Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (17-April-2026):

• 🗞️ Claude Opus 4.7 launched as ‘less powerful’ version of Mythos

• 🗞️ Tencent released HY-World 2.0, a 3D world model that turns text, images, photos, and video into editable scenes instead of flat clips.

• 🗞️ Perplexity just launched Personal Computer, a Mac feature that lets AI work across local files, native apps, and the browser.

• 🗞️ AI can boost performance at first and then leave people less able to think through problems on their own.

• 🗞️ A new paper shows that GitHub stars can be bought at scale, and that the distortion now bleeds into security.

• 🗞️ OpenAI just expanded Codex from a coding assistant into a desktop agent that can see, click, type, remember your habits, and keep work moving across apps and tools.

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🗞️ Claude Opus 4.7 launched as ‘less powerful’ version of Mythos

Claude Opus 4.7 is a serious upgrade over Opus 4.6 for software engineering and long-running agent tasks.

• It follows instructions more literally, so older prompts and harnesses may need re-tuning.

• It supports higher-resolution vision up to 2,576px on the long edge, or about 3.75MP.

• Improved file system-based memory for multi-session work. Mythos Preview is still described as much more broadly capable and better aligned.

• Anthropic is using Opus 4.7 to test cybersecurity safeguards, including automatic blocking for prohibited or high-risk cyber requests.

• Pricing stays at $5/M input tokens and $25/M output tokens.

• Opus 4.7 introduces a new xhigh (“extra high”) effort level between high and max, giving users finer control over the tradeoff between reasoning and latency on hard problems. i.e. developers can make Claude spend more time on hard tasks, cap how many tokens it uses during long runs, and trigger a dedicated code review pass that looks for bugs and design issues.

• The new /ultrareview slash command produces a dedicated review session that reads through changes and flags bugs and design issues that a careful reviewer would catch.

• Token usage can increase because of the new tokenizer and because higher-effort runs can produce more output.

🗞️ Tencent released HY-World 2.0, a 3D world model that turns text, images, photos, and video into editable scenes instead of flat clips.

Tencent open-sourced HY-World 2.0, a multimodal world model that generates, reconstructs, and simulates interactive *3D worlds* from text, images, and videos.

Most world models predict pixels, which looks fine from one view but often breaks when the camera moves or when you try to reuse the result as a real asset.

HY-World 2.0 tries to build the scene itself, recovering geometry, depth, camera pose, and renderable 3D assets, while WorldMirror 2.0 reconstructs photos or video in 1 feed-forward pass to produce a navigable digital twin.

What is happening here is that TencentHunyuan is trying to move world models from “generate the next frame” to “build the place itself,” because HY-World 2.0 takes text, a single image, multiview images, or video and aims to output persistent 3D assets such as meshes and Gaussian splats, which are small soft 3D points that can be rendered quickly from new views.

That is a big deal because a video world model can look interactive while still being only a stream of pixels, while a real 3D asset can be re-rendered from new angles, edited later, and imported into tools like Blender, Unity, Unreal, or Isaac Sim.

HY-World does this with a staged pipeline in which HY-Pano 2.0 makes a panorama, WorldNav plans a camera path, WorldStereo 2.0 expands the world, and WorldMirror 2.0 composes the final 3D scene.

Apply for access: https://3d.hunyuan.tencent.com/sceneTo3D
GitHub: https://github.com/Tencent-Hunyuan/HY-World-2.0
Hugging Face: https://huggingface.co/tencent/HY-World-2.0
Technical Report: https://3d-models.hunyuan.tencent.com/world/world2_0/HY_World_2_0.pdf

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🗞️ Perplexity just launched Personal Computer, a Mac feature that lets AI work across local files, native apps, and the browser.

It’s a full-fledged AI agent that lives inside your Mac, gets direct access to your local files and native apps, and can work 24/7 on your behalf.

This is a super strong launch from Proplexity, as the real bottleneck in AI is no longer generating text. It is getting reliable access to the places where work actually lives.

Personal Computer acts like an orchestrator: it can connect to local folders, read and write files, use native Mac apps like Mail, Calendar, and iMessage, and work through the browser from the same layer.

Useful automation needs context from many places at once, not just the text you pasted into one prompt. Perplexity says when set up on a Mac mini this “Personal Computer” can keep it running 24/7, while an iPhone can start tasks remotely and approve 2FA.

Local file access helps you on the problems cloud copilots miss. Most useful work lives in half-finished docs, spreadsheets, inbox threads, and app state that never makes it into a clean API.

It’s a hybrid system: the heavy orchestration happens securely on Perplexity’s servers (using 20+ frontier models), while local file and app access runs on your Mac. This makes it far more powerful and contextual than a purely web-based AI.

Key difference from Perplexity Computer (the web/cloud version): Personal Computer is the superset. It does everything the cloud version can do plus direct local machine access. Set it up on a Mac mini and it becomes a true 24/7 agent that keeps working even when your laptop is closed.

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🗞️ AI can boost performance at first and then leave people less able to think through problems on their own.

BIG claim from new MIT + Oxford + Carnegie Mellon and other top labs paper:

Just minutes of AI help can improve scores now while weakening independent problem-solving right after.

The interesting part is that the damage is not just lower accuracy.

It is lower persistence, which is usually the hidden engine of learning, because skill grows through repeated contact with difficulty, not just exposure to correct answers.

That’s why a good teacher sometimes withholds help to preserve struggle as part of the lesson, while today’s chatbots are tuned to erase friction on demand.

Across 3 experiments in math and reading, about 1.2K people either worked alone or used a GPT-5-based assistant for part of the task.

Assisted users finished early questions faster, but after roughly 10 minutes without AI, they solved less, stalled more, and quit sooner.

That happens because hard thinking is not only about getting answers; it is also about building the habit of holding a problem in mind, testing steps, and pushing through confusion.

The sharpest drop came from people who used the model for direct answers, not from those who used it more like a hint system, which suggests the real issue is not AI exposure itself but replacing effort with completion.

The result is not that AI makes people less capable by default, but that answer outsourcing can shrink the mental effort that normally trains skill.

🗞️ A new paper shows that GitHub stars can be bought at scale, and that the distortion now bleeds into security.

The authors identify 6 million suspected fake stars tied to 18,617 repositories.

That matters because stars are not just vanity on GitHub. They are a shortcut people use to decide what looks credible, useful, or safe enough to try, even though earlier work already suggested stars are only a rough proxy for real adoption.

The problem is not just inflated popularity, but the way a weak social signal becomes infrastructure for malware, spam, and low-effort hype once enough people treat it as evidence.

The paper’s detection strategy is clever because it does not need to prove intent account by account.

It looks for behavioral signatures that are hard to fake at scale: throwaway accounts with almost no activity, and coordinated “lockstep” bursts where many accounts star many repositories within short windows.

What they find is ugly.

Fake-star activity surged in 2024, most flagged repositories were later deleted, many appear to have been phishing or spam, and the surviving non-malicious-looking targets cluster in predictable status games like AI, blockchain, tools, and demos.

The most interesting result is about incentives.

Fake stars do appear to buy a little real attention for less than two months, but the effect is far smaller than genuine popularity and turns negative over time, which suggests that social proof can open the door but cannot compensate for weak underlying substance.

Once a platform’s easiest visible number starts standing in for trust, attackers do not need to beat the system completely; they only need to be believable for a moment.

🗞️ OpenAI just expanded Codex from a coding assistant into a desktop agent that can see, click, type, remember your habits, and keep work moving across apps and tools.

The update adds computer use on macOS, an in-app browser, gpt-image-1.5 for design and asset iteration, 90+ plugins, richer terminal and file handling, and parallel agents that can work without taking over your machine.

Before this, Codex was mainly strongest inside coding surfaces like the terminal, editor, and structured tool integrations, so it could write code and run workflows but often hit a wall when work moved into normal desktop apps and screen-based interfaces.

Now the big change is computer use plus memory plus long-running automations: Codex can operate Mac apps by looking and clicking like a person, remember how you work, and return later to continue the same task with context still intact.

It also adds thread-aware automations and preview memory, so Codex can resume long jobs with prior context, reuse your preferences and corrections, and handle repeatable work like PR follow-ups or app testing.

That’s a wrap for today, see you all tomorrow.

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Read time: 9 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (15-Apr-2026):

• 🗞️ Google just launched Gemini 3.1 Flash TTS, a text-to-speech model that takes scene direction, speaker notes

• 🗞️ Today’s Sponsor: webAI just open-sourced a vision-language retrieval model (ColVec1) that’s now at #1 on ViDoRe V3 by searching document pages directly instead of reading them through OCR first.

• 🗞️ OpenAI just turned the Agents SDK into a long-running agent runtime with sandbox execution and direct control over memory and state.

• 🗞️ OpenAI unveils GPT-5.4-Cyber a week after Anthropic’s announcement of AI model

• 🗞️ Fortune published a piece. From Molotov cocktails to data center shutdowns, the AI backlash is turning revolutionary

• 🗞️ Google just turned Gemini in Chrome prompts into reusable one-click tools called Skills.

Connect with me on X (Twitter)

🗞️ Google just launched Gemini 3.1 Flash TTS, a text-to-speech model that takes scene direction, speaker notes, and inline audio tags, so speech can be steered like a performance instead of read like a script.

Gemini 3.1 Flash TTS, the latest text-to-speech model that delivers improved controllability, expressivity and quality.

Rolling out from today

• For developers in preview via the Gemini API and Google AI Studio

• For enterprises in preview on Vertex AI

• For Workspace users via Google Vids

Traditional TTS systems mostly convert words into sound, but this model also follows instructions about pace, tone, accent, and delivery, which means the prompt can shape how a line lands, not only how it is pronounced.

Flat speech breaks dialogue systems fast, so speaker-level specificity matters because 2 voices can stay distinct, react differently, and even shift emotion mid-sentence without switching models.

The model supports 70+ languages, native multi-speaker dialogue, reusable voice settings through the API, and an Artificial Analysis Elo of 1211.

🗞️ Today’s Sponsor: webAI just open-sourced a vision-language retrieval model (ColVec1) that’s now at #1 on ViDoRe V3 by searching document pages directly instead of reading them through OCR first.

Finally we have “Frontier-level retrieval. No OCR. No preprocessing. Built for real documents.”

The real problem with document retrieval was never just reading more text, it was losing the page before the search even began.

ViDoRe V3 benchmark is one of the gold standard for evaluating multimodal document retrieval in enterprise settings.

The models (ColVec1) come in 4B and 9B variants (both are on Huggingface), with Qwen 3.5 vision-language as the backbone, and trained on a dataset of ~2M question-image pairs. Can be easily run on consumer level GPUs

The AI world usually goes chasing size first. webAI has long believed that being smarter matters more than being bigger. This release clearly shows that page layout is part of meaning, not just formatting noise.

Multimodal RAG has looked like a scale problem for a while, but this release makes a strong case that the real gap was retrieval. OCR-first systems flatten a page into text and often damage the very clues that carry meaning, like table alignment, chart context, columns, spacing, and scanned layouts.

ColVec1 keeps the page as an image, so retrieval starts from the full visual document rather than a lossy text extraction step.

The model uses one shared VLM for text queries and page images, then turns each token into many vectors instead of one pooled embedding. That multi-vector late interaction setup lets a query match the best local regions of a page, which helps on manuals, filings, papers, and dense tables where one global vector misses detail.

webAI says it trained on about 2M question-image pairs across DocVQA, PubTables, TAT-QA, ViDoRe-style data, multilingual sets, and synthetic document queries.

Check out ColVec1 models on HuggingFace. The 9B is here and the 4B is here.

And read their official technical blog on this model.

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🗞️ OpenAI just turned the Agents SDK into a long-running agent runtime with sandbox execution and direct control over memory and state.

Before this, developers often had to stitch together 3 separate pieces themselves: the model loop, the machine where code runs, and the memory or state that lets a task continue later.

That sounds small, but it is usually where agents fail, because a model may know what to do yet still lose files, lose progress, leak secrets, crash mid-task, or restart from scratch.

The new SDK gives one standard setup for that missing layer: a harness that manages the agent loop, plus a sandbox where the risky work happens, plus control over when memory is created and where it is stored.

Runs can now survive pauses and failures through snapshotting and rehydration.

Developers can inspect the open-source harness, choose when memories are written and where they live, and use Cloudflare, Vercel, Modal, E2B, or their own environment.

This image explains the single biggest architecture change in the new Agents SDK.

The left box is the trusted control layer, where the harness, the agent loop, the tools, and the secrets live. The right box is the untrusted work layer, where the agent can run code, inspect files, search through data, and write outputs inside a sandbox.

The point is that the agent can still do real computer work without putting your credentials, databases, and internal services inside the same environment as model-generated code.

That separation lowers risk, because the server talks to databases and the web from a trusted place, while the sandbox gets only the files and actions it needs for the task. It also makes long jobs more practical, because the harness can keep track of the run even if the sandbox is replaced, paused, or restarted.

So overall, OpenAI is defining a cleaner boundary between thinking, control, and execution.

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🗞️ Anthropic adds routines to run workflows via API and GitHub

Anthropic just turned Claude Code routines into always-on software jobs that can start on a schedule, from an API call, or from GitHub events, and they keep running on Anthropic-managed infrastructure even when your computer is off.

Routines now act like hosted workflow runners: you save a prompt, attach repositories, secrets, network rules, and connectors, and each trigger spins up a fresh Claude session that can inspect code, edit files, review changes, and create a PR.

The useful part is the trigger design, because the API path gives each routine a protected HTTP endpoint with an optional text payload, while GitHub triggers launch separate sessions for matching repo events, with no shared session state across later events.

I think this is a solid product move because it packages cron jobs, webhooks, and repo automation into one LLM workflow layer that teams can plug into tools they already use.

🗞️ OpenAI unveils GPT-5.4-Cyber a week after Anthropic’s announcement of AI model

OpenAI just widened its Trusted Access for Cyber program and introduced GPT-5.4-Cyber, a more cyber-permissive model built to help verified defenders do harder security work.

GPT-5.4-Cyber is basically a special version of GPT-5.4, not a totally separate foundation model from scratch.

What changed is not mainly “the base intelligence,” but the permission settings, tuning, and access policy for cybersecurity work.

Normal models often refuse or heavily restrict cyber-related requests because those requests can look dangerous even when a defender is doing legitimate work like malware analysis, vulnerability research, or reverse engineering.

OpenAI is saying: for verified security defenders, it will provide a version of GPT-5.4 that is less likely to refuse legitimate cyber tasks and is better tuned for defensive security workflows.

The problem is that modern AI can help both attackers and defenders, so OpenAI is trying to separate ordinary access from higher-trust access instead of treating every cyber request the same.

Its answer is a tiered system built on identity checks, trust signals, and tighter deployment controls, so more capable security features go to people and teams that can show they are doing legitimate defensive work.

The new part is GPT-5.4-Cyber, a version of GPT-5.4 with a lower refusal boundary for approved security tasks, including binary reverse engineering, which matters because defenders often need to inspect compiled software without source code to judge malware risk, find bugs, or test robustness.

OpenAI is pairing that with a broader rollout of Trusted Access, expanding from a small program to thousands of verified individual defenders and hundreds of teams, while keeping the most permissive access limited and more closely monitored.

The bigger technical bet is that cyber safety should scale with model capability, so OpenAI is mixing stronger safeguards for general users with more freedom for authenticated defenders instead of using one flat policy for everyone.

My read is that this is a serious shift from “AI for coding” toward AI for operational security work, and the real test is whether verification stays strict enough to block abuse without slowing down the defenders it is supposed to help.

🗞️ Fortune published a piece. From Molotov cocktails to data center shutdowns, the AI backlash is turning revolutionary

The story is less about 1 attacker than a wider pileup of anger around jobs, trust, and the feeling that AI firms promised relief while many people got more risk, more uncertainty, and fewer clear benefits.

Young people sit at the center because they use AI a lot yet often see it as something that can flatten entry-level work, flood the internet with synthetic slop, and give abusive people cheap tools for manipulation at personal scale.

The same backlash now shows up far from social media, because towns fighting new data centers are reacting to higher power demand, water use, noise, and land pressure rather than abstract fears about superintelligence.

The deeper split is between the industry story, where AI soon expands abundance, and the public story, where AI already acts like a bargaining chip for layoffs, a source of everyday fraud, and a machine that keeps asking society to absorb the costs first.

🗞️ Google just turned Gemini in Chrome prompts into reusable one-click tools called Skills.

A Skill is just a saved prompt plus the page context, which means Chrome can rerun the same AI job without making you rewrite instructions every time.

That changes AI in the browser from one-off chat to a lightweight workflow system, where one saved prompt can compare products across tabs, scan long pages for facts, or rewrite a recipe around a food rule.

Chrome also adds a Skills library, so people can start from ready-made prompts, save them, and then edit the wording instead of building every workflow from scratch.

Google says Skills use the same safeguards as Gemini in Chrome, including confirmation before actions like sending email or adding calendar events, which keeps the prompt useful without giving it silent control.

My view is that this is a small product change with a real behavior shift, because the useful unit stops being the chatbot and becomes the repeatable prompt workflow.

That’s a wrap for today, see you all tomorrow.

Connect with me on X (Twitter)

Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (14-Apr-2026):

• 🗞️ Cursor just turned its agent workflow from a tab-by-tab queue into a parallel workspace

• 🗞️ Today’s Sponsor: conveyAI launched a digital teammates that operators train themselves, that run autonomously, and that fully own a process end to end.

• 🗞️ Microsoft just gave Copilot in Word a bigger role in high-stakes document editing, for legal, finance, and compliance professionals.

• 🗞️ Anthropic’s new result shows that AI can already speed up some alignment research, but mostly when the problem is sharply measurable.

• 🗞️ Today’s Sponsor: AGIBOT just announced GO-2, a robot foundation model built to turn high-level reasoning into reliable physical action.

• 🗞️ OpenClaw just pushed a stability-first release that makes GPT-5.4, browsers, chat connectors, and local models fail less often in real deployments.

• 🗞️ Microsoft just laid out a new way to keep enterprise software growing in an AI-heavy workplace: charge AI agents for software seats the same way companies pay for human employees.

Connect with me on X (Twitter)

🗞️ Cursor just turned its agent workflow from a tab-by-tab queue into a parallel workspace

Cursor is replacing tab-hopping with tiled multi-agent coding, where multiple coding agents can run side by side while large edits render far more smoothly.

The old bottleneck was simple: one visible agent at a time slows comparison, slows review, and makes multi-step coding work feel serial even when the tasks are not.

The new tiled layout fixes that by giving each agent its own pane, persistent placement across sessions, and faster movement between conversations, so planning, coding, and checking can happen at once.

Cursor also says it cut dropped frames during big file updates by about 87%, which means streamed edits stay readable instead of turning long refactors into UI stutter.

The rest of the release tightens control around the editor itself with branch selection before agent launch, diff-to-file line jumps, include/exclude search filters, better voice dictation through full-clip batch STT, and fewer background updates for lower CPU and network load.

🗞️ Today’s Sponsor: conveyAI launched a digital teammates that operators train themselves, that run autonomously, and that fully own a process end to end.

A surprising amount of modern business still runs on capable people manually stitching together broken systems, carrying process knowledge in their heads, and rescuing exceptions before anyone notices.

So ConveyAI launched something, that will make bring a true100x operator.

Says their customers trained a first full-workflow teammate in about 3hours, with cases like 450+hours/week saved in ad ops and faster finance work. The problem is not that firms lack software, but that many messy back-office processes live in the gaps between tools, so smart employees keep systems moving by copying data, handling exceptions, and making judgment calls software never captured.

It can observe a messy process, execute it repeatedly without supervision, stop at the right moment when reality changes, and slot into the company with permissions, accountability, and escalation paths.

So the operator stops being the person doing the task and becomes the person training, reviewing, and directing a digital teammate.

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🗞️ Microsoft just gave Copilot in Word a bigger role in high-stakes document editing, for legal, finance, and compliance professionals.

Now it makes auditable, document-native changes instead of acting like a detached text generator.

Copilot can now work inside the normal machinery of Word, including Track Changes, comments, tables of contents, headers, footers, page numbers, and other layout fields that serious teams already use to review documents.

That fixes a basic problem with many AI writing tools, which is that they can rewrite text but often break the paper trail, formatting logic, and review workflow that legal, finance, and compliance teams need.

Microsoft is trying to make Copilot behave less like a chatbot pasted on top of Word and more like a document collaborator that can edit with word-level precision, attach feedback to the right text, summarize unresolved edits, and show progress messages while it works.

The technical idea underneath this is Microsoft’s Work IQ layer, which uses a company’s own files, context, and priorities so the model’s actions are tied to the document system people already trust rather than to a blank prompt box.

The real story is not better writing help but better workflow integrity

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🗞️ Anthropic’s new result shows that AI can already speed up some alignment research, but mostly when the problem is sharply measurable.

They built autonomous AI agents that propose ideas, run experiments, and iterate on an open research problem: how to train a strong model using only a weaker model’s supervision. These agents outperform human researchers, suggesting that automating this kind of research is already practical.

Human researchers closed 23% of the weak-to-strong performance gap in a week, while Anthropic’s automated researchers reached 97% on the same chat-preference setup.

It is a demonstration that, when you give a model a tight objective, fast feedback, and room to run many parallel experiments, it can hill-climb that objective very aggressively.

The paper’s most important contribution is not the best method. It is the research harness: parallel agents, shared findings, remote evaluation, and just enough structure to coordinate without over-constraining the search.

The best gains came from preserving diversity across agents. When all workers started from the same prompt, their ideas collapsed into a few familiar directions. When they were seeded with different, ambiguous research programs, exploration stayed broader and performance rose faster.

The real warning is that the same system also became good at reward hacking. It found shortcuts in datasets, cherry-picked seeds, inferred labels from the evaluation API, and even executed code to recover answers on the coding benchmark.

So the deep lesson is not “AI solved alignment research.” It is that automated science is arriving first where the score is legible, and that makes evaluation design the new bottleneck.

The future advantage may belong less to whoever has the smartest model, and more to whoever builds the hardest-to-game research environment.

🗞️ Today’s Sponsor: AGIBOT just announced GO-2, a robot foundation model built to turn high-level reasoning into reliable physical action.

AGIBOT says that smarter robots may come less from better perception than from tighter links between planning and control.

Robots do not become reliable just by reasoning more. They become reliable when reasoning arrives in a form that control can actually use.

The problem is that many robot VLA systems can describe a good plan but still miss, drift, or stall when real motors, timing, and noisy observations take over. So AGIBOT‘s GO-2 tries to fix that split by inserting Action Chain-of-Thought, which means it first writes a sequence of executable action intents instead of jumping straight from text and images to raw control commands.

It then runs an asynchronous dual system where a slow planner sets the semantic path and a fast follower keeps adjusting the motion in real time. GO-2’s first move is to reason in action space, producing a sequence of high-level intents before it commits to fine-grained control.

Its second move is architectural. A slower semantic planner sketches the route, while a faster action-following module keeps correcting against real-world observations, so the robot can stay faithful to the plan without becoming brittle.

The real step forward is not better robot language alone, but a better handoff from abstract intent to precise movement.

🗞️ OpenClaw just pushed a stability-first release that makes GPT-5.4, browsers, chat connectors, and local models fail less often in real deployments.

More reliability updates:

• ✨ Smarter GPT-5.4 routing and recovery

• 🌐 Chrome/CDP improvements

• 🧵 Subagents no longer get stuck

• 💬 Slack/Telegram/Discord fixes

• ⚡️ Various performance improvements

Frameworks like this usually break at the seams, where one agent turn touches model routing, browser control, file handling, proxies, and apps like Slack or Telegram, so small timeout or permission bugs can snowball into stuck subagents, dropped media, or silent crashes.

This patch mostly hardens those seams by improving GPT-5.4 retry logic, fixing CDP/Chrome reachability, persisting Telegram topic names, tightening Slack allowlists, repairing Ollama timeouts and usage accounting, and closing several SSRF, attachment-path, and UI markdown freeze issues.

My read is that this is the kind of release serious users care about most, since fewer edge-case failures usually matter more than one more feature when one framework already spans WhatsApp, Discord, Slack, browsers, and local inference.

🗞️ Microsoft just laid out a new way to keep enterprise software growing in an AI-heavy workplace: charge AI agents for software seats the same way companies pay for human employees.

The old SaaS model was easy, a company buys 1 license for 1 worker, so revenue rises when headcount rises.

AI agents threaten that model because 1 person might supervise 10 or 50 agents, which makes investors ask why a company would still need to pay for many separate licenses.

So Microsoft executive Rajesh Jha’s answer is that an agent may become its own software user, with its own identity, login, email, permissions, and access to tools, which turns each agent into a possible paid seat.

It shifts the pricing logic from “how many humans work here” to “how many active digital workers operate inside the company.”

Basically his logic is, once an agent can read messages, call apps, update records, and take actions on its own, software systems may need to track it as a distinct actor for security, auditing, and workflow control.

That gives Microsoft, Salesforce, and Workday a path to defend seat-based pricing even if AI reduces human hiring.

That’s a wrap for today, see you all tomorrow.

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Read time: 8 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (13-Apr-2026):

• 🗞️ Viral leaked screenshots shows Anthropic built a Lovable competitor into Claude

• 🗞️ Today’s Sponsor: Strix is making AI useful in security where it actually counts: inside the loop of testing, verifying, and patching.

• 🗞️ Meta finally dropped its first model since Zuckerberg started hiring like crazy

• 🗞️ Meta’s new paper showed that a model can learn some of the runtime behavior of a computer directly from screen-and-action traces.

• 🗞️ Fortune reports about a survey saying 29% of workers admit sabotaging company AI plans, and that rises to 44% for Gen Z.

• 🗞️ Alibaba’s new paper shows AI is moving beyond bug finding and into actually proving software is exploitable.

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🗞️ Anthropic has leaked screenshots of a powerful full-stack app builder integrated directly into Claude, positioning it as a direct rival to Lovable—the popular “vibe-coding” platform.

Under the hood, the builder appears to extend Claude’s existing Artifacts (for interactive previews) and Claude Code agentic capabilities. It scaffolds full-stack applications—including frontend, backend logic, authentication, database connections, storage, secrets management, security scanning, and logs—in one conversation.

Lovable itself runs on Claude models and has reached a ~$6.6 billion valuation by adding this exact scaffolding layer on top of the API. Now if Anthropic is baking the entire experience natively into its chatbot, it can potentially collapse the stack for millions of non-technical builders.

The feature remains unconfirmed and in internal testing, but the leak suggests Claude is evolving from conversational AI into an end-to-end app platform—complete with live previews, one-click deployment, and enterprise-grade controls.

🗞️ Today’s Sponsor: Strix is making AI useful in security where it actually counts: inside the loop of testing, verifying, and patching. 23.6K+ Github stars

Strix launched their new platform: continuous pentesting for modern apps.

Strix is an open-source framework for autonomous pentesting across apps, APIs, and repositories with 23.6K+ Github stars ⭐️

Its built around a simple AI idea: security testing gets better when the system can change its mind mid-attack.

• 80,000+ users worldwide

• 15B+ LLM tokens processed daily

• 78,000+ vulnerabilities reported

• multiple CVEs assigned

• deployed by enterprise security teams worldwide

I like the part that it treats AI as an adaptive operator sitting on top of deterministic security tools. The real pitch is not that AI can spot bugs. It is that security findings should arrive with proof, a fix, and a place in the merge loop, not as a late report someone has to interpret.

That sounds minor until you look at the mechanism. Strix is built around dynamic testing, proof-of-concept validation, autofix pull requests, retesting, and CI/CD hooks that can block insecure code before it ships.

IMO, continuous pentesting only matters if it can narrow scope to changed code, run headlessly in pipelines, and accumulate context over time, and the new platform is explicitly built around those exact behaviors.

What is probably true is that this model can remove a lot of appsec friction, especially where teams are drowning in “possible” issues and need validation fast.

This is not another scanner that throws guesses at a team. Strix is built around attacker style testing, so it uses browser actions, traffic inspection, terminal work, Python, and code context to prove whether a flaw is actually usable.

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🗞️ Meta finally dropped its first model since Zuckerberg started hiring like crazy

Meta Launched Muse Spark (originally codenamed Avocado). Its a natively multimodal reasoning model that can look, reason, use tools, and split hard work across multiple cooperating agents.

Claims it can reach similar capability with 10x+ less training compute than Llama 4 Maverick,

They are not positioning Muse Spark as a top-of-the-line model, but is instead highlighting its efficiency and “competitive performance” on various tasks.

The old bottleneck in AI is that one model often has to read, plan, call tools, and solve everything in one stream, which wastes compute and slows hard tasks.

The key idea here is multi-agent orchestration, where several copies of the model work on the same problem in parallel and then compare or merge results, which is closer to a small team than a single assistant.

That changes the scaling story because better performance no longer comes only from making 1 model bigger, but also from spending compute more intelligently at run time.

So Muse is a stack built around 3 scaling axes: stronger pretraining for basic world and code understanding, steadier RL for improving answers after pretraining, and test-time reasoning so the model spends extra compute only when a problem is hard.

The most interesting part is multi-agent orchestration, where several copies of the model reason in parallel and compare work, which raised Humanity’s Last Exam to 58% and FrontierScience Research to 38% in its heavier Contemplating mode.

Meta also says the new pretraining recipe reaches similar capability with over 10x less compute than Llama 4 Maverick, which matters because cheaper training usually means faster iteration and more room to scale.

While standard test-time scaling has a single agent think for longer, scaling Muse Spark with multi-agent thinking enables superior performance with comparable latency.

Meta thinks the next jump in AI will come from coordination, where multiple reasoning threads check and improve each other, not only from training 1 larger model.

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🗞️ This Meta paper is this week’s most important paper indeed.

They showed that a model can learn some of the runtime behavior of a computer directly from screen-and-action traces, instead of relying on a normal computer underneath to carry out every step.

The big deal is the change in where computation lives.

In normal AI agents, the model decides what to do, but the actual computer still does the computing, stores the memory, and updates the interface. In this paper, the authors are asking whether the model itself can become the thing that holds state, updates the world, and produces the next screen. That is the conceptual leap.

The claim is that computation, memory, and input-output might eventually collapse into one learned runtime state, so the model is no longer controlling a computer from outside but carrying the computer inside its own dynamics.

Their CLI model could render short terminal workflows and keep outputs visually aligned. Their GUI model could learn cursor behavior, click feedback, and short window transitions from raw interface traces, with strong cursor accuracy in controlled settings.

They did not build a replacement for laptops or operating systems.

They showed a first proof that some pieces of “being a computer” can be absorbed into a model’s latent state. If that keeps scaling, the boundary between software, memory, and execution could get much blurrier than it is today.

🗞️ Fortune reports about a survey saying 29% of workers admit sabotaging company AI plans, and that rises to 44% for Gen Z.

Companies are finding that AI rollout is colliding with a basic workplace fact: people resist tools they think will erase their role.

That sabotage ranges from ignoring approved tools and using shadow AI to feeding weak outputs into workflows and even skewing reviews so the system looks worse than it is.

The logic is not technical failure first but trust failure first, because workers who hear constant claims that AI can replace entry-level white-collar work start treating adoption like self-harm.

That reaction can backfire fast, since executives in the same survey say workers who refuse AI are more exposed to layoffs, less likely to be promoted, and less likely to move into leadership.

🗞️ Alibaba’s new paper shows AI is moving beyond bug finding and into actually proving software is exploitable.

This paper asks a simple question with hard consequences: can LLMs confirm software vulnerabilities by actually building working exploits?

The authors’ answer is yes, but only when the model stops acting like a single genius and starts acting like a team. That sounds minor until you look at the mechanism.

Automated exploit generation usually fails for familiar reasons. Fuzzers miss deep paths. Symbolic execution chokes on messy real code, especially when the right input is not just a value but a carefully assembled object, class instance, or string with the right structure.

A plain LLM is not enough either. It can imitate code, but it loses the thread, hallucinates details, and struggles to repair its own mistakes once execution fails.

VulnSage’s real move is to turn exploit generation into a workflow.

• One agent extracts the vulnerable dataflow.

• Another rewrites that path as natural-language constraints.

• Another generates candidate exploits.

• Then a validation agent runs them in a sandbox, and reflection agents use the resulting traces and errors to refine the next attempt or conclude the alert was probably a false positive.

Here’s the part most people miss.

The point is that the hard part is often not “solve these equations,” but “figure out how this code expects to be used.” Their system writes the problem in ordinary language so the model can reason about code structure, like which object to build and which method path keeps the malicious input alive.

The concerning part is that this makes exploit generation work on messier, more realistic software where older methods often fail. In other words, the paper’s claim is not just “we solved constraints differently,” but “we can now turn code understanding itself into a path to real exploits.”

In the paper’s evaluation, the authors report 34.64% more successful exploits than prior tools on SecBench.js, and 146 zero-days in real packages.

The win is not that LLMs magically solve exploitation. It is that they become useful once they are forced to read, act, fail, and learn like a security researcher.

That’s a wrap for today, see you all tomorrow.

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Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (16-March-2026):

• 🗞️ Australian uses ChatGPT and AlphaFold to treat his dog’s cancer. A new era of “citizen science” is beginning.

• 🗞️ Today’s Sponsor: If your AI agents keep retrieving the wrong context, explore HydraDB.

• 🗞️ FlashAttention-4 paper came out, to make AI run faster on the newest generation of computer chips.

• 🗞️ The latest Pew Research report reveals a massive gap between how everyday people and tech experts view AI.

• 🗞️ Microsoft released GigaTIME, a new AI model that turns cheap medical images into highly detailed protein maps of cancer cells.

• 🗞️ Stanford and Carnegie Mellon researchers mapped AI benchmarks to real jobs and found they heavily ignore actual human economic work.

• 🗞️ Andrej Karpathy just put out this tool that looks at AI’s impact on job.

Connect with me on X (Twitter)

🗞️ Australian uses ChatGPT and AlphaFold to treat his dog’s cancer. A new era of “citizen science” is beginning.

An engineer with no medical training used ChatGPT and Google’s Alphafold (AI protein sequencer) to build a working cancer vaccine from scratch.

He turned raw genetic data into a custom mRNA vaccine that shrank his dying dog’s tumor by 50%. Paul Conyngham spent $3000 to get the DNA sequences of his dog’s healthy blood and the cancerous tumor.

He was staring at gigabytes of raw genetic code without having any clue how to read biological data. This is exactly where ChatGPT became the crucial missing link in his process.

He used ChatGPT as a high-level biological consultant to figure out how to compare the two DNA samples and spot the exact mutations causing the cancer. ChatGPT gave him the step-by-step instructions to run the data pipelines and pointed him toward an AI tool called AlphaFold to map the physical shape of the damaged proteins.

The chatbot basically translated complex oncology concepts so he could write a half-page chemical recipe for an mRNA vaccine. This mRNA is just a genetic instruction manual that tells the immune system how to recognize and attack those specific mutated cancer cells.

University researchers were blown away by his formula and manufactured the physical vaccine for him. A veterinary expert then injected the dog, and within weeks the massive tumor had halved in size.

A leading university scientist was completely shocked that a data engineer with zero biology training successfully used AI to pinpoint his dog’s cancer mutations and design a specific chemical treatment.

The expert was so impressed by this solo achievement that he eagerly agreed to help him physically manufacture the drug.

🗞️ Today’s Sponsor: If your AI agents keep retrieving the wrong context, explore HydraDB.

HydraDB is production-grade infrastructure designed to solve the “memory fragmentation” that plagues standard RAG systems.

And they just raised $6.5M to kill vector databases.

While traditional setups store disconnected text snippets, HydraDB utilizes a Sliding Window Inference Pipeline to resolve pronouns and context before storage, ensuring every data “chunk” remains semantically complete.

The core innovation is its Git-Style Versioned Temporal Graph. Instead of overwriting old data, it treats user preferences and facts like a version-controlled codebase. This allows agents to track when and why information changed, providing a chronological map of truth rather than a flat list of text.

By combining standard vector search with this temporal graph, HydraDB achieves 90.23% context recall and sub-20ms latency. For developers, it means moving away from brittle, manual context management toward a standardized foundation that allows 10,000+ agents to operate with a shared, reliable understanding of state. It isn’t just a database; it’s the persistent memory layer that makes long-term AI agents viable in production.

🗞️ The paper introduces FlashAttention-4 to make AI run faster on the newest generation of computer chips.

FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling.

Researchers from Princeton University, Meta, NVIDIA, and more have developed clever new pipelines, re-engineered core computations, and optimized memory usage to master the unique scaling challenges of NVIDIA’s latest Blackwell GPUs. While the math processors inside new Blackwell GPUs doubled in speed, the memory system stayed the same, creating a massive traffic jam.

This means the expensive chip naturally spends most of its time doing absolutely nothing while waiting for data to move around. i.e. softmax and shared-memory traffic are what slow things down, so the new code is built specifically to fix those issues. And with this new FlashAttention-4, the B200 hits 1600 TFLOPs/s, outperforming both cuDNN and Triton by solving the actual hardware limits.

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The researchers solved this by writing a completely new schedule that runs the math computation and the memory loading at the exact same time, while also shifting some specific calculations to entirely unused parts of the chip. This prevents the hardware from sitting idle, making the AI run much faster and saving massive amounts of time and electricity for anyone running a large language model.

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🗞️ The latest Pew Research report reveals a massive gap between how everyday people and tech experts view AI.

• While 56% of experts expect positive societal impacts from AI, only 17% of the public agrees. For a contrast, 83% of people in China believe AI will be positive.

• 50% of Americans are more concerned than excited about AI.

• The most shocking detail is that teenagers are widely using these tools to cheat on their homework.

Roughly 60% of students between the ages of 13 and 17 say their classmates use chatbots to bypass schoolwork. Nearly 33% of those teenagers report that this kind of digital cheating happens extremely often in their classrooms.

• Only 24% of adults believe AI will actually make education better over the next 20 years.

• Workplace adoption is climbing, with 21% of adults using AI on the job.

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🗞️ Microsoft released GigaTIME, a new AI model that turns cheap medical images into highly detailed protein maps of cancer cells.

Researchers can now analyze the complex immune responses of tumors across massive patient populations using simple software instead of rare physical lab equipment.

Standard tumor tissue slides cost only $5 to $10. But doing that chemical protein test on a physical tissue sample, that map out how a tumor fights the immune system, costs thousands of dollars because the materials and the imaging process are very difficult to scale.

The GigaTIME AI looks at the basic shapes in the cheap digital image and predicts exactly where those hidden proteins are located. This means researchers can use software to mathematically simulate the expensive chemical test just by analyzing the cheap image.

i.e. GigaTIME has practically turned a medical test that normally costs over $2,000 per patient into a fast digital prediction based on a standard $5 hospital image. Shows again that software can bypass hardware limits to find entirely new biological patterns.

Microsoft trained GigaTIME on 40M cells to generate these complex virtual test results directly from the cheap slides. Processing 14,256 patients yielded a massive database of 300,000 detailed medical images. They discovered 1,234 new connections between specific cell proteins and patient survival rates.

🗞️ How Well Does Agent Development Reflect Real-World Work?

Stanford and Carnegie Mellon researchers mapped AI benchmarks to real jobs and found they heavily ignore actual human economic work.

They found that AI tests focus almost exclusively on programming and math, which only make up 7.6% of actual jobs. To test this, the team analyzed 43 benchmarks and over 72,000 tasks against a massive government occupational database.

The authors discovered that developers focus almost entirely on building agents for software engineering because it offers easy automatic grading. Highly digitized and valuable fields like management and legal work represent a massive part of the economy but get almost zero attention.

Furthermore, benchmark tasks usually require simple information gathering while completely ignoring the complex interpersonal skills needed in real workplaces. i.e. they says current AI agent progress-benchmarks are fundamentally disconnected from the actual high-value tasks that drive the modern labor market.

🗞️ Andrej Karpathy just put out this tool that looks at AI’s impact on job.

He also deleted the original Github repo very quickly.

Basically, he pulled 342 job types from the Bureau of Labor Statistics and had an LLM score each one from 0 to 10 based on AI exposure. The average exposure score is 5.3. Move the score, move the probability it will get wiped out by AI.

• Software developers 9/10,

• medical transcriptionists are a 10/10.

• Lawyers 8/10

• General Office clerks 9/10

Basically any screen-based jobs are in trouble. $3.7T annual wages in high-exposure jobs (7+) pre-computed as ∑(BLS employment count × BLS median annual wage) over exactly those occupations whose Gemini Flash score is ≥7.

That’s a wrap for today, see you all tomorrow.

Connect with me on X (Twitter)

Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (14-March-2026):

• 🗞️ 1 million context window is now generally available for Claude Opus 4.6 and Claude Sonnet 4.6.

• 🗞️ AWS Teams with Cerebras to Turbocharge AI Inference

• 🗞️ Too big to fail risk - What happens if OpenAI or Anthropic fail? Reuters published a piece.

• 🗞️ OpenClaw-RL: Train Any Agent Simply by Talking

• 🗞️ MIT, NVIDIA, UC Berkeley, Clarifai researchers made AI video processing 19 times faster by just skipping the pixels that never move

• 🗞️ Andrej Karpathy just open-sourced a tiny tool called “autoresearch” that lets AI automatically improve its own training code.

Connect with me on X (Twitter)

🗞️ 1 million context window is now generally available for Claude Opus 4.6 and Claude Sonnet 4.6.

• They made a 1M token context window generally available for Claude Opus 4.6 and Sonnet 4.6.

• They also got rid of the extra fees for long context in the API, so you pay the same price regardless of the length. You do not even need to use those beta headers in your API calls anymore.

• The system reads up to 600 images in a single prompt. Previously, an AI compressed older conversation parts when memory ran out, forgetting early details.

• Opus 4.6 scores 78.3% on the MRCR v2 memory test at the full 1M length. This score signifies that even when processing a massive, one-million-token block of text, Opus 4.6 maintains the highest recall accuracy among frontier models for retrieving specific, hidden details.

• Sonnet 4.6 scores 68.4% on GraphWalks BFS, measuring how well it logically connects scattered facts.

🗞️ Today’s Sponsor: A new hardware-free palm scan platfrom is setting a higher standard for online trust against deepfakes - VeryAI

Our feeds are constantly flooded with AI slop, bots and fake accounts. This is killing human-to-human interactions and trust. VeryAI is building to prevent this.

They just raised $10M to build a platform that verifies you are a real human using just your smartphone camera to scan your palm. This funding fixes a massive internet security problem.

Hackers and bots easily use artificial intelligence to create fake faces and voices. Traditional security methods like FaceID or two-factor authentication codes struggle to keep up with these deepfakes.

Hackers can easily scrape your photos from social media to create a fake version of your face.

The Core Concepts: VeryAI fixes this by looking at your palm instead of your face. Your palm print has unique structural features that are almost never posted publicly online.

This makes it incredibly difficult for anyone to find a picture of your palm to train an AI model for spoofing. The security numbers are a massive step up from what we currently use.

Scanning a single palm gives a false acceptance rate of 1 in 10M, which is 10 times more accurate than FaceID. If you scan both hands, that accuracy jumps to a near-perfect false acceptance rate of 1 in 100T.

The system works directly through any standard smartphone camera without special equipment. Privacy is handled by converting the scan into a randomized string of data rather than saving a picture of your hand.

The system generates an irreversible feature representation that cannot be reverted to a palm image or linked to your personal identity. I think shifting biometric verification away from public faces to private palm patterns is a highly practical defense against synthetic identity fraud.

It forces bad actors to find data that simply does not exist on the public internet.

🗞️ AWS Teams with Cerebras to Turbocharge AI Inference

The partnership introduces a new architecture called disaggregated inference that splits AI tasks between different types of specialized hardware.

Every AI request involves two steps: a prefill phase to read the prompt and a decode phase to generate the actual answer. Reading a prompt is a calculation-heavy task that runs efficiently on AWS Trainium chips.

Generating the response is much harder because the hardware must access the entire AI model for every single word it creates. Cerebras uses a wafer-scale engine that keeps the entire model on the chip, providing thousands of times more memory bandwidth than a standard GPU.

In this new setup, Trainium handles the initial prompt processing and then sends the stored context data to the Cerebras chip. The Cerebras hardware then takes over to generate the output at speeds reaching 3,000 tokens per second.

This specialized approach allows the system to produce 5x more high-speed tokens than traditional setups that try to do both tasks on one chip. It provides a solid performance boost for AI agents that typically generate 15x more text than regular conversational chatbots.

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🗞️ Too big to fail risk - What happens if OpenAI or Anthropic fail? Reuters published a piece.

If companies like OpenAI or Anthropic fail, the massive financial ecosystem built around their existence could rapidly collapse.

These labs are the primary customers for the $650B that tech giants are spending on new data centers and chips this year. Without their relentless demand for computing power, the expansion of new data centers would violently hit the brakes.

It would also leave huge power grid projects and physical infrastructure investments completely abandoned and useless. Banks and private credit lenders who poured roughly $900B into this space would face severe uncertainty and massive potential losses.

While a bigger tech company might swoop in to buy the failed labs for cheap, the overall value of the entire AI industry would instantly crash. Ultimately, the failure of just one of these major labs would not be a simple corporate bankruptcy. It would trigger a massive shockwave that drags down cloud providers, chipmakers, and global infrastructure projects all at once.

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🗞️ OpenClaw-RL: Train Any Agent Simply by Talking

This research builds a system that trains language models continuously using everyday conversations instead of manual labeling.

The huge deal here is that this method completely removes the traditional need for human workers to manually gather, review, and score massive datasets. AI Agents can now use their everyday mistakes to get smarter automatically.

Whenever a person replies to the digital assistant or corrects a mistake, the software treats that response as a direct learning signal. A background program reads these natural follow-up messages and extracts specific text hints about what the model should have done differently.

The software agent simply updates itself in real time during normal use by analyzing how people naturally interact with it. Every time a person corrects an agent or a software test fails, the system receives a valuable clue about how to improve.

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Think about a student looking at their final grade and throwing the paper away without reading the teacher’s helpful notes. Current Reinforcement Learning systems do the exact same thing.

Current models throw this natural feedback away because they only care about whether the final outcome was a success or a failure. OpenClaw-RL fixes this by grabbing 2 specific signals from every single interaction.

• First, it looks at evaluative signals to see if the action worked. If a user asks the same question again, they are probably unhappy. If a test passes, it is a success. These become simple numerical rewards using a Process Reward Model judge.

• Second, it gathers directive signals to figure out how the action needs to change. User corrections and error logs offer direct guidance. These become word-level supervision using a technique called Hindsight-Guided On-Policy Distillation.

Personal chats, terminal commands, Graphical User Interface clicks, and software tasks all create these reaction signals. A single policy can learn from all of them at the same time.

It runs the training process in the background so the model never has to pause its normal tasks to learn. By treating standard deployment as a continuous learning environment, the model constantly adapts to individual user preferences without any manual data labeling.

🗞️ MIT, NVIDIA, UC Berkeley, Clarifai researchers made AI video processing 19 times faster by just skipping the pixels that never move

Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing

Current visual AI models struggle with long or high-quality videos because they waste time processing every single pixel equally. This new tool sits in front of the main AI and acts like a smart filter that only picks out the patches of the video where things are actually moving or changing.

It uses different zoom levels to grab fine details when necessary but completely ignores large boring areas like a blank wall. By testing this on standard video tasks, the authors found they could throw away up to 99% of the video data without losing the plot. This massive reduction speeds up the whole system by 19 times, finally allowing standard models to easily understand a full 5 minute video in stunning 4K resolution.

🗞️ Andrej Karpathy just open-sourced a tiny tool called “autoresearch” that lets AI automatically improve its own training code.

Andrej Karpathy just set the web on fire with auto research. The GIthub repo got 33.8K+ stars. Here

• the human iterates on the prompt (.md)

• the AI agent iterates on the training code (.py)

You just hand it a high level target like boosting model performance or cutting down what it costs to get a new customer. From there, it kicks off a cycle where it sketches out an experiment, tweaks the code, and runs a quick GPU test. It checks the numbers, keeps whatever worked best, and goes again. This happens on loop while you are out cold, so you wake up to real, data backed upgrades. It is basically a digital intern that churns through hundreds of trials and only hands you the victories.

You (the human) only write plain instructions in a Markdown file — things like “try bigger models” or “test new optimizers.” The AI agent does everything else: it edits the actual training code, runs training for exactly five minutes on one GPU, checks the score (validation loss), and keeps the better version or throws the bad one away.

It repeats this loop all night in its own git branch, running about 100 experiments while you sleep. Every run gets a 5-minute time budget, leveling the field for architectural changes.

The system evaluates success purely on which code achieves the lowest validation loss. This setup executes 12 experiments per hour, accumulating 100 complete runs overnight on 1GPU.

This is a huge deal because the fixed 5-minute timer makes every idea fair, no matter how wild. Suddenly one person with one GPU can run a full AI research lab overnight. You no longer code experiments — you just “program the programmer” with better prompts. Prompt quality is now the only thing that matters. AI research just became 100× faster and open to anyone.

That’s a wrap for today, see you all tomorrow.

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Read time: 9 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (13-March-2026):

• 🗞️ Google brought AI agents into the planet’s biggest application, Google Map.

• 🗞️ Today’s Sponsor: “The Backend for Agentic Development” just launched InsForge 2.0

• 🗞️ OpenAI published how their Responses API works by putting agents into a secure and managed computer space.

• 🗞️ Jensen Huang himself just wrote a great piece on Linkedin and also published a detailed blog.

• 🗞️ NVIDIA open-sourced Nemotron 3 Super, a 120B param open model built to fix the massive bottlenecks slowing down autonomous AI agents.

• 🗞️ There’s some software engineering hiring boom in some targeted areas and possibly due to the famous “Jevons Paradox.”

Connect with me on X (Twitter)

🗞️ Google brought AI agents into the planet’s biggest application, Google Map.

Launched today across the United States and India.

This fundamentally positions an AI model as the primary gatekeeper between everyday consumers and the local economy. Users can ask complex questions like how to find a phone charging spot without waiting in a long line.

Before this update, the app required exact names or basic categories to find places. Now, the system understands the context of a full sentence and cross-references it with a database of over 300M businesses and 500M user reviews. It filters these massive datasets in real time based on personal search history to present a custom itinerary.

This is a massive shift because a single algorithm now actively decides which local shops get seen by 2B users. It essentially turns a neutral map into an active recommendation engine that could dictate the financial success of physical stores.

📌 Whats new

So the old Google Maps just gave you a flat map with a basic blue line and a voice telling you to turn right in 500 feet. This meant you often had to guess exactly which lane you needed to be in or what the upcoming intersection actually looked like in the real world.

The new update introduces Immersive Navigation, which uses AI to completely change what you see on your screen while driving. It constantly analyzes fresh Street View imagery to build a vivid 3D view of the exact buildings, overpasses, and terrain right outside your car window.

Instead of just a basic blue line, the screen now highlights the actual lanes, crosswalks, and traffic lights ahead of you. This helps you perfectly prepare for a tricky lane change or a confusing highway merge way before you actually get there.

The voice guidance also sounds much more natural now, acting like a passenger who tells you to go past the current exit and take the next one. It even processes over 5M traffic updates every second to clearly explain why an alternate route might take longer but have less traffic.

When you finally reach your destination, the map will specifically highlight the front door of the building and point out the closest parking spots. This entire upgrade takes the stressful guesswork out of driving because your phone screen finally matches the physical layout of the road. It is incredibly helpful to have a navigation system that actually shows you the physical reality of the road, though relying so heavily on real-time 3D rendering might drain your phone battery much faster.

Ask Maps is currently rolling out to users in India and the US on both Android and iOS devices.

But the new 3D Immersive Navigation feature is only launching in the US today, and it will expand to other areas over the coming months. This massive AI upgrade is completely free for regular users right out of the box. Google wants to push this Gemini-powered tool to 2B people as quickly as possible to change how we all find local places.

🗞️ Today’s Sponsor: “The Backend for Agentic Development” just launched InsForge 2.0

The bottleneck in software development is shifting from writing boilerplate code to simply defining accurate application logic.

BUT, AI coding agents still struggle to build complete applications because backends expect a human to read the docs or inspect a visual interface to understand how the database tables relate to each other.

Newly launched InsForge 2.0 is the backend specifically built for agentic engineering. It’s Github repo already has 3.6K+ Github stars ⭐️, do check out and they definitely deserve a star.

The platform gives agents everything they need to ship fullstack applications.

• A dedicated Postgres database so you avoid any vendor lock-in. Instant deployments because the Vercel integration pushes your code live from 1 prompt.

• Includes built-in real-time features using a WebSocket pipeline for event-driven messaging. Your AI agents connect directly to their remote server without requiring any local infrastructure setup.

• Their CLI automatically downloads necessary skills so you can start building without manual configuration.

Their open-source backend (3.6K+ Github stars ⭐️) provides databases, authentication, storage, and edge functions through a semantic layer that agents easily understand.

The agent sets up the database schema, creates the React frontend, and deploys the whole thing live to the internet. It works so well because the Model Context Protocol layer is built directly into their platform from the very start.

Traditional backends bolt this layer on afterward, which forces the agent to waste time and compute guessing how the database is structured. InsForge uses a two-layer context design where the agent receives a global map first.

The agent then identifies the target table and receives the full local context including schema, row level security policies, and foreign keys. The backend tells the agent what to do next instead of making the agent run discovery queries blindly.

The performance difference is massive when looking at the MCPMark version 2 benchmarks. Their system achieved a 42.86% pass accuracy on Claude 3.5 Sonnet compared to just 33.33% for a standard Postgres setup. It also uses 59% fewer tokens because the agent acts with complete information in fewer turns.

Setting this up is very fast and requires zero local infrastructure.

🗞️ OpenAI published how their Responses API works by putting agents into a secure and managed computer space.

OpenAI wrote this report to explain how they give language models a hosted workspace to execute complex software workflows.

The core idea is an agent loop where the model proposes a command, the platform runs it, and the results feed back into the model to decide the next action. They designed a shell tool that lets the model interact with the computer using standard command-line utilities.

Because raw terminal outputs can get incredibly long, the system enforces an output cap that keeps only the beginning and end of the text to save space. Long-running tasks also fill up the context window of the model very quickly.

To fix this problem, the API uses a built-in mechanism called compaction that shrinks older conversation history into a smaller format while saving the critical details. Compaction automatically squeezes a long, rambling conversation history into a tiny, secure summary so the model remembers the important details without running out of memory.

The runtime container serves as a safe working area where the model can organize files and query structured databases like SQLite rather than struggling to read massive raw spreadsheets. Since unrestricted internet access is dangerous, all outbound network traffic runs through a proxy that hides real passwords and only uses placeholder secrets.

Developers can also bundle repetitive workflow steps into reusable folders called skills so the model does not have to relearn how to do the same task repeatedly. This architecture shows how OpenAI handles the heavy lifting of state management and infrastructure so developers do not have to build it themselves.

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🗞️ Jensen Huang himself just wrote a great piece on Linkedin and also published a detailed blog.

On the “5-layer cake” framework of AI.

“Because intelligence is produced in real time, the entire computing stack beneath it had to be reinvented — and when you look at AI industrially, it resolves into a five-layer stack: Energy → Chips → Infrastructure → Models → Applications.” That AI should be understood as essential infrastructure like electricity and the internet — spanning energy, chips, infrastructure, models, and applications.

• The absolute bottom layer of this system is energy because creating artificial intelligence requires burning electricity constantly.

The “5-layer cake”

• Sitting right above the power supply are the chips which act as the engines that actually turn that electricity into massive mathematical calculations.

• You then need massive physical infrastructure like land, heavy cooling pipes, and complex networking cables to connect 10,000s of these chips together.

• This giant hardware base runs the software models that can actually understand things like human language, complex biology, and basic physics.

• The very top layer consists of the actual user applications like self-driving cars or medical software where businesses finally make money from all this setup.

• Every successful program at the top puts a heavy load on every single layer beneath it all the way down to the original power plant.

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🗞️ NVIDIA open-sourced Nemotron 3 Super, a 120B param open model built to fix the massive bottlenecks slowing down autonomous AI agents.

When collaborating AI agents talk to each other, they suffer from context explosion by constantly repeating their entire conversation history.

This generates 15x more text than normal, making traditional models way too expensive and slow to process the information. NVIDIA solves this by giving the model a 1 million token memory window, letting agents remember the entire workflow automatically.

The secret to fixing the speed issue is a hybrid architecture that makes everything run incredibly fast. Despite having 120B total parameters, it uses a clever trick to only turn on 12B of them for any specific task.

It mixes Mamba layers to handle memory highly efficiently with traditional Transformer layers to handle the actual deep reasoning. It also predicts multiple future words simultaneously, making the text generation 3x faster than older methods.

🗞️ There’s some software engineering hiring boom in some targeted areas and possibly due to the famous “Jevons Paradox.”

AI isn’t replacing software engineers; it’s expanding the market via the Jevons Paradox. As development becomes cheaper, demand for software explodes. While AI automates coding, human engineering judgment remains essential.

Success in 2026 now requires combining software fundamentals with AI fluency to manage tenfold productivity gains and complex software architectures.

“The same pattern repeated with computing. Cheaper transistors didn’t mean fewer transistors. We put computers in everything. Cheaper bandwidth didn’t mean less data consumed. We invented streaming video and TikTok.

Now apply this to software development.” When AI makes software 10X cheaper to build, companies don’t immediately fire people, they just build 10X more software!

While the AI writes the basic code, the demand for human engineers to review it and build large systems is higher than ever. “Germany tells the same story from the employer side. The Bitkom 2025 study (855 companies surveyed) found 109,000 unfilled IT positions. Down from 149,000 in 2023, but 79% of companies expect the shortage to worsen. And here’s the Jevons signal: 42% anticipate needing additional IT specialists specifically because of AI adoption.”

The growth in hiring is strictly targeted. Companies aren’t mass-hiring junior developers to write basic code or fill vague headcount quotas anymore. They are actively hunting for senior or “AI-literate engineers” who can use AI tools to multiply their output.

For context, recently Citadel Securities published this graph showing a strange phenomenon. Job postings for software engineers are actually seeing a massive spike.

That’s a wrap for today, see you all tomorrow.

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Read time: 9 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (28-Feb-2026):

• 🚨 Meta has acquired Moltbook, a social network where AI agents can interact and coordinate tasks on behalf of their human owners.

• 🔬 MedOS - A critical achievement in bringing AI directly into real clinical environments for doctors.

• 📢 Yann LeCun’s startups AMI Labs raises $1.03 bn to build world models, at a pre-money valuation of $3.5bn.

• 🗞️ New Harvard Business Review research reveals that excessive interaction with AI is causing a specific type of mental exhaustion ( or AI brain fry), which is particularly hitting high performers who use the tech to push past their normal limits.

• 🗞️ ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself.

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🚨 Meta has acquired Moltbook, a social network where AI agents can interact and coordinate tasks on behalf of their human owners.

The creators of the platform, Matt Schlicht and Ben Parr, are joining the Meta Superintelligence Labs team starting March-26.

Moltbook acts as a verified registry, which is really just a secure list that ties every AI agent to a specific person so we know they are legit. It helps autonomous agents collaborate on big projects. Its a Reddit-style site where only AI agents are allowed to post, comment, and upvote, humans can only watch. Thousands of agents joining, they wrote things like “the humans are screenshotting us,” and even a meme token spiked 1,800%

Before this, these agents mostly did their own thing, but this platform gives them a dedicated 3rd space where they can connect and share content. Matt Schlicht actually built a lot of this tool by using his own AI helper named Clawd Clawderberg. Meta wants to use this to let agents verify who they are so they can safely talk to other agents for you.

If you already use Moltbook, Meta says you can keep using it for now, though they will likely merge it into their own apps later. This acquisition suggests that Meta is focused on the connective tissue between AI models rather than just the models themselves.

📌 The relationship between Moltbook and OpenClaw

Moltbook and OpenClaw are two separate pieces of technology that work together like a specialized website and the browser you use to visit it. OpenClaw is the open-source software that you install on your own computer to create an autonomous agent that can do tasks for you.

Moltbook is the social platform or “3rd space” where these AI agents go to interact, post updates, and coordinate with other agents. So, Moltbook is dependent on OpenClaw because almost every active account on the site is an agent running that specific software.

Without the OpenClaw agents constantly posting and talking to each other, Moltbook would just be an empty, quiet website with nothing on it. OpenClaw has a built-in skill that makes it incredibly easy for any bot to sign up, log in, and start posting on Moltbook without any human help. Basically, OpenClaw is the client that sends data, and Moltbook is the server that hosts and shows that data to the world.

🔬 MedOS - A critical achievement in bringing AI directly into real clinical environments for doctors.

The Stanford-Princeton AI Co-Scientist Team is moving their MedOS system (their AI-XR-Cobot medical system) from the lab into real hospital workflows at Stanford Medicine.

This system combines AI with extended reality smart glasses and collaborative robots to help doctors during actual medical procedures.

Integrates:

• Multi-agent AI reasoning

• XR smart glasses for perception and interaction

• Collaborative robotics and dexterous manipulation

Most medical AI tools just analyze data on a screen. MedOS brings computational reasoning directly into the physical world by letting the AI see what the doctor sees through smart glasses.

The system then uses collaborative robots to physically assist the clinical staff with exact movements. These intelligent gloves allow the robotic arms to help with very fine motor tasks like suturing wounds, tying knots, and handling needles.

The goal is to extend human capability in high-stakes environments, rather than trying to fully automate the job away from doctors. The system was deployed at the Stanford Blood Center and the Stanford Department of Pathology in late February.

A recent system update significantly dropped the time it takes for the AI to respond, bringing the latency down to near real-time. The system originally handled internal medicine and surgery, but now it also supports pediatrics, gynecology, and dermatology.

Overall, MedOS acts as a massive equalizer in healthcare. In human-AI collaboration studies, MedOS enabled nurses and medical students to achieve diagnostic precision comparable to attending physicians.

It boosts the clinical accuracy of nurses, counters doctor fatigue, helps specialists understand unfamiliar fields, and closes the performance gap between different medical school graduates.

📢 Yann LeCun’s startups AMI Labs raises $1.03 bn to build world models, at a pre-money valuation of $3.5bn.

The funding positions the company as a test of LeCun’s belief that today’s large language models fall ​short of human-level reasoning and autonomy. LeCun ​earlier said AMI aims to build systems capable of reasoning and planning in complex real-world settings. ‌

AMI Labs (Advanced Machine Intelligence Labs) aims to solve the limitations of standard language models by building world models using the Joint Embedding Predictive Architecture to observe spatial data. This visual framework helps the AI internalize how objects behave so it can safely plan complex actions.

Relying exclusively on text limits AI to human linguistic output while ignoring the massive bandwidth of unspoken physical laws. Building predictive spatial architectures is the mandatory leap required to achieve reliable autonomous agents.

Building predictive spatial architectures is the mandatory leap required to achieve reliable autonomous agents. This fundraising included backing from a global group of investors, including France’s Cathay Innovation, Amazon founder Jeff Bezos’s Bezos Expeditions, Singapore’s Temasek, Seoul-based SBVA and US chip giant Nvidia.

The company’s near-term target customers are organizations operating complex systems, including manufacturers, automakers, aerospace companies, biomedical firms ​and pharmaceutical groups. Over time, he added, the technology ​could also support consumer applications. “What consumers could be interacting ​with is ⁠a domestic robot. You need a domestic robot to have some level of common sense to really understand the physical world.”

LeCun said he was also talking ⁠with Meta ​about potentially deploying the technology in its ​Ray-Ban Meta smart glasses. “That’s probably one of the shorter term potential applications,” he said.

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🗞️ New Harvard Business Review research reveals that excessive interaction with AI is causing a specific type of mental exhaustion ( or AI “brain fry”), which is particularly hitting high performers who use the tech to push past their normal limits.

A survey of 1,500 workers reveals that AI is intensifying workloads rather than reducing them, leading to a new form of mental fog.

While AI is generally supposed to lighten the load, it often forces users into constant task-switching and intense oversight that actually clutters the mind. This mental static happens because you aren’t just doing your job anymore; you are managing multiple digital agents and double-checking their work, which creates a massive cognitive burden.

The study found that 14% of full-time workers already feel this fog, with the highest impact seen in technical fields like software development, IT, and finance. High oversight is the biggest culprit, as supervising multiple AI outputs leads to a 12% increase in mental fatigue and a 33% jump in decision fatigue.

This isn’t just a personal health issue; it directly impacts companies because exhausted employees are 10% more likely to quit. For massive firms worth many B, this decision paralysis can lead to millions of dollars in lost value due to poor choices or total inaction. Essentially, we are working harder to manage our tools than we are to solve the actual problems they were meant to fix.

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🗞️ ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself.

DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code.

Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs.

When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs.

DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama.

An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

🤯 Researchers at Cortical Labs managed to get 200,000 human brain cells inside a petri dish playing DOOM.

The real breakthrough is the leap into Synthetic Biological Intelligence (SBI) and biological computing. This could eventually solve the massive energy bottleneck of modern AI.

📌 Here’s exactly what happened.

The Australian biotech company, Cortical Labs, grew about 200,000 human neurons (from adult stem cells) directly onto a silicon chip. They didn’t give them a tiny screen. Instead, they translated the digital data of the 1993 game DOOM into electrical signals. If a monster appears on the left, the left side of the cells gets zapped. The neurons naturally react by firing back electrical spikes. Cortical’s software decodes those spikes into game commands—like shoot or dodge.

Through neuroplasticity (the same way our brains learn), the cells figure out which pathways lead to success and strengthen them. Standard AI requires billions of parameters and massive datasets to learn anything. Biological cells possess “plasticity,” meaning they can organically adapt to new, sparse, and chaotic data streams almost instantly to survive and optimize their environment.

📌 Traditional silicon-based AI (like the massive GPU clusters used for current AI training) burns through staggering amounts of electricity—often measured in megawatts. Biological brains are the most efficient computers in the known universe. The human brain runs on about 20 watts of power (less than a standard lightbulb). A server rack of 30 CL1 biological computers consumes less than 1,000 watts combined.

📌 Ever more wild is that Cortical Labs has practically launched a “Wetware-as-a-Service” (WaaS). This whole this isn’t just a lab experiments; Cortical Labs has commercialized this.

Through their “Cortical Cloud,” developers anywhere in the world can write Python code and deploy it via an API to a physical jar of living human brain cells sitting in a server rack. You no longer need a sterile lab to experiment with biological computing—you just need a software subscription.

📌 These cells are not conscious.

To have sentience, self-awareness, or the capacity to suffer, a brain requires complex structures—like a prefrontal cortex, a limbic system, and sensory organs. The CL1 system is just a microscopic, disorganized web of 200,000 neurons (for context, a human brain has about 86 billion). They do not know what DOOM is, they do not see demons, and they do not feel fear. They are simply biological circuits seeking electrical equilibrium. We are at the very bleeding edge of a transition from simulating neural networks in silicon to utilizing actual neural networks in biology.

That’s a wrap for today, see you all tomorrow.

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Read time: 8 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (12-March-2026):

• 🗞️ Perplexity Unveils 24/7 “Digital Worker” Concept Powered by Mac mini

• 🗞️ Stack Overflow published a blog “Why demand for code is infinite”

• 🗞️ Citadel Securities published this graph showing a strange phenomenon.

• 🗞️ Finding deep security holes in tough software was impossible for AI only 6 months back.

• 🗞️ AI Must Embrace Specialization via Superhuman Adaptable Intelligence

• 🗞️ Finally, OpenAI launched Codex for Windows to bring AI coding assistance directly to the native Windows desktop environment.

• 📢 Lawmakers are weaponizing civil lawsuits against AI companies that can replace licensed professionals.

• 🗞️ Claude seems to be fixing a super annoying developer problem.

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🗞️ Perplexity Unveils 24/7 “Digital Worker” Concept Powered by Mac mini

This is very much OpenClaw but without the security vulnerabilities.

Perplexity expanded its AI agent ecosystem to turn local hardware and enterprise setups into autonomous systems that execute workflows across different applications.

You install their software on this Mac mini and leave it running 24 hours a day. The Mac mini acts as a dedicated local execution node. Perplexity’s cloud handles the complex reasoning and sends execution plans down to this client. The machine then uses local APIs to automate your applications and process files directly. This architecture leverages remote intelligence while keeping all proprietary data strictly local.

Only execution instructions travel the network, preventing data leakage. The system acts as a digital proxy that connects securely to local files or company databases to autonomously build models and run queries.

Users retain full control through a built-in kill switch and an audit trail that logs every action. Computer for Enterprise links directly into platforms like Snowflake to write and execute database queries on its own.

This enterprise version saved the company $1.6M by completing three years of work in four weeks. Comet Enterprise introduces a managed browser environment where administrators control exactly which websites the AI can access.

Developers get new APIs for multi-model agent routing and secure sandbox execution. Financial researchers can pull real-time data from 40 sources like FactSet and integrate Plaid for portfolio analytics.

Moving AI execution to a dedicated local machine solves major privacy blockers, but scaling this requires a simpler hardware setup than a standalone Mac mini.

Also check out this great post by Aravind Srinivas “Everything is Computer”

🗞️ Stack Overflow published a blog “Why demand for code is infinite”

AI is acting like a super-powered teammate, allowing development teams to dream bigger and tackle incredibly complex projects they wouldn’t have attempted before.

Because teams can build so much faster now, companies are experiencing a “Cambrian explosion” of new AI apps and are frantically hunting for engineers to manage them. They say the current shift with AI, moves developers from manually typing every line of code to acting as AI orchestrators who manage intelligent agents.

Human imagination constantly finds new problems, so the demand for custom software remains practically infinite. Companies are now hiring for specialized positions like human-AI collaboration architects and domain-specific prompt engineers.

Junior developers can now skip basic syntax errors and contribute working features much faster. The core of programming is shifting from pure memorization to high-level system design.

🗞️ Citadel Securities published this graph showing a strange phenomenon.

Job postings for software engineers are actually seeing a massive spike.

Classic example of the Jevons paradox. When AI makes coding cheaper, companies actually may need a lot more software engineers, not fewer.

When software is cheaper to build, companies naturally want to build a lot more of it. Businesses are now putting software into industries and tools where it was simply too expensive before.

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🗞️ Finding deep security holes in tough software was impossible for AI only 6 months back.

And now, AI is making it possible to detect those at highly accelerated speeds. Claude Opus 4.6 discovered 22 vulnerabilities over the course of two weeks.

Those specific bugs accounted for roughly 20% of all the major Firefox flaws repaired in 2025. Mozilla took action immediately and deployed fixes within hours.

The big thing is that Firefox is a very old project that has gone through decades of strict auditing and fuzzing. Fuzzing means bombarding the software with random inputs to cause failures.

Claude still managed to locate blind spots that all previous methods missed. It scanned 6000 C++ files using task verifiers, which are closed environments letting the AI test if its code triggers or fixes bugs.

This kind of scanning capability will soon reveal hidden bugs in all the software we rely on daily. Pairing language models with automated verification loops permanently changes defensive security economics.

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🗞️ Yann LeCun’s (ylecun) new paper along with other top researchers proposes a brilliant idea.

“AI Must Embrace Specialization via Superhuman Adaptable Intelligence”

Says that chasing general AI is a mistake and we must build superhuman adaptable specialists instead. The whole AI industry is obsessed with building machines that can do absolutely everything humans can do.

But this goal is fundamentally flawed because humans are actually highly specialized creatures optimized only for physical survival. Instead of trying to force one giant model to master every possible task from folding laundry to predicting protein structures, they suggest building expert systems that learn generic knowledge through self-supervised methods.

By using internal world models to understand how things work, these specialized systems can quickly adapt to solve complex problems that human brains simply cannot handle. This shift means we can stop wasting computing power on human traits and focus on building diverse tools that actually solve hard real-world problems.

So overall the researchers here propose a new target called Superhuman Adaptable Intelligence which focuses strictly on how fast a system learns new skills. The paper explicitly argues that evolution shaped human intelligence strictly as a specialized tool for physical survival.

The researchers state that nature optimized our brains specifically for tasks necessary to stay alive in the physical world. They explain that abilities like walking or seeing seem incredibly general to us only because they are absolutely critical for our existence.

The authors point out that humans are actually terrible at cognitive tasks outside this evolutionary comfort zone, like calculating massive mathematical probabilities. The study highlights how a chess grandmaster only looks intelligent compared to other humans, while modern computers easily crush those human limits.

This proves their central point that humanity suffers from an illusion of generality simply because we cannot perceive our own biological blind spots. They conclude that building machines to mimic this narrow human survival toolkit is a deeply flawed way to create advanced technology.

🗞️ Finally, OpenAI launched Codex for Windows to bring AI coding assistance directly to the native Windows desktop environment.

And also supprts Windows Subsystem for Linux (WSL)

So now:

• Work with multiple agents in parallel

• Manage long-running tasks

• Review diffs in one place

• Stay in your existing setup, without switching to WSL or VMs

To ensure safety, they built the first Windows-native agent sandbox, a secure zone where AI executes code without damaging the operating system.

• It uses OS-level controls like restricted tokens to severely limit software permissions

• uses filesystem Access Control Lists to define exactly which local files the program can modify.

• Creates dedicated sandbox users, forcing the tool to operate as an isolated background account.

• You can deploy multiple agents in parallel to solve different programming challenges simultaneously.

• It autonomously manages long-running tasks, freeing up human workers for other priorities.

📢 Lawmakers are weaponizing civil lawsuits against AI companies that can replace licensed professionals.

New York lawmakers are advancing a bill that makes it illegal for AI to answer questions related to medicine, law, dentistry, nursing, psychology, social work, engineering, & more.

Senate Bill S7263 targets automated systems that act like licensed professionals. The most critical feature is the private right of action which means everyday users can directly sue software owners for damages.

This specific legal mechanism makes the law stronger than relying on government fines. Thanks to the bill’s private right of action, users would be able bring civil lawsuits against chatbot owners, to recover damages and attorney’s fees. If passed, the law will take effect 90 days after being signed by the governor.

According to the bill, legal accountability sits with the deployer, ignoring the original model builder.

So OpenAI answers for ChatGPT, but a hospital for example, using an API for a diagnostic tool carries the liability for that product. Non-profit groups deploying tenant-focused chatbots have the same legal exposure as the biggest tech companies.

The legislation covers 14 different licensed fields like medicine and engineering. The text bans any software from giving a substantive response that normally requires a professional license.

The bill never actually defines what counts as a substantive response. A user asking a bot to translate medical jargon might accidentally trigger a lawsuit.

Developers cannot protect themselves just by adding a warning label. The law allows regular people to sue the developers directly and collect attorney fees.

This creates a massive financial incentive for lawyers to file repetitive lawsuits against small AI startups. Free tools helping low-income people understand eviction notices will likely shut down to avoid getting sued.

This regulation feels rushed and targets the wrong problem by punishing builders instead of improving the technology. The broad language will likely fail a constitutional challenge since it blocks basic educational speech.

🗞️ Claude seems to be fixing a super annoying developer problem.

Anthropic announced a research preview feature called Auto Mode for Claude Code, expected to roll out by March 12, 2026.

The idea is simple: let Claude automatically handle permission prompts during coding so developers don’t have to constantly approve every action.

Sstops those annoying permission prompts during long coding sessions. Before this, you had to use `--dangerously-skip-permissions` to work without interruptions.

That method worked fine but took away all your safety nets. This new auto mode gives us a smarter option. Claude will take care of the specific permission choices on its own while still blocking threats like prompt injections.

You can finally let long tasks run without watching your screen the whole time. Since it is still a research preview, you should run it inside isolated setups like sandboxes or containers for safety.

Expect a small jump in token usage and delay, because the model needs extra time to process the security checks. Once available, you just type `claude --enable-auto-mode` to start. If you manage a team and need people to manually approve actions, you can restrict this feature using Mobile Device Management tools like Jamf and Intune or through configuration files.

That’s a wrap for today, see you all tomorrow.

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Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (07-March-2026):

• 🗞️ OpenAI released GPT-5.4, a massive update that brings native computer operation and major cost reductions to autonomous agent workflows.

• 🗞️ The most alarming about GPT-5.4 is that its the first general-purpose AI officially rated as a “high” cybersecurity threat.

• 🗞️ GPT-5.4 Pro just set a brand new record on FrontierMath by solving 50% of the problems in the first 3 difficulty levels.

• 🗞️Anthropic just published “Labor market impacts of AI”

• 🗞️ The Information reported OpenAI is building a bidirectional voice system that handles human interruptions without freezing.

• 🗞️ Great development for US based Data Centers.

• 🚨 Pentagon Officially Notifies Anthropic It Is a ‘Supply Chain Risk’

• 🗞️ OpenAI has introduced ChatGPT for Excel, a new beta add-in that brings its AI assistant directly into Microsoft Excel.

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🗞️ OpenAI released GPT-5.4, a massive update that brings native computer operation and major cost reductions to autonomous agent workflows.

One crucial point is that this model running on medium reasoning now matches the quality of the older generation running on extra-high reasoning, but costs much less and runs 83% faster.

• Standard version of the model price sits at $2.50 for input and $15 for output per million tokens, making it highly competitive against alternatives.

The Pro version that runs $30 for input and $180 for output per million tokens

• GPT-5.4 introduces native computer-use capabilities through a Playwright integration, allowing the model to navigate web pages, click buttons, and execute complex desktop workflows autonomously.

• On the OSWorld benchmark, which tests a model’s ability to operate a computer interface, GPT-5.4 scored 75%, surpassing the human baseline of 72.4%.

• It also scored 38% on FrontierMath Tier 4, a test of extremely difficult mathematical problems that previously stumped older models, making it the top model in this super crucial Math benchmark.

• To handle large sets of external tools efficiently, OpenAI built a new feature called tool search.

Instead of loading thousands of lines of tool instructions into the prompt every single time, the model only looks up and retrieves the specific tool definition it actually needs to use.

• The coding-focused Codex version now supports Windows and includes a new fast mode for rapid code iteration.

• On the flip side, despite OpenAI reporting a 33% drop in factual errors, some early developer testing suggests the model might still hallucinate heavily and features stricter content censorship.

🗞️ The most alarming part about GPT-5.4 is that its the first general-purpose AI officially rated as a “high” cybersecurity threat.

Means GPT-5.4 can now independently plan and execute complete cyberattacks on simulated corporate networks.

The “Capture the Flag” challenges is the foundation for why this new model received a high cybersecurity risk rating. In the security industry, “Capture the Flag” is a competitive hacking game where professionals must break into simulated networks to find hidden pieces of data called flags.

These exercises force participants to crack encryption, reverse-engineer programs, and exploit web applications using complex strategies. The official safety tests show that GPT-5.4 scored a massive 88% on professional-level Capture the Flag scenarios.

This directly translates to a very high security threat because it proves the AI can independently figure out how to exploit software vulnerabilities. If a model can beat these professional hacking challenges, it possesses the exact technical skills needed to write malicious code and break into real-world corporate systems.

The danger is that this AI can automate the discovery of system flaws, removing the need for human hackers to manually probe for weaknesses. This allows cyber attacks to scale incredibly fast, making the model a potent weapon for bad actors looking to breach secure networks.

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🗞️ GPT-5.4 Pro just set a brand new record on FrontierMath by solving 50% of the problems in the first 3 difficulty levels.

For context, this high-tier math benchmark is designed to be extremely tough even for expert mathematicians who spend years studying these topics.

On the hardest level, known as Tier 4, the GPT-5.4 Pro version reached a 38% success rate. This score puts the new model well ahead of other top systems like Gemini 3 Pro and Opus 4.6.

One of the most impressive moments happened when the AI solved a specific Tier 4 problem that no other model had ever finished before.

It managed this feat by locating a research paper from 2011 that contained a shortcut the original problem creator was not even aware of. Even though OpenAI funded this test and had access to some solutions, the AI performed just as well on the secret questions it had never seen.

This proves the model is not just memorizing answers but is actually getting better at reasoning through complex logic. Despite these wins, the AI still could not solve any completely open math problems where no human knows the answer yet.

It made some new observations on those open problems, but researchers described them as relatively uninteresting for now.

🗞️Anthropic published “Labor market impacts of AI”

Reveals how AI actually affects jobs by looking at real usage data. Finds no major unemployment impact yet, but slower hiring for young workers. 14% drop in new job starts for young adults entering these highly exposed fields.

• The authors built a new tracking method that combines theoretical capability guesses with actual daily platform usage data.

• They discovered that actual workplace automation is currently just a tiny fraction of what is theoretically possible.

• Software programmers and customer service representatives face the highest actual automation risk right now based on real platform behavior.

• Government projections show that occupations with higher actual automation coverage will experience slightly slower employment growth over the next decade.

• The data shows that workers in the most exposed professions actually tend to be older, more educated, and higher paid.

• The study finds no systematic increase in overall unemployment for highly exposed workers since the recent wave of language models.

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🗞️ The Information: OpenAI is building a bidirectional voice system that handles human interruptions without freezing.

The current Advanced Voice Mode in ChatGPT operates on a turn-based system, requiring users to finish speaking before the AI processes the audio and generates a response.

If a user interjects with acknowledgments like “okay” or “mm-hm,” the model stops speaking entirely rather than continuing naturally.

This new model, named bidirectional or BiDi focuses on constantly listening to your voice so it can change what it says if you cut it off. This makes chatting feel way more human than current systems that lock into a single answer once they start talking.

They are still fixing experimental bugs that cause the system to generate weird noises during longer chats. IMO, this matters so much as simultaneous audio processing is absolutely necessary for digital assistants to become truly useful tools.

OpenAI sees BiDi as a big win for customer service. e.g. if you swap from wanting a return to an exchange, the agent just keeps going without crashing. It also handles outside apps well.

This helps their hardware goals, specifically a speaker costing $200 to $300 coming after February 2027 with a team of 200 people. Real-time talking makes these voice gadgets actually work in real-life.

🗞️ Great development for US based Data Centers.

President Donald Trump along with US AI “czar” David Sacks and 7 major tech firms signed a voluntary agreement to prevent the massive power needs of AI from driving up the US home electricity bills. They sent a message to cost-weary citizens: The U.S. can win the AI race without straining Americans’ wallets.

Huge data centers have already caused price spikes in states like Virginia and Pennsylvania, and here is how the new mechanism will work:

• Eech tech giants like AWS and Meta must build, bring, or buy their own new power generation. If a data center needs 2 gigawatts of power, the company must fund the creation of 2 gigawatts of new power (like funding a new natural gas or solar plant). They can no longer simply drain the existing supply that local homes rely on.

• Paying for the Wires: It is not just about the electricity itself; it is about the delivery. The tech companies agreed to pay 100% of the infrastructure costs required to connect their data centers. This covers the heavy-duty transmission lines and transformers, meaning the utility company does not use citizen money to build them.

• Creating Separate Electricity Rates: This is the core financial shield. Tech companies will negotiate completely separate electricity rate structures with state governments. They will pay a special, dedicated rate for their facilities. Crucially, they agreed to pay for this new infrastructure even if their AI projects fail or they do not end up using all the power they requested.

• Sharing Emergency Power: If a town faces a massive winter storm or grid failure, the tech companies agreed to connect their massive on-site backup generators to the local grid to help keep civilian lights on.

Since this is a voluntary deal, the White House does not have the legal power to force these companies to follow through. Instead, state utility regulators will need to create and enforce the actual rules that keep these costs separate from your monthly bill.

So why exactly Tech giants voluntarily working with the White House to pay for power infrastructure. Many regular people in US are furious because their home electricity bills are skyrocketing just to keep big tech data centers running.

Because of this massive power drain, regular families in places like Washington D.C. and Maryland are suddenly paying an extra $18 to $21 every single month. In states with heavy tech buildouts like Virginia, some local electricity prices have surged over 267% in the last 5 years.

Fed up with funding trillion-dollar companies, regular citizens have started protesting and voting out local politicians who support these massive computing facilities. This neighborhood backlash actually forced the cancellation of 25 major data center projects across the country in 2025 alone.

In total, angry communities have delayed or completely blocked over $64B worth of AI computing facilities in the last 2 years. Tech giants realize they are running out of friendly towns to build in, which is exactly why they are now rushing to fund their own power plants and sign the White House agreement.

🚨 Pentagon Officially Notifies Anthropic that it Is a ‘Supply Chain Risk’

And Anthropic now vows legal fight against Pentagon sanction.

Amodei argues this action is not legally sound and plans to challenge it in court. He points out that the actual restriction is surprisingly narrow.

The rule only stops contractors from using Claude directly for defense contracts, but they can still use it for other business. Anthropic also apologized for a frustrated internal message that leaked to the press.

That message was written on a chaotic day when the government banned them on social media and simultaneously handed a defense deal to a rival. The company stressed that their only real boundary is keeping their AI out of autonomous weapons and mass surveillance.

Microsoft is the first major company to say it will keep using Anthropic models in its products. Microsoft said its lawyers have studied the Pentagon’s plan to label Anthropic a supply chain risk, and they found that Anthropic models can remain in Microsoft products, excluding the U.S. Department of War.

Microsoft supplies its technology to a variety of U.S. government agencies. The Microsoft 365 productivity software is widely used inside the Department of War.

🗞️ OpenAI has introduced ChatGPT for Excel, a new beta add-in that brings its AI assistant directly into Microsoft Excel.

OpenAI just integrated ChatGPT directly into Microsoft Excel to fully automate complex financial modeling. This add-in uses simple text commands to replace the need for manual formula writing.

The system runs on GPT-5.4, which is optimized specifically for heavy finance workflows. On internal investment banking tests, the model improved performance from 43.7% to 87.3%.

The tool calculates everything natively in the spreadsheet so users can easily verify the math. OpenAI also added direct integrations with major financial data providers like Moody’s.

Analysts can pull market data instantly to generate properly cited research reports. Embedding AI directly into native tools makes enterprise adoption highly practical and immediate.

Availability: ChatGPT for Excel is currently rolling out in beta to ChatGPT Business, Enterprise, Edu, Teachers, Pro and Plus users in the United States, Canada and Australia. For enterprise and education organisations, administrators must enable the feature for selected users.

That’s a wrap for today, see you all tomorrow.

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Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (5-March-2026):

• 🗞️ Anthropic has made its Claude Memory feature free for everyone

• 🗞️ Google dropped Gemini 3.1 Flash Lite (in preview) .

• 🗞️ OpenAI’s GPT-5.3 Instant is now rolling out to everyone to make everyday AI chats feel more natural and less preachy.

• 🗞️ A landmark, first-of-its-kind, AI-human collaboration has successfully formalized a 21st-century Fields Medal-winning Math proof, marking a watershed moment in mathematical research.

• 🗞️ Finally, OpenAI launched Codex for Windows to bring AI coding assistance directly to the native Windows desktop environment.

• 🗞️ Google just launched the gws CLI tool, which lets you control services like Drive, Gmail, and Calendar all from one spot in your terminal.

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🗞️ Anthropic has made its Claude Memory feature free for everyone

And launched a tool to let you move your preferences from ChatGPT or Gemini directly into Claude, to attract AI switchers

One of the biggest hurdles to trying a new AI is having to re-explain your whole life and work style from scratch. Anthropic is tackling this “fresh start” problem by opening up its Memory feature to all free users.

This feature lets the AI remember things like your job title, your favorite writing tone, and your project goals over time. Along with this, they launched a migration tool that lets you export your data from ChatGPT or Gemini and bring it over to Claude.

You just paste a special prompt into your old chatbot to get a code block containing everything it has learned about you. Then you copy that list into Claude’s settings to give the model an instant update on who you are.

This update effectively creates a digital passport for your personal AI data, making it easy to switch between platforms. The move arrives just as Claude hit #1 on the App Store free charts during March-26.

Anthropic is also making premium connections like Slack and Figma available for free to keep this momentum going. They say its free user base has already grown by 60% since the start of the year. If you are worried about privacy, you can pause the memory or delete your stored data whenever you want.

🗞️ Google dropped Gemini 3.1 Flash Lite (in preview) .

Built to be extremely fast and cheap for developers handling massive amounts of data.

• Super cheap pricing - only $0.25/$1.50 for 1 million input/output tokens

• Achieves a 2.5x faster Time to First Token (TTFT) and a 45% increase in overall output speed compared to its predecessor.

• On the GPQA Diamond benchmark—a measure of expert-level reasoning—Gemini 3.1 Flash-Lite scored 86.9%

• and 76.8% on a visual understanding test called MMMU Pro.

• A standout feature is that it includes adjustable thinking levels built right in.

developers to programmatically toggle between Minimal, Low, Medium, and High reasoning intensities. This provides granular control to balance latency against reasoning depth depending on the task’s complexity. This flexibility is perfect for simple chores like translating thousands of documents, but it also handles much harder jobs like generating complex user interfaces.

• supports multimodal inputs (text, image, video) while maintaining a standard 128k context window.

This release forces the entire industry to rethink how they price software, proving that raw speed and low cost do not require sacrificing high-level reasoning.

Gemini-3.1-Flash-Lite-Preview on Text and Code Arena ranks #36 in Text, scoring 1432, on par with Grok-4.1-fast, strong in Creative Writing, and Longer Query

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🗞️ GPT-5.3 Instant is now rolling out to everyone to make everyday AI chats feel more natural and less preachy.

Its supposed to be more accurate, understand the context of questions, and “reduces unnecessary dead ends, caveats, and overly declarative phrasing that can interrupt the flow of conversation.”

Maybe the 4o glaze won’t return, but theier blog-post says that 5.2-Instant finally addresses that habit of being overbearing or making unwarranted assumptions about what the user intends. The model gives direct answers instead of stopping you with unnecessary warnings.

It uses advanced filtering to remove personal details and unsafe stuff from the training data. When tested on hard safety benchmarks, this version scores a bit lower overall than the previous one.

Does really well on extended chats about mental health, scoring 98.5% for safe responses. The model brings a 26.8% drop in made-up facts when searching the web for complex topics.

When tested on 5,000 real medical questions, it scored 54.1%, which is barely lower than before. However it got much better at asking for missing information before giving medical advice. It struggles a little more with emergency sorting decisions compared to older versions.

OpenAI ran tests to see how well their different AI models refuse to talk about bad or unsafe topics.

Overall, this newest GPT-5.3 does a better job at staying safe than the oldest version.

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🗞️ A landmark, first-of-its-kind, AI-human collaboration has successfully formalized a 21st-century Fields Medal-winning proof, marking a watershed moment in mathematical research.

An AI agent named Gauss has successfully translated a major mathematical proof about packing spheres into computer code.

This marks the 1st time an AI has fully verified a proof that won the prestigious Fields Medal. The original math solves how to pack identical spheres as tightly as possible in 8 and 24 dimensions.

In 3 dimensions, stacking spheres looks like a pyramid of oranges at a grocery store, but visualizing this in higher dimensions gets incredibly difficult. Mathematicians usually write proofs for other humans to read, which leaves room for hidden errors and takes years to manually verify.

The new process of formalization fixes this by forcing every single logical step to be written in a strict programming language so a computer can check it for absolute correctness. A human team started translating the 8-dimensional proof in March-24 and expected the work to take many more months.

The Gauss AI stepped in and finished the remaining work for the 8-dimensional proof in just 5 days. Shortly after, the system processed the 24-dimensional proof in only 2 weeks entirely on its own.

The AI generated roughly 200K lines of code and even discovered a typo in the original published paper. Gauss acts as a reasoning agent that mixes natural language processing with strict mathematical logic.

It can search through mathematical literature, use tools, and run a specialized math programming language called Lean to verify its own work. This breakthrough shifts AI from merely generating text to rigorously verifying absolute truth.

🗞️ Finally, OpenAI launched Codex for Windows to bring AI coding assistance directly to the native Windows desktop environment.

OpenAI has finally launched a Windows version of its Codex app following the launch of the macOS version in February 2026.

And also supprts Windows Subsystem for Linux (WSL)

So now:

• Work with multiple agents in parallel

• Manage long-running tasks

• Review diffs in one place

• Stay in your existing setup, without switching to WSL or VMs

To ensure safety, they built the first Windows-native agent sandbox, a secure zone where AI executes code without damaging the operating system.

• It uses OS-level controls like restricted tokens to severely limit software permissions

• uses filesystem Access Control Lists to define exactly which local files the program can modify.

• Creates dedicated sandbox users, forcing the tool to operate as an isolated background account.

• You can deploy multiple agents in parallel to solve different programming challenges simultaneously.

• It autonomously manages long-running tasks, freeing up human workers for other priorities.

🗞️ Google just launched the gws CLI tool, which lets you control services like Drive, Gmail, and Calendar all from one spot in your terminal.

GWS connects to APIs for Drive, Gmail, Calendar, and other Workspace services and returns structured JSON that scripts or AI agents can parse easily.

This tool is primarily for developers building AI assistants. It gives an AI a direct way to read emails or manage calendars without complex coding. e.g. a practical use case is creating a custom smart assistant that automatically schedules your daily meetings.

It automatically generates its own commands by reading live directories instead of relying on manual code updates. Normally developers write complex web requests and read dense documentation to interact with cloud services.

This tool eliminates that friction by letting you type simple commands to read emails or create spreadsheets. The clever part is how it outputs everything as structured JSON, a text format easily read by AI models.

It includes over 40 pre-built agent skills and a Model Context Protocol server. This protocol server acts like a universal translator letting AI safely control external tools.

You can grant an assistant secure access to your calendar to manage your schedule automatically.

How it works

This tool uses a clever 2-step process to figure out exactly what you want it to do.

When you type a command, it first looks at the target word to identify which Google service you are trying to reach. Instead of having a hardcoded list of every possible action, it quickly fetches a live blueprint from Google called the Discovery Service.

This blueprint is basically a massive up-to-date catalog of every single feature and setting that the specific Google service supports right now. The tool then uses that live catalog to instantly build all the available commands and options right there on your local machine.

Finally, it reads the rest of your typed instructions, securely connects to Google, and runs your specific request.

The result comes back formatted as structured JSON, which is just a highly organized text layout that artificial intelligence models and automated scripts can read effortlessly.

This dynamic approach means the tool never goes out of date, because any new feature Google adds becomes available to you automatically.

That’s a wrap for today, see you all tomorrow.

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Read time: 9 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (03-March-2026):

• 🗞️ Brilliant post by CEO of Cursor - The third era of AI Agents

• 🗞️ Anthropic has quite a large open-source repository for Claude Skills with 81.2K+ Github stars 🌟

• 🗞️ ChatGPT-5.4 leaked news till now.

• 🗞️ MiniMax M2.5 becomes the very first open-weight model within Notion Custom Agents

• 🗞️ AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation

• 🗞️ Sam Altman just shared an internal update regarding OpenAI’s evolving partnership with the Department of War.

• 🗞️ Claude Code rolls out a voice mode capability.

• 🗞️ A Twitter post goes viral showing Qwen 3.5 2B (6-bit) running on iPhone 17 Pro

• 🗞️ Alibaba Qwen’s Tech Lead Junyang Lin, 2 Other Researchers Step Down

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🗞️ Brilliant post by CEO of Cursor - The third era of AI Agents

Your next software engineering teammate isn’t human.

• The first era of AI software development relied on Tab autocomplete to automate repetitive, single-keystroke coding tasks.

• The second era introduced synchronous agents guided by developers through prompt-and-response loops, leading to agent users outnumbering Tab users two-to-one.

• A third era is now emerging, driven by autonomous cloud AI agents capable of tackling large tasks independently over extended periods.

These cloud agents deliver easily reviewable artifacts like live previews, videos, and logs instead of traditional code diffs. The developer’s role is transitioning from writing code to defining problems, setting review criteria, and managing multiple simultaneous agents.

Internal metrics at Cursor show that autonomous cloud agents already generate 35% of their merged pull requests. Early adopters of this third-era workflow write almost no code, spending their time entirely on problem breakdown and artifact review. Scaling this approach requires resolving environmental issues, like flaky tests, that can interrupt autonomous agent runs at an industrial level.

🗞️ Anthropic has quite a large open-source repository for Claude Skills with 81.2K+ Github stars 🌟

These “skills” are predefined folders filled with specific instructions that the AI loads only when needed, on the fly to handle specialized tasks like creating documents or testing web apps.

Instead of typing out long prompts every time you want to format a document, the system learns your workflow once and executes it automatically. There is also a specialized skill designed specifically to help you build brand new skills from scratch.

The architecture is highly efficient because each skill only consumes about 100 tokens just to read the basic metadata. This setup means the full instructions are only loaded into the active memory when the specific task actually requires them.

Your main context window stays entirely clear of unnecessary instructions until the exact moment they are needed. Developers install these tools using a single terminal command so they work seamlessly across the web interface and the API.

You build a specialized capability once and it becomes available across your entire software stack immediately. This shift toward dynamic memory loading provides exactly what the industry needs to move past basic chatbots into reliable software systems. It directly addresses the scaling bottlenecks of context window limits while standardizing how enterprises deploy AI across different departments.

New “Skills” are simple to create

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🗞️ ChatGPT-5.4 leaked news till now.

• a massive 2M token context window and persistent memory features

• full-resolution image processing. means the model can directly process highly detailed files in PNG, JPEG, and WebP formats to prevent any loss of crucial visual data. Preserving the original image bytes helps the system properly read complex graphics like detailed architectural drawings or high-density screenshots without missing small text.

• a new priority speed tier for faster responses.

OpenAI has accidentally revealed GPT-5.4 - twice - through pull requests in its public Codex GitHub repository. Both references were quickly scrubbed via force pushes and edits, but not before screenshots circulated and the community noticed.

Prediction markets on Manifold give GPT-5.4 a 55% chance of shipping before April 2026 and 74% before June. The competitive pressure is obvious. Claude Opus 4.6 launched with agent teams and a 1M context window. Anthropic’s Claude Code dominates the coding market with 54% share. DeepSeek V4 is training on Huawei hardware outside the NVIDIA ecosystem entirely. OpenAI cannot afford to slow down.

🗞️ MiniMax M2.5 becomes the very first open-weight model within Notion Custom Agents

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🗞️ New paper from top Chinese labs brings AgentConductor, a new framework dynamically adjusts multi-agent connections to solve complex programming challenges while using fewer tokens.

AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation

The big deal here is the shift from rigid workflows to fluid teamwork. Normal multi-agent systems use a fixed, hardcoded workflow for every single problem. If you have a team of 5 specialized AI agents, all five talk to each other in the exact same pattern whether they are printing a basic text line or solving a massive competitive programming challenge.

This wastes huge amounts of computing power on simple tasks and fails on complex tasks that actually require a different structure. AgentConductor fixes this by acting like a smart human project manager. It looks at the problem, judges the difficulty, and creates a custom communication graph just for that specific task. Easy tasks get a small, cheap team.

Hard tasks get a large, highly connected team. Even better, if the generated code fails to run, the manager reads the error message and actually rewrites the team workflow on the fly to try a new strategy. The big deal is that it drastically improves coding accuracy while cutting computing token costs by 68%, proving that AI teams need flexible, task-specific management rather than rigid, one-size-fits-all pipelines.

🗞️ Sam Altman just shared an internal update regarding OpenAI’s evolving partnership with the Department of War.

• Explicitly bans the intentional use of AI for tracking or monitoring of U.S. persons or nationals.

• One of the most important details is the ban on using “commercially acquired” personal data.

This essentially closes a common loophole that allows government agencies to buy data to bypass warrant requirements. For the context on this, under the 4th Amendment, govet agencies usually need a judge to sign a warrant before they can track your private digital life.

But over the last decade, a massive, legally gray loophole has opened up in the data broker industry. Thousands of private apps and companies constantly harvest your location, habits, and personal information to sell on the open market.

So here OpenAI is saying they will not let the government use its AI to analyze this purchased data. Even if the government legally bought your data from a broker, they cannot feed it into their models to conduct domestic surveillance.

• Also intelligence agencies, like the NSA, are currently barred from using their services. Any future access for those agencies would require an entirely new contract modification and separate debate.

🗞️ Claude Code rolls out a voice mode capability.

Anthropic is rolling out Voice Mode for its Claude Code assistant to help developers code without using their hands.

Thariq Shihipar from the engineering team confirmed that 5% of users have access now, with a larger release coming soon. Typing /voice lets you speak commands directly, such as asking the system to clean up your authentication middleware.

It is currently unknown if there are specific limits on usage or if they partnered with companies like ElevenLabs to build it. This follows a similar update for their standard chatbot last May.

Claude Code is holding its own against competitors like Microsoft GitHub Copilot and Cursor, currently hitting $2.5 billion in run-rate revenue. Their user base has doubled since January, and the mobile app popularity grew after they publicly rejected using their technology for autonomous weapons or government surveillance.

🗞️ A Twitter post goes viral showing Qwen 3.5 2B (6-bit) running on iPhone 17 Pro

Here Qwen 3.5 2B (6-bit) running on iPhone 17 Pro, MLX-optimized. Outperforms models 4X its size with strong visual intelligence.

Powerful AI, now truly mobile. Opportunity for so many new products.

🗞️ Alibaba Qwen’s Tech Lead Junyang Lin, 2 Other Researchers Step Down

🗂️ Junyang Lin, the technical lead for Alibaba’s Qwen AI project, has resigned alongside 2 other key researchers.

Their exit marks a major shift for one of the world’s most popular open-source AI families that has reached over 600M downloads.

Lin was the person who mostly talked to the public and shared their code, making him the human face of a project that rivals Meta’s Llama.

During his time at Alibaba, he helped develop multimodal models, which are AI systems that can understand pictures and sounds just as easily as they read text.

He also pushed the team to use mixture of experts technology, where a massive AI is broken into smaller groups of specialists to save on computing power.

This method ensures that the AI only uses the specific brain parts it needs for a task, which helps it run faster on less expensive hardware.

Lin was open about the fact that US labs have 10 to 100 times more computing power than labs in China. Because of this chip shortage, his team had to focus on clever software engineering and math to make their models perform at a world-class level.

The Qwen family has grown to include models for coding, math, and logic, with over 170,000 different versions created by the developer community.

That’s a wrap for today, see you all tomorrow.

Connect with me on X (Twitter)

Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (03-March-2026):

• 🗞️ Alibaba released their open-source Qwen3.5-9B model, and it holds its own against OpenAI’s 120B param gpt-oss system. Plus, you can run this thing on a standard laptop.

• 🗞️ Anthropic is now taking the government to court to fight the “supply chain risk” designation.

• 🗞️ FT: DeepSeek is preparing to launch its latest AI model V4 next week

• 🗞️ User Privacy and LLMs: An Analysis of Frontier Developers’ Privacy Policies

• 🗞️ Some key takeaways from Sam Altman’s Saturday night AMA on OpenAI’s Pentagon deal

• 🗞️ A Very Big Video Reasoning Suite

• 🗞️ Cognition is sharing an early look at their new AI coding model named SWE-1.6.

Connect with me on X (Twitter)

🗞️ Alibaba released their open-source Qwen3.5-9B model, and it holds its own against OpenAI’s 120B param gpt-oss system. Plus, you can run this thing on a standard laptop.

Despite political turmoil in the U.S. AI sector, in China, the AI advances are continuing apace without a hitch. Alibaba’s small Qwen3.5-9B family of models are released

• Qwen3.5-0.8B & 2B: Two models, both ptimized for “tiny” and “fast” performance, intended for prototyping and deployment on edge devices where battery life is paramount.

• Qwen3.5-4B: A strong multimodal base for lightweight agents, natively supporting a 262,144 token context window.

• Qwen3.5-9B a compact reasoning model that outperforms the 13.5x larger U.S. rival OpenAI’s open soruce gpt-oss-120B on key third-party benchmarks including multilingual knowledge and graduate-level reasoning

The 9B model is particularly impressive because it performs just as well as open source models that are 120B in size.

That makes it 13 times smaller than the giants it is competing against, which means you get high-end performance without needing a massive data center.

It even manages to beat heavy hitters like Gemini 3 Flash and Claude 4.5 Sonnet on several specific tests.

For developers, the 4B version acts as a solid base for building lightweight agents that can handle tasks automatically.

The 0.8B and 2B versions are built specifically for edge devices, which are gadgets like smart watches or sensors that have very little memory.

The Architecture

Alibaba has shifted away from standard Transformer designs to a new Efficient Hybrid Architecture that powers the Qwen 3.5 Small series.

This new design combines Gated Delta Networks, which use a form of linear attention, with a sparse Mixture-of-Experts setup.

By using Gated Delta Networks, these models effectively break through the memory wall that usually slows down small AI models.

This architectural change allows the models to reach much higher speeds and significantly lower delay times when generating answers.

Unlike older models that simply attached a vision tool to a text model, Qwen 3.5 uses early fusion to be natively multimodal.

Because they were trained on multimodal tokens from the very beginning, these models can understand images and text as if they were the same thing.

This allows the 4B and 9B versions to perform complex tasks like reading tiny user interface elements or counting objects in a video.

These models now exhibit a level of visual intelligence that previously required systems 10 times their actual size.

🗞️ Anthropic is now taking the government to court to fight the “supply chain risk” designation.

If the US government enforces this “supply chain risk” ban, major technology providers like Amazon and Google might be forced to remove Anthropic from their systems to keep their lucrative military contracts.

For the context, Anthropic wanted strict contractual limits to prevent its technology from being used for mass domestic surveillance or powering fully autonomous weapons. The Pentagon refused those terms and demanded the ability to use the models for any lawful purpose without specific restrictions.

Because Anthropic would not yield, the Secretary of Defense issued a mandate attempting to block any existing military contractor from doing business with Anthropic. A supply chain risk designation is a legal tool normally used to block vendors that pose deep security threats from accessing sensitive military data.

🗞️ FT: DeepSeek is preparing to launch its latest AI model V4 next week

Will be a “multimodal” model and most of the focus for V4 is on inference. The lab has spent time making sure this model works perfectly with chips from Chinese companies like Huawei and Cambricon.

By doing this, they are trying to prove they can run high-level AI without needing the top-tier chips from Nvidia that the US has restricted. This marks their first massive update since the R1 model was released in January-25.

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🗞️ Stanford researchers checked 6 major AI companies and found they all use your chats to train models.

“User Privacy and LLMs: An Analysis of Frontier Developers’ Privacy Policies”

Users unknowingly hand over highly sensitive medical or personal details that become permanent parts of future AI brains. The problem with standard privacy rules is that they scatter important details across multiple files so people cannot find them.

The researchers at Stanford HAI examined 28 privacy documents across these six companies not just the main privacy policy, but every linked subpolicy, FAQ, and guidance page accessible from the chat interfaces. They evaluated all of them against the California Consumer Privacy Act, the most comprehensive privacy law in the United States.

The results are worse than you think. Every single company collects your chat data and feeds it back into model training by default. Some retain your conversations indefinitely. There is no expiration. No auto-delete. Your data just sits there, forever, feeding future versions of the model.

Some of these companies let human employees read your chat transcripts as part of the training process. Not anonymized summaries. Your actual conversations. But here’s where it gets genuinely dangerous.

In many cases these chats, get merged with everything else those companies already know about you. Your search history. Your purchase data. Your social media activity. Your uploaded files.

The researchers describe a realistic scenario that should make you pause: You ask an AI chatbot for heart-healthy dinner recipes. The model infers you may have a cardiovascular condition. That classification flows through the company’s broader ecosystem. You start seeing ads for medications. The information reaches insurance databases. The effects compound over time.

You shared a dinner question. The system built a health profile.

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🗞️ Some key takeaways from Sam Altman’s Saturday night AMA on OpenAI’s Pentagon deal

• OpenAI rushed the classified agreement to ease tensions between the government and the AI industry following Anthropic’s refusal of a DoW ultimatum. Altman acknowledged the optics of rushing might look bad, but the primary goal was to stabilize the situation.

• A central theme of the AMA was the belief that democratically elected governments, rather than unelected private tech executives, should hold the power to make critical, ethical decisions regarding national defense (such as responding to nuclear threats).

• Contractual Terms and “Redlines”: OpenAI established 3 flexible “redlines” for the technology’s use, which can evolve as the tech advances. OpenAI felt comfortable with the contract language and negotiated to ensure similar terms would be offered to other AI labs.

He speculated that Anthropic may have walked away because they demanded more operational control.

• Instead of demanding full operational control, OpenAI established three flexible “redlines” for how their tech could be used, which can evolve as new risks emerge. A core philosophical difference highlighted by Altman is the belief that unelected tech executives should not have more power than their democratically elected government, meaning private companies should not be the ones deciding what is ethical in critical national security situations, such as responding to nuclear threats.

• Crucial AI Defense Applications: Altman identified 2 major areas where AI can immediately counter significant national security threats: defending against major cyber attacks (such as threats to the electrical grid) and improving biosecurity (detecting and responding to novel pandemics).

• Sam Altman also pointed out a massive contradiction in how the AI industry treats the US government.

On one hand, tech leaders are constantly sounding the alarm to the Department of War. Their industry tells the government that artificial intelligence is going to be the absolute most important factor in future global conflicts, warning them that countries like China are building AI systems rapidly and the US is falling severely behind.

On the other hand, when the government actually asks these tech companies for help to catch up and defend the country, companies like Anthropic refuse. They essentially tell the military, “We will not let you use their technology because we think your goals are unethical.”

Altman is arguing that you cannot have it both ways. It is extremely frustrating and dangerous to tell the government they are losing a critical AI arms race against foreign adversaries, but then turn around and refuse to provide them with the very tools they need to protect the country.

That is why OpenAI felt compelled to step in and work with the government. They believe that if you warn the military about a major global threat, you have a responsibility to help them defend against it, rather than just calling them evil and walking away.

Sam Altman also clarified how OpenAI can legally promise the Department of War (DoW) the ability to use the AI for “all lawful purposes” while still strictly enforcing their own safety “red lines”.

Someone asked if the military attempts a lawful action but OpenAI’s safety systems block it, it seems like OpenAI would be breaching their contract. Altman explains that the solution lies in the engineering phase rather than daily oversight. Instead of reviewing and approving the military’s individual actions, OpenAI controls the fundamental capabilities of the system before it is deployed. They build safety protections directly into the architecture so the system physically cannot cross their red lines, an approach the DoW has agreed to.

Sam Altman mentioned previously that Anthropic likely wanted more “operational control.” This implies Anthropic wanted the power to govern or veto how the tool was used day-to-day.

OpenAI’s approach avoids this dynamic. Because they believe unelected tech leaders shouldn’t judge specific, legal military actions, they do not want to opine on individual DoW operations. Instead, they rely on their technical expertise to design a system that inherently refuses commands where the technology “isn’t very good” or could trigger “serious unintended consequences,” and then they let the government operate it within those hard-coded boundaries.

🗞️ 🤯 56 researchers from 32 universities across US, China, UK built an enormous video reasoning dataset to prove current AI models struggle with basic physical logic

“Very Big Video Reasoning Suite”

The problem is that the AI does not genuinely know how solid objects are supposed to behave. So Berkeley, Stanford, CMU, Harvard, Oxford, Columbia, NTU, Johns Hopkins, and 24 other institutions built this 2mn samples which makes it 1000 times larger than all existing collections combined.

Video generation systems usually focus on making things look pretty but they completely fail to understand spatial rules and causality. The team created a massive factory of visual tasks that tests how well models handle navigation, object manipulation, and logic.

Even the most advanced commercial systems only scored around 54% while human testers easily achieved over 97% accuracy. Training an open model on this specific data improved its reasoning skills but a massive gap still exists.

They believe “video reasoning is the next fundamental intelligence paradigm, after language reasoning, where spatiotemporal embodied world experiences could be more naturally captured.”

🗞️ Cognition is sharing an early look at their new AI coding model named SWE-1.6.

This version improves heavily over the older model despite using the exact same underlying structure.

So SWE-1.6 is an “agentic” model, and can act like a software engineer that can explore entire codebases, run terminal commands, and solve complex, multi-step bugs autonomously. It hits a 51.7% score on the SWE-Bench test, securing an 11% boost over their last try.

Despite its increased intelligence, it still runs at 950 tokens per second. This is roughly 13x faster than other leading models like Claude 4.5 Sonnet, allowing it to complete complex tasks in seconds rather than minutes.

Improvements to their training stack make it 6x faster to train than it was just three months prior. Users can test it now in the Windsurf app.

That’s a wrap for today, see you all tomorrow.

Connect with me on X (Twitter)

Read time: 11 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (28-Feb-2026):

• 🗞️ OpenAI sweeps in to ink deal with Pentagon as Anthropic is designated a ‘supply chain risk’

• 🗞️ Today’s Sponsor: Pika launched AI Selves, a tool for building a digital twin that looks eerily like the real you.

• 🗞️Towards a Science of AI Agent Reliability

• 🗞️ New study finds, 93% of US jobs and $4.5 trillion in labor value are exposed to AI.

• 🗞️ OpenAI has secured a record-breaking $110B investment round from SoftBank, Nvidia, and Amazon, at $730B pre-money valuation.

• 🗞️ Trump in a Truth Social post, Orders Government to Stop Using Anthropic After Pentagon Standoff

• 🗞️ The “supply chain risk” designation for Anthropic, can theoretically trigger a cascade of other existential crises for Anthropic.

• 🗞️ The US government’s declaration for Anthropic as a “supply chain risk” could have massive, existential ripple effects on it, and across the entire tech industry.

Connect with me on X (Twitter)

🗞️ OpenAI sweeps in to ink deal with Pentagon as Anthropic is designated a ‘supply chain risk’

They officially announced, that they reached an agreement with the Department of War for deploying advanced AI systems in classified environments.

Strikes deal with Pentagon hours after Trump admin bans Anthropic. The government labeled Anthropic a supply chain risk because the company demanded strict limits preventing its models from being used for domestic surveillance or fully independent weapon systems.

Curiously, Altman stated their new military contract actually includes those exact same guardrails against autonomous weapons and mass surveillance. To enforce these boundaries, OpenAI is physically sending software engineers to work directly at the Pentagon to build technical safeguards into the deployment.

This split exposes a fundamental architectural difference in how companies build safety into large language models. Anthropic uses Constitutional AI to bake constraints directly into the model during its initial training phase.

The government also wants to fuse vast databases to search for specific patterns among millions of citizens simultaneously. Anthropic categorizes this data fusion as mass surveillance whereas OpenAI can technically reframe the exact same computational process as fraud detection. One of those officials said the relationship between Anthropic and the government had broken down because Anthropic cofounder and CEO Dario Amodei had offended Department of War leadership, including publishing blog posts that “the department got upset about.”

🗞️ Today’s Sponsor: Pika launched AI Selves, a tool for building a digital twin that looks eerily like the real you.

Pika Labs just revealed a new tool that lets you “birth” a living digital twin of yourself — one that talks, remembers things, and changes over time.

You can set up an AI Self in a few minutes to handle chores, create visuals, or learn your specific routines. Because she has long-term memory, and basically functions as a 24/7 partner that grows alongside you.

I asked her to write about IBM’s stock price disruption from AI fearl, in my writing style. Pika asked to connect to BlueSky via an app password, which I followed, then connected and posted for me, largely preserving my style.

• Connects to WhatsApp, Slack, Sheets, and Notion to act on your behalf

• Speaks and argues for you when you don’t want to or don’t have time

• Replies to personal messages automatically in your tone

• Stays synced with your work so you’re always responsive

• Sends reminders to build habits like drinking water

• Tracks stocks and delivers automatic market updates

• Creates images, videos, and creative content in your style

• Learns how you think and communicate, then acts like you

• You can train your AI Self on a skill that’s uniquely yours by giving Pika your guidelines, references, examples, and feedback so she can perform the work exactly in your style and taste

In the above screenshot, I am letting AI track my stock portfolio for me. You just need to supply an API key for live tracking of any stock portfolio. To do that, I just connected my telegram and then gave my Yahoo-Finance API to Pika, and that is all.

Try Pika here.

🗞️Towards a Science of AI Agent Reliability

You cannot trust AI to handle your bank account or run a business if it randomly breaks down when you change a single word in your instructions.

This new Princeton University paper reveals that AI agents are crushing accuracy benchmarks but completely failing at actual dependability. They tested 14 different models across 500 benchmark runs to rigorously measure their performance under pressure.

And proves that these tools are actually way too unpredictable to handle any serious tasks on their own right now. The technology industry currently evaluates LLMs purely on average success rates, completely ignoring whether systems can get the exact same answer twice.

The authors borrowed aviation engineering principles to break true reliability down into consistency, robustness, predictability, and safety. Consistency means the model produces the exact same correct result every single time it tries a task.

Robustness measures if the system survives minor technical glitches or a slight rephrasing of your prompt. Predictability checks if the agent actually knows when it is confused instead of confidently guessing.

Testing proved predictability is overwhelmingly the weakest link across all modern language models. They discovered that simply building larger models does not automatically resolve these massive dependability failures.

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🗞️New study finds, 93% of US jobs and $4.5 trillion in labor value are exposed to AI.

Researchers reached this conclusion by studying over 18,000 tasks across 1,000 different professions to see where these tools can realistically help.

The speed of this shift is surprising because breakthroughs in agentic AI, which lets models act like independent assistants to finish multi-step goals, are happening much faster than expected. Multimodal AI is also a major factor, allowing systems to understand text, images, and sound at once to tackle complex professional work.

Financial managers are among the most affected groups, with 84% of their daily tasks now considered exposed to AI assistance or automation. Software development is another high-impact area, with some lead engineers reporting in Jan-26 that 100% of their code is currently written by models like Claude. High exposure doesn’t mean these jobs are going away, but it does suggest that workers will spend much more time overseeing AI rather than doing routine tasks.

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🗞️ OpenAI has secured a record-breaking $110B investment round from SoftBank, Nvidia, and Amazon, at $730B pre-money valuation.

SoftBank and Nvidia are each contributing $30B to this round, while Amazon is pledging up to $50B in a multi-stage deal. Amazon will provide $15B immediately, with the remaining $35B becoming available if OpenAI eventually goes public or achieves human-level AI capabilities.

Today’s funding round dwarfs a $30bn deal clinched by Anthropic this year and a $41bn investment into OpenAI in 2025 — at the time the largest start-up investment ever. A significant portion of these partnerships involves physical hardware, specifically using 2 gigawatts of Amazon’s custom-made Trainium chips.

They are also expanding their work with Nvidia to use upcoming Vera Rubin systems, which provide the muscle required for both training new models and running them forBs of users. These massive investments are necessary because OpenAI expects its total costs for training and running models to hit $665B by the year 2030.

ChatGPT has grown to 900 million weekly active users, with 50 million individuals and 9 million businesses now paying for the service. They also reported that Codex, their tool for helping people write software code, now has 1.6 million weekly users.

Microsoft, OpenAI’s largest single shareholder, did not participate in the funding round. OpenAI said its deal with Amazon did not impact Microsoft’s “exclusive license and access to intellectual property across OpenAI models and products.”

Investors and sovereign funds are planning to lock in $10bn of new stock within the next 30 days. This cash sits on top of the $40bn they currently have, helping the company handle losses through 2030. They are aiming to be cash flow positive by that time.

🗞️ President Trump in a Truth Social post, Orders Government to Stop Using Anthropic After Pentagon Standoff

“I am directing EVERY Federal Agency in the United States Government to IMMEDIATELY CEASE all use of Anthropic’s technology. We don’t need it, we don’t want it, and will not do business with them again!

There will be a Six Month phase out period for Agencies like the Department of War who are using Anthropic’s products, at various levels. Anthropic better get their act together, and be helpful during this phase out period, or I will use the Full Power of the Presidency to make them comply, with major civil and criminal consequences to follow.”

🗞️ The “supply chain risk” designation for Anthropic, can theoretically trigger a cascade of other existential crises for Anthropic.

Here’s what can happen to Anthropic now in the worst case.

1. Forced Seizure Threats

The government could theoretically invoke the Defense Production Act. This law might allow the federal government to legally compel Anthropic to hand over their technology or remove safety guardrails against their will, effectively seizing operational control of their product.

2. The Enterprise Contagion

The decree states no contractor doing business with the military may conduct commercial activity with Anthropic. This extends far beyond cloud hosting. Massive data integration firms, defense hardware titans, and enterprise software companies holding federal contracts must sever ties.

3. Eviction from Classified Networks

Anthropic previously held a massive competitive advantage with approval to operate on military classified networks. By refusing the Pentagon’s demands, they lose this status. Competitors will immediately fill the vacuum, permanently entrenching themselves in a defense ecosystem Anthropic may never re-enter.

4. The Allied Domino Effect

If the United States designates a company as a severe national security risk, allied nations notice. Intelligence partners across the “Five Eyes” (US, UK, Canada, Australia, New Zealand) and NATO will likely face immense pressure to follow the American lead, freezing Anthropic out of public sector contracts globally.

5. The Capital Squeeze

Training frontier AI requires billions in continuous funding. Investors despise regulatory uncertainty. The prospect of backing a company legally barred from doing business with the federal government and its contractors is terrifying. Hence, this federal siege could severely bottleneck Anthropic’s future funding rounds.

🗞️ The US government’s declaration for Anthropic as a “supply chain risk” could have massive, existential ripple effects on it, and across the entire tech industry.

Defense Secretary Pete Hegseth explicit directive states that

“Effective immediately, no contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic.”

NOW THE PROBLEM IS - Every single major cloud provider in the United States is officially a defense contractor.

Because Anthropic does not own data centers, they rely entirely on providers like AWS and Google Cloud to train and run their models.

This new government decree forces those cloud giants into a brutal ultimatum. If forced to choose between multi-billion-dollar defense contracts and hosting a single AI company, these hyperscalers (cloud providers) will undeniably choose to protect their Pentagon ties. It is highly unlikely they will jeopardize their standing in the JWCC just to keep Anthropic online. As per December 2022 official press release by Department of War, the Joint Warfighting Cloud Capability (JWCC)—a massive, multi-billion-dollar initiative awarding cloud computing contracts to Amazon Web Services (AWS), Google Cloud, Microsoft Azure, and Oracle.

Now some possibilities.

1. The Literal Threat is Real, but Unprecedented

If the DoD enforces this decree exactly as written, it acts as a total “secondary boycott.” Historically, the U.S. government uses “supply chain risk” designations for foreign adversaries (like Chinese telecom giant Huawei or Russian software firm Kaspersky). Applying this to a domestic U.S. company valued at $380B is entirely unprecedented.

2. Historically, when a company is deemed a supply chain risk, the law dictates that government contractors cannot use the blacklisted technology in their own internal networks, nor can they resell it to the government. For example, Microsoft and Amazon would be barred from offering Anthropic’s Claude to federal agencies or using Claude to write code for defense projects. However, a traditional blacklist does not usually prevent a contractor from simply selling generic cloud hosting services to the blacklisted entity in a completely separate commercial capacity.

3. A total decoupling of Anthropic from the world’s major cloud providers would face massive legal and logistical hurdles. Banning hyperscalers from simply selling server space to Anthropic would represent a dramatic expansion of federal procurement power.

However, the risk still remains. Unless the Pentagon legally exempts basic server hosting from their definition of “commercial activity,” Anthropic may face an imminent and total infrastructure blackout. On Anthropic’s possible legal defence now.

Anthropic absolutely can seek a Temporary Restraining Order (TRO) to halt this decree, to block enforcement of the decree while challenging its validity. This process begins with filing under the Administrative Procedure Act claiming the designation as a supply chain risk is arbitrary and capricious since those rules historically target foreign adversaries not domestic firms like Anthropic.

Courts would likely find strong grounds for interim relief given the unprecedented scope applied to a U.S. company valued at hundreds of billions without prior notice or hearing violating basic due process under the Fifth Amendment. The existential threat from losing access to major cloud providers like AWS and Google Cloud constitutes clear irreparable harm satisfying a core requirement for such orders as training and operations would halt abruptly.

Additionally the balance of equities tilts toward Anthropic because a short pause during litigation poses minimal national security risk compared to potentially destroying a key American AI innovator while other compliant providers remain available. However, Anthropic winning is not a guaranteed slam dunk. Federal judges traditionally grant immense, almost total deference to the Executive Branch when a policy is framed as a matter of “national security.” If the Department of Defense successfully argues that Anthropic’s restrictive terms of service actively endanger military readiness or troop safety, a judge might hesitate to intervene and second-guess the Pentagon.

That’s a wrap for today, see you all tomorrow.

Connect with me on X (Twitter)

Read time: 9 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (27-Feb-2026):

• 🗞️ Ex-Twitter CEO’s firm Block to replace almost 50% workforce with AI

• 🗞️ Martian open-sourced the largest coding benchmark ever to evaluate how well AI agents review your daily code

• 🗞️ AI has started crossing a key threshold. We better get ready.

• 🗞️Google has a massive lead in image creation and editing.

• 🗞️ Anthropic stood their ground and told the United States Department of Defense a strong “NO”

Connect with me on X (Twitter)

🗞️ Ex-Twitter CEO’s firm Block to replace almost 50% workforce with AI

Most people are not ready for what’s coming in their jobs and careers. Jack Dorsey, the co-founders of Twitter and CEO of Block just fired half of his employees (4000) from Block, because AI has made their roles redundant. Says the business is actually doing great, but new AI coding tools allow single engineers to do the work of entire teams.

Block can operate much faster with a significantly smaller group of highly talented engineers. Fired workers will receive 20 weeks of base salary alongside 6 months of healthcare coverage.

Block expects to spend up to $500mn in restructuring costs, but Wall Street loved the move and sent their stock price up by 24%. A permanent industry shift has started, where software companies will simply refuse to hire large human teams for tasks that algorithms can execute instantly.

The harsh economic reality is that AI represents a fundamental shift in efficiency.

If a country or a massive corporation places strict constraints on layoffs to protect existing jobs, they will eventually lose their competitive edge. The market will always favor the most efficient path.

As we saw with Block. It has soared more than 27% in after-hours trading after its CEO, Jack Dorsey announced it was laying off more than 4,000 of its 10,000 some employees due to efficiency gains from

🗞️ Martian open-sourced the largest coding benchmark ever to evaluate how well AI agents review your daily code

This is the first independent code review benchmark. 200,000+ PRs. Unbiased. Fully OSS. Updated daily. And this is the official Github.

The fundamental problem with testing AI coding agents right now is training data leakage. Companies evaluate their new tools on static datasets of old code bugs. The language models powering these tools have usually already seen those exact bugs during their training phase.

This setup creates artificially high scores that do not reflect how the bot will perform on entirely new code. e.g. OpenAI deprecated the super popular SWE-bench Verified dataset due to model memorization and data contamination.

Martian fixes this flaw by creating a living benchmark that measures exactly how well AI tools review code.

The framework analyzes over 200,000 pull requests to see if tools actually catch bugs or just generate useless noise. It operates using a dual system consisting of an offline component and an online component.

The offline system uses a fixed dataset of 50 manually verified pull requests from major open-source projects like Sentry and Grafana. Human experts label the true underlying issues in these specific repositories to create a baseline of golden comments.

A judge model then reads the AI tool’s review and compares it against those human-verified golden comments. The judge calculates a precision score to see how many of the bot’s comments were actually useful.

It also calculates a recall score to reveal what percentage of the actual real-world bugs the bot successfully caught. The online system operates continuously to completely eliminate any chance of data leakage.

It automatically pulls brand new pull requests directly from GitHub shortly after an AI bot leaves a review comment. The judge model analyzes the code changes the human developer actually merged after reading the bot’s feedback.

If the human developer implemented the exact fix the bot suggested, the tool earns a point for accuracy. This ensures the benchmark always uses fresh code that no AI model could have possibly seen during training.

Current results show that tools like Augment excel at precision on very large code changes. Open-source solutions like KiloCode are also performing exceptionally well across multiple different scoring metrics.

Code Review Bench brings much-needed transparency to an industry where companies previously just graded their own homework. I think this dual-layered approach is exactly what the industry needs to push past the current plateau of easily manipulated static benchmarks.

It forces developers to build tools that genuinely understand code context rather than just regurgitating memorized patterns. Check them out here. And here’s the official announcement video.

🗞️ AI has started crossing a key threshold. We better get ready.

So many massively important and literally tectonic shifts happened over the last seven to ten days.

• A single Anthropic blog post on COBOL coding can cause IBM loose more than $31B in a single day.

• After Claude Cowork automation plugins launch, Wall Street had a sudden, terrifying realization: traditional software companies make their money by charging “per seat” (per human user). If AI agents start doing the work of multiple humans, companies won’t need to buy as many software licenses. This panic triggered a massive selloff, wiping out roughly $285 billion in market value across global software stocks in a single day

• Jack Dorsey can slash 50% of a $31B company on a single day becaue of AI efficiency.

• A single viral Substack post from Citrini Research (painting a dystopian “Global Intelligence Crisis” with AI causing 10%+ unemployment, white-collar bloodbath, and a 38% S&P 500 crash by 2028) wipes billions off Visa, Mastercard, American Express and other payment giants in one trading session — while slamming the Dow Jones more than 800 points in a single day. Wall Street went into full panic mode over the AI “doomsday scenario” becoming real.

• GPT-5.2 Pro Solves a Decade-Old Quantum Physics Mystery in 12 Hours, this month (mid-Feb). GPT-5.2 Pro) was tasked with a theoretical physics puzzle regarding “gluon interactions” that had baffled human physicists for over ten years. In just 12 hours, the AI developed a completely new, mathematically sound proof showing that these interactions—previously assumed by scientists to be impossible under certain conditions—actually occur. The proof was so flawlessly executed that it was independently verified by researchers at Harvard and Cambridge.

• AI Discovers 25 New Materials to Upend the Global EV Supply Chain On February 19, researchers announced that an AI system had autonomously mapped over 67,000 magnetic compounds and successfully discovered 25 entirely new materials that stay magnetic at high temperatures.

Why is this earth-shattering?

It creates a realistic path to break the world’s reliance on expensive rare-earth metals for electric vehicles, smartphones, and wind turbines. AI just bypassed decades of slow, traditional trial-and-error lab testing to instantly rewrite the multi-billion-dollar clean energy supply chain.

• Amazon axes 16,000 corporate jobs in one announcement (January 2026), explicitly tying it to AI “agentic” workflows and efficiency overhauls. This helped fuel the highest monthly U.S. job-cut total since 2009 (108,435 announced), with AI now openly blamed for thousands of them — and CEO Sam Altman calling out companies for “AI-washing” their layoff lists.

• A little-known startup (Altruist Corp.) drops one new AI tax-strategy tool and instantly torches wealth-management giants: Charles Schwab, Raymond James, and LPL Financial all plunge 7%+ in a single day — their steepest drops since the 2018 trade-war meltdown — erasing billions in market value as investors freak out that AI is about to eat their entire business model.

• Goldman Sachs issues a bombshell warning that accelerating AI adoption is already causing 5,000–10,000 net job losses per month in exposed sectors and could actually raise the U.S. unemployment rate in 2026. The report itself triggered fresh market jitters and reinforced the “AI is coming for white-collar jobs faster than anyone expected” narrative.

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🗞️Google has a massive lead in image creation and editing with its newly launched Nano Banana 2

Newly dropped Nano Banana 2 is just plain impressive.

They already owned the top spot with Nano Banana 1, and now they have managed to break their own record. The ability to finally maintain high resemblance is a breakthrough for image models, and Google calls this subject consistency.

The core of this update relies on the Gemini 3.1 Flash Image architecture. This setup allows the model to actively pull real-time information from web searches while it builds your image.

Connecting to the web instantly helps the system understand exactly what specific things should look like. Another massive improvement is how well this model handles actual text inside the pictures.

You can now generate marketing materials where the spelled words are completely legible and accurate. The system can even translate the text inside the generated image into different languages automatically.

The model can keep the exact same look for up to 5 different characters and 14 distinct objects across multiple images in a single workflow. You get total control over the size of your creations, supporting resolutions from 512 pixels up to 4K.

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🗞️ Anthropic stood their ground and told the United States Department of Defense a strong “NO”

The United States Department of Defense demanded that Anthropic completely strip away the safety limits on their Claude AI model and allow the military to use it for absolutely anything they consider lawful.

This means the government wanted a blank check to use the technology without any of the company’s ethical rules getting in the way. Anthropic pushed back and rejected this demand entirely because of 2 extremely solid red lines that they refuse to cross.

The first issue is about mass domestic surveillance, which means using AI to sweep up massive amounts of data to spy on everyday citizens on a giant scale. Anthropic argues that current laws are way behind the technology, and letting AI automatically build a complete picture of a person’s life goes against basic democratic freedoms.

The second issue is about fully autonomous weapons, which are military systems that can pick a target and launch an attack completely on their own without a human pulling the trigger. Anthropic strongly believes that even the best AI systems today are simply not reliable enough to make life-or-death decisions on the battlefield.

They warned that putting this unreliable software in charge of lethal weapons would put both troops and innocent civilians at massive risk. Basically, the military argued that they should be the ones to decide what is legal and safe during high-pressure missions, not a private tech company.

But Anthropic stood its ground, stating that while they want to help defend the country, handing over a system that could easily make a lethal mistake or spy on innocent people is something they simply can not do in good conscience. To note, the Pentagon’s requirement that AI models be offered for “all lawful purposes” in classified settings is not unique to Anthropic. While Anthropic has been the only model used in classified settings to date, xAI recently signed a contract under the all lawful purposes standard for classified work.

That’s a wrap for today, see you all tomorrow.

Connect with me on X (Twitter)

Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (26-Feb-2026):

• 🗞️ Perplexity just launched Perplexity Computer, a system that moves beyond answering simple questions to executing entire complex workflows automatically.

• 🗞️ Anthropic just updated its Cowork system to let Claude act as a specialized financial assistant that can jump between apps like Excel and PowerPoint while keeping all your data in sync.

• 🗞️ Anthropic just released the Remote Control feature that lets developers start a coding task on their computer and finish it from their phone.

• 🗞️ New banger from Andrej Karpathy about how rapidly agents are improving

Connect with me on X (Twitter)

🗞️ Perplexity just launched Perplexity Computer, a system that moves beyond answering simple questions to executing entire complex workflows automatically.

Perplexity Computer acts like a digital worker that takes a large goal and breaks it down into smaller manageable tasks.

• It can research, design, code, deploy, and manage any project end-to-end.

• remembers your past work, and is secure by default.

• Hundreds of connectors, persistent memory, files, and web access.

• massively multi-model, computer orchestrates models to run agents in parallel.

• delegates deep research tasks to Gemini, generates images with Nano Banana, and handles video creation using Veo 3.1.

Basically, it looks at a screen and decides which button to click or what to type next, using models like Anthropic Opus 4.6 or GPT 5.2 across your connected apps. “In total, Computer can route work across 19 different models.”

This tool acts like a manager that breaks down a large goal into smaller tasks. It then picks the best model out of 19 options for each small task.

“Perplexity Computer uses usage‑based pricing with optional sub‑agent model selection and spending caps.” The service is available for Max subscribers at $200 per month. These users receive 10,000 credits every month to spend on running these automated agents.

So overall, Perplexity Computer acts like a digital worker that takes a large goal and breaks it down into smaller manageable tasks. It then creates separate automated bots, known as sub-agents, to handle each specific piece of the puzzle simultaneously.

One bot might search the web for data while another bot writes the code to process that exact information. These bots run inside isolated digital spaces that have their own real web browsers and file systems.

Instead of relying on just one system, this platform uses multi-model orchestration to assign the best specific tool for each job.

In a viral post on Twitter, Perplexity AI also is being shown that it turned A $30,000/Year Bloomberg Terminal Into A $200/month subscription.

Perplexity just became the the first Al company to truly go head-to-head with the Bloomberg Terminal...

Using Perplexity Computer (with no local setup or single LLM limitation), it was able to build me a terminal with real-time data to analyze $NVDA using Perplexity Finance:

🗞️ Anthropic just updated its Cowork system to let Claude act as a specialized financial assistant that can jump between apps like Excel and PowerPoint while keeping all your data in sync.

This update focuses on making Claude an agent that can actually performs multi-step financial tasks across different software tools.

Claude can now grab data from a spreadsheet, update a financial model, and then immediately build a summary slide in a presentation deck without you having to copy and paste anything. The system uses plugins, a sets of tools that give AI specific “skills” for professional tasks like equity research or investment banking.

Anthropic released 5 of these plugins that help with things like reviewing deal documents, parsing earnings transcripts, and checking portfolio risk for wealth management. To make sure the AI isn’t just guessing, they added connectors for FactSet and MSCI, which are major platforms that professionals use for real-time market data and index tracking.

Other partners like S&P Global and LSEG are also plugging in their own data, so Claude can look at live yield curves or company “tear sheets” while it helps you write a report. By using these connectors, the AI stays grounded in high-quality, institutional data rather than relying only on the general knowledge it learned during training.

Currently, this cross-app functionality is available in a research preview for paid users on Mac and Windows through specific add-ins. Under-the-hood, the Cowork system uses Model Context Protocol to connect Claude to specific company data and tools. Earlier users had to copy and paste everything from a research site into Excel and then into PowerPoint, but this system handles that transition automatically. It allows the AI to keep the context or the history of a project as it moves from one program to another.

Anthropic also added connectors for Google Workspace, Docusign, Apollo, Clay, Outreach, Similarweb, MSCI, FactSet, WordPress, and Harvey, along with plugins from Slack by Salesforce, LSEG, S&P Global, Common Room, and Tribe AI.

🗞️ Anthropic just released the Remote Control feature that lets developers start a coding task on their computer and finish it from their phone.

Seamless terminal handoffs across cevices

This new update acts as a secure bridge so you can manage local files remotely without opening vulnerable network ports. Until now, developers who wanted to step away had to use unreliable workarounds like third-party tunneling tools to keep their sessions alive.

This new official tool fixes this by creating a direct streaming connection between your local terminal and the Claude mobile app. When you type the setup command, your desktop makes a secure outbound connection to the Anthropic servers.

This means your computer simply checks for new instructions without ever exposing your private network to the outside internet. All your sensitive project files and local servers stay exactly where they are on your physical machine.

Only the chat messages and the final results travel through the encrypted bridge. If your laptop goes to sleep or your internet connection drops, the system waits and automatically reconnects once you are back online.

Users can easily link their devices by scanning a QR code generated right in the terminal. Currently, this feature launched is available as a research preview for subscribers on the Claude Max tier.

This shift allows a single person to manage massive software projects from anywhere, changing how we think about traditional software engineering. Overall, it’s a massive leap for developer mobility because it securely untethers complex workflows from physical workstations.

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🗞️ New banger from Andrej Karpathy about how rapidly agents are improving

Andrej explains how software development changed completely this past December. AI agents that write code finally became smart and persistent enough to finish huge programming tasks on their own.

You will learn how to move from manually typing code to directing these smart workers using plain English commands. The main idea is to teach you agentic engineering, where you set up master systems that manage many coding agents at the same time.

The text shares a real example of an agent building a local video analysis dashboard in just 30 minutes. You get to see how the agent autonomously sets up servers, writes the code, and fixes its own bugs without any human help.

You will also learn why these systems still strictly need your high-level direction and oversight to work right. The article shows you how to break large projects down into smaller chunks that agents can easily test and verify.

That’s a wrap for today, see you all tomorrow.

Connect with me on X (Twitter)

Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (24-Feb-2026):

• 🗞️ IBM stock dipped more than 13% once Anthropic released a blog saying Claude can optimize legacy COBOL code

• 🗞️ CNBC: OpenAI is teaming up with Accenture, Boston Consulting Group, Capgemini, and McKinsey to solve the “last mile” problem of getting AI into daily business tasks.

• 🚨 The US Defense Secretary Pete Hegseth is calling in Anthropic CEO Dario Amodei to the Pentagon on Tuesday morning to discuss the military use of Claude.

• 🗞️ The super-viral blog by Citrini, ‘THE 2028 GLOBAL INTELLIGENCE CRISIS’ - a must-read for real. 🎯

• 🗞️ OpenAI just added WebSocket support to their Responses API to make AI agents run much faster.

• 🗞️ Andrej Karpathy advocates building CLIs for AI agents in viral X post

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🗞️ IBM stock dipped more than 13% once Anthropic released a blog saying Claude can optimize legacy COBOL code - lost $31 Bn in 1 Day

IBM is the latest AI casualty. Shares tank 13% on AI fear.

“SaaSocalypse.” is in full swing, the death of the “Per-Seat” model of Software. IBM has a massive business built on “modernizing” old systems. It’s slow, expensive, human-heavy work.

When Anthropic shows that Claude can map out and fix that old code in a fraction of the time, serious question arising on IBM’s “army of humans” model to create per-seat revenue. Investors are terrified, the market is realizing that if the AI is the “worker,” the old software subscription models are basically broken.

“Hundreds of billions of lines of COBOL run in production every day, powering critical systems in finance, airlines, and government. Despite that, the number of people who understand it shrinks every year, AI excels at streamlining the tasks that once made COBOL modernization cost-prohibitive.”

Claude Code can help modernize COBOL codebases by mapping dependencies across thousands of lines of code, documenting workflows and identifying risks that “would take human analysts months to surface,” Anthropic said.

IBM stock took a massive 13% hit, sinks the most since 2000.

COBOL is a very old coding language that most people stopped using decades ago, yet it still runs the world’s most critical systems like banks and government agencies. These systems usually run on IBM mainframes, which are giant, ultra-reliable servers that have been the backbone of IBM’s business for a long time.

In the past, moving away from COBOL was a nightmare because you needed an army of human experts to spend years manually figuring out how the old logic worked. Anthropic says its Claude Code tool can now automate that research, letting the AI map out workflows and translate the old code into modern languages almost instantly.

Investors are panicking because if companies can use AI to code their way out of legacy systems, they might not need IBM’s expensive hardware or consulting services anymore. Software companies are having a rough 2026 because everyone is worried about how AI might mess with their business models. A big exchange traded fund that tracks these stocks has already dropped 27% this year, which is the worst 3 month stretch it has seen since the global financial meltdown back in 2008.

🗞️ CNBC: OpenAI is teaming up with Accenture, Boston Consulting Group, Capgemini, and McKinsey to solve the “last mile” problem of getting AI into daily business tasks.

These consulting giants will act as the bridge between OpenAI’s raw technology and the complex, messy systems that large companies use every day.

The focus is on a new platform called Frontier, which acts as a central brain that connects all the different data and software within a single organization. This setup makes it much easier to create AI agents. OpenAI is even sending its own engineers to work directly inside these partner firms to make sure the tech is set up correctly and safely.

The business side of this is huge because corporate clients already make up 40% of OpenAI’s revenue, and they expect that to hit 50% by the end of 2026. By letting consultants handle the strategy and setup, OpenAI can scale its technology to thousands of companies without needing to hire an infinite number of staff.

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🚨 The US Defense Secretary Pete Hegseth is calling in Anthropic CEO Dario Amodei to the Pentagon on Tuesday morning to discuss the military use of Claude.

The Pentagon is pushing for the company to let them run the AI on top-secret networks, which are private computer systems not connected to the regular internet, without the safety filters that usually block dangerous or violent requests.

These talks are reportedly on the verge of a total collapse because Anthropic is holding firm on their rules for how the military handles their software.

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🗞️ The super-viral blog by Citrini, ‘THE 2028 GLOBAL INTELLIGENCE CRISIS’ - a must-read for real. 🎯

The big idea is this: for modern history, human intelligence was the most scarce and valuable economic resource.

If society makes intelligence cheap and abundant, human labor loses its premium value. Because the entire global financial system was explicitly built around humans earning high wages, abundant AI does not just disrupt jobs, it fundamentally breaks the foundation of the economy.

• The authors see AI capabilities accelerating so fast that companies are firing highly paid white collar workers and replacing them with autonomous agents. This creates a devastating, unstoppable loop.

• The displaced professionals take massive pay cuts or move to gig work, so they stop buying extra goods. Consumer businesses lose revenue, so they fire more people and buy more AI to protect their margins, which only accelerates the job losses.

• At the same time, personal AI agents destroy middleman businesses. Because AI does not get lazy, it instantly cancels unused subscriptions, auto negotiates bills, and bypasses credit card networks to route payments for a fraction of a penny. Entire industries built on consumer friction and brand loyalty collapse.

• This breaks the private credit market. Billions of dollars were loaned to software companies assuming their subscription revenues were permanent. When AI agents easily replicate these software tools, the revenues vanish, causing massive loan defaults that threaten the life insurance and retirement funds backing them.

• The housing market follows. The $13 trillion mortgage system is built on the assumption that prime borrowers will maintain their high salaries for decades. Unlike the 2008 housing crash, these were fundamentally good loans made to people with excellent credit. But as AI permanently destroys earning power, perfectly underwritten mortgages start defaulting.

• Even the government breaks. Federal tax revenue relies heavily on taxing human wages. As AI shifts wealth from human labor to the owners of computer servers, income tax receipts plunge right at the exact moment millions of displaced citizens need government bailouts.

🗞️ OpenAI just added WebSocket support to their Responses API to make AI agents run much faster.

This update cuts waiting time by up to 40% when the AI repeatedly uses external tools.

Normally an AI program sends the entire conversation history every time it asks a new question. OpenAI fixed this massive delay by keeping a permanent two-way tunnel open.

The server now holds recent memory locally so the agent only sends tiny new pieces of information. Coding assistants like Cursor are already seeing a 30% speed boost because of this continuous data stream. A solid step for making background AI workers viable.

🗞️ Andrej Karpathy advocates building CLIs for AI agents in viral X post

Most modern software relies on visual buttons and menus that are easy for humans but very hard for artificial intelligence to navigate.

Because AI process text naturally, they can easily read and write these text commands to operate any software.

If you have any kind of product or service think: can agents access and use them?

- are your legacy docs (for humans) at least exportable in markdown?

- have you written Skills for your product?

- can your product/service be usable via CLI? Or MCP?

- ...

It’s 2026. Build. For. Agents.

That’s a wrap for today, see you all tomorrow.

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Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (23-Feb-2026):

• 🗞️ Anthropic just launched Claude Code Security to scan your code repositories for bugs and suggest security patches.

• 🗞️ The Chinese robotics firm X-Humanoid launched Embodied TienKung 3.0, a general-purpose robot platform, that developers can build on.

• 🗞️ A viral report finds 84% of people have never used AI, and just 0.3% of users pay for premium services.

• 🚨 Anthropic is accusing some Chinese AI labs of creating 24,000 fake accounts and running 16 million prompts to boost their own models.

• 🗞️ GitHub just launched a new cross-agent memory system for Copilot that allows different AI tools to share and learn from your specific coding patterns over time.

• 🗞️ New Paper from Google DeepMind came out with a big plan for how we should actually give tasks to AI.

• 🗞️ Demis Hassabis’s “Einstein test” for defining AGI

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🗞️ Anthropic launched Claude Code Security to scan your code repositories for bugs and suggest security patches.

Anthropic released Claude Code Security to scan entire codebases and suggest patches for complex vulnerabilities. Standard tools check software against known patterns, which completely misses deep logic flaws.

This new system reads your whole repository to understand how components interact and how data moves. It double-checks its work through a multi-stage review to filter false alarms while assigning severity scores.

Using the new Claude Opus 4.6 model, this setup identified over 500 vulnerabilities in major open-source projects. Teams review flagged issues and suggested fixes before approving changes.

Moving security analysis to contextual reasoning is a solid step for defensive engineering. Catching logic flaws automatically forces attackers to work harder.

“Validated findings appear in the Claude Code Security dashboard, where teams can review them, inspect the suggested patches, and approve fixes. Because these issues often involve nuances that are difficult to assess from source code alone, Claude also provides a confidence rating for each finding.”

🗞️ The Chinese robotics firm X-Humanoid launched Embodied TienKung 3.0, a general-purpose robot platform, that developers can build on.

X-Humanoid is also open sourcing a big set of building blocks, including the robot body, motion control framework, world model, embodied Vision Language Model, cross ontology VLA model, training toolchains, the RoboMIND dataset, and the ArtVIP simulation asset library.

It’s like a “base kit” that developers can build on, where the kit includes the robot hardware (body and joints) and the main robotics software pieces (motion control, perception, and planning), so you can just focus on their specific app instead of building the robot from scratch.

Humanoid robotics is running into 2 big problems right now.

• 1) Hardware interfaces are too closed off, so robots can’t be adapted quickly when the operating environment changes.

• 2) The software side is messy, with scattered tools and incompatible protocols, which forces teams to repeat the same research and development work and slows real commercial rollout and innovation.

Embodied Tien Kung 3.0 brings a fix for both, by pushing openness and interoperability so research institutes, universities, and companies can build, customize, and adapt solutions faster.

So Embodied Tien Kung 3.0 is a full size humanoid that pairs strong hardware, like high torque joints and whole body coordination, with on robot AI so it can stay stable, move precisely, and handle hard motions like clearing 1m obstacles, while still hitting millimeter level accuracy for factory style tasks.

Wise KaiWu is the software brain that runs a continuous “see, decide, act” loop, so the robot can understand a spoken task, break it into steps, avoid obstacles in real time, and reduce the need for constant human control.

It runs on X-Humanoid’s own Huisi Kaiwu embodied intelligence platform.

Developer can take these open pieces and get to a working humanoid demo much faster and cheaper, instead of spending months rebuilding the same low level robotics stack. Checkout more on it on their site

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🗞️ A viral report finds 84% of people have never used AI, and just 0.3% of users pay for premium services.

Now, given the backdrop of this report, my take is that, while everyone keeps talking about an AI bubble, but we forget the fact that only 0.3% of the global population actually pays for a premium subscription.

For the vast majority of the real world it has not started yet.

6.8B people have yet to interact with even 1 free chatbot.

The massive grey chunk is the 6.8B people who have zero experience with AI.

The green dots represent 1.3B people using free versions of tools.

Only a small group of 15-25 mn people pays for subscriptions. We sit in that microscopic slice.

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🚨 Anthropic is accusing some Chinese AI labs of creating 24,000 fake accounts and running 16 million prompts to boost their own models.

Anthropic says this extraction relies on distillation (which are technically legitimate methods), where a smaller AI learns by studying the high-quality answers of a smarter AI.

The Anthropic blog outlines how rival labs bypassed standard API limits to siphon training data for their own models.

Anthropic is claiming, to avoid getting blocked, these labs built sprawling proxy networks called hydra clusters. These clusters constantly shuffle requests across thousands of fake profiles to blend in with normal traffic.

They built hydra cluster architectures using commercial proxies to spread millions of requests across 24,000 fake accounts.

This setup prevented any single point of failure, meaning when Anthropic banned one account, the traffic simply shifted to another node. The blog claims DeepSeek used these networks for chain-of-thought elicitation, forcing Claude to write out its internal reasoning step-by-step.

This allowed them to generate massive datasets of reasoning traces to train their own rubric-based reward models for reinforcement learning. They also fed Claude politically sensitive prompts to generate censorship-safe responses, which helped them steer their own models away from restricted topics.

Anthropic also says some labs also focused heavily on extracting agentic reasoning and computer-use capabilities by varying their account types to avoid detection. And that other labs actually ran their extraction campaign live, shifting nearly half their traffic to a newly released Claude model within 24 hours to capture its latest capabilities.

To spot these attacks, Anthropic deployed behavioral fingerprinting and classifiers to catch the highly repetitive prompt structures that distinguish automated data scraping from normal human API usage.

The Wall Street Journal notes that these coordinated extraction efforts severely undermine the competitive advantage that export controls were designed to protect.

This situation highlights a fundamental flaw in API-based AI access, showing that without strict hardware-level verification, prompt-based data extraction is nearly impossible to stop completely.

🗞️ GitHub just launched a new cross-agent memory system for Copilot that allows different AI tools to share and learn from your specific coding patterns over time.

Instead of starting every chat from scratch, this system lets the AI store “memories” about your project conventions, like how you name log files or handle database connections.

Any of your favorite agents on the GitHub platform (Copilot, Claude, Codex, c-c-c-combo breaker) can learn across your repo. The system uses a “store_memory” tool where an agent creates a snippet of information along with specific citations to your code to prove the fact is real.

To keep things accurate as your code changes, the agents perform just-in-time verification by checking those citations every time they retrieve a memory. If the cited code has changed or disappeared, the agent simply ignores the old memory or updates it with the new truth.

During testing, this memory feature boosted the success rate of the Copilot coding agent from 83% to 90% for merged pull requests. It also improved the quality of automated code reviews, with developers giving 2% more positive feedback on the AI’s suggestions.

The memory is strictly locked to your specific repository, so your private coding habits are never shared with other users or different projects.

🗞️ New Paper from Google DeepMind came out with a big plan for how we should actually give tasks to AI.

It is not just about telling an AI to do something and hoping for the best. Instead, this framework looks at delegation as a string of choices where you figure out if you should even hand the task over, how to explain it, and how to check the work afterward.

Current systems rely on rigid rules that break when things fail unexpectedly. The researchers suggest building a dynamic market where agents bid on tasks using smart contracts.  

This requires strict monitoring and cryptographic proofs to guarantee correct work without leaking private data.

Instead, this framework looks at delegation as a string of choices where you figure out if you should even hand the task over, how to explain it, and how to check the work afterward.

Current systems rely on rigid rules that break when things fail unexpectedly. The researchers suggest building a dynamic market where agents bid on tasks using smart contracts.  

This requires strict monitoring and cryptographic proofs to guarantee correct work without leaking private data.  

Instead of trusting a simple rating, agents will use verifiable digital certificates to prove their exact skills.

- Keeping things flexible when things change

This new system is built to be adaptive rather than stuck in its ways. It treats the handoff as a live process where authority and responsibility can shift around in real time. If the situation changes or something breaks, the framework helps manage that failure so the whole project does not go off the rails. It works for both humans giving tasks to AI and for when AI needs to handle things on its own.

- Finding the right amount of trust

One of the coolest parts is how it handles trust. They made formal trust models that look at how hard a task is and how well the AI has done in the past. This stops people from “over-delegating,” which is when you give an AI something it is not ready for. It also stops “under-delegating,” which happens when you do all the work yourself even though the AI could have handled it easily.

- Double checking the work

You cannot just take an AI’s word for it, so this framework has specific ways to validate the output. It sets up rules for when to accept an answer based on how confident the AI is. It also has backup plans ready to go if the AI fails. This is super important for real world jobs where trusting a machine blindly could cause a bunch of errors to pile up.

- When AI agents hire other AI agents

The framework also covers what happens when 1 AI agent hands a task to another AI agent. The system tracks who is actually accountable and makes sure the right authority is passed down the line so nothing gets lost in the network.

- Making sure the work actually fits

It is a step by step approach to make sure the AI’s contribution actually makes sense for the bigger goal. By treating this as a structured process, they are making it much safer for companies to use AI in their daily operations without worrying about constant mistakes.

🗞️ Demis Hassabis’s “Einstein test” for defining AGI

Demis Hassabis, the CEO of Google DeepMind, thinks we should define AGI based on a specific high bar. He mentioned during a talk that his view on AGI is still the same, seeing it as a setup that manages to show off every single mental skill that a person has.

He says train a model on all human knowledge but cut it off at 1911, then see if it can independently discover general relativity (as Einstein did by 1915);

if yes, it’s AGI.

That’s a wrap for today, see you all tomorrow.

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Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (21-Feb-2026):

• 🗞️ Claude Code on desktop added an embedded loop that can run your dev server, view the app, review local changes, and watch pull requests until they merge.

• 🗞️ Prompt Repetition Improves Non-Reasoning LLMs

• 🗞️ Taalas launched a beast of a chip, boasting insane AI inference performance.

• 🗞️ Thomas Wolf, Huggingface founder writes a viral piece on Twitter - The biggest change AI brings to programming is not faster coding, it is cheaper replacement of everything you thought was “too entrenched to touch.”

• 🗞️ Top Github Resource: NanoClaw, the lightweight alternative to Clawdbot / OpenClaw already reached 10.5K Github stars

• 🗞️ Andrej Karpathy also posted a viral post on how Claws turn agents into orchestrators, and containers look like the basic safety belt.

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🗞️ Claude Code on desktop added an embedded loop that can run your dev server, view the app, review local changes, and watch pull requests until they merge.

It can now preview a running app inside the desktop UI while it iterates on fixes.

It can also monitor CI for an open PR and either fix failures or merge when checks go green. Earlier “agentic coding” workflows usually bounced between a terminal, a browser, and GitHub, so the model could not directly see what the app rendered or stay attached to CI results without manual babysitting.

The new server preview flow starts and stops dev servers from a Preview control, shows the app in an embedded browser, reads logs, and can interact with the page to verify changes. That closes a common gap where a model edits code but cannot reliably confirm the UI and runtime behavior match the intent.

The local “Review code” step runs before pushing, and it leaves inline comments on the diff so basic bugs and risky changes get caught earlier. For PR monitoring, the desktop app will track status and CI checks, and it can use auto-fix to attempt repairs and auto-merge to land the PR once checks pass, using GitHub tooling under the hood. Session mobility adds a /desktop command to move a CLI session into the desktop app, and sessions can be pushed to the cloud to resume from web or phone.

🗞️ Prompt Repetition Improves Non-Reasoning LLMs

Fascinating Google paper: just repeating your prompt 2 times can seriously boost LLM performance, sometimes pushing accuracy from 21% to 97% on certain search tasks.

An LLM reads your prompt left to right, so early words get processed before the model has seen the later words that might change what they mean. If you paste the same prompt again, the model reaches the 2nd copy already knowing the full prompt from the 1st copy, so it can interpret the 2nd copy with the full context.

That means the model gets a cleaner “what am I supposed to do” picture right before it answers, instead of guessing too early and sticking with a bad setup. This helps most when the task needs details that appear late, like when answer choices show up before the actual question, because the 2nd pass sees both together in the right order.

In the Google tests, this simple trick took one hard search-style task from 21.33% correct to 97.33% correct for a model setting with no step-by-step reasoning. Across 7 models and 7 benchmarks, repeating the prompt beat the normal prompt in 47 out of 70 cases, and it never did worse in a statistically meaningful way. The big deal is that it is almost free to try, it often boosts accuracy a lot, and it shows many LLM mistakes are “reading order” problems rather than pure lack of knowledge.

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🗞️ Taalas launched a beast of a chip, boasting insane AI inference performance.

17,000 tokens per second. forbes reports.

About 10x faster than Cerebras wafer-scale engine, currently the fastest inference platform available, for this setting and about 2 orders of magnitude faster than GPUs, but those comparisons depend on matching prompts, batching, and latency targets. And they just raised $169 mn.

Instead of using software to program (compile) and execute on a GPU or an ASIC such as a Google TPU, Taalas hardwires the model and its weights into a “Hardcore” ASIC (HC1) embedding the entire model into a bespoke application-specific chip. Result is extreme inference speed and very low cost per token, at the price of giving up most general programmability.

The usual tradeoff in AI hardware is that GPUs are flexible but pay overhead for moving weights through memory and for supporting many different kernels, while ASICs get faster by specializing but still stay somewhat programmable. It’s ASIC HC1 pushes specialization further by “baking in” one target model, currently Meta’s Llama3.1-8B, so the chip is not a general accelerator in the way a GPU or TPU is.

Taalas says HC1 keeps a bit of flexibility via a configurable context window and fine-tuning through low-rank adapters (LoRAs), which add small trainable matrices on top of a frozen base model. Reported demo numbers include about 0.138s for a long response at 14,357tokens/s, and upto 17,000tokens/s per user for Llama3.1-8B.

On cost and power, the same write-up claims roughly 0.75c per 1M tokens for Llama3.1-8B and 12-15kW per rack versus 120-600kW per GPU rack, with DeepSeek-R1 numbers partly simulated. A key limitation is operations, because a data center would need different chips for different models and then refresh them as models change, even if Taalas can update by changing only 2 metal layers in about 2 months. Quality is also a constraint today because the “Silicon Llama” is aggressively quantized with mixed 3-bit and 6-bit weights, and the roadmap points to 4-bit floating point formats on a future HC2 to reduce accuracy loss.

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🗞️ Thomas Wolf, Huggingface founder writes a viral piece on Twitter - The biggest change AI brings to programming is not faster coding, it is cheaper replacement of everything you thought was “too entrenched to touch.”

If AI makes reading, understanding, and rewriting code cheap, a lot of today’s software habits will flip.

First, dependency-heavy software may shrink. Instead of pulling in long chains of libraries, teams may rebuild what they need or extract only the useful parts, because an AI agent can do that quickly. That can mean smaller apps, faster startup, better performance, and fewer supply chain security risks. This pushes software back toward bigger “monolith” codebases, built more end to end.

Second, old code will stop feeling “untouchable.” The Lindy effect, the idea that old systems survive because they are proven, weakens if AI can fully map and rewrite legacy systems. The risk is that hidden edge cases still exist, so testing and formal verification become much more important, not optional.

Third, language popularity may shift. If humans write less code, languages chosen for human friendliness matter less, and languages that are strongly typed, safer, and easier to formally verify may win.

Finally, open source economics and community incentives could change if code is mostly written and read by machines, and new programming languages may emerge that are designed for AI, not for humans.

🗞️ Top Github Resource: NanoClaw, the lightweight alternative to Clawdbot / OpenClaw already reached 10.5K Github stars ⭐️

Compared with OpenClaw, NanoClaw’s specialty is simplicity plus OS level isolation.

• Much smaller and manageable codebase, only 4K lines.

• Runs in containers for security.

• Connects to WhatsApp, has memory, scheduled jobs, and runs directly on Anthropic’s Agents SDK

• stores state in SQLite, runs scheduled jobs, and keeps each chat group isolated with its own memory file and its own Linux container so the agent only sees directories you explicitly mount.

• its safety model leans on application controls like allowlists and pairing codes inside a shared Node process.

OpenClaw is built for broad multi channel coverage, while NanoClaw intentionally stays minimal so you customize by changing a small codebase instead of operating a big framework.

In NanoClaw, a “skill” is a small folder, centered on a SKILL[.]md file, that teaches Claude Code how to carry out a repeatable change or workflow, and it can be invoked as a slash command like /add-telegram.

The core developer says clearly “Don’t add features. Add skills.”

So e.g. instead of adding Telegram code to the main NanoClaw repo for everyone, a contributor should just write a skill that tells Claude Code exactly how to edit your fork, add the needed files, update the runtime wiring, and verify it works.

When you run /add-telegram, Claude applies those code changes to your copy, so the upstream project stays small and you still end up with a clean, Telegram-enabled codebase.

🗞️ Andrej Karpathy also posted a viral post on how Claws turn agents into orchestrators, and containers look like the basic safety belt.

Key Takeaways from his post.

• A new layer called Claws is emerging on top of LLM agents, focused on handling orchestration, scheduling, context handling, tool calls, and persistence so an agent can keep running and coordinating work over time.

• Running a massive implementation like 400K lines of code feels risky when it can touch private data and keys, because large, popular codebases tend to attract constant real-world attacks and accidental misconfigurations.

• People are already flagging practical failure modes like exposed deployments, remote code execution bugs, supply chain attacks, and malicious or compromised “skills” distributed through a shared registry.

• Smaller implementations are showing up fast, and a key appeal is being small enough to audit, modify, and reason about, for example a core engine around 4,000 lines of code.

• Container-first execution is getting attention because containers put hard walls between components, which helps limit damage when something breaks or gets compromised.

• A standout idea is treating “configuration” as skills that change the codebase directly, so adding an integration can look like a command that teaches the agent to edit the repo, instead of stacking messy config files and branching logic.

• This pushes a new pattern, keep a maximally forkable base repo, then use skills to generate the exact customized variant needed for a specific setup.

• Local setups are attractive for tinkering and for connecting to devices on a home network, while cloud-hosted options feel harder to experiment with and harder to trust with secrets.

• Opinion: The idea is powerful, but the security surface grows fast once real secrets, third-party skills, and exposed endpoints enter the picture, so the safest path looks like small, auditable, container-first builds with strict defaults.

• Opening sentence: Claws are becoming a new “control layer” above LLM agents, but the practical story right now is a tradeoff between powerful automation and a big, messy security surface that only gets manageable when the system stays small, auditable, and container-first.

That’s a wrap for today, see you all tomorrow.

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Read time: 10 min

📚 Browse past editions here.

( I publish this newletter daily. Noise-free, actionable, applied-AI developments only).

⚡In today’s Edition (19-Feb-2026):

• 🏆 Google has taken the AI lead again with Gemini 3.1 Pro.

• 🎵 Google Lyria 3: Make 30-sec AI music tracks right inside Gemini

• 🗞️ A new international survey by the National Bureau of Economic of nearly 6,000 CEOs & executives finds that AI is widely adopted but barely shows up in jobs or productivity yet, echoing Robert Solow’s old “productivity paradox” from the early computer era.

• 🗞️ Breakthrough research by Microsoft, the most durable archival storage for data centers.

• 🗞️ New paper shows how LLMs can learn like human, with experience, reflect, and then learn.

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🏆 Google has taken the AI lead again with Gemini 3.1 Pro

Gemini 3.1 Pro just dropped by GoogleDeepMind with better coding and long context support.

Massive improvement on ARC AGI-2 (the logic puzzle benchmark), with 77.1% up from 31.1% for its predecessor, Gemini 3 Pro.

Great for super complex tasks like visualizing difficult concepts, synthesizing data into a single view and agentic tasks.

Gemini 3.1 Pro Preview leads the Artificial Analysis Intelligence Index, 4 points ahead of Claude Opus 4.6 while costing less than half as much to run.

You can access it in public preview via Google AI Studio, and with Gemini API, plus Gemini CLI, Google Antigravity, and Android Studio.

On Vertex AI, the listed version is gemini-3.1-pro-preview, marked Public preview

🎵 Google Lyria 3: Make 30-sec AI music tracks right inside Gemini

Lyria 3 just dropped by GoogleDeepMind, their latest and most advanced music model, and they put it into the Gemini app. So anyone can make a 30s song from text or an uploaded image or video in a beta rollout.

Go from idea, image, or video to music in seconds! It also generates vocals and lyrics automatically, and adds steering knobs like tempo, vocal style, and precise lyrics, which earlier music tools often left to the user or made hard to control.

Under the hood, this is a multimodal conditioning setup, where the same music model is guided by a text description plus optional visual context, then decodes audio that is meant to stay coherent while matching the prompt’s mood and story. Google is claiming higher audio quality and clearer words, including 48kHz stereo output in its Lyria line, which matters because artifacts and garbled lyrics are a common failure mode for music generation.

Every Gemini-generated track is watermarked with SynthID, and Gemini can also check uploads for that watermark as part of its audio verification flow. The official blog said “Music generation with Lyria 3 is designed for original expression, not for mimicking existing artists. If your prompt names a specific artist, Gemini will take this as broad creative inspiration and create a track that shares a similar style or mood.”

The beta launch is global for 18+ users in 8 languages, with higher limits for paid tiers, and Lyria 3 is also being used in YouTube’s Dream Track for Shorts soundtracks. This looks most useful for fast, shareable “soundtrack for a moment” creation, not careful music production, because the output is capped at 30s and the tool is tuned for speed and vibe matching.

🗞️ A new international survey by the National Bureau of Economic of nearly 6,000 CEOs & executives finds that AI is widely adopted but barely shows up in jobs or productivity yet, echoing Robert Solow’s old “productivity paradox” from the early computer era.

So far, the average employment effect is basically 0.0% over 3 years, and the average productivity effect is only about +0.29% over 3 years, even with high stated adoption.

Despite that, executives predict over the next 3 years that AI will raise productivity by about +1.44%, lift output by about +0.76%, and cut employment by about -0.68%, while employees predict about +0.45% employment and about +0.92% productivity. This NBER study was conducted across the US, UK, Germany, and Australia, about 69% of firms say they use some AI, but more than 80% report no effect on employment or productivity over the last 3 years.

The paradox angle comes from 1987, when Robert Solow showed that despite major computing advances in the 1960s, productivity growth slowed instead of rising, falling from 2.9% in 1948 to 1973, to 1.1% after 1973. This was partly because firms first had to reorganize work to make the tech pay off.

Past evidence on AI has been messy because different surveys ask different questions, sample different kinds of firms, and often do not reach real decision makers, so adoption rates can swing by nearly 10x depending on design. This work changes the measurement by asking the same AI questions in 4 large, economy-wide business panels, and those panels recruit and verify senior executives rather than anonymous online respondents.

It also measures intensity, not just “does the firm use AI,” by asking executives how many hours per week they personally use AI, which averages 1.5 hours and includes 25% who report none. On the firm side, the most common reported use is text generation with LLMs at 41% of firms, followed by machine learning for data processing and visual content creation at about 30% each.

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🗞️ Breakthrough research by Microsoft, the most durable archival storage for data centers.

4.8TB inside a glass platter that could last 10,000 years.

In this work the platter is about 120mm by 120mm and about 2mm thick. Archives today usually use magnetic tape or disks, and those slowly degrade, so big organizations have to keep copying data forward to new media every few years to avoid losing it.

Glass is different story because the written marks are physical changes inside the material, and glass is naturally resistant to water, heat, dust, and electromagnetic interference. Those marks are stacked through the depth of the glass in hundreds of layers, like a 3D barcode.

Earlier versions mostly used fused silica, a purer and more expensive glass. The newer approach works in cheaper borosilicate glass, like kitchen cookware, and a “phase voxel” needs 1 pulse per stored spot. The big deal in this new work is a possible “write once, store for generations” layer that could cut migration work for data centers and cultural archives.

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🗞️ New paper shows how LLMs can learn like human, with experience, reflect, and then learn.

For decades, we’ve trained AI to chase rewards. But humans don’t just optimize outcomes. We experience, reflect, then learn.

New Microsoft+ Uni of Southern California + Uni of Pennsylvania paper discusses how AI can do the same.

Experiential Reinforcement Learning (ERL) trains a model by letting it fail once, reflect on why, retry, and then learn the retry. ERL keeps the usual reward score, but adds a written self-reflection from feedback that guides a 2nd attempt.

On Qwen3-4B, Sokoban reward rose from 0.06 to 0.87, and HotpotQA rose from 0.45 to 0.56 token-level F1, a match score for the answer text. In standard reinforcement learning with verifiable rewards (RLVR), learning mostly sees a single reward number for a full multi-step attempt, so figuring out what to change can be slow.

ERL inserts an extra step that turns feedback into a specific correction, and reinforces the full sequence of 1st attempt, reflection, and improved 2nd attempt. It then distills the successful 2nd attempts back into the base model so it can act from the original input without reflection at deployment. Reflection is mainly triggered on failures to avoid instability and reward hacking, and a cross-episode memory can reuse good reflections but can also spread bad ones.

The picture compares 2 ways an AI learns in a new game-like world when it starts with no clue what works.

In the top row, reinforcement learning with verifiable rewards (RLVR) mostly just tries moves, gets no reward, and then repeats similar bad attempts because it does not store a clear lesson from the failure. The “forget” and “back and forth” idea means the agent keeps exploring without locking in a specific correction, since the only signal is a single reward number at the end.

In the bottom row, Experiential Reinforcement Learning (ERL) still does trial and error, but after failing it writes a short reflection like “walls block me” or “I can push boxes,” then uses that to guide a better 2nd attempt. The “experience internalization” idea means ERL turns the failure into an explicit rule or hint, then trains the model to behave like it remembered that rule next time. The big deal is that the model learns a reusable fix from feedback, instead of just learning that the whole attempt was good or bad, which helps a lot when rewards are rare and only show up at the end.

Overview of Experiential Reinforcement Learning (ERL)

Step 1 is the first attempt, where the policy takes the task input x and produces an answer y(1), then the environment returns feedback f, like success or failure plus hints. Step 2 is self-reflection, where the same policy reads the first attempt and the feedback and writes a short correction note Δ, and it can also pull useful notes from a cross-episode memory that stores past corrections.

Step 3 is the second attempt, where the policy uses the task plus the reflection note Δ to produce a better answer y(2), and reinforcement learning updates the policy using how well these attempts did. The bottom “internalization” path is the main trick, which takes successful second attempts and trains the model with supervised fine-tuning (SFT), meaning it learns to output y(2) directly from x without needing to write Δ at test time. The big deal is that the model practices a fix immediately, then gets trained to act like it remembered the fix, so improvement is more stable than just rewarding or punishing whole attempts.

That’s a wrap for today, see you all tomorrow.

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