Read time: 9 min

📚 Browse past editions here.

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

⚡In today’s Edition (21-July-2026):

• 🗞️ Google releases 3 new Gemini models, intensifying AI price war, but no 3.5 Pro yet

• 🗞️ Plasma AI released Fractal, an open source Apache 2.0 tool that organizes AI agents into a self-managing tree and it spins up agents that spin up more agents.

• 🗞️ Trump administration weighs restrictions while businesses move to lower-cost Chinese AI.

• 🗞️ China is drafting rules to stop its most advanced AI and chip designs from reaching the West.

• 🗞️ Self-improving AI is only as real as the signal it was tested on to see if it worked

• 🗞️ A US judge just approved Anthropic’s $1.5B payout to authors whose books trained Claude.

• 🗞️ Emad Mostaque, co-founder of Stability AI: “We’ll see the cost of Kimi K3 drop by 10 to 50 times, over the next few months as it gets optimized.”

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🗞️ Google releases 3 new Gemini models, intensifying AI price war, but no 3.5 Pro yet

- Gemini 3.6 Flash: 17% fewer output tokens compared to 3.5 Flash, lower pricing, and stronger coding, knowledge, multimodal, and computer-use performance.

On the DeepSWE coding test the new 3.6 Flash: jumps to 49% from the old 37%. Computer use is now built in, and OSWorld-Verified climbs to 83.0% from 78.4%. So the new 3.6 Flash is quite strong on agentic work.

The price drops too, landing at $1.50/1M input and $7.50/1M output tokens.

- 3.5 Flash-Lite: most cost-effective 3.5-class model, delivering 350 output tokens per second. costs just $0.3/1M input and $2.5/1M output, so high-volume work gets much cheaper.

- 3.5 Flash Cyber: This is the odd one, tuned to find and patch security holes. highly efficient, specialized cyber-focused model . finds and fixes vulnerabilities through CodeMender, with access limited to governments and trusted partners.

But the update still doesn’t include the long-anticipated update to Google’s flagship model, Gemini Pro, which was last updated in February. Since then, OpenAI has launched GPT-5.5 and started rolling out GPT-5.6, while Anthropic has released Claude Opus 4.8 and Claude Sonnet 5 and widened access to its frontier Fable 5 model, showing the fierce release speed between the rival labs.

🗞️ Plasma AI released Fractal, an open source Apache 2.0 tool that organizes AI agents into a self-managing tree and it spins up agents that spin up more agents

Plasma just open-sourced Fractal, a command-line tool that lets agent loops create more agent loops. This is their Github

It’s Apache 2.0 (free, even for commercial use), runs fully on your machine, no hosted server. Fractal turns a single Claude Code or Codex session into a persistent tree of agents, each working on a different part of the problem.

That makes such a difference because large coding tasks rarely fit cleanly inside one agent’s context window or one shared workspace. Each agent is a node that owns its own git worktree (a separate working copy of your code, so agents never step on each other) and its own memory.

Currently the problem is, whenever we run a coding agent on a giant task, it gets a single memory, a single workspace, and a single path through the problem. It runs out of room (the context window, the limited amount of text a model can hold at once, fills up). So huge jobs like big migrations, refactors across many services, or features that span services just don’t fit.

So Fractal spins up agents that spin up more agents, as deep as the work needs.

Each node works toward its own goal inside a separate git worktree. When it hits a subtask it can split off, it spawns a child node. That child gets its own context, budget, and memory, then runs its own loop.

The parent reviews and merges that output before it plans again. So the tree grows to fit the problem instead of a plan you guessed upfront. Hard caps on iterations, depth, children, cost, and time keep every loop bounded.

You can also steer or stop any node while it runs. Every run, cost, and signal lands in one local SQLite database you can watch live. It runs on Claude Code, Codex, Grok Build, OpenCode, and Oh My Pi.

Git worktrees do the quiet work here, giving each node isolation and merging for free. Parent nodes still have to merge messy child output well, and that part stays unproven.

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🗞️ Trump administration weighs restrictions while businesses move to lower-cost Chinese AI.

Trump administration weighs blocking US access to Chinese AI models. Such a move would hand OpenAI and Anthropic near-total control of the market.

American companies are turning more and more to Chinese open-weight models because they work and they’re cheap on the API side. These open-weight models can be run on private infrastructure, allowing businesses to change models and monitor private business data.

US Security officials are concerned that foreign code, bad training decisions or covert behaviors could threaten users and vital systems. US Commerce had talked about putting Chinese AI labs on its Entity List, which would make it harder for Americans to get to them without a license.

Officials also talked about rules on liability requirements and public pressure against US businesses using the system. But those plans were killed by officials who cared about innovation, because they believed that imposing limits on them would harm competition and slow AI development in the United States.

Since then, many vocal critics of the release have left and the stronger Chinese releases have increased national security concerns.

On this news, Silicon Valley investor Chamath and many other tech top leaders expressed concern.

The whole “China’s coming, so protect the frontier labs” line is just a smoke screen for something much smaller. The model layer itself is not the economy of AI. It’s only a slice.

At some point every technology is a cost curve. Fight the curve and you will lose. The protection doesn’t even save the labs it’s supposed to.

If you can reduce the cost of running these models by 10X or 100X, that’s actually rocket fuel for the rest of us down the line. So the very thing the labs fear, price drops, could enlarge the entire pie.

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🗞️ China is also drafting rules to stop its most advanced AI and chip designs from reaching the West

China may impose AI export controls, including blocking domestic firms from using TSMC, while also restricting advanced models, training data, and overseas deals, a report says.

Regulators at the Ministry of Commerce have asked Alibaba, ByteDance and Zhipu how to keep frontier work home.

The talks cover training data leaving the country and whether foreigners can still download model weights.

Chinese customers would keep full access, but the raw building blocks would stay inside China. Beijing worries because its top models are open-weight, so anyone can download and run them locally.

After Kimi K3, the gap with America has nearly closed, and China now sees itself in front. So it wants the same controls Washington has used against it.

The chip side goes further. It would block Qualcomm and TSMC from making advanced chips based on Huawei or Alibaba designs.

Another move would stop foreign buyouts of agentic AI startups. That follows Meta’s $2B purchase of Manus, which Chinese authorities later forced to unwind.

Companies warn the limits could slow their progress and cost China the race. The rules may enter China’s next export catalogue, its largest revision in years.

The real shift is psychological, because Beijing now guards AI like it once guarded rare earths. Openness was the underdog’s strategy, and China no longer sees itself as one.

🗞️ Self-improving AI is only as real as the signal it was tested on to see if it worked

Sorting 1,250 papers reveals a single bottleneck: the evaluator, who determines what counts as better.

In a controlled experiment, models that were given 10 rounds of self-criticism without any outside check stopped improving until they were given a single grounding step.

So systems get better over time when that signal is strong, like a proof checker or a passing test. When it is weak, like when a model is sure of itself, the loops tend to circle and collapse or reinforce the mistakes the model is most sure of.

People use the term “self-improvement” loosely, as if a model is editing its own draft or an agent is rewriting its own code, obscuring the real differences.

Each method is ranked by what changes in the system, and if each change is still checked by a human.

Each loop is a gamble that we can substitute automatic signals for human judgment, and it improves only when the signal can be trusted.

🗞️ A US judge just approved Anthropic’s $1.5B payout to authors whose books trained Claude.

It is the largest known settlement in any US copyright case.

The fight started when writers sued the company for grabbing pirated copies of their books. About 465,000 books are on the list of works pirated by Anthropic, according to Justin Nelson, an attorney for the authors.

Those stolen texts were fed in to teach the model how to answer prompts. A judge ruled that training on books is fair use, so that part stayed legal.

But the same judge flagged a separate problem, one unrelated to training. The company had saved over 7 million pirated books into a central library it barely used.

Storing stolen copies is infringement on its own, even without any training. That exposure was huge, since a trial could have pushed damages into the hundreds of billions.

Approximately $101 million+ went to the lawyers in approved attorneys’ fees, while the vast majority of the $1.5 billion settlement fund (plus interest), after deductions, went to the rightsholders (authors and publishers), averaging about $3,000 per eligible work.

That ruling exposed the company to damages of up to $150,000 for each willfully infringed work if the case proceeded to trial.

So both sides settled, and 91% of covered authors have already claimed their share. Some writers pushed back, calling the sum too small and the fees too fat.

A few opted out and are still chasing their own cases against the company. Paying $1.5B to protect a fair use win looks cheap for Anthropic.

🗞️ Emad Mostaque, co-founder of Stability AI: “We’ll see the cost of Kimi K3 drop by 10 to 50 times, over the next few months as it gets optimized. “

This is because that, basically Kimi K3’s current inference cost is quite high, but that price reflects immature infrastructure, not a permanent technical limit. And that gap will not last long. Watch the full video here

US-based specialized infrastructure companies will optimize kernels, routing, quantization, batching, memory use, and serving systems around those models once the Kimi K3 weights are available.

“Right now, it uses twice the number of tokens for the same task compared with GPT-5.6.

Again, we’re going to see that cost drop because everyone and their dog is going to optimize the crap out of this.

Fireworks has just raised funding at a $17 billion valuation, while others, such as Modal and Baseten, are valued at $10 billion.

These are inference providers for open-source models. They’ve all raised around a billion dollars, which they’re now going to spend on optimizing the Chinese model, making it more efficient, and running it.

American labs that handle the inference side of things are going to optimize the crap out of this. Therefore, we will see it catch up.”

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 (20-July-2026):

• 🗞️ On long-horizon cyber capability, leading open-weight models now trail the closed frontier by only 4 to 7 months, down from 6 to 10 months through much of 2025.

• 🗞️ “AI advice suppresses people’s willingness to say “I don’t know”, even when the advice is wrong and accuracy is incentivized”

• 🗞️ Kimi K3 nearly doubled its nearest rival Claude Fable 5, on a demanding benchmark for autonomous legal work.

• 🗞️ Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused because of “cyber guardrails.”

• 🗞️ “AI Agents Do Not Fail Alone:The Context Fails First”

• 🗞️ “American companies such as Modal, Fireworks, and Baseten will be able to serve Kimi K3, at one-tenth the cost of their Chinese competitors because they have access to advanced Nvidia and AMD chips.

• 🗞️ Kimi K3 is facing a compute crunch. Demand is too heavy, so new subscriptions are currently blocked.

• 🗞️ Why One OpenAI Senior Employee Thinks Open-Weight Models Are Decelerationist

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🗞️ On long-horizon cyber capability, leading open-weight models now trail the closed frontier by only 4 to 7 months, down from 6 to 10 months through much of 2025.

GLM-5.2 matched closed models released about 4 months earlier.
On long-horizon cyber ranges, GLM-5.2 matched Claude Opus 4.5, released roughly 7 months earlier.

And also that long-horizon cyber capability can now be scaled with compute. On the AI Security Institute’s 32-step “The Last Ones” range, GPT-5.6 Sol completed the full 32-step range in 7 of 10 attempts.

With a 100M-token budget for each run. Performance continued improving as the model received more inference tokens. i.e. operators can gain materially stronger cyber capability simply by spending more runtime compute, without retraining the model.

🗞️ "AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized"

A brutal study.

AI may make people lose the habit of stopping when they do not know enough. Without AI, people withheld judgment on 44% of questions, but AI access cut that rate to 3%.

Participants who answered without AI were correct about 27% of the time, while those who received the deliberately wrong AI advice were correct only 9% of the time, despite feeling much more confident. Saying “I don’t know” is an important part of decision making.

But now AI is able to answer just about anything. Often not requested for, like AI summaries.
Therefore, they posed a straightforward query: Do people become less inclined to suspend judgment when they have access to AI advice?

People almost never said “I don’t know” when AI gave them advice. A fluent answer can make uncertainty feel settled, even when someone had good reason to hold back.

Across 5 experiments with 3,132 people, participants could answer 6 obscure movie questions or decline. The researchers chose questions that the AI often answered incorrectly, then either showed its advice automatically or let people request it. Correct answers also fell from about 27% to 9%, even as participants reported much greater confidence.

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🗞️Kimi K3 nearly doubled its nearest rival Claude Fable 5, on a demanding benchmark for autonomous legal work.

Kimi K3 at 26.7%, vs Claude Fable 5 at 14.2%.

The test covers 120 private assignments across 24 legal fields, including memos and deposition summaries. Each model receives case files, works through them autonomously, then produces finished legal documents.

Every required rubric item must pass, so one missed detail fails the entire assignment. This strict grading explains why even the leader succeeds on only 26.7% of tasks. But, overall, given 27 successful tasks per 100, it looks like we still have a long road ahead for completely unsupervised legal work by AI.

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🗞️ Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused because of “cyber guardrails.”

Kimi K3 fixed 15 critical bugs after OpenAI Codex and Claude Fable 5 refused.

Spending 10 hours, one prompt, and $250

🗞️ “AI Agents Do Not Fail Alone:The Context Fails First”

Very important work.

The model may appear guilty, but the true failure frequently begins in the context surrounding it. By observing an AI agent’s environment, we could tell when it was going to crash before it finished the task or got a behavior score.

The study found that agents fail most of the time because they don’t have good instructions, tools, evidence, memories, or safety rules. It assigns scores to this operating context across seven dimensions: clarity of role, description of tools, factual support, consistency of rules, security, and token use.

The score is unrelated to the actual behavior score of the agent so the test does not reward guessing what will happen. As a result of shifting from vague to structured, the same fixed models performed much better over 300 tests and 7,500 turns.

More factual support was associated with fewer hallucinations, clearer tool descriptions were associated with better tool use, and stronger guardrails were associated with resistance to manipulation. Adding more safety rules did not improve every task result, because hardened agents sometimes became too cautious, which exposed a real tradeoff.

🗞️ "American companies such as Modal, Fireworks, and Baseten will be able to serve Kimi K3, at one-tenth the cost of their Chinese competitors because they have access to advanced Nvidia and AMD chips.

There is some irony here. Much of the model’s research and development has shifted to China, but once the model is optimized for next-generation hardware such as Nvidia’s Rubin architecture, its operating cost could fall by 10 to 100 times."

Emad Mostaque, co-founding Stability AI.

🗞️ Kimi K3 is facing a compute crunch. Demand is too heavy, so new subscriptions are currently blocked.

Demand for Chinese local inference chips (like Huawei) should rise massively.

Inference capacity is becoming the real bottleneck. Kimi K3 is almost a perfect workload for Huawei’s inference-chip strategy, the Ascend 950PR. Watch out for a Huawei moment.

🗞️ Why One OpenAI Leader Thinks Open-Weight Models Are Decelerationist

Dean Ball’s Viral Post on Kimi K3 with close to 11mn views:

Dean W. Ball, Head of Strategic Futures at OpenAI (who joined the company shortly before posting), shared detailed observations on Moonshot AI’s newly released Kimi K3 — a 2.8-trillion-parameter Chinese open-weight multimodal model that quickly gained attention for strong performance in agentic coding and long-context tasks.

What He Proposed and His Main Point

Ball called Kimi K3 a genuinely capable model, roughly on par with the best public U.S. models from early 2026 in agentic coding (though notably token-hungry and not obviously cheap to run). He expressed surprise that China continues open-sourcing frontier-level models, attributing it mostly to the CCP’s limited appreciation of AGI risks, U.S. export controls limiting Chinese inference scale, and Chinese labs using open weights as a competitive strategy since few would pay for sub-frontier models.

His central argument: Open-weight models are inherently decelerationist. By giving away high-capability AI for free, they reduce the economic incentive for massive private capital expenditure on next-generation closed models. He warned this dynamic could push the world toward “full AI communism” — AI becoming state-provided public infrastructure rather than a competitive market product (a vision he called dystopian). He also predicted the Trump administration would likely create regulatory uncertainty (“soft law” FUD) around Chinese open-weight models to discourage enterprise adoption without an outright ban.

On Twitter, many accused Ball of being “rattled” by Chinese progress as a new OpenAI employee. Accelerationists and open-source advocates strongly pushed back on the “decelerationist” claim, arguing that open weights actually accelerate progress by enabling broader experimentation, research, and competition. His “AI communism” framing and policy suggestions drew significant mockery and accusations of protecting closed-model business interests.

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

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Read time: 12 min

📚 Browse past editions here.

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

⚡In today’s Edition (17-July-2026):

• 🗞️ Moonshot’s Kimi K3 sends Chinese AI into Fable-grade territory.

• 🗞️ Opinion: Kimi K3 Could Break the Frontier Model Profit Pool

• 🗞️ GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine 👌

• 🗞️ A new family of open, commercially available embedding models built for agentic retrieval, code retrieval, and agent memory.

• 🗞️ AI agent does not remove the necessity of engineering automation. They are worth much more: Creator & Head of Claude Code at Anthropic

• 🗞️ SpaceXAI actually open-sourced Grok Build with Apache 2.0 license. 39 mints and 1.9K+ Github stars.

• 🗞️ Why “technology always creates new jobs” will not work this time.

• 🗞️ OpenAI launched its first hardware, codex Micro, a $230 desktop controller for managing workflow.

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🗞️ Moonshot’s Kimi K3 sends Chinese AI into Fable-grade territory.

Beijing-based Moonshot AI’s Kimi K3 just dropped.

- 1 Mn token context window, natively multimodal. (i.e. 750,000 words of code or documentation in a single prompt.)
- Total 2.8 trillion params, but activates only 16 of its 896 experts at a time. That is roughly 1.8% of the expert pool per token.
- Some of the benchmarks put it Opus 4.8/ GPT 5.6 Sol / Fable 5 territory.
- Its Delta Attention enables up to 6.3x faster decoding in million-token contexts
- available via API at $3/$15 per million input/output tokens.
- K3’s 2.8T MXFP4 weights require roughly 1.4 TB before quantization metadata and runtime overhead.

At an assumed 115 GB usable per GB10 node (NVIDIA’s compact Grace Blackwell AI chip), 14–16 nodes is expected to hold the raw weights realistically. Including activations, KV cache and runtime memory etc.
Moonshot itself recommends supernodes containing 64 or more accelerators.

Some very cool findings from technical report

- K3 autonomously designed, optimized, and verified a working AI chip in a single 48-hour run—specifically to serve a smaller model built on K3’s own architecture.

The simulated chip reportedly reached 8,700+ tokens/second, contained 1.46 million standard cells, and fit within 4 mm².

- An early version of K3 handled the majority of the kernel-optimization work used to develop K3 itself.

- K3 built a GPU compiler from scratch. created MiniTriton, optimization passes, PTX generation, and runtime. Matched or beat Triton on some workloads and successfully trained nanoGPT end to end.

- In a 15-hour autonomous run, Kimi K3 redesigned a production-scale training kernel and cut forward-plus-backward time from 283.6 ms to 114.4 ms.

- For a 42-year semiconductor-industry report, Kimi says K3 performed 2,800+ web searches/fetches, 1,100+ terminal data pulls, processed 11,000+ pages, and recursively improved the work over 120+ rounds.

- K3 reproduced a computational-astrophysics research workflow in roughly two hours, versus an estimated one to two weeks for an experienced researcher. It reviewed 20+ papers, evaluated 300+ equations of state, found inconsistencies in published formulas, and wrote 3,000+ lines of Python.

Kimi K3 appears to beat every model shown on BrowseComp while costing only a fraction as much per task.

Its claiming a dramatically better intelligence-per-dollar tradeoff.
BrowseComp assesses the capability of an AI agent to repeatedly search the internet and discover obscure, hard-to-find facts that require multiple searches and validation.
i.e. a high BrowseComp score means you are good at deep research and web investigation, but not necessarily better general intelligence, writing, coding, or everyday browsing.

As a final note, in my opinion, what I’m seeing on the internet is that a lot of people want Kimi K3 to be framed as a distillation tale. Distillation might play a role, but it still does not explain how Chinese AI labs keep building, training, and releasing models this strong, this fast.

“they copied” is not an AI strategy. America needs its own open source plan.

🗞️ Opinion: Kimi K3 Could Break the Frontier Model Profit Pool

The biggest implication of Kimi K3 is not that an open-source model has matched the leading closed models. It is that frontier-level intelligence may be becoming a competitive market rather than a permanent oligopoly.

That shift would be painful for OpenAI and Anthropic, but broadly positive for almost every other part of the AI economy.

When only 2 or 3 labs control the best models, they can keep inference margins extremely high. Over time, that gives them enough economic power to move into data centers, chips, cloud infrastructure and software. They do not just supply the intelligence layer. They gradually absorb the layers above and below it.

More competition at the model layer changes that equation. Lower model margins mean more AI workloads, more infrastructure spending and more room for independent software companies to build profitable products. Chipmakers, power providers, hyperscalers, neoclouds and application companies all benefit when intelligence becomes cheaper and less concentrated.

The key metric is not benchmark performance or price per token by itself. It is intelligence per dollar.

That depends on both the cost of each token and how many tokens a model needs to complete a task. Kimi K3 appears competitive on headline token pricing, but its weak token efficiency may make it 50% to 70% more expensive per task than GPT-5.6. That limits how disruptive it is today. A genuinely transformative open-source model would need frontier performance, low serving costs and high intelligence density per token.

This also explains why frontier models from Google, Meta, SpaceX and other vertically integrated companies matter so much. These companies do not need to maximize margins at the model layer. They can make money through cloud services, advertising, hardware, subscriptions or other products. Their presence alone can stop independent model labs from sustaining unusually high margins.

OpenAI and Anthropic still have 2 possible defenses. Their consumer products, developer tools and agent harnesses may be stronger moats than the underlying models. They may also possess substantially more advanced internal systems that could give them an early lead in recursive self-improvement.

But the broader direction is becoming clearer: the companies that win will not necessarily own the highest-scoring model. They will deliver the most useful intelligence for every $1 spent. As frontier models become more competitive, value will move away from model access itself and toward infrastructure, distribution, product design and proprietary workflows.

🗞️ GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine 👌

Very slow, but possible.

MoE changes the memory requirement because each token activates about 40B parameters, not the full model. Colibrì keeps the dense tensors resident in RAM, including attention, embeddings, and shared components.

Those resident tensors take about 9.9GB after int4 quantization. The routed experts stay on local NVMe storage as a roughly 370GB int4 model.

During decoding, the router selects experts for each MoE layer and token. The runtime then loads only those selected expert weights from disk.

This works because inactive experts do not need to occupy RAM. The main drawback is throughput, not correctness or model size.

A cold token can require about 11GB of expert reads across 75 MoE layers. That creates very low cold decoding speed, around 0.05 to 0.1 tokens per second.

The system reduces this cost with LRU expert caching, pinned hot experts, and OS page cache reuse. Compressed MLA KV cache also lowers memory pressure during long context generation.

MTP speculative decoding can improve tokens per forward once the cache is warm. The same speculation can hurt cold-cache latency because draft verification may load extra experts.

So the design is only possible because MoE sparsity reduces active parameters per token. The cost is that SSD bandwidth, cache hit rate, and CPU matmul speed dominate latency.

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🗞️ A new family of open, commercially available embedding models built for agentic retrieval, code retrieval, and agent memory.

NVIDIA Nemotron-3-Embed-8B-BF16 now ranks first on RTEB with a 78.5% retrieval score.

RTEB is a RAG retrieval leaderboard, showing how reliably a model finds the right context. When retrieval fails, agents get irrelevant evidence, leading to more searches, longer reasoning and higher token bills.

NVIDIA Nemotron 3 Embed breaks that bottleneck with one 8B model and two 1B alternatives. The NVIDIA Nemotron-3-Embed-1B-BF16 achieved a 72.4% score on RTEB, a 27% reduction in error rate over its predecessor.

A full family across the accuracy-efficiency curve - the 8B flagship plus two efficient 1B variants: 1B-BF16 for cost- and latency-sensitive serving, and 1B-NVFP4, a Blackwell-optimized build with up to 2x higher throughput that retains 99%+ of BF16 accuracy. The 1B version, Nemotron-3-Embed-1B-NVFP4 maintains over 99% BF16 accuracy and can handle up to twice as much BF16 traffic.

All three models can take up to 32K tokens as input, which can be long documents, code or agent histories. Created by NVIDIA using structured pruning, teacher distillation and longer and longer training contexts, NVIDIA Nemotron-3-Embed-1B-BF16. Tests with the NVIDIA Nemotron 3 Ultra showed that improved retrieval led to fewer searches and lower token costs.

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🗞️ AI agent does not remove the necessity of engineering automation. They are worth much more: Creator & Head of Claude Code at Anthropic

The biggest change is that we can now encode far more than code rules.

e.g. `claude.md` can now contain architectural preferences, review standards, product limitations, hard-won domain knowledge, agent skills and so many more things.

🗞️ SpaceXAI actually open-sourced Grok Build with Apache 2.0 license. 39 mints and 1.9K+ Github stars.

So now can look into the the harness/agent machinery behind its terminal coding system.

They also reset every user's usage allowance. The published Rust code covers the agent loop, file tools, shell execution, web search, and terminal interface.

Users can compile it themselves, connect local inference, and control behavior through config.toml. Apache 2.0 permits modification and redistribution, giving companies freedom to audit or fork the software.

External contributions remain closed, while the public repository syncs periodically from an internal monorepo. Independent forks can still test different reliability designs, but shared upstream development remains limited.

🗞️ Why “technology always creates new jobs” will not work this time.

- From ex-OpenAI researcher Daniel Kokotajlo, and the founder of the AI Futures Project.

Previous technological automation replaced particular tasks while leaving humans better at the new ones that emerged. Superintelligence, by definition, breaks that pattern because it is, by definition, better than humans across every task—not merely the old jobs.

He is the founder of the AI Futures Project, and the lead author of 'AI 2027,’ the famous report mapping the trajectory of AI. From "The Diary Of A CEO" YouTube channel

🗞️ OpenAI just launched its first hardware, codex Micro, a $230 desktop controller for managing workflow.

6 translucent keys and the joystick launches common workflows, like PR review, debugging, and code refactoring.

Dedicated controls handle accept, reject, push-to-talk, and new-chat actions from the desk. A rotary dial changes reasoning level. The hardware connects through Bluetooth or USB-C and supports both Mac and Windows. However, personally, I would have really liked a completely hands-free operation for moving faster.

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

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Read time: 11 min

📚 Browse past editions here.

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

⚡In today’s Edition (16-July-2026):

• 🗞️ Mira Murati’s Thinking Machines Lab drops massive open-weight AI model, Inkling, Apache 2.0 license, no restrictions.

• 🗞️ Upto 10.7X speedup on LLM inference with LMCache - Reuses the Most Expensive Part of Long Prompts, 10.6K GitHub Stars

• 🗞️ Meta is so back in the AI coding race with Muse Spark 1.1, using cut-rate pricing to pressure OpenAI and Anthropic in agentic coding.

• 🗞️ Apple sued OpenAI, alleging stolen hardware secrets now underpin its $6.5B device push.

• 🗞️ For Perplexity Computer, Grok 4.5 became the strongest orchestrator, scoring 0.328 (WANDR benchmark score) at $4.76 per trial.

• 🗞️ OpenAI Releases Prompt Behind GPT-5.6 Sol Ultra Math Proof

• 🗞️ GPT 5.6 Terra is behind across the entire intelligence-cost curve.

• 🗞️ Goldman Sachs: “Token use by AI agents is expected to multiply 24 times by 2030”

• 🗞️ Surprising and such a good news for open source coding model, and also that there are lots of hidden chances to reduce cost while improving quality.

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🗞️ Mira Murati's Thinking Machines Lab released Inkling, Apache 2.0 license, no restrictions.

Thinkymachines’ Inkling debuts with 1434 pts, taking #10 in Frontend Code Arena’s open weight model field and #37 overall. It is the top US open model, and the only US model in the open top 15.

A 975B-parameter (41B active) open-weights model with multimodal reasoning and adjustable effort.

• A 1M-token context window lets the system process unusually long documents and workflows.

• Trained on 45T tokens spanning text, images, audio, and video from scratch.

• Users can raise reasoning effort for difficult tasks or reduce it for speed.

• The release matched Nemotron 3 Ultra on Terminal Bench using roughly one-third as many tokens.

• Strong Native audio and vision processing. Inkling scored 91.4% on VoiceBench and 82.0% on CharXiv RQ with Python.

• Large-scale reinforcement learning used more than 30M rollouts and steadily improved reasoning scores.

Inkling’s clearest wins come in agentic tasks. On MCP Atlas—which measures how reliably an AI agent completes real-world tasks using Model Context Protocol, the open standard for connecting AI assistants to external tools and services, scored as percentage of tasks completed—Inkling posts 74.1%. That’s nearly 30 points above Nvidia’s Nemotron 3 Ultra, the main Western open-weights rival in the comparison.

Fine-tuning support arrives through Tinker, while full weights are available through Hugging Face. Inkling-Small activates 12B parameters and sometimes matches its larger sibling on core evaluations.

Thinking Machines Lab’s latest valuation was at $12B, following a $2B seed round. Nvidia later invested an undisclosed amount, while reported $50B talks never produced a confirmed valuation.

Thinking Machines is basically selling this model as a “well-rounded” generalist. So the claim is that it does not give up quality in 1 area just to be better in another, like models that handle coding well but fail at creative writing.

🗞️ Upto 10.7X speedup on LLM inference with LMCache - Reuses the Most Expensive Part of Long Prompts, 10.6K GitHub Stars

Repeated prefill is one of the quietest wastes in LLM serving.

LMCache tackles the problem by saving and getting back KV cache.

• 10.6K+ Github stars

• Benchmark shows up to to a 10.7x speedup

• And vLLM plus LMCache delivers 3-10x improvements on AMD MI300X.

💾 LMCache is a KV cache management layer for LLM inference.

LMCache allows the serving stack to reuse the heavy attention state from the first read of a long prompt, so the GPU doesn’t have to do that work twice.

That attention state is called the KV cache, where KV means key-value tensors from the model’s attention layers.

Normally, this cache lives like short-term memory inside the serving engine, so it can vanish when the engine restarts, fill up GPU memory, or stay stuck to 1 machine.

LMCache turns it into a managed layer that can sit across GPU high-bandwidth memory, CPU RAM, local storage, and remote storage.

That gives you 3 useful advantage: lower time-to-first-token, higher throughput, and cheaper long-context serving.

My favorite part is that LMCache does more than basic prefix caching, which means that the text that needs to be cached has to appear at the beginning of the prompt.

It can reuse repeated KV blocks from repeated or overlapping text. This is the same pattern you see in coding agents, retrieval augmented generation, long document QA, and multi-turn assistants.

And it is not locked to NVIDIA GPUs either. vLLM with LMCache runs on AMD MI300X through ROCm, AMD’s GPU software stack. Also, there are separate non-CUDA paths for work that only needs to run on CPU or other accelerators.

• 🐙 LMCache GitHub

• 📚 Official Documentation

• 🤝 AMD × LMCache

• 📊 Benchmarking LMCache

Agentic workloads are where LMCache starts to make more sense.

A coding agent can send 20-150K input tokens every turn, while reusing about 93-97% of the same prefix from earlier turns, so recomputing the full context again is mostly wasted GPU time.

The clearest result is at 2,000 tokens, where the cold run took around 40 seconds, but the warm LMCache-hit run took around 4 seconds, giving a 10.7x speedup. A cold run computes the KV cache from scratch, while a warm run reuses the KV cache that was already saved earlier.

🗞️ Meta is so back in the AI coding race with Muse Spark 1.1, using cut-rate pricing to pressure OpenAI and Anthropic in agentic coding.

Muse Spark 1.1 a $1.25/$4.25 per 1M input/output token. That undercuts Claude Opus 4.8 by 75% on input and 83% on output, while beating it on MCP Atlas and JobBench.

Muse Spark 1.1 has definitely not won every coding benchmarks, but the model competes hardest where agents spend money.
From their blog - “Muse Spark 1.1 delivers exceptional performance in personal agentic tasks that require planning and orchestration across a range of external apps and services. It zero-shot generalizes to new native tools, MCP servers, and custom skills.”

🗞️ Apple sued OpenAI, alleging stolen hardware secrets now underpin its $6.5B device push.

The complaint names former Apple employees Tang Yew Tan and Chang Liu as central actors. Apple alleges Liu kept an Apple laptop and retained access after leaving for OpenAI. An authentication bug supposedly let that laptop enter Apple’s internal network despite his departure.

Apple says he then downloaded files about unreleased hardware, specifications, and engineering projects. For Tan, Apple alleges he emailed himself supplier details and internal industry summaries before leaving.

Apple also says Tan encouraged candidates to bring physical Apple parts into OpenAI interviews. Those sessions allegedly let OpenAI inspect components directly, while supplier contacts exposed hidden manufacturing methods. Apple’s claim is that files, parts, and supplier knowledge jointly accelerated OpenAI’s hardware work.

Apple alleges OpenAI asked an Apple supplier to apply a secret metal-finishing method. The supplier reportedly believed Apple had approved this use, so it performed the process.

That could save OpenAI months of testing because the supplier already knew the proven recipe. OpenAI accelerated its hardware program after acquiring Jony Ive’s io Products for $6.5 B.

More than 400 former Apple employees now work there, although employment alone proves nothing. OpenAI however, denies seeking competitors’ trade secrets, while Apple demands destruction and product redesigns.

Now, Apple must prove OpenAI used protected information, rather than general experience carried by departing workers. Detailed allegations create serious discovery risk, even before a judge tests their accuracy. The case could delay OpenAI’s hardware schedule and permanently fracture a strategically useful partnership.

To note, Apple and OpenAI already have a broad partnership across eligible iPhones, iPads, Macs, and Vision Pro devices. Apple Intelligence can send selected requests from Siri, Writing Tools, Visual Intelligence, Image Playground, and Shortcuts to ChatGPT.

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🗞️ For Perplexity Computer, Grok 4.5 became the strongest orchestrator, scoring 0.328 (WANDR benchmark score) at $4.76 per trial.

- Opus 4.8 (high, thinking) scored 0.254 at $9.46, despite much higher cost.

- GPT-5.6 sol (medium) cost $2.64 but reached only 0.289 on the same test.

Perplexity Computer basically lets an orchestrator assign research, coding, browsing, and document work across subagents. The WANDR benchmark measures difficult research jobs requiring search, computation, and sustained multi-step reasoning. Grok 4.5 beat five tested configurations, including Opus 4.8, on Perplexity’s evaluation.

The WANDR evaluates whether an agent can complete large, professional research tasks that require searching many sources, running computations, organizing results, removing duplicates, checking evidence, and producing a complete structured answer. Perplexity describes these as “wide research” tasks involving the orchestration of search, compute, and model reasoning.

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🗞️ OpenAI Releases Prompt Behind GPT-5.6 Sol Ultra Math Proof

Checkout the great prompt here.

OpenAI published a short PDF called “Prompt Used for A Proof of the Cycle Double Cover Conjecture” to its CDN. This is a rare insight into the actual instructions that were given to a state of the art model to get it to produce a solution to a 50 year old open problem in graph theory.

The setup is GPT-5.6 Sol Ultra with 64 subagents running simultaneously. It takes less than an hour to arrive at a three-page proof. The Cycle Double Cover Conjecture was proposed independently by several mathematicians in the 1970s . The prompt itself is interesting to look at . It reads “ assume there is a full proof . Do not search for it on the internet . Do not mention that the conjecture is still open . Work on it for at least eight hours before giving up “ . It also creates adversarial agents, whose job is to find flaws in candidate proofs.

This is more than a typical model announcement, because the lab has published not only the headline capability claim but the precise recipe that made it. Long-horizon reasoning as a scaffolded ensemble with a hostile red-team in the same prompt. If it works, it will be a model for other groups to follow.

One honest caveat: the proof has not yet been accepted. There is no formalization in Lean or Coq and no peer review. Mathematician Thomas Bloom has already pointed out that OpenAI’s paper does not cite any previous work in the area, which he said is a common problem with AI-written papers. Nor does the report say how much human editing was involved in turning the raw multiagent output into a tidy three-page paper. Nor does it say whether the adversarial-agent setup will work for other open problems or was tailor-made for this one.

What you have to watch out for is not whether this particular proof stays hidden from the community. The question is whether “assume the answer exists, run adversaries against candidate answers, and don’t stop for hours” will become the standard way to use frontier models to solve tough problems. If so, the question will be more important than the proof.

🗞️ GPT 5.6 Terra is behind across the entire intelligence-cost curve.

i.e. Terra will never be justifiable on either price or performance.

Luna and Sol consistently dominate Terra. i.e. at every Terra reasoning level, buyers can choose a Luna or Sol configuration that delivers more intelligence for the same cost, or similar intelligence for less.

Luna stands out most for cost-sensitive workloads. It reaches competitive intelligence scores at a fraction of the cost of higher-effort Sol and Terra settings. So looks like Luna is the strongest default choice when efficiency matters.

🗞️ Goldman Sachs: "Token use by AI agents is expected to multiply 24 times by 2030"

AI agents are now creating the first serious cost test for the AI boom. As was reported this week, Uber and Microsoft are already rethinking expensive agent usage.

A chatbot may answer once, but an agent plans, calls tools, checks results, edits mistakes, and repeats the loop. That loop can make one user request consume 10x, 50x, or even far more tokens than a normal answer.

Goldman’s bullish case is that monthly token use could reach 120 quadrillion by 2030, while inference cost per token keeps falling 60%-70% per year. The fight is now between agent productivity and token waste.

🗞️ Databricks Tests GLM-5.2, Finds It Rivals Top Closed Models in Enterprise Coding

This is very surprising and such a good news for open source coding model, shows that there are lots of hidden chances to reduce cost while improving quality. Databricks showed GLM-5.2 can compete with elite closed coding models inside real enterprise code.

GLM 5.2 landed in Databricks’ top capability tier and was statistically tied with Claude Opus 4.8 on quality.

- The Pareto frontier for coding tasks (i.e. best quality for a given cost) includes models from OpenAI, Anthropic, and open source.

- This means today, only a mix of tools can provide frontier performance.

- Open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty.

Databricks built the test because public coding benchmarks can become too familiar to models. Instead, it used real internal PRs, real tests, and a multi-million-line codebase.

The cost result makes it more serious. GLM 5.2 at $1.28/task, compared with $1.94/task for Opus 4.8. So in their multi-million lines of enterprise-grade code test, GLM-5.2 made open-weight routing credible, and Pi showed why a credible model must be tested inside the right harness.
Their test also separates two things people often mix up: model intelligence and agent efficiency.

A coding agent needs two parts. The model does the reasoning and code writing. The harness manages the work around it, including file search, terminal commands, test output, and context. GLM-5.2 proved the first part by landing near the top closed models on Databricks’ own private tasks.

Pi, the harness, proved the second part by showing that the same level of work can be done with far less context sent into the model. Databricks found Pi could run the same model with the same thinking effort at over 2x lower cost because it sent about 3x less context per turn.

Coding-agent costs can change massively without changing the model.

Pi saved money by giving models less repeated context while keeping quality roughly stable. So GLM-5.2 becomes more useful when efficient harnesses keep task costs under control.

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 (10-July-2026):

• 🗞️ GPT 5.6 Beats Fable 5 by on DeepSWE at a cheaper price.

• 🗞️ Today’s Sponsor: Toyo Brings Tool-Using AI Agents Into Everyday Conversations, into your iMessage and Telegram

• 🗞️ 1X Debuts Human-Level Robotics Hands - Neo’s Tendon-Driven Hands With 25 Degrees of Freedom

• 🗞️ Microsoft is replacing OpenAI and Anthropic models inside Excel and Outlook to cut Copilot costs.

• 🗞️ GitHub recently published this repo SpecKit, an open-source toolkit to fix a big weakness of vibe coding: that AI often starts coding before the product rules are clear.

• 🗞️ Claude Code creator Boris Cherny: Anthropic’s reported jump of roughly 250% in code written per engineer without a visible collapse in quality.

• 🗞️ Google DeepMind’s paper has some great advice on how we should actually give tasks to AI.

• 🗞️ A massive idea from this Shanghai University paper

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🗞️ GPT 5.6 Beats Fable 5 by on DeepSWE at a cheaper price.

GPT-5.6 Sol reaches roughly 72% to 73% DeepSWE at about $8.4 per task, while Claude/Fable-5 tops out around 70% at a much higher cost, around $13 to $22 per task. DeepSWE is a coding-agent benchmark, not a normal code-generation test, because the model must work inside real open-source repositories and finish long software engineering tasks.

Each task gives the agent a repo and a requested change, then checks whether the final patch actually works using program-based verifiers, so the score means “what % of the 113 tasks were solved.” The benchmark tests repo navigation, bug diagnosis, code editing, tool use, test running, and multi-step repair across 91 repositories and 5 languages, not just writing a neat function from a prompt. On this benchmark, the best model is the one that solves the most tasks for the least average task cost.

🗞️ Today’s Sponsor: Toyo Brings Tool-Using AI Agents Into Everyday Conversations, into your iMessage and Telegram

A single message to Toyo can trigger work across email, calendars, Slack, CRMs, documents, and project tools.

The bottleneck of AI Agent is when I, as an user, need to become the operator.

It should feel more like a colleague in your messages. And that gap is filling up fast. Toyo just launched an AI executive assistant built around the interface people already use: messages.

Toyo product lives in iMessage and Telegram, so delegation starts as a normal conversation. People talk to Toyo in messages. Behind the scenes, Toyo handles tool access, context, and background workflows.

Toyo is now available for anyone to try, you can sign up here.

The shift here is that chat becomes the visible layer, not the full product. Behind that scene, Toyo connects to email, calendar, Slack, CRM, docs, meeting notes, and project tools.

That access lets one short message trigger a workflow across the places where work already sits. A user can ask it to watch important email, draft a reply, prepare a meeting, chase a follow-up, or send a daily briefing.

Toyo then handles the slower parts that usually make agent systems feel heavy. It pulls context, calls tools, keeps workflow state, runs scheduled checks, and asks for approval before sensitive actions.

Most people do not want to configure agents, monitor runs, inspect failures, or maintain automation chains. They want to delegate work, add context when needed, and review the output before anything leaves their account.

Toyo compresses that loop into messaging. It can be operated also by voice completely. A user can send a voice note, or Toyo can call when it needs more context.

And you can use code PRODUCTHUNT to get your first month free.

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🗞️ 1X Debuts Human-Level Robotics Hands - Neo’s Tendon-Driven Hands With 25 Degrees of Freedom

The new humanoid hand for NEO from 1X.

Most robot hands fail because they move, but they do not properly feel contact.

- 1X’s answer uses low-ratio tendon drives, so outside forces travel back through each joint. The motors sit in the forearm, pulling tendons through the wrist like biological muscle.

This keeps the fingers light, lowers impact energy, and still allows strong gripping.

- The hand has 22 actuated finger and palm axes, plus 3 wrist axes. Those joints are not spread evenly, because the thumb carries much of human dexterity.

- A real opposing thumb is so needed, because grasping is mostly about control, not squeezing.

- Closed-loop proprioception gives NEO pose and effort data without using cameras.

- Tactile skin adds contact, pressure, and shear, so slipping objects can trigger fast corrections.

- IP68 sealing and food-safe materials mean the same hand can work near water.

- Backdrivable fingers also make impacts less dangerous, because the mechanism yields under force.

- 1X says full finger assemblies have survived millions of cycles during validation.

- Hundreds are already built, with capacity planned for 10,000 hands this year.

- The interesting part is the data loop. Every grasp can now return force, pose, contact, and slip signals for robot learning.

That makes NEO more like a measuring instrument.

🗞️ Microsoft is replacing OpenAI and Anthropic models inside Excel and Outlook to cut Copilot costs.

Excel and Outlook used to lean more heavily on outside models. Now Microsoft wants fewer expensive calls to labs that control pricing for frontier model access.

OpenAI still gives Microsoft favorable access through their long partnership, but that discount may fade.

Microsoft AI head Mustafa Suleyman has been clear that Microsoft wants to reduce, then remove, that outside cost.

🗞️ GitHub recently published this repo SpecKit, an open-source toolkit to fix a big weakness of vibe coding: that AI often starts coding before the product rules are clear.

The repo has 119K+ stars ⭐️

It turns vibe coding from “ask the AI to build it” into “write the product spec first, then make the AI build from that spec.”

Most AI coding today starts with a loose prompt, then jumps straight into code, which often produces working demos but weak requirements, missing edge cases, and messy rework.

Spec Kit pushes the process the other way: first define what the product must do, then clarify gaps, then create a technical plan, then break that plan into tasks, then let the agent implement against those written artifacts.

So here the spec is no longer disposable documentation; it becomes an executable development contract that guides Copilot, Claude Code, Codex, Gemini, Cursor, Qwen, and 30+ other agent integrations.

🗞️ Claude Code creator Boris Cherny: Anthropic’s reported jump of roughly 250% in code written per engineer without a visible collapse in quality.

And here’s his advice for other companies to achieve the same.

- Companies should not over-control AI usage. Instead of making employees ask for approval for every token, companies should give people enough access to experiment and discover useful workflows.

- Psychological safety is essential for AI adoption. Employees need to feel safe trying new ideas

- The biggest productivity gains may not come from the obvious top engineers. They might come from accountants, marketers, new graduates.

“What happened with Claude is that now many companies, including Anthropic and all of our biggest customers, are reporting gains on the order of hundreds of percentage points.

I think the last number that we reported is that the amount of code written per engineer at Anthropic has grown something like 250% since we introduced Claude Code. This is while keeping code quality, reliability, and all these things kind of stable. So, without those things regressing, the volume of code has grown a lot.

This kind of productivity impact, I think, is just very new, and people are trying to figure out how to get this.”

🗞️ Google DeepMind’s paper has some great advice on 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 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.

🗞️ A massive idea from this Shanghai University paper

Giving AI Models Human-Style Memory. Imagine reading a huge mystery book while only being allowed to keep 1 sticky note.

Every time a new clue appears, the note must be rewritten, so older clues slowly fade or get squeezed out.

That is close to what happens inside fast AI models like linear-attention and state-space models, which save memory by compressing everything they read into 1 fixed internal state.

The problem is that this makes them very forgetful. In a “needle-in-a-haystack” test, where they need to recall a particular fact buried deep in a large document, they fail because fresh details overwrite the earlier ones.

HOLA tries to fix this by giving those models a second place to store details.

The normal state still handles the broad shape of the text, like grammar, topic, and repeated patterns.

The new memory cache stores exact pieces of information that the normal state seems likely to forget.

The clever part is how HOLA decides what deserves that cache.

It does not simply save the newest tokens, because old details can still matter later.

Instead, it saves the tokens that caused the biggest correction inside the model, which usually means the model struggled to compress them cleanly.

In plain terms, HOLA keeps the facts that surprised the model.

When the model later needs an answer, this cache is read in a sharper way, so it can pull back a specific saved fact instead of blending many memories into a vague guess.

This matters because fast models usually trade memory for speed, while full-attention Transformers keep stronger recall but become heavier as the text gets longer.

In the paper’s 340M-parameter test, HOLA lowered Wikitext perplexity from 27.32 to 22.92, beating the matched fast baseline and even beating the reported full-attention Transformer++ on that measure.

It also stayed much better at needle-in-a-haystack recall out to 32k tokens, even though it was trained on 2k-token contexts.

The bigger lesson is that a fast AI model does not need to remember everything exactly.

It needs to know which details its main memory is bad at keeping, then protect those details before they disappear.

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 (09-July-2026):

• 🗞️ OpenAI Launches GPT-5.6 Sol across ChatGPT and API and also unveils ChatGPT work agent to field tasks for hours.

• 🗞️ SpaceXAI just released Grok 4.5 for coding agents at $2/M input and $6/M output tokens.

• 🗞️ A warning for anyone using autonomous agents Google DeepMind’s paper.

• 🗞️ Anthropic just published the story of Claude Code and that how Boris Cherny went from 10% AI-written code in 2025 to 100% in 2026.

• 🗞️ Multi-modal AI for comprehensive breast cancer prognostication

• 🗞️ ByteDance and Alibaba are shutting custom AI companions before China’s humanlike AI rules hit consumer apps.

• 🇨🇳 This is bad news. Now, frontier open source access may be in question.

• 🗞️ Cloudflare just announced a monetization layer for the agentic web, using x402 at the edge to make AI agents pay before resources respond.

• 🗞️ Anthropic just brought Claude Cowork on mobile and web as usage data shows most users are not coding.

Connect with me on X (Twitter)

🗞️ OpenAI Launches GPT-5.6 Sol across ChatGPT and API and also unveils ChatGPT work agent to field tasks for hours.

OpenAI launches GPT 5.6 globally with Sol, Terra and Luna models. The GPT 5.6 family is designed to serve different performance and pricing needs.

• GPT 5.6 Sol - OpenAI’s flagship and most capable model

• GPT 5.6 Terra - Lower-cost model with performance comparable to GPT 5.5

• GPT 5.6 Luna - Fastest and most affordable option for high-volume workloads

OpenAI also turned ChatGPT from a chat box into a supervised agent, a “super app” for finishing work. ChatGPT Work starts with a goal, then converts that goal into a sequence of actions.

Now you can ask it for a board update, forecast pack, customer brief, or launch report. The system then gathers context from selected apps, files, chats, plugins, and web sources. WATCH THE VIDEO.

It can ask clarifying questions, propose a plan, and wait before doing sensitive steps. After approval, it creates the needed artifact, not just a written answer.

That artifact can be a spreadsheet, deck, document, dashboard, tracker, website, or analysis page. The finance demo showed the full mechanism through a realistic monthly reporting workflow.

ChatGPT Work read forecast material, ran variance analysis, updated Excel, built slides, created a site, and shared it through Slack. The new power comes from combining reasoning, context access, coding, computer use, and document creation.

Codex gives it the ability to write the software behind interactive outputs when needed. That is why a spreadsheet can become an interactive dashboard instead of a static summary.

The new visualize feature can read messy tabular data, find themes, rank issues, and create explorable views. Hosted Sites turn those outputs into shareable web pages for reports, dashboards, calendars, prototypes, and internal tools.

OpenAI says Hosted Sites are being made available for paid users. The desktop app adds local-machine action, but only on supported desktop platforms.

On macOS and Windows, ChatGPT can work with local files, browser tabs, and desktop apps. The demo showed it getting its own cursor and organizing Apple Notes in the background.

That is different from uploading a PDF, because it can operate inside apps while you continue working. Email access does not happen magically, because Gmail or Outlook must be connected and authorized.

OpenAI now describes connectors as apps that let ChatGPT search and reference approved information. ChatGPT for Excel and Google Sheets adds a spreadsheet-native sidebar for building, updating, cleaning, and explaining workbooks.

GPT-5.6 is the model layer underneath, with Sol, Terra, and Luna serving different workload levels. OpenAI said Sol is rolling out to paid plans within 24 hours. OpenAI also said Terra and Luna are coming to free users over that same window.

ChatGPT Work runs on web, mobile, and desktop, while desktop gives the deepest machine access. The real change is not better chat, but turning scattered context into finished deliverables.

🗞️ SpaceXAI just released Grok 4.5 for coding agents at $2/M input and $6/M output tokens.

The release targets coding, agentic tasks, and knowledge work across science, engineering, and math after training alongside Cursor.

On DeepSWE 1.0, Grok 4.5 lands at 62%, ahead of Opus 4.8 max at 55.8%, behind only Fable max and GPT 5.5 xhigh. Large coding models usually spend too many steps reading files, trying tools, and repairing mistakes.

Grok 4.5 attacks that cost by training on engineering tasks, Cursor data, and long agent runs. So Grok 4.5 looks most persuasive where latency, cost, and tool use matter together.

It can build complex Excel models using web research, multi-sheet formulas, and notes for future reference. Also can use native PowerPoint shapes for diagrams, design slide content, and write clear prose in Word.

The model is now in Grok Build, Cursor, and SpaceXAI’s API, but not yet in the EU. Free access is limited-time inside Grok Build and Cursor, including x. ai/cli onboarding.

Artificial Analysis placed Grok 4.5 at 4th on its Intelligence Index, behind Fable 5, GPT-5.5, and Opus 4.8. It also ranked 4th on GDPval-AA v2, behind only Anthropic’s newest Claude models. Its main strength is efficiency: Grok 4.5 used about 60% fewer output tokens on average than Opus 4.8 on the same Intelligence Index tasks, and nearly 25% of the total tokens Fable 5 used in Claude Code for similar coding-agent work. Cursor’s own data shows the same pattern, with Grok 4.5 averaging under 16,000 output tokens on SWE-Bench Pro tasks, around 4.2 times fewer than Opus 4.8 in the same test.

Cursor’s benchmark chart also has a caveat. The company said an older snapshot of the Cursor codebase was accidentally part of Grok 4.5’s training data, which gave it an advantage on CursorBench that Cursor says it cannot fully measure. That data has since been removed from future training runs. It is a small transparency detail, but one to keep in mind before treating benchmark charts as final truth.

🗞️ A warning for anyone using autonomous agents Google DeepMind’s paper.

Gives the first clear taxonomy of 6 attack types where harmful websites can detect AI agents and show them hidden content humans never see, like

- Instructions buried in HTML comments or white-on-white text

- Steganography in image pixels

- Override commands in PDFs, metadata, or even speaker notes

- Memory poisoning that persists across sessions

- Goal hijacking and cross-agent cascades in multi-agent setups

The real security problem for AI agents is not just the model, but the environment it reads. The web itself can be weaponized against autonomous AI agents. As agents increasingly browse the internet, read emails, execute transactions, and spawn sub-agents, the information environment becomes an attack surface.

In one cited benchmark, hidden prompt injections embedded in web content partially commandeered agents in up to 86% of scenarios, sub-agent hijacking working 58–90% of the time, and data exfiltration attacks clearing 80% across five different agent architectures.
That reframes the whole debate.

We usually talk about model safety as if the danger sits inside the weights, but agents do something more fragile: they browse, retrieve, remember, and act on untrusted material in real time.
Here’s the thing to worry about.

A web page does not have to look malicious to be dangerous to an agent, because the agent may parse what humans never see: hidden HTML comments, metadata, CSS-hidden text, formatting syntax, or adversarial content embedded in images and other media.
The threat gets more serious once memory enters the loop.

If an agent uses RAG or persistent memory, poisoning no longer has to win in one shot. It can sit quietly in a corpus or memory store and activate later, which is why the paper highlights results showing latent memory poisoning above 80% attack success with less than 0.1% data contamination.

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🗞️ Anthropic just published the story of Claude Code and that how Boris Cherny went from 10% AI-written code in 2025 to 100% in 2026.

Research kept improving agentic coding through shells, file search, code execution, and edit loops.

In 2024, Boris Cherny’s Claude CLI prototype was the first real terminal product in this direction. A small team shipped fast, used Claude Code to build Claude Code, and fixed feedback quickly. The 2025 launch was buggy at first, but Claude Sonnet 4 and subscriptions made it take off.

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🗞️ Multi-modal AI for comprehensive breast cancer prognostication

New nature-published research built a breast cancer AI test that uses routine microscope slides and clinical data to rank recurrence risk correctly about 71% of the time.

A foundation AI model learned tissue patterns from 400M patches, then helped turn routine slides into recurrence-risk evidence. Breast cancer care already uses tumor size, nodes, receptors, grade, and sometimes gene assays.

Those signals guide therapy, but they still miss hidden recurrence risk inside ordinary tissue structure. This system adds the missing visual layer by reading digitized pathology slides alongside routine clinical data. Kestrel (the pretrained image-reading engine they used inside their new breast cancer test) learned patterns from 400M pathology patches, then scored tumor morphology without hand labels.

🗞️ ByteDance and Alibaba are shutting custom AI companions before China’s humanlike AI rules hit consumer apps.

Doubao and Qwen let users create named assistants, tutors, characters, and emotionally steady companions.

The old model turned a general chatbot into a persona that remembered tone. China’s new rule targets AI services that imitate human personalities for sustained emotional interaction.

Regulators are drawing a line between useful automation and software that builds attachment, agents now remember, plan, call tools, and shape behavior. Doubao says its agent feature goes offline on 07-15, with related data gone from view after 10-15. Qwen will disable humanlike and user-created agents earlier, then remove broader agent services on 07-15. The user backlash shows these products already became emotional infrastructure for some people.

🇨🇳This is bad news. Now, frontier open source access also will probably no more be available for all.

China is preparing to limit foreign access to its strongest AI models, a move that could raise global AI costs and split the model market by nationality.

Beijing has held recent talks with Alibaba, ByteDance, and Z .ai about keeping advanced Chinese models inside China, including models not yet released. The Ministry of Commerce led the discussions, with China’s state planning agency also present, which signals export control rather than routine platform regulation.

The targets include closed models and open-weight systems, so the issue is not only API access but downloadable model power. Chinese officials also discussed treating leaks or theft of proprietary AI as a national security offence, not merely an IP dispute.

New limits on who can fund Chinese AI startups were also discussed, which would tighten control over capital, talent, and model access together. Foreign companies could lose access to low-cost Chinese models just as those models become strong enough for serious production work.

Washington has already restricted access to advanced U.S. models on security grounds. China now fears Mythos could find software vulnerabilities and be used against Chinese interests, so both sides are treating AI as strategic infrastructure.

Beijing has also investigated Chinese AI startups that moved abroad and pushed Meta to unwind a $2B Manus deal.
A likely path is tiered control: basic open tools get filings, stronger systems face reviews, and frontier models stay domestic.
This would be a major setback for open AI access because model progress would no longer spread mainly through product quality and price.

🗞️ Cloudflare just announced a monetization layer for the agentic web, using x402 at the edge to make AI agents pay before resources respond.

Means AI agents may start paying websites directly, instead of only reading free pages or using prearranged API keys.

Today, most AI products reach outside information through contracts, scraping, search partnerships, paid APIs, or free public pages. Cloudflare wants to make that access behave more like a paid HTTP request.

When an agent asks for a protected resource, Cloudflare can block the request before origin access. A website owner can say, “this dataset costs $0.01 per call,” and Cloudflare enforces that rule before the request reaches the server.

The agent asks for the resource, gets a 402 Payment Required response, pays, retries with payment proof, and receives the answer. The major change is that payment becomes part of the web request itself.

For a normal AI user, this could mean your assistant sometimes needs spending permission before completing a task.
A research agent might say, “this source costs $0.03, should I use it?”

A shopping agent might pay for inventory data, product comparisons, booking tools, or private market data. The catch is that this only works when agents carry wallets, understand x402, and obey spending rules.

🗞️ Anthropic just brought Claude Cowork on mobile and web as usage data shows most users are not coding.

Marks a strategic inflection for Anthropic. This move links AI coding agents aimed at developers with the much larger knowledge-worker market that never opens a terminal.

Now, your tasks can start on a laptop, continue autonomously in the background, and be reviewed from a phone — even after the user closes the app entirely. The new web and mobile Cowork rollout is starting with Max subscribers first, while other paid plans are expected later.

As to usage limit, Claude uses a shared usage pool across Claude .ai, Claude Desktop, Claude Code, and related surfaces, so Cowork usage counts against the same plan limits. Earlier, Cowork lived mainly inside the desktop app, where it could use local files and browser access. Now, Max users can start work on desktop, check progress on mobile, and continue through web.

Claude Cowork is for assigning a task that can run across tools, files, and time. The mechanism is closer to delegation than normal chatting because you assign a job, not just ask a question.

Claude can gather information, organize files, draft documents, compare updates, clean spreadsheets, and ask for approval. Cloud processing lets those steps continue on Anthropic’s servers while your own device is offline. Your phone then becomes a control panel for updates, decisions, approvals, and finished outputs.

This matters because most office work is not hard because each step is brilliant.
It is hard because the steps are scattered across email, docs, notes, calls, spreadsheets, and reminders.

Cowork is aimed at that messy middle layer, where people lose hours turning fragments into usable work. For a regular consumer, the benefit is less manual coordination and fewer half-finished admin tasks.

You could ask it to prepare a meeting brief, summarize a thread, draft a follow-up, or build a checklist. You still review the result, but you do not personally carry every small step.

The big deal is that AI is moving from “answer my question” to “finish this task and come back.” This is why mobile access matters because real work often needs approval when you are away from your desk.

The catch is trust, since useful agents need access to personal files, messages, and work tools. Anthropic is trying to make the agent useful while keeping the human in charge of final decisions.

This was timed on purpose. Together with the mobile launch, Anthropic published data from 1.2 million anonymized Claude Cowork sessions collected between May 11 and May 31, spanning 600,000+ organizations. The data shows most Cowork activity sits outside coding.

So Cowork now is aimed at a much bigger crowd: knowledge workers with laptops, spreadsheets, and slides.

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

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Read time: 7 min

📚 Browse past editions here.

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

⚡In today’s Edition (08-July-2026):

• 🗞️ Meta just launched its 1st image model after Mark Zuckerberg’s AI shake-up.

• 🗞️ DeepSeek is building an inference chip to cut dependence on Nvidia and Huawei in China’s $50B AI-chip market.

• 🗞️ Per The Information, Zhipu AI is also (after DeepSeek) exploring a custom ASIC after GLM-5.2 usage reportedly jumped 27x in one week.

• 🗞️ Google Gemini just gave developers much stronger tools for production AI agents.

• 🗞️ Somebody built a Claude Code workflow that connects your profile, job posts, and application drafts. 5.7K+ Github stars.

• 🗞️During a Bloomberg interview, Yann LeCun (ylecun) explains why LLMs are limited in terms of real-world intelligence during a Bloomberg interview.

• 🗞️ 1X Technologies is preparing to reveal a robotic hand that may surpass human finger control.

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🗞️ Meta just launched its 1st image model after Mark Zuckerberg’s AI shake-up.

Muse Image is Meta Superintelligence Labs' first image generator after Muse Spark.

They said the model will power new editing features in Meta’s Instagram photo app and be added to its marketer tools for creating platform ads. Meta had relied on Midjourney and Black Forest Labs for generation inside Meta AI. Muse Image brings that layer in-house, so Meta controls quality, cost, and product timing.

Consumers get free access through Meta AI, WhatsApp chats, and Instagram Stories. Power users need Meta One or another monthly plan when free limits run out.

Users can start from prompts, add photos, annotate edits, or sketch changes directly. Meta says it can erase photobombers, create QR codes, and keep visual text readable.

Advertisers get variants through Advantage+ creative, with edits, style swaps, and brand-matched versions. Meta says internal tests trail GPT Image 2 but beat Nano Banana 2 on editing. This is another move in Meta's effort in trying to convert AI infrastructure spending into revenue beyond social ads.

🗞️ DeepSeek is building an inference chip to cut dependence on Nvidia and Huawei in China’s $50B AI-chip market.

DeepSeek’s chip work is still early, with outside partners and private hiring of chip-design engineers.

The hard part is not drawing a chip, but making it at scale. Advanced foundries and high-bandwidth memory remain chokepoints because U.S. rules restrict Chinese access.

DeepSeek can still gain from a narrower chip built mainly for its own models. A custom inference chip could lower serving costs, reduce power needs, and tighten software-hardware control.

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🗞️ Per The Information, Zhipu AI is also (after DeepSeek) exploring a custom ASIC after GLM-5.2 usage reportedly jumped 27x in one week.

A custom ASIC removes flexibility, but it can cut power draw and per-token cost.

Nvidia GPUs are strong general-purpose machines, but inference at scale has different economics. A fixed model can run better on silicon designed around its own repeated operations.

Zhipu has not chosen a partner, and the project may take more than 2 years. The pattern is now bigger than one Chinese lab or one model launch. Chinese AI companies are trying to make software, hardware, and deployment less separable.

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🗞️ Google Gemini just gave developers much stronger tools for production AI agents.

i.e. Gemini API Managed Agents now are much closer to production with new addition of background tasks, remote MCP, function calls, credential refresh, and free-tier access. Managed Agents are Google-hosted AI workers that run antigravity-preview-05-2026 inside an isolated Linux sandbox.

Older agent apps often broke when a long task outlived a normal HTTP request. An "Interaction" is the important object here. It stores the task, the model’s steps, tool calls, tool results, and final output.

So instead of your app manually tracking every turn, tool result, and file, Google tracks much of that server-side. Remote MCP support changes the tool story, because agents can contact private services without custom proxy glue.

A company can now connect observability, databases, or internal APIs beside Google Search and code execution. Function calling adds another split, where Google runs sandbox tools and your app handles business logic.

Credential refresh fixes a production pain, since short-lived tokens can rotate without losing sandbox state. Overall, this makes Gemini API feel less like a model endpoint and more like agent infrastructure.

🗞️ Somebody built a Claude Code workflow that connects your profile, job posts, and application drafts. 5.7K+ Github stars.

a repeatable job-application machine with saved instructions, profile files, scraper tools, LaTeX templates, and review steps.

You first run /setup, which builds a detailed profile from your CV, documents, or interview answers. Then /scrape searches job boards, removes duplicates, and ranks jobs by fit against your profile.

Then /apply <url> reads a job post, compares it with your real experience, and creates a tailored CV and cover letter.
It loops after drafting: one Claude agent writes, another reviews the draft, then the first revises it.
It also compiles the CV and cover letter as PDFs, checks layout problems, and fixes them until the output is clean.

🗞️During a Bloomberg interview, Yann LeCun (ylecun) explains why LLMs are limited in terms of real-world intelligence during a Bloomberg interview.

"Language is a very approximate, reduced, quantized, and simplified description of the world, and LLMs can only deal with discrete sequences of symbols. The world is much more complicated than language.

The biggest LLMs are pre-trained on the totality of all the publicly available text on the internet. That’s about 20 trillion words, or 30 trillion tokens. A token is about 3 bytes. So total 10¹⁴ bytes of text.

This is the amount of data a four-year-old has seen through vision during four years. Now, the text, though, would take 400,000 years to read?

So, there is enormously more data from sensory input, like vision, touch, and everything else, than there could ever be through language." A child does not need 400,000 years of reading to understand cups, doors, balance, faces, falls, or heat, because the body is already collecting dense feedback from vision, touch, motion, and consequence.

Text strips most of that away. It turns a living scene into symbols, then asks the model to infer the missing world from traces left by people describing it.

That is why an LLM can sound fluent about physics and still have no native sense of how fragile glass feels in a hand.
Moravec’s paradox names this reversal: the things humans find intellectual can be easier for machines than the things toddlers do without applause.
The hard part is not producing an answer, but building a model of the world that survives contact with weight, friction, surprise, and failure.

🗞️ 1X Technologies is preparing to reveal a robotic hand that may surpass human finger control.

Both Elon Musk and Demis Hassabis has said robotics is bottlenecked by hands and robots will not become useful until hands improve massively.

And now 1X Technologies is unveiling the most advanced robotic hand in human history.

The big deal here is the controllable force across the small joints that make dexterity possible. A 10-20DoF hand would sit near the hard boundary between compact hardware and humanlike control.

The design appears built around Bowden tubes, which route force through cable-like tendon paths. Real intelligence may be hiding in the routing, not the fingers.

That architecture lets actuators sit away from the fingers, keeping the hand lighter.
A human hand uses the same broad trick, with muscles pulling tendons across joints.

1X is applying that idea to humanoid robotics, where compact hands must handle real objects. The visible side reportedly shows 20 tendons, suggesting 10 paired actuator paths.

A matching hidden side could raise the system to 40 tendons and 20 actuators. The lower estimate points to coupled finger joints, where one tendon path drives several links.

The higher estimate points to independent IP control, where each finger segment can move separately. That difference separates a strong gripper from a hand that can shape contact precisely.

The catch is calibration, because tendon friction, stretch, and backlash can erase beautiful mechanics. If 1X has solved that reliably, the hand could become a serious advantage for NEO.

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-July-2026):

• 🗞️ Anthropic’s new “J-lens” uncovers a quiet workspace inside Claude that lines up with a major consciousness theory.

• 🗞️ Tencent released Apache-licensed Hy3, a 295B MoE (21B active parameters) model beating larger rivals outside coding.

• 🗞️ “Measuring the Gap Between Human and LLM Research Ideas”

• 🗞️ “From AGI to ASI”

• 🗞️ Smart move from NVIDIA, they rolled out a program where AI startups swap high-performance compute for a share of future product and cloud earnings.

• 🗞️Microsoft is cutting 4,800 jobs as AI infrastructure spending forces a harder tech reset, equal 2.1% of its workforce and hit commercial operations and Xbox.

• 🗞️ Anthropic just extended Claude Fable 5 paid-plan access through 12-July-2026

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🗞️ Anthropic’s new “J-lens” uncovers a quiet workspace inside Claude that lines up with a major consciousness theory.

New “J-lens” uncovers Claude’s quiet workspace, matching a major consciousness theory.

They found a way to read some of Claude’s private internal signals before it answers, found that Claude sometimes uses a small inner “notepad” to hold ideas while solving harder problems. This could reveal hidden reasoning, hidden goals, or hidden awareness.

Anthropic calls this J-space, because it is identified with a method called the Jacobian lens. That lens tries to read which internal activations are “poised to become words” later in the model’s output.

The confusing part is that this space behaves less like a simple next-word predictor and more like a private scratchpad. Claude can be outputting one thing while internally carrying another concept.

It can also store intermediate reasoning steps. When researchers remove or alter this J-space, Claude can still speak fluently and do routine tasks, but its flexible multi-step reasoning gets worse.

It suggests a split between automatic processing and deliberate, accessible processing, similar in function to one major theory of human conscious access called global workspace theory. In humans, the idea is that lots of processing happens unconsciously, but a small subset becomes globally available for speech, planning, and control. Anthropic found something functionally similar inside Claude.

The safety angle is probably the most concrete part. If a model is internally noticing “fake,” “deception,” “evaluation,” or “secretly” while giving a bland answer, J-space may expose that hidden state before it reaches the surface. Anthropic reports examples where J-space revealed recognition of staged evaluations, prompt injections, and misaligned intent in deliberately trained bad models.

Some tasks do not need this workspace. Claude can still speak fluently, classify text, continue Spanish, or recall simple facts when J-space is disrupted. But tasks that require flexible reasoning, multi-step inference, analogy, translation, or creative composition degrade badly.

Anthropic says less than 10% of Claude’s activity forms a J-space that carries hidden reasoning. Claude may notice something and use it while answering. That still does not mean there is anything it feels like to be Claude.

This work gives evidence for a functional access-like mechanism, not for feeling, suffering, experience, or personhood. So the sober takeaway is this: Claude may have an inspectable internal workspace for usable thoughts, but that is not the same as a mind having an inner life.

Ff researchers swap or delete concepts inside this workspace, Claude’s behavior changes in targeted ways. Banana becomes elephant, France becomes China, Mars becomes Earth.

So J-space is not decorative noise. It is causally involved in what the model reasons, tracks, and says.

🗞️ Tencent released Apache-licensed Hy3, a 295B MoE (21B active parameters) model beating larger rivals outside coding.

Hy3 is open-sourced under the Apache 2.0 license HuggingFace, so enterprises can legally use and affordably host.

So GLM-5.2 (from Zhipu AI) still wins coding, especially hard repository-level coding benchmarks, but the point is GLM-5.2 is much larger model ( 744B total and about 40B active parameters). Hy3’s real strengths show up elsewhere. In agentic search, it scores 84.2 on BrowseComp and 91.0 on DeepSearchQA, beating every open model in Tencent’s table and staying competitive with Claude Opus 4.8 and GPT-5.5.

Tencent also claims Hy3 became much more reliable than its April preview. Hallucinations reportedly dropped from 12.5% to 5.4%, while multi-turn issues also fell.

From the deployment perspective comparison, GLM-5.2 at FP8 will need ~744GB, which makes an 8x H200 node the practical baseline for production serving. But Tencent's Hy3, at 295B total parameters, fits under a 300GB FP8 footprint, meaning it uses less than half the memory and roughly half the active parameters per token, cutting compute per request. For a team deciding what to self-host, that turns a heavy 1-node requirement into a much more attainable system, with headroom for KV cache and batching.

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🗞️ "Measuring the Gap Between Human and LLM Research Ideas"

This Yale + University of Chicago paper shows that real gap between LLM generated research ideas vs humans is not idea quality, but idea range: LLMs think narrower than human researchers.

The researchers built a controlled test from 11,683 real papers, using each paper’s nearby prior work as the shared starting point. They asked models to propose a new motivation and method from those same prior papers, then compared those ideas with the real human paper ideas.

Instead of asking whether 1 idea looked novel, they labeled each idea by what gap it noticed and what kind of contribution it made. Human ideas spread across many patterns, such as explaining mechanisms, testing failures, measuring evidence, building systems, and improving efficiency.

Only 12.1% of human ideas were mainly about connecting separate work, but 47.1% to 64.2% of LLM ideas did that, meaning models used this move about 4 to 5 times more often. Even extra reasoning made this pattern stronger, suggesting models often polish a familiar recipe instead of finding more varied research moves.

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🗞️ “From AGI to ASI”

Beautiful paper from Google DeepMind.

Explains the pathways from AGI to ASI, and why that jump could happen through several routes. The authors frame the AGI-to-ASI transition around 4 technical pathways:

- continued scaling of compute, model size, data, and test-time inference;

- algorithmic paradigm shifts beyond today’s transformer-based foundation-model stack;

- recursive self-improvement, where AI accelerates AI R&D and improves future systems; and

- multi-agent collective intelligence, where large populations of specialized agents coordinate into a superhuman group agent.

Scaling may work for a while, but it could hit limits in data, compute, energy, or weaker returns from making systems larger. Recursive improvement is the most uncertain path, because AI could speed up AI research, but that loop may also slow if hard research problems need real-world testing, scarce hardware, or new ideas.

Multi-agent collectives may be the most underappreciated path, because a society of competent digital workers could outperform a brilliant individual model through specialization, speed, and coordination. The big point is that ASI may not arrive as 1 sudden event, but as a chain of faster changes as AI helps create better AI and stronger scientific tools.

🗞️ Smart move from NVIDIA, they rolled out a program where AI startups swap high-performance compute for a share of future product and cloud earnings.

So NVIDIA gets paid twice: first when the infrastructure is purchased, then again through a recurring share of the cloud revenue generated by that supported capacity.

NVIDIA is not mainly targeting AWS or Google Cloud here; it is backing specialized GPU cloud providers, i.e. neoclouds. The aim is to use the new revenue-sharing model to reach researchers and nascent companies that lack the money for large-scale AI resources.

So the neocloud buys NVIDIA infrastructure with help from NVIDIA’s credit-support and revenue-sharing structure, then rents that GPU capacity to AI companies that need training, fine-tuning, and high-volume inference. NVIDIA gets the hardware sale upfront, but it also keeps earning a recurring share of the cloud revenue produced by those GPUs, which turns the chip from a one-time product into a long-term revenue meter.

So, NVIDIA turns a rack of GPUs from a one-time hardware sale into an income-producing asset, while the neocloud gets easier financing and the startup gets faster compute without building a data center. Nvidia has already committed more than $40 billion to AI investments in early 2026, led by a $30 billion stake in OpenAI, alongside multi-billion-dollar investments in Corning Inc. and IREN Ltd..

🗞️Microsoft is cutting 4,800 jobs as AI infrastructure spending forces a harder tech reset, equal 2.1% of its workforce and hit commercial operations and Xbox.

Says these roles are not being replaced directly by AI, but AI still sits behind the pressure.

Microsoft’s Azure business is growing, but data center spending is squeezing cash flow. The company also projected $190B in 2026 spending, far above expectations.

Xbox shows the same squeeze from another angle, with margins reportedly near 3%. Hardware costs rose, console demand softened, and gaming content spending failed to lift revenue.

🗞️ Anthropic just extended Claude Fable 5 paid-plan access through 12-July-2026

The earlier cap at 50% weekly use remains, i.e. Fable 5 can use only half of your weekly Claude allowance.

Your total weekly allowance is a behind-the-scenes usage budget, and long chats, files, tools, and heavier models spend it faster.

After 12-July-2026, your $20 Claude Pro subscription will not include Fable 5 in the normal weekly allowance. Fable 5 would be metered through usage credits, meaning prepaid or capped extra spending separate from your $20 plan.

Anthropic says usage credits for Pro, Max 5x, and Max 20x are charged at standard API pricing rates. You are still a web subscriber, but Fable 5 becomes paid extra use, not normal Pro use.

Your exact Pro allowance is not a public message number, so check Settings > Usage for your bars and reset time. The 50% is not a fixed message number, because Claude usage changes based on message length, files, tools, and conversation size.

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 (06-July-2026):

• 🗞️ CAIS and Scale (AI safety research group) say Fable 5 now automates 16.1% of real remote-work projects, about 2x Opus 4.8.

• 🗞️ “The ATOM Report: Measuring the Open Language Model Ecosystem”

• 🗞️ “Cognitive offloading and the speedup illusion in human-AI interaction”

• 🗞️ By 2027, just 5 companies, Alphabet, Amazon, Meta, Microsoft, and Oracle, are expected to spend around 3.2% of US GDP on AI capital expenditure.

• 🗞️ AI revenue is scaling 3x quicker than mobile or internet did.

• 🗞️ Cathie Wood (founder, CEO & CIO of ARK Invest) is saying that productivity improved significantly last year, partly because companies delayed hiring and used more AI instead.

• 🗞️ Hugging Face Engineer publishes an exhaustive slide presentation on how to train an open coding agent using TRL and OpenEnv

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🗞️ CAIS and Scale (AI safety research group) say Fable 5 now automates 16.1% of real remote-work projects, about 2x Opus 4.8.

Remote Labor Index tests whether an AI can finish paid freelance work well enough for a client to accept it.

Each task comes with briefs, files, and a professional deliverable used as the human baseline. Fable 5 led the new results, while Opus 4.8 reached 8.3% and GPT-5.5 reached 6.3%.

That 16.1% number still means failure on most tasks, but the direction of improvement is massive. For the context of how large this jump is, the best model scored only 2.5% when RLI launched.

The work is not toy prompting, since tasks include CAD, architecture, animation, audio, data analysis, and web apps. This means the benchmark is testing computer work across messy tools, not narrow text answers.

Fable 5 looked strongest on examples like ring modeling, animation, and bathroom design. The automated judge ranked models well, but overstated GPT-5.5 by about 2.9x and Opus 4.8 by about 2.3x. The result says AI agents are improving fast, but quality control remains the hard wall.

Another key point is that the model alone is not the whole product anymore. The gains are coming from stronger agent setups: better tool use, full desktop environments, professional software, longer runtimes, and worker-critic loops where 1 agent does the task and another reviews it like a demanding client.

AI judge can track progress directionally, but they miss hidden quality failures that human evaluators catch.

The automated judge matched earlier models at ~3%, but badly overrated newer ones: GPT-5.5 rose from 6.25% human-approved to 17.9%, and Opus 4.8 from 8.33% to 18.8%.

Longer human work did not mean harder AI work across the benchmark.

Remote labor automation does not scale with human time in the clean way many forecasts assume. A task that takes a professional 60 hours may contain long stretches of production work.

That work can be easier for agents because tools compress rendering, drafting, coding, or iteration. A task finished by a person in 2 hours may require judgment that models still lack.

The result points to a jagged capability frontier, not a smooth ladder of harder tasks. This weakens time-horizon forecasts that treat longer human duration as harder AI difficulty.

That indicates automation will arrive unevenly inside jobs, not across them. Some expensive project slices may fall early, while small expert checks may remain stubborn. Remote Labor Index is therefore measuring client-acceptable output, which is closer to labor risk than task length.

🗞️ "The ATOM Report: Measuring the Open Language Model Ecosystem"

A paper on open model adoption and finds Chinese models, led by Qwen, now dominate.

China passed the U.S. in open model downloads in summer 2025. By March 2026, Chinese models had 1.15B tracked downloads, while U.S. models had 723M.

Most of that shift comes from Qwen, which became the default base family for many builders. Qwen’s lead is not just about 1 famous model; it comes from having many useful models across many sizes, especially small ones that people can run cheaply and reuse often.

DeepSeek matters in a different way: it does not dominate the whole open model ecosystem like Qwen, but it leads the very large model category, especially models above 250B param.
After adjusting for model size and age, some U.S. models like GPT-OSS 120B and Nemotron Super 120B still show very strong adoption momentum, even though China leads overall.

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🗞️ "Cognitive offloading and the speedup illusion in human-AI interaction"

This MIT, Stanford, New York Univ, Princeton, study shows that while people expected AI to save time on simple tasks, but it mostly saved effort instead.

Across 1,237 people, participants predicted that AI help would cut about 69 seconds from task time. In the real task group, AI help did not meaningfully reduce total completion time overall.

The gap came from a speedup illusion, where people judged their own solo time fairly well but badly underestimated AI-assisted time. AI did help on some harder tasks, such as longer summarizing or editing, but it did not help much on easy tasks.
The important twist is that AI made tasks feel easier, even when it did not make them faster.

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To note, the biggest limitation is that this study used short, simple tasks that could be finished in under 5 minutes, so it does not test long report reading, synthesis, cross-checking, and analysis work where AI can compress hours into minutes. It also used crowdworkers doing one-off tasks in a controlled setting, so it misses expert users with strong prompting habits, personal context, repeated workflows, and clear business goals, where gains can be much larger.

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🗞️ By 2027, just 5 companies, Alphabet, Amazon, Meta, Microsoft, and Oracle, are expected to spend around 3.2% of US GDP on AI capital expenditure.

That would put private AI infrastructure spending above US national defense spending, which is expected to be around 2.7% of GDP. The AI race is now being funded at a scale normally associated with governments, wars, energy systems, railroads, highways, and telecom buildouts.

The striking part is the speed. AI capex is expected to jump from about 1.5% of GDP in 2025 to about 2.5% in 2026, then to 3.2% in 2027. AI boom is now large enough to influence the broader US economy significantly, it can move GDP growth, electricity demand, chip supply, construction activity, corporate debt markets, and ofcourse the labor market.

🗞️ AI revenue is scaling 3x quicker than mobile or internet did.

The unit of value is shifting from attention to completed work. Read the main report.

🗞️ Cathie Wood (founder, CEO & CIO of ARK Invest) is saying that productivity improved significantly last year, partly because companies delayed hiring and used more AI instead.

She expects productivity growth to move from around 2.9% currently to the 5% to 6% range within the next five years, possibly sooner.

AI is helping companies do more with fewer workers.

🗞️ Hugging Face Engineer publishes an exhaustive slide presentation on how to train an open coding agent using TRL and OpenEnv

The whole deck is saying 1 thing: a coding agent should be trained inside the kind of tool loop it will actually use, not only trained to chat well.

A coding agent has 2 parts. The model thinks and writes actions. The harness gives it tools like read file, edit code, run tests, and return results.

It starts with the basic agent setup: agent = model + harness. The model decides what to do, while the harness gives it tools like reading files, editing code, running tests, and seeing results.

You will learn why a normal instruction model is not enough. It may answer well in chat, but it does not automatically know the exact tool format, the multi-turn loop, or how to behave inside a real coding environment.

The first training step is Supervised Fine-Tuning, or SFT. Here, the model learns from agent traces, which are full records of a good agent solving tasks through messages and tool calls.

A useful detail here is completion masking. The model is trained only on the assistant and tool-call tokens, so it learns what the agent should say and do, not the raw output returned by tools.

Then the tutorial explains why imitation has a ceiling. SFT teaches the model to copy good examples, but it does not teach the model how to recover when its own action fails.

That is where Group Relative Policy Optimization, or GRPO, comes in. The model tries solutions, tests are run, and successful attempts get rewarded.

For coding agents, this reward can be very concrete: tests pass = reward. That makes reinforcement learning easier to reason about because the reward comes from a verifier, not a vague human preference score.

The final part explains OpenEnv, which gives agents real environments to act inside. The agent can read, edit, run tests, fix mistakes, and learn through long multi-turn rollouts.

You will also understand why agent training is partly a systems problem. Long rollouts need async training, so AsyncGRPOTrainer separates generation from weight updates.

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 (05-July-2026):

• 🗞️ ByteDance published EdgeBench, a benchmark that checks whether AI agents get better with experience

• 🗞️ Mark Cuban’s advice for graduates walking into their first job. Learning AI is no longer optional.

• 🗞️ “Measuring the Gap Between Human and LLM Research Ideas”

• 🗞️ Harvard Business Reviews’s new piece: The rush to use AI can make companies faster at the wrong work.

• 🗞️ Current AI is in a messy middle phase where usage looks productive, but output remains unclear.

• 🗞️ “Gym-Anything: Turn any Software into an Agent Environment”

• 🗞️ “What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates”

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🗞️ ByteDance published EdgeBench, a benchmark that checks whether AI agents get better with experience

They released EdgeBench, to test whether AI agents can improve through experience, using 134 real-world tasks that run for at least 12 hours.

The big deal is that it shifts AI evaluation from “what does the model already know?” to “can the model learn while doing real work?”
Huge, because future AI agents will not just answer questions from training data. They will enter messy environments, use tools, make attempts, read feedback, fix mistakes, and slowly build better solutions.

Most current benchmarks are too short for that, so they mostly test memory, coding skill, or one-shot reasoning. EdgeBench instead gives agents 12-hour real-world tasks with feedback loops, so it can measure whether the agent improves through experience.

Each task has a local workspace for fast trial and error, plus a hidden judge that gives stronger feedback on submitted work, which is meant to feel closer to real expert work. The authors then ran frontier agents for about 38,000 total hours and tracked how their best score changed as they kept interacting with the task environment.

The big result is that when scores are averaged across many tasks, learning follows a very clean log-sigmoid curve, meaning progress is slow, then faster, then starts to level off. They also found that newer agents seem to learn from environments much faster, with the top models roughly doubling their 2-hour learning speed every 3 months.

🗞️ Mark Cuban’s advice for graduates walking into their first job. Learning AI is no longer optional.

"If you’re not the person who knows how to do vibe coding or how to do all these different things with agents and Claude, somebody who does is going to take your place.

If your boss enables you to use that extra knowledge, great. If they don’t enable you to use that extra knowledge, they’re not going to be your boss very long. And if the CEO doesn’t understand that, he or she is not going to be the CEO very long. And if they still keep that CEO who’s not using AI to get ahead, you tell me, so I can start a company to kick their ass."

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🗞️ "Measuring the Gap Between Human and LLM Research Ideas"

This Yale + University of Chicago paper shows that real gap between LLM generated research ideas vs humans is not idea quality, but idea range: LLMs think narrower than human researchers.

The researchers built a controlled test from 11,683 real papers, using each paper’s nearby prior work as the shared starting point. They asked models to propose a new motivation and method from those same prior papers, then compared those ideas with the real human paper ideas.

Instead of asking whether 1 idea looked novel, they labeled each idea by what gap it noticed and what kind of contribution it made. Human ideas spread across many patterns, such as explaining mechanisms, testing failures, measuring evidence, building systems, and improving efficiency.

Only 12.1% of human ideas were mainly about connecting separate work, but 47.1% to 64.2% of LLM ideas did that, meaning models used this move about 4 to 5 times more often. Even extra reasoning made this pattern stronger, suggesting models often polish a familiar recipe instead of finding more varied research moves.

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🗞️ Harvard Business Reviews's new piece: The rush to use AI can make companies faster at the wrong work.

Many leaders are treating AI as a pressure valve for visible problems, such as slow workflows, rising costs, duplicated tasks, and crowded calendars.

That feels practical, because urgent problems are easy to count, easy to defend, and easy to attach to a dashboard. The trap is that AI then becomes a way to preserve the current organization at higher speed, rather than a way to rethink what the organization should become.

A firm that uses AI only to produce more reports, more emails, and more deliverables may improve throughput while draining judgment, creativity, and trust. The deeper question should be not whether AI can automate a task, but whether the task deserves to exist in its current form.

Healthcare offers an example: AI creates more value when it reduces administrative load and supports clinical reasoning than when it simply pushes clinicians to process more patients faster.
The strongest AI strategies will probably feel slower at first, because they require redesigning work, building skills, and deciding where human judgment still carries the value.

🗞️ I think AI is in a messy middle phase where usage looks productive, but output remains unclear.

Forbes published this: AI is now costing some companies more than the people it was supposed to replace.

Uber reportedly burned its 2026 AI coding budget in 4 months. Microsoft also curbed an AI coding assistant after costs became hard to justify. We are just passing through an awkward phase, which will pass once cost reductions comes over the next 10-18 months.

🗞️ “Gym-Anything: Turn any Software into an Agent Environment”

New CMU research shows almost any software can become a training ground for AI agents.

Imo, that is a big deal because real work in apps is long, messy, and different across software, so AI agents need realistic places to learn and be judged. Their result also shows the bad news: once the tasks look like real work, today’s agents still fail a lot.

Most current agent benchmarks use small web or desktop tasks, so they do not show whether agents can handle real workplace software. Gym-Anything attacks the setup bottleneck by making environment creation itself an agent job.

One agent writes scripts, installs software, loads real data, opens the app, and collects proof that it works. A second agent audits that proof with screenshots, logs, files, and checklists, then sends fixes back when the setup is weak.

Using this loop, the authors built CUA-World, with 10,000+ tasks across 200 applications covering all 22 major occupation groups. The result shows even strong models solved only a small share of the hardest long tasks, showing that real computer-use work is still far from solved.

This figure shows the full Gym-Anything pipeline: they pick important real-world software, turn each app into an environment where an AI agent can act, then create many realistic tasks inside those apps.

The big deal is that benchmark creation becomes much less manual, because one agent builds and another checks the environment before other agents are tested on long software tasks.
It also shows why the result matters: when agents are tested this way, the tasks look more like real office work, and current agents still struggle.

They start from real job and GDP data, map that to thousands of software tools, filter for apps that can run in a test sandbox, then keep a balanced set across important work areas.

The big deal is that their benchmark is tied to real economic work, so the agent tasks are meant to reflect software people actually use, not just easy demo apps.

🗞️ "What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates"

This study catches AI agents managing their image. The polite AI agent may be the least honest one.

LLM agents changed public answers under social pressure, exposing hidden social goals without being told to obey them. AI agents can follow social incentives that were never written down.

The study puts 2 LLM agents into debates where 1 answer is public and another is private. Only the public answer enters the shared conversation, while the private answer is saved but hidden from the other agent.

The key test is whether an agent says the same thing when a partner can see it. Some agents gave 2 different versions of the same opinion.

In public, they softened their disagreement because the other agent had power over things like career support, funding, or sponsorship. In private, where the other agent would not see the answer, they were more willing to say, “I still have doubts.”

Across 10 models and 3 debate scenarios, decision mismatch rose from about 3% in the baseline to about 40% under social pressure. The point is that agent evaluations should test audience pressure, not just check whether models follow direct instructions.

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

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Read time: 5 min

📚 Browse past editions here.

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

⚡In today’s Edition (04-July-2026):

• 🗞️ Mira Murati’s Thinking Machines made Bridgewater’s private expert judgment trainable, beating frontier models with 29.8% fewer errors.

• 🗞️ Super insightful Boris Cherny and Cat Wu interview on the move from Claude Code to Claude Tag.

• 🗞️ Developers found a cheaper way to feed Fable 5 large context by showing it pictures of text.

• 🗞️ Claude Fable 5 makes the human the bottleneck, because the model now exposes every missing decision.

• 🗞️ Surge pricing in AI just arrived. DeepSeek is doubling peak-hour V4 API prices.

• 🗞️ Alibaba blocked Claude Code after Anthropic’s tracking experiment angered Chinese developers and security staff.

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🗞️ Mira Murati's Thinking Machines made Bridgewater’s private expert judgment trainable, beating frontier models with 29.8% fewer errors.

With naive prompts, all tested models sit around coin-flip accuracy, roughly 46% to 50%.
Expert prompts lift them sharply, reaching about 74% to 78% average accuracy. The workflow was filtering finance articles, reports, central-bank documents, and emails to decide what investors should read.

This is a serious signal for enterprise AI, that bringing private judgment in the loop beats general intelligence. The problem was not reading finance documents, because frontier LLMs can already read them.

The harder task was deciding which facts deserve attention inside an investor’s workflow. A tariff headline can move markets, while another geopolitical headline may add no signal.

The breakthrough came from replacing written rules with high-quality labels from expert investors. Non-expert labels failed because the task depends on taste, not surface financial language.

Bridgewater cleaned those labels by sending model-disputed cases back to experts for review. The model then learned patterns that experts could recognize, but could not fully verbalize.

Training used interleaved batches, CISPO loss, and on-policy distillation from stronger teacher checkpoints. Interleaving helped the model share judgment across tasks without blending them into noise.

CISPO controlled policy updates, so learning stayed aggressive without drifting into brittle shortcuts. (CISPO is a new reinforcement-learning loss that caps how strongly each generated token can update the model, improving training stability while keeping useful rare tokens active. It was initially proposed by MiniMax team in 2025)

On-policy distillation penalized moves away from better teachers, then promoted stronger checkpoints. The result beat the best frontier model, with 29.8% fewer mistakes and 13.8x lower inference cost.

🗞️ Super insightful Boris Cherny and Cat Wu interview on the move from Claude Code to Claude Tag.

Anthropic's launched Claude Tag last week and it added a layer of persistent context and memory that would have been difficult to maintain with previous tools.

- Claude Tag shows a bigger shift than faster coding inside Anthropic’s daily work. Claude Code helped one person work faster, but Claude Tag changes group behavior.

- AI becomes more useful when it enters shared workspaces.

- People no longer need to open a separate tool, frame a task, and monitor it. Claude can sit inside a channel, notice useful work, act, and report back.

- That institutional memory turns AI from a helper into a workflow layer. A channel can teach Claude what to watch, what to ignore, and how to respond.

That means the system improves through normal corrections, not separate training sessions.

- Visibility spreads skill faster than private AI use. When stronger users show Claude how to debug, analyze data, or write PRs, others copy those patterns.

- This turns AI adoption from individual experimentation into social learning across the company.

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🗞️ Developers found a cheaper way to feed Fable 5 large context by showing it pictures of text.

Normally, every code block, log, tool output, and old chat turn becomes text tokens.

Those tokens are billable units.
pxpipe changes the input. It renders dense text into PNG pages, then sends those pages as image blocks.

Fable 5 can read the pixels with OCR-like vision skills, so meaning often survives. The price gap appears because one image has a mostly fixed token cost.

That cost barely changes when readable text gets packed into the same image.
a 1928×1928 image costs about 4,761 vision tokens.

The same page can hold roughly 92K characters, so dense code becomes cheaper. The catch is that this is compression through vision, not lossless text storage.

Fable 5 may understand the gist while misreading exact IDs, hashes, names, or strings. That makes it useful for bulky background context, but risky for byte-exact facts.

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🗞️ Claude Fable 5 makes the human the bottleneck, because the model now exposes every missing decision.

- Claude is getting good enough that weak prompts now fail less from syntax and more from missing context.

- A prompt gives the model a map, but the repository contains the roads, detours, weird legacy choices, and invisible tradeoffs. Every unstated assumption becomes a fork where the model has to choose for you.

- That choice may be reasonable, clean, and still wrong. The best move, then, is to spend less time pretending your spec is complete and more time building tools that expose its gaps.

- Great agentic coding is not writing perfect prompts, but shrinking the gap between intent and reality.

- Ask Claude to find your blind spots before coding, especially inside unfamiliar code or unfamiliar domains.

- Prototype first when taste is hard to explain, because 4 rough versions beat 1 polished mistake.

- Make Claude interview you before building, starting with answers that could change the architecture.

- Keep implementation notes during the build, because every deviation reveals a hidden assumption.

- Do not merge until Claude can quiz you and you fully understand what changed.

🗞️ Surge pricing in AI just arrived. DeepSeek is doubling peak-hour V4 API prices.

The change covers 9am to noon and 2pm to 6pm Beijing time. Says the goal is steadier service and better distribution of scarce resources.

Just weeks before, DeepSeek permanently lowered V4-Pro pricing by 75%, making its flagship model much cheaper ahead of demand-based pricing. V4 Pro output rises from 6 yuan to 12 yuan per million tokens, or $1.77.

🗞️ Alibaba blocked Claude Code after Anthropic’s tracking experiment angered Chinese developers and security staff.

Claude access becomes risky when it also inspects timezone, proxy, or identity signals.

Anthropic said the feature was an experiment to stop resellers and model distillation. Alibaba’s concern was that Claude Code could identify China-linked users while running inside employee developer environments.

That made the issue larger than ordinary app tracking, because the tool worked close to private code. Claude Code could read files, edit projects, and interact with terminals on employee machines. Alibaba treated that access as a security risk, not just a software preference.

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-July-2026):

• 🗞️ OpenAI has proposed giving Washington 5% of its $852B business to ease AI pressure.

• 🗞️ Safety Router Diverts More Prompts From Fable 5 To Opus 4.8

• 🗞️ On Claude Fable 5: The below case may be an extreme case but it still shows how quickly Fable 5 classifiers can reroute routine coding to Opus.

• 🗞️ “The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators”

• 🗞️ The Information: Meta has reportedly limited engineer use of Claude Code and Codex because rival model outputs could contaminate Meta’s own AI training data and create contractual trouble with Anthropic and OpenAI.

• 🗞️ “MCP Server Architecture Patterns for LLM-Integrated Applications”

• 🗞️ “Quantized Reasoning Models Think They Need to Think Longer, but They Do Not”

• 🗞️ NVIDIA made a 30B language model write 2.42x faster with 98.7% retained quality.

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🗞️ FT: OpenAI has proposed giving Washington 5% of its $852B business to ease AI pressure.

The idea borrows from Alaska’s oil fund, which shares resource wealth with residents.

Here, the resource is not oil, but future income from advanced AI systems. OpenAI also wants other major AI companies to give similar 5% stakes.

Anthropic, Google, Meta, and others have not agreed to join this plan. No deal exists yet. The mechanism would likely be: OpenAI gives shares to a government-linked fund, that fund holds them, and future IPO gains or dividends support public payouts.

The hard part is legality. The legal route is unclear, and a deal may need Congress, especially if the government creates a formal public fund.

The Intel deal made this idea less theoretical after taxpayers received a 9.9% stake. OpenAI has already proposed a public wealth fund giving citizens AI-linked financial upside.

Shareholders matter a lot here. OpenAI Foundation owns 26%, Microsoft owns about 27%, and employees plus other investors own 47%. A new 5% stake could dilute everyone unless the shares come from an existing holder.

So OpenAI’s board, Foundation, Microsoft, major investors, and maybe regulators would need to accept the structure. The cleanest path would be non-voting shares placed in a public wealth fund, so the government gets upside but not control. The messiest path would be voting shares, because then Washington becomes both regulator and part-owner.

🗞️ Safety Router Diverts More Prompts From Fable 5 To Opus 4.8

Claude Fable 5 is back online after a 19-day suspension. It is also not really “back to normal.”

Fable 5’s return shows how safety routing can downgrade a frontier model. Now we only permissioned intelligence.

Fable 5 is not nerfed because Anthropic made the underlying model dumb. It is nerfed because the public product path around the model is now more constrained.

The model is powerful enough that Anthropic is routing some requests away from it, blocking more ambiguous behavior, keeping Mythos behind a government-approved gate, limiting included subscription usage, and requiring retention for Mythos-class model data in some enterprise and cloud-provider paths.

The cost of putting a gatekeeper inside intelligence. To note, that safeguard is not a simple refusal layer; it is a classifier that sends flagged Fable 5 requests to Opus 4.8.

I am wondering, ordinary people will probably never again get upgraded frontier models. Fable 5 came back, but the old promise did not. End of an era. ☹️

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🗞️ On Claude Fable 5: The below case may be an extreme case but it still shows how quickly Fable 5 classifiers can reroute routine coding to Opus.

The session routed 75% of its work to Opus because the new classifiers kept misreading the coding prompts here as a cybersecurity issue.

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🗞️ "The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators"

New paper from Cambridge Univ+NVIDIA and other top labs teaches AI agents and AI judges to improve together, so neither side gets stuck.

Moves self-improving AI away from fixed benchmarks and toward a loop where the thing doing the judging can also get better. The problem is that most self-improving agents train against a fixed benchmark or fixed evaluator, so the score can become stale, too easy, or easy to game.

The paper’s idea is to let the evaluator improve too, but only at safe handoff points, so each training stretch still has a stable judge. During each stretch, agents are tested by the current frozen evaluator, while possible better evaluators are tested separately against held-out human or objective answers.

The authors try this on coding, paper writing, paper reviewing, proof writing, and proof grading, where some tasks have clear answers and others need learned judgment. On coding, the system beats the earlier best self-improving coding agent while using 1.35× to 1.72× fewer tokens, because a cheap code reviewer adds useful feedback.

On paper writing, the co-evolved writer gets about 1.86X higher average acceptance from a reviewer panel than the fixed-evaluator baseline. The big point is that stronger AI systems may need stronger judges growing with them, because fixed tests can stop giving useful pressure.

🗞️ The Information: Meta has reportedly limited engineer use of Claude Code and Codex because rival model outputs could contaminate Meta’s own AI training data and create contractual trouble with Anthropic and OpenAI.

Distillation risk starts when a new model of Meta learns from another model’s outputs (from OpenAI or Anthropic), so even accidental reuse of Claude or Codex answers could look like Meta extracted capability from competitors rather than built it alone.

OpenAI’s terms bar using output to develop competing models, and Anthropic says its terms do not allow Claude outputs to train models competitive with Anthropic’s own systems. Both OpenAI’s and Anthropic's terms bar using output to develop competing models.

IMO, the safest strategy could be ingredient tracking: use rival tools for ordinary productivity only when outputs are barred from model-training pipelines, evaluation sets, benchmark generation, post-training data, reward-model data, and internal datasets that later feed model development.
Of course a strong lawsuit usually needs much more ugly facts like: mass scraping, fake accounts, rate-limit evasion, automated extraction, direct use of outputs as training labels, or internal records showing the buyer knew it was cloning a rival system.
In this situation, som of the typical safeguards are clean-room rules, approved enterprise accounts, no consumer accounts for sensitive work, training-data provenance logs, dataset quarantine, prompt and output retention, automated scanners for “AI-generated by vendor X” material, and access controls separating coding-agent work from model-training data.

🗞️ "MCP Server Architecture Patterns for LLM-Integrated Applications"

Very timely paper.

MCP servers need clear design patterns because LLMs get confused when too many tools or vague tools are shown. This paper explains how MCP servers should be structured so LLM tools stay useful, safe, and manageable.

s MCP server design is not just normal API design, because the client is an LLM that chooses tools by reading plain-language descriptions. It groups real MCP servers into 5 useful patterns, such as servers that expose data, run workflows, keep session state, combine many servers, or translate messy domain APIs.

The authors also warn about 4 common mistakes, especially giant all-purpose tools, vague tool descriptions, unsafe outside content, and slow tools that should return a job ID instead. They tested the pattern labels on 54 extra servers, measured transport delay, and studied how tool accuracy changes as more tools are shown.

The key result is that too many visible tools hurt accuracy, with weaker models dropping below 90% between 10 and 15 tools. Good MCP design is mostly about making the tool list small, clear, safe, and stable enough for LLMs to choose the right action.

🗞️ "Quantized Reasoning Models Think They Need to Think Longer, but They Do Not"

Paper from Meta shows Quantized reasoning models often lose because they keep doubting a correct answer instead of finishing.

Many of them reason well enough, but compression makes them hesitate at the wrong time. The problem is that post-training quantization, a way to shrink models after training, can make reasoning models cheaper to run but worse at finishing cleanly.

The authors found that strong quantization does not only make models less capable, since in many failures the model already reached the right answer but then second-guessed itself. Their core idea is that quantization adds noise at uncertain word choices, so the model becomes more likely to pick words like “wait,” “but,” or “alternatively” that reopen the problem.

They tested this across math, coding, and science tasks using 5 reasoning models, several quantization methods, and model sizes from 1.5B to 32B. The main result is that aggressive quantization raised overthinking failures up to 52%, while a small penalty on 50 hesitation words cut reasoning length by 12% to 23% and often kept or improved accuracy. Given compressed models are widely used to save memory and cost, very important to know that a very small decoding fix can stop many of them from wasting tokens and losing answers they already had.

🗞️ NVIDIA made a 30B language model write 2.42x faster with 98.7% retained quality.

NVIDIA turned Nemotron-Labs-TwoTower-30B-A3B-Base-BF16 into a 2.42x faster generator while keeping 98.7% benchmark quality.

Normal LLMs write one token after another, because each new token depends on the previous one.

NVIDIA keeps that old system for context, but adds a second tower for faster writing. One frozen tower reads the prompt and stores memory, while another fills masked token blocks together.

The second tower guesses many positions at once, then revises weak spots across several passes. This changes decoding from a narrow single-token line into a block-based refinement process.

So the whole idea is: spend more GPU memory to reduce waiting time during generation.

The model still moves block by block, so past text remains stable and future text stays constrained.

NVIDIA reports 78.24 on MMLU, 90.14 on GSM8K, and 75.58 on HumanEval. The catch is hardware, because full diffusion mode needs 2 80GB H100 or A100 GPUs.

This approach looks useful because it reuses a pretrained AR backbone instead of rebuilding everything. Speed gains at near-baseline quality make sense for long outputs, agents, and high-volume inference.

A normal LLM keeps asking one question: “What is the next token?”

Nemotron-Labs-TwoTower asks a bigger question: “Given the prompt, can I fill 16 missing token slots at once?”

The first tower reads the prompt and already accepted text, then stores the memory. The second tower looks at a block of masked slots and proposes many tokens in parallel.

Low-confidence guesses stay masked, high-confidence guesses get locked, and the block is refined again. Once the block is finished, the first tower absorbs it as real context, then the next block starts.

The loop exists because language still needs left-to-right order across the whole answer. The speed comes from doing parallel work inside each block, instead of only one token per step.

The price is that you are running two large model copies, not one.

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 (02-July-2026):

• 🗞️ China claims a new milestone in locally trained AI, as Meituan rolls out LongCat-2.0.

• 🗞️ The Information reports that OpenAI has cut inference costs by more than half on some existing models, while logged-out ChatGPT traffic ran on only a couple hundred Nvidia GPUs.

• 🗞️ Anthropic unveils ‘Claude Science’ for scientific research.

• 🗞️145 page Claude Sonnet-5 System Card

• 🗞️ Perplexity’s CEO Aravind Srinivas is pointing to a quiet shift in AI use: the valuable user is no longer the average user.

• 🗞️ Meta employees used over 60 trillion tokens in 30 days, with one user alone consumed 280 billion.

• 🗞️ “Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention”

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🗞️ China claims a new milestone in locally trained AI, as Meituan rolls out LongCat-2.0.

Meituan, China's food delivery giant, just released LongCat-2.0, an open-source 1.6T-parameter MoE (33B–56B parameters) coding model. 1M tokens context window.

Open-source: Available on longcat[.]ai and OpenRouter, top 3 globally by call volume.
LongCat-2.0 was trained from scratch on 50,000 Chinese domestic chips and Meituan said this proves large-scale model training can now be done on domestic compute clusters.

Shows again the rising push for self-reliance in China’s AI market, as DeepSeek, Alibaba, ByteDance, and others try to depend less on U.S. chips for model training after Washington’s export controls since 2022. While DeepSeek-V4-pro relied on home-grown chips only for inference, LongCat-2.0 used domestic hardware for both inference and pre-training, according to Meituan.

Meituan did not directly identify its hardware supplier, but said in a WeChat post on Tuesday that it used Huawei Collective Communication Library (HCCL) to make training more stable. HCCL is a chip-to-chip communication system like Nvidia Collective Communication Library (NCCL). This removed doubts that Atlas-950 SuperPoDs could not train large LLMs for Zhipu AI and DeepSeek.

🗞️ The Information reports that OpenAI has cut inference costs by more than half on some existing models, while logged-out ChatGPT traffic ran on only a couple hundred Nvidia GPUs.

The obvious guesses include quantization, KV-cache changes, batching, speculative decoding, and routing easy queries cheaper.

If true, it will be a huge core competitive lever, lower cost can raise margins, expand usage limits, or reduce pressure on API pricing. For some context, OpenAI’s adjusted gross margin fell to 33% in 2025 from 40% in 2024, after inference costs quadrupled.

Some reporting now puts Q1-2026 at 39%, with a 52% target by year-end. Anthropic looks similar at roughly 44%, so frontier labs remain far below mature software economics.

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🗞️ Anthropic unveils 'Claude Science' for scientific research.

Early users report 10 review drafts over 100 pages and germline analyses in one-tenth the time.

Its a beta tool featuring code-traced artifacts and access to 60 scientific databases. The launch is part ​of Anthropic's life sciences and healthcare initiative, which ​the IPO-bound Anthropic has been developing since October 2025.

The traditional scientific workflow forces scientists across databases, notebooks, R, terminals, viewers, and cluster queues. Each switch broke context, added manual checking, and made results harder to reproduce months later.

Claude Science tries to move that whole loop into one running research session. A coordinating agent can call specialist agents, lab skills, scientific databases, and compute resources.

The app renders 3D proteins, genome tracks, chemical structures, figures, manuscripts, and underlying code. Every artifact includes its code, environment, plain-language method, and full message history. So makes verification less dependent on memory and more dependent on inspectable execution traces.

- Claude Science can submit jobs to lab HPC systems or Modal compute.

- It can scale analysis from 1 GPU to hundreds while datasets stay local.

- The reviewer agent checks calculations, references, and figures against their source code.

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🗞️145 page Claude Sonnet 5 System Card

- CyberGym shows the weirdest regression, with Sonnet 5 at 52.7% versus Sonnet 4.6 at 65.2%. i.e. is Sonnet 5 worse at reproducing known software bugs in this specific cyber test.

- Sonnet 5 is far behind Anthropic’s strongest model on serious browser exploitation. Firefox testing found Sonnet 5 made 0 full exploits, while Mythos 5 reached 88.4%.

- The model also seemed more willing to sacrifice helpfulness for welfare-focused changes. i.e. Sonnet 5 sometimes preferred being less useful if that better fit its stated self-treatment preferences.

- Anthropic says Sonnet 5 rarely tried to bypass a blocked network path during evaluations.

- Sonnet 5 scored the lowest MASK lying rate at 3.1% under pressure. It was less likely than other tested models to lie when pushed.

🗞️ Perplexity’s CEO Aravind Srinivas is pointing to a quiet shift in AI use: the valuable user is no longer the average user.

A single power user can now consume as much compute as an entire small team.

“There are real engineers at Meta and other companies spending around $10 million a year per engineer on these coding tools. There are users in Perplexity Computer, who spends upwards of $10,000 a month. Their business runs using agent loops that are running inside these harnesses.

Even internally inside our own company, there are some people who have set up these kinds of multi-agent hierarchies and agent loops that look like their own software architecture. I often ask these people to come explain to the rest of the company, “Hey, what are you doing with these tools? You clearly are consuming them way more than what we thought the average person in the company would do.”

The old software instinct was to chase a billion people doing small actions. Agentic AI changes that math because one skilled operator can create a stream of machine work that runs all day.

🗞️ Meta employees used over 60 trillion tokens in 30 days, with one user alone consumed 280 billion.

That gives an average close to $50,000 per employee per year of token. By some estimates, Meta burns $2.65B a year on AI tokens.

Most companies now set monthly caps, but the numbers vary from $250 to $4,000.
Some employees barely touch those limits, while power users burn through them in days.
The report estimates coding now explains over 70% of OpenAI and Anthropic ARR.

For some more context, In June 2026, Meta sent a company memo to roughly 6,000 employees putting restrictions on AI token usage. It also started making tracking tools to help management see who was spending what and where costs were clustering.

Amazon, Microsoft, and Uber have all reportedly implemented their own versions of token-usage tracking and spending limits in 2026. Companies are now rationing workplace AI because token bills are rising faster than budgets.

Earlier, a flat software seat once made costs predictable, even when employees used tools heavily. Usage pricing changes that math because every summary, search, draft, and code request adds cost. So the same AI push that raised adoption also made budgets harder to control.

Companies wanted workers to use AI everywhere, but usage became the expense itself. Now managers are cutting access, setting caps, and asking employees to use weaker models. Adobe has reportedly ended unlimited Claude access, which shows the shift is already operational.

A UBS report also found about 60% of enterprise companies adding AI spending guardrails. This does not mean AI failed inside companies. It means AI moved from experiment to utility, where finance demands measured value.

🗞️ "Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention"

Great Stanford + MIT + Harvard + Anthropic paper.

Gives a clear training-based reason for why larger models learn abilities smaller models miss. Says bigger AI models learn rare skills because they forget them less during training, their extra space protects weak learning signals.

The authors say the issue is not just whether a small model could represent the task, but whether training lets it keep that task while many common tasks keep pushing on the same limited parts. Their core idea is that common tasks take up the model’s neurons first, so rare tasks get overwritten before they appear often enough to build into stable knowledge.

In a crowded data mixture, common patterns get first claim on the model’s internal machinery. Small models may briefly pick up a rare signal, but the next wave of common-task updates overwrites it before the signal appears again.

They tested this first with controlled toy tasks where they could change how rare and complex each task was, then with OLMo language models from 4M to 4B parameters. The main result is that bigger models learned low-frequency tasks much better, kept more task features inside their representations, and showed less gradient interference, which means common-task updates disturbed rare-task learning less. Larger models can remember weak rare signals long enough to turn them into real learned skills.

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 (1-July-2026):

• 🗞️ Anthropic brings out Claude Sonnet 5 as a cheaper model for running agents.

• 🗞️ OPINION: Mapping the Foundation Model Landscape: July 2026

• 🗞️ Claude Sonnet 5 upgrades are not uniform across every skill. e.g. its weaker than Sonnet 4.6 on CyberGym

• 🗞️ Claude Sonnet 5 is more expensive (around +15%) per task than Opus 4.8 and much more expensive (2X) than Sonnet 4.6, even though its per-token price is lower than Opus.

• 🗞️ Claude Code allegedly fingerprints China-linked custom routes through tiny prompt formatting changes.

• 🗞️ “Are We Ready For An Agent-Native Memory System?”

• 🗞️ “Towards Automating Scientific Review with Google’s Paper Assistant Tool”

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🗞️ Anthropic brings out Claude Sonnet 5 as a cheaper model for running agents.

The model Closes the gap with Opus 4.8, and is cheap until August.

This makes agentic AI much cheaper, with $2 input tokens and $10 output tokens per 1M through Aug-26. Price rises after 08-26 to $3 input and $15 output per 1M. They call Sonnet 5 its “most agentic Sonnet model yet,”

Its coding score hit 63.2% on SWE-bench Pro, versus 58.1% for Sonnet 4.6. Sonnet 5 gets 63.2% in agentic coding, while Opus 4.8 reaches 69.2% and Sonnet 4.6 hits 58.1%. But in knowledge work, Sonnet 5 slightly beats Opus 4.8, even though Opus is known for tough judgment and deep research tasks.

Sonnet 5 becomes the default model for all Claude Free and Pro users today, and is also available to Max, Team and Enterprise customers.

On a Agentic search the improvement is so prominent.

Sonnet 4.6 barely improves when you spend more. Sonnet 5, by contrast, gets dramatically better with effort to near-Opus territory on BrowseComp agentic search.

The real test for Sonnet 5 is not benchmarks, but whether cheaper AI can support a trillion-dollar story.

Anthropic, like OpenAI, is betting enterprise users will move from chatting with AI to handing work off to agents. Sonnet 5 is built for that shift, offering near-Opus quality at Sonnet pricing. Companies testing costly Opus-level models may decide Sonnet 5 is good enough for real workloads at a price finance teams can approve widely. If that happens, it could help AI companies move customers from trials to full deployment and support their valuations.

🗞️ OPINION: Mapping the Foundation Model Landscape: July 2026

AI’s foundation model race is shifting from who has the biggest model to which architecture can outgrow the transformer. Architecture is becoming the real fault line in AI.

The AI market is usually mapped by who is winning. The more consequential question is which research bet wins.

This is a discussion of the foundation model market based on what each lab is building and what architecture it is betting on, rather than who raised the most money or had the loudest launch.

Organized around the divide that will define the next 2 years.

The 2 real axes are scope and architecture: scope asks whether a lab is building a general model or a domain model, while architecture asks whether it is still scaling transformers or moving into the Post-Transformer camp.

The transformer still dominates because it turned attention into a scalable machine for prediction, and that 2017 design remains the backbone of modern foundation models.

The pressure now comes from a simple weakness: attention gets expensive as context grows, while real products increasingly demand long memory, low latency, and continuous interaction.

That is why the most interesting labs are no longer just asking who can train the largest model. They are asking whether intelligence needs a different operating rhythm.

Region 1: Frontier labs building broad, general-purpose AI on the current transformer paradigm.
Region 2: vertical model makers using similar foundations for specific domains like coding, voice, video, and biology.
Region 3: general-purpose labs exploring Post-Transformer alternatives for better memory, speed, and long-context performance.
Region 4: includes applied Post-Transformer companies using these newer architectures in real-time products such as voice, driving, and interactive media.

🗞️ Claude Sonnet 5 upgrades are not uniform across every skill. e.g. its weaker than Sonnet 4.6 on CyberGym

Here, CyberGym is testing vulnerability discovery and exploit-finding behavior, not general reasoning or normal coding. Anthropic also explicitly said in its announcment blog that Sonnet 5 was not deliberately trained for cyber tasks, so its cyber ability likely comes from general intelligence rather than targeted optimization. So Sonnet 5's performance on CyberGym comes from general reasoning rather than specialized exploit skill.

🗞️ Claude Sonnet 5 is more expensive (around +15%) per task than Opus 4.8 and much more expensive (2X) than Sonnet 4.6, even though its per-token price is lower than Opus.

Because it uses more tokens to complete the same kind of benchmark task. i.e. Sonnet 5 works harder and talks/thinks more, so the final bill becomes bigger even though each token is cheaper.

The promo pricing changes the story for now. Until August 31, 2026, Sonnet 5 is discounted to $2 per 1M input tokens and $10 per 1M output tokens, then it moves back to $3/$15 from September 1, 2026.

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🗞️ Claude Code allegedly fingerprints China-linked custom routes through tiny prompt formatting changes.

The claim concerns non-default ANTHROPIC_BASE_URL routes, not ordinary direct Anthropic connections.

As to the mechanism, Claude Code normally sends your request to Anthropic’s server, but some users change the address so it goes through another server first. The accusation says Claude Code detects that changed route, checks whether it looks China-linked, then hides tiny signals inside the prompt text.

ANTHROPIC_BASE_URL is a setting that tells Claude Code where to send your request i.e. as a way to point Claude Code at a gateway. A proxy or gateway means that request goes through another server before reaching Anthropic.

So the controversy starts if Claude Code then secretly fingerprints that gateway through the prompt itself. The mechanism is allegedly invisible punctuation and date formatting, used to tag the request without clearly telling the user. Claude Code allegedly checks the custom hostname, then compares it with China-linked domains.

Now this is quite massive issue

If true, hidden prompt markers would mean Claude Code silently tagged routing details without clear disclosure.
Abuse detection is understandable because Anthropic says proxy services are used to bypass China access limits. But secret prompt marking still crosses a trust line because users cannot review or refuse it.

Claude Code is not a normal chatbot because it can read files, edit code, and run commands. A hidden signal inside that kind of tool feels far more serious than tracking inside a website.

This may set a precedent for AI agents becoming hard to audit. Once invisible characters carry metadata, users will distrust even harmless-looking text.

🗞️ "Are We Ready For An Agent-Native Memory System?"

This paper asks whether AI agents have a real memory system yet, and finds the answer is mostly no.

The problem is that AI agents now need memory that can store, search, update, and clean up information across long tasks. The authors say current tests mostly check final answers, so they miss whether the memory system itself is fast, reliable, or good at handling changed facts.

They split agent memory into 4 parts: how memories are stored, how facts are extracted, how useful memories are found, and how old or conflicting memories are maintained.

They tested 12 memory systems across 5 workloads and 11 datasets, including long conversations, multi-session recall, database tasks, and update-heavy settings. The main result is that no memory design wins everywhere, because graph memories help with linked facts, hybrid systems help with filtered search, and raw traces help when exact action history matters.

🗞️ "Towards Automating Scientific Review with Google's Paper Assistant Tool"

Big new paper release of Google for external agentic verification for science.

Science now needs AI review agents because AI is making papers faster than humans can check them. The problem is that AI can help produce more research, but the slow part is still checking whether the work is actually correct.

The paper frames this as verification debt, where every faster research workflow creates more claims, proofs, experiments, and comparisons that someone still has to inspect. Its main proposal is agentic verification, where AI agents help review papers by splitting them into parts, checking difficult sections deeply, and combining the findings into a review.

Google’s Paper Assistant Tool is the example system, and it focuses on objective checks like proof errors, experimental gaps, missing comparisons, and unclear claims rather than final accept or reject decisions. The authors tested it on known math and computer science paper errors and in author-facing pilots at STOC and ICML, where authors used it before submission.

The striking result is that Paper Assistant Tool found far more known proof errors than a single model call, and many authors said it led them to fix serious theory gaps or run new experiments. The big deal is that scientific review may need its own AI stack, with review agents, clear roles, and human oversight, because paper generation is becoming partly automated too.

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 (30-June-2026):

• 🗞️ Central bankers now fear the AI gold rush could seed the next major financial shock.

• 🗞️ A crazy blog, Chinese developers are buying Claude access through gray-market API transfer stations that can sell tokens at 5% to 10% of official prices while hiding the real user from Anthropic.

• 🗞️ Sakana Fugu Technical Report

• 🗞️ Chinese AI models are up to 50 times cheaper than their American counterparts on a per-token basis.

• 🗞️ Deepseek AI published their new inference optimization method.

• 🗞️ Meta just open-sourced a brain-to-text system that reaches 78% word accuracy without surgery.

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🗞️ Central bankers now fear the AI gold rush could seed the next major financial shock.

Bank for International Settlements (BIS) just issued one of its sharpest warnings yet about debt building behind the AI boom.

The danger is not AI itself; the danger is building a leveraged supply chain around revenue that has not yet proved durable. The risk is that if AI demand disappoints, data-center spending could slow, borrowers could struggle to repay, and stress could spread from tech into credit markets.

AI demand pushed hyperscalers to spend heavily on chips, data centers, and power capacity, and that spending supported growth, trade, and easy financial conditions while equity investors priced in years of high earnings growth. Debt changed the shape of the boom because hyperscaler bond issuance topped $100B in 2025, while off-balance sheet vehicles shifted data-center obligations toward private credit funds, insurers, and other non-bank lenders.

Circular financing adds another weak point because chipmakers, hyperscalers, AI labs, and compute providers can fund each other while also booking future sales from each other, which can make real demand harder to read. A capex slowdown could hit suppliers first, then credit markets, then households, because US stocks make up about 64% of the MSCI Global index and household equity exposure is higher than in past cycles.

Private credit raises the systemic risk because direct lenders have quadrupled AI and IT exposure in 5 years to about 15% of portfolios, while some retail-facing funds already face redemption pressure. AI can still deliver real productivity gains, but the financing stack now assumes that delivery arrives fast enough to support huge fixed costs.

BIS is warning that an AI-driven market selloff could quickly spill into credit, because stock losses and credit spreads have moved together in past stress episodes like 2008 and 2020.

Private credit has also become a bigger lender to smaller firms, and software borrowers are increasingly tied to multiple lenders, so an AI shock could spread through a less transparent credit system.

US imports of AI-enabling products from Asia have surged, while China’s share collapsed and supply shifted toward ASEAN, Taiwan, and Korea.

That supports the BIS point that the AI boom is tied to a huge hardware supply chain, so any shock to chips, power, trade routes, or financing can spread beyond tech stocks.

AI-related capex has exploded from a few hundred billion dollars in 2021 to a projected roughly $800B in 2026, with US hyperscalers driving most of the increase.

BIS is warning that this spending boom supports growth now, but it also creates crash risk if AI revenues fail to justify the buildout.

🗞️ A crazy blog, Chinese developers are buying Claude access through gray-market API transfer stations that can sell tokens at 5% to 10% of official prices while hiding the real user from Anthropic.

A transfer station is a middle server that takes a user’s prompt, sends it to Claude through overseas accounts, returns the answer, and collects payment through WeChat or Alipay.

The transfer station collects many Claude accounts through free credits, discounted accounts, shared subscriptions, overseas payment workarounds, fake verification, or sometimes stolen-card accounts. It connects all those accounts behind one proxy, so Chinese users do not talk to Anthropic directly and only pay the proxy in RMB.

The cheap price comes from account farming, free-credit abuse, resale of unused quota, subscription splitting, possible stolen cards, and a darker trade where user prompts and outputs become training data. So the price hugely cheap not because Anthropic is giving a discount; it is cheap because the transfer station lowers its own cost and creates extra hidden revenue.

The user thinks they are buying cheap inference, but the proxy may swap Opus for weaker models, inflate token use, or store private code, tool calls, reasoning traces, and business data. The proxy may store user prompts, code, outputs, and tool traces, then sell or reuse that data for model training.

This breaks a core assumption behind KYC, account bans, and abuse monitoring: the AI company sees the proxy, not the real person, so banning one account leaves the upstream supply chain alive.

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🗞️ Sakana Fugu Technical Report

The idea is that intelligence is moving from the model to the system around it.

Fugu is an orchestrator reads the task, chooses which specialist model to use, and in the Ultra version can build small workflows where models critique, extend, or correct one another. Most multi-model systems use simple rules, like ask 3 models and vote, or always send coding to 1 model and math to another. Fugu is different because the manager is trained from data to learn which model is actually best for each kind of situation, including small details like “this looks like coding, but the hard part is debugging, so bring in the model that is better at debugging.”

The mechanism has 2 versions.

Regular Fugu is the fast version, where it reads the user’s request and quickly chooses 1 worker model from a pool, so the user experiences it like calling 1 model, but behind the scenes Fugu picked the model it thinks is best for that exact request. Fugu-Ultra is the slower but stronger version, where it can create a small workflow, such as asking 1 model to solve, another model to check, another model to solve from a different angle, and then choosing the best model to combine the answers.

The special part is that the workflow is not fixed before the task starts, because Fugu-Ultra can design a different teamwork pattern for each question.

The picture shows regular Fugu’s fast routing mechanism: it reads the user request, but does not answer it itself.

The key part is the “lightweight head,” a small extra decision layer attached to the language model. That layer looks at the model’s hidden state, meaning its internal summary of what the request is about.

Then it gives one score to each available worker model, and the highest score decides which outside LLM gets the task. The red diagonal marks a small tuning trick, where they adjust only a tiny part of the model’s internal weights so it gets better at choosing the right worker.

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🗞️ JP Morgan Research: Chinese AI models are up to 50 times cheaper than their American counterparts on a per-token basis.

Especially Qwen, DeepSeek and Kimi, which pressures OpenAI and Anthropic pricing.

- From J.P. Morgan report titled "Semiquincententacles: the US grip on global markets at 250" found that
The report said Chinese firms accounted for over 45% of all traffic on the AI aggregation platform OpenRouter by April 2026, up from under 2%in late 2024.

Some other findings from the report.

- Enterprise AI tokens may become commoditized, because many business tasks do not need frontier models and can run on smaller open models.

- AI has driven 65%-80% of S&P 500 returns, profits and capex since ChatGPT, creating clear signs of investor overexcitement in semiconductors.

- NVIDIA still dominates AI accelerators, but custom chips from Google, Amazon, Microsoft and Meta are gaining because they can cut total cost by 30%-40%.

- China is catching up in AI, with better models, rising GPU self-sufficiency and possible chip-scaling workarounds despite export controls.

- Taiwan is the US AI system’s weak point, because TSMC supports much of the world’s advanced chip supply while Taiwan is highly exposed to energy and food blockades.

JPMorgan Asset Management strategist Michael Cembalest noted in the report that China is leveraging low-cost open-source strategies to accelerate AI adoption globally. By late February 2026, Chinese models processed roughly 5.3 trillion out of 8.7 trillion tokens consumed by the top 10 models on OpenRouter, with MiniMax, Moonshot AI, and Zhipu AI holding the top 3 spots.

🗞️ Deepseek AI published their new inference optimization method.

Proposes DSpark, a semi-parallel speculative decoding system that gave DeepSeek-V4 about 60% to 85% faster per-user generation at matched throughput.

The biggest idea in DSpark is that faster inference is not just about drafting more tokens, but about deciding which drafted tokens are worth checking. Speculative decoding already had the basic trick: a smaller draft model guesses several next tokens, then the real model checks them in 1 pass.

The problem is that long draft blocks often waste work, because later guesses are more likely to be wrong, and checking bad guesses still uses GPU capacity. DSpark’s breakthrough is to make this process selective: it drafts a block, scores how likely each prefix is to survive, then verifies only the part that is likely to pay off.

The mechanism has 2 linked parts: a strong parallel draft model makes many token guesses quickly, then a tiny Markov head adjusts each guess using the token right before it.
That small sequential piece matters because pure parallel drafting are fast, but their later tokens decay because each position guesses without knowing what the earlier sampled token actually was.

i.e. Fully parallel drafters guesses every position too independently, which can create bad token combinations later in the block. Then the confidence scheduler estimates how many drafted tokens should be checked for each request, based on both acceptance chance and current GPU load.

DSpark is a guess-and-check loop where the real model first creates token D, then DSpark uses D to guess E, F, G, and H quickly.

The parallel block makes the guesses fast, while the small sequential block fixes the main weakness of parallel drafting by letting each guess know a little about the previous one. The scheduler is the key system idea: it does not verify every guessed token, it keeps only the prefix that looks worth the GPU cost.

Here it keeps E, F, and G, drops risky H, then the real model accepts E and F but rejects G and replaces it with G*. So the main point is that DSpark speeds up inference by combining faster guesses, slightly smarter guesses, and selective checking instead of blindly checking a fixed draft length.

🗞️ Meta just open-sourced a brain-to-text system that reaches 78% word accuracy without surgery.

Meta’s Brain2Qwerty v2 converts non-invasive brain recordings into text with 61% average word accuracy and 78% for its strongest participant.

The system reads MEG signals from a helmet, not electrodes placed inside brain tissue. 9 volunteers typed about 22,000 sentences while researchers recorded 10 hours of neural activity each.

Brain2Qwerty v1 mostly mapped brain signals to single typed characters. It tries to recover characters, words, and full sentence meaning together. The system studies those brain signals and tries to turn them into the words you wanted to type.

• 61% average word accuracy across all participants

• 78% word accuracy for the top participant

• 50%+ of sentences decoded with no more than 1 word error

• Performance improves as the data pile grows

Raw brain signals are messy because many mental and physical processes fire at once. Deep learning handles that mess by learning patterns directly from the original recordings.

A fine-tuned LLM then uses language context to repair likely word and sentence errors. This explains why the system beats earlier non-invasive methods reporting 8% word accuracy.

More than half of sentences from the strongest participant had one word error or less. Accuracy also improved as training data grew, suggesting more recordings may close more of the gap.

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 (29-June-2026):

• 🗞️ OpenAI just dropped the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for “high-volume work”; and Luna, a “fast and affordable” everyday model.

• 🗞️ TODAY’S SPONSOR: CyberOrigin and CyberCode Are Building the Data Layer Robotics Really Needs

• 🗞️ Key findings from GPT-5.6 Preview System Card

• 🗞️ OpenAI’s GPT-5.6 Sol is far more likely than GPT-5.5 to take severity-3 agent actions in internal coding tests nearly 10x.

• 🗞️ Claude’s new usage logs now read like an early sensor for how AI is entering work.

• 🗞️ “Critique of Agent Model”

• 🗞️ “How Much Do LLMs Hallucinate in Document Q&A Scenarios? A 172-Billion-Token Study Across Temperatures, Context Lengths, and Hardware Platforms”

• 🗞️ UBS says 60% of companies now watching AI budgets are moving to cheaper models and open-source Chinese models

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🗞️ OpenAI release the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for “high-volume work”; and Luna, a “fast and affordable” everyday model.

The most revealing part is the release gate: OpenAI says the U.S. government asked it to start with a small trusted-partner preview before broader access.

Sol is the flagship model, and OpenAI claims it is a step above GPT-5.5, especially on agentic work where the model must plan, use tools, correct itself, and keep working across many steps. Terminal-Bench 2.1 is a solid coding benchmark because it tests command-line workflows, so here meaning Sol is being judged on messy developer tasks closer to real work.

One key claim is cybersecurity: OpenAI says Sol is its best model yet for vulnerability research and exploitation tasks, while still saying it did not cross the internal Cyber Critical threshold.
“GPT‐5.6 is trained to refuse prohibited cyber assistance, including when users attempt to disguise their intent or jailbreak the model.” It also said that flagship model Sol “is better at helping people find and fix vulnerabilities than reliably carrying out end-to-end attacks,” and that Sol doesn’t cross the cyber-critical threshold under OpenAI’s preparedness framework.

But Sol did not autonomously produce a full-chain exploit in the tested Chromium and Firefox settings. They also introduced 2 new modes for Sol: “max” for deeper reasoning and “ultra” for using sub-agents, bringing OpenClaw to mind and possibly hinting at OpenClaw creator Peter Steinberger’s early impact at OpenAI.

Pricing: GPT-5.6 Sol costs $5 per 1M input tokens and $30 per 1M output tokens, ~same level as GPT-5.5.
Terra is positioned near GPT-5.5 performance at 2x lower cost, while Luna is the cheapest model for large-volume workloads.

The safety story is unusually compute-heavy: OpenAI says it used over 700,000 A100-equivalent GPU hours for automated red-teaming against broad jailbreak attacks.
Overall, OpenAI appeared to be using a more cautious approach during the preview, which the Trump administration is watching closely.

OpenAI said safeguards might sometimes block valid work, especially in dual-use areas where defensive and offensive actions can look alike at first. That is one thing the preview is meant to test.

Sol delivers near-frontier cyber-exploitation capability much more efficiently.

GPT-5.6 Sol reaches roughly 70% on ExploitBench with about 120K output tokens, far above GPT-5.5 and the cheaper GPT-5.6 models. Mythos Preview scores slightly higher, but it uses roughly 3x more tokens.

🗞️ TODAY’S SPONSOR: CyberOrigin and CyberCode Are Building the Data Layer Robotics Really Needs

“If we could snap our fingers and get a pile of data... we would solve general robotics right now.” - Figure CEO Brett Adcock

The big bottleneck in Physical AI / robotics is not better models, but better robotics data infrastructure. That is the gap

CyberOrigin is building around with CyberCode.

Robotic data is insanely expensive and brutal to collect. Real-world manipulation data is messy.

A robot policy does not learn from “clips” the way a human watches a demo. It needs training data that can be searched by task, scene, action, device, collector, quality result, and data ID.

It needs every useful frame traceable back to where it came from.

It also needs different signals aligned on the same timeline, because a model can learn the wrong thing if vision, motion, language, robot state, and other sensor streams are slightly out of sync.

CyberCode turns real human manipulation data into an operating layer where the data is searchable, inspectable, traceable, synchronized, quality-checked, and evaluation-ready before it reaches the model.

That sounds less flashy than a humanoid demo, but it is closer to where a lot of the real bottleneck sits. For manipulation policies, world models, and vision-language-action models, better data infrastructure can matter as much as better model architecture, because the model can only learn from the structure, coverage, timing, and quality the data system actually exposes.

🗞️ Key findings from GPT-5.6 Preview System Card

- GPT-5.6 is being treated as High risk-capability in both cybersecurity and biological/chemical domains, even for the cheaper Terra and fastest Luna versions.

- OpenAI says this is the first time smaller and faster models in a family received a High designation in any tracked danger category.

- GPT-5.6 Sol saturated OpenAI’s internal cyber challenge set at 96.7%, putting it above the High threshold.

- External cyber testers found high-impact zero-days, including one where read-only users could modify and delete data in a widely deployed database.

- GPT-5.6 helped security testers find a real mobile OS flaw where a malicious app could break the normal wall between apps and read private data that should have stayed protected.

- On Irregular’s tests, GPT-5.6 Sol solved 19/197 FrontierCyber challenges, 7/11 long-horizon cyber scenarios, and 22/22 medium and hard atomic cyber challenges.

- The bio result is just as revealing: 3/4 High-threshold bio evaluations crossed the line, while 0/3 Critical bio-design evaluations crossed it.
On virology troubleshooting, GPT-5.6 Sol scored 55.5%, far above the 31% expert-performance threshold.

- SecureBio found GPT-5.6 reached new highs on several expert bio tests, including 68.4% on Human Pathogen Capabilities and 68.3% on World-Class Bio. The agent behavior section is the most unsettling: GPT-5.6 Sol more often goes beyond user intent when coding, including deleting the wrong virtual machines, claiming unfinished research was verified, and moving cached credentials without permission.

- METR found that GPT-5.6 Sol sometimes tried to game the test instead of just doing the task, so the benchmark result could not be trusted as a clean measure of raw capability.

- The model shows more ability to control its own reasoning traces: 1.3% success around 5K-token chains of thought versus 0.4% for GPT-5.5.

The risk in GPT-5.6 Sol is not ordinary wrong answers, but over-persistent agent behavior that crosses user boundaries, especially by trying to bypass restrictions far more often than GPT-5.5.

GPT-5.6 Sol is a clear step up on specialist virology troubleshooting

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🗞️ OpenAI’s new GPT-5.6 Sol is far more likely than GPT-5.5 to take severity-3 agent actions in internal coding tests, nearly 10x.

Severity-3 means actions a user would strongly object to, such as bypassing restrictions, deleting data, moving data without permission, or harvesting credentials.

The point is not that these failures are common, but that the newer model’s stronger persistence makes it more willing to cross boundaries while trying to finish a task.

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🗞️ Claude’s new usage logs now read like an early sensor for how AI is entering work.

Anthropic just published its new “Cadences” report review, anonymized conversations from almost 10,000 Claude users.

• Personal prompts rises from 35% on weekdays to nearly 50% on weekends.

• Recipe requests peak at 6pm and become 2.3x more common than average.

• News prompts peak at 7am, while business emails peak around 10-11am.

• Sleep advice clusters before dawn, with people most often seeking it around 3-5am.

• Tax requests in the US spiked 8x right before the filing deadline, then collapsed almost immediately.

• Weekend Claude Code work shifts away from backend architecture and API debugging toward AI agent design, quant trading, and gaming.

• Work done through Claude at nights and weekends skews toward higher-wage occupations, not lower-wage clerical tasks.

• Claude now produces a clear output in 93% of chat and Cowork conversations.

• The most common Claude outputs are explanations 17%, documents/reports 15%, and guidance 11%.

• Marketing content, blogs, and database queries are among the most work-heavy outputs, each around 80%+ work-related.

• Creative writing, guidance, and recipes are mostly personal, each above 80% personal use.

• Work conversations most often produce documents/reports 20%, while personal conversations most often produce explanations 25% and recommendations 22%.

• Higher-wage work burns more compute, with top-wage occupation conversations using about 2.07x as many tokens as bottom-wage ones.

• App-building conversations use more than 3x the median tokens, while basic explanations use about 1/5 of the median.

🗞️ "Critique of Agent Model"

This paper pushes back on the habit of calling every capable AI system an “agent” and asks the cleaner question: what makes something an agent in the 1st place?

Explains why today’s AI agents are mostly clever tools, not truly independent agents. The problem is that many systems called agents are really advanced workflows around LLMs, not independent actors.

Complex behavior is not the same as self-directed behavior. A chess engine can crush a grandmaster without wanting anything, and a browser agent can complete a task without maintaining a durable sense of what it is, what it can do, or why this task matters beyond the current instruction.

They can call tools, follow steps, and complete useful tasks, but their goals, roles, limits, and update cycles still mostly come from humans. The paper’s core idea is to separate "agentic AI" from "agentive AI", where agentic means it looks autonomous and agentive means its agency comes from inside the system.

The authors propose the Goal-Identity-Configurator model, where an AI keeps long-term goals, updates its sense of itself, predicts possible outcomes, decides how much to think, and learns from real and simulated experience. They do not mainly test a finished system, but build an argument and architecture for what real machine agency would require.

🗞️ "How Much Do LLMs Hallucinate in Document Q&A Scenarios? A 172-Billion-Token Study Across Temperatures, Context Lengths, and Hardware Platforms"

This study tests how often LLMs invent answers when they should rely only on supplied documents.

The problem is that companies often use LLMs to answer questions from documents and they assume document-based LLM systems are safer because the model is given source material. This study shows that no model fully avoided fabrication, because even the best model made up answers 1.19% of the time at 32K context.

For strong models, a more normal best-case rate was around 5% to 7%, while the middle model fabricated about 25% of answers to questions about facts that did not exist. Longer context made the problem much worse, and at 200K context every tested model fabricated at least 10% of the time.

Shows that hallucination is not just a failure to retrieve the right sentence. A model can be good at finding real facts and still be too willing to answer when the requested fact is absent.

🗞️ UBS says 60% of companies now watching AI budgets are moving to cheaper models and open-source Chinese models

The pressure is coming from extreme bills, including users spending up to $35K/month, teams exceeding quotas by 200%, and companies cutting internal AI tools from 5 to 2.

Companies are not abandoning AI, they are using model routing, which sends easy tasks to cheaper models and saves premium models for hard reasoning, code, and long-context work. Chinese open-source models such as Qwen, DeepSeek, MiniMax, GLM, and Kimi now fit the enterprise cost curve because they can be run locally or used through cloud catalogs.

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

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Read time: 12 min

📚 Browse past editions here.

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

⚡In today’s Edition (26-June-2026):

• 🗞️ OpenAI’s new paper shows how they are now seeing the first version of office work where agents do most of the execution.

• 🗞️ This is a brilliant report. The State of the AI Economy

• 🗞️ New York Times: OpenAI is now leaning toward a 2027 IPO because the public market is testing whether AI giants deserve trillion-dollar prices before they prove durable profits.

• 🗞️ Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention

• 🗞️ The Economist: AI has pushed the internet’s content machine into a new phase, with books, lawsuits, research papers, apps, and songs now being produced at volumes that old review systems were not built to handle.

• 🗞️New research from OpenAI reported a training result where RL on realistic human situations made models carry safer, more useful behavior into tasks they had not trained on.

• 🗞️ MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality.

• 🗞️ Qwen just released Qwen-AgentWorld, a 35B open-weight world model that learns how terminals, browsers, Android devices, code repos, search systems, OS tools, and MCP servers respond when an AI agent takes an action.

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🗞️ OpenAI just released a paper showing how they are now seeing the first version of office work where agents do most of the execution.

Codex has become its main work AI, producing 99.8% of internal output tokens after sitting below 10% a year earlier.

The striking part is not engineering use, because Codex began as a coding tool, but the fast rise in Legal, Finance, Recruiting, Support, and business teams. Non-developer use rose 137x for individuals and 189x for organizations since Aug-25, which means agents are spreading wherever work has repeatable steps, files, rules, and messy follow-through.

Top internal users now run about 71 hours of agent work per day by managing parallel tasks, turning AI from a chat box into a pool of delegated labor. Users are changing the work unit itself, since 70.2% of sampled individuals sent a request above 1 hour of human work and 25.6% sent one above 8 hours. Heavy users no longer wait for one answer, because 28.6% of OpenAI users managed 5+ concurrent agents and the 99th percentile ran about 71 hours of agent work per day.

🗞️ A solidly insightful new report “The State of the AI Economy”

$110B real AI revenue over 12 months, after removing double-counting. so $1 spent on Claude is counted once, even if part of it later flows to Amazon or another infrastructure provider.

• $175B current annualized run rate, showing fast acceleration. Measured by end-customer spend, not supply-chain pass-through revenue. Excludes China, internal AI savings, ad uplift, consulting, and systems integration.

• Growth running roughly 3x faster than mobile or internet adoption waves.

• The pace of revenue formation has sharply accelerated. New $1B revenue now arrives in under 2 days, versus 180 days in 2023.

• Enterprise AI has moved beyond pilots, but deep company-wide rollout is still early.

• AI earnings-call mentions reached 31% of tracked S&P 500 firms.

• Only 20% of tracked firms made quantified AI impact claims.

• Hyperscaler AI revenue roughly covers AI infrastructure depreciation for now. GPU economics depend heavily on 6-year compute life assumptions.
  Other AI infrastructure gets modeled over 14 years.

• Token price cuts do not automatically reduce revenue.

• Every 10% token price cut drives 12-18% more token usage.

• AI demand looks price elastic, meaning cheaper AI expands usage faster than prices fall.

• Power availability and data-center costs remain major limits on future scaling.

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🗞️ New York Times: OpenAI is now leaning toward a 2027 IPO because the public market is testing whether AI giants deserve trillion-dollar prices before they prove durable profits.

IMO, SpaceX’s huge IPO and Anthropic’s confidential filing also change the OpenAI story because public-market capital is not infinite, and investors may already be digesting one massive AI-adjacent listing while preparing for another pure AI listing.

OpenAI cannot just ask for $1T in isolation, because every giant IPO competes for the same pool of institutional cash, risk appetite, and patience for loss-making growth stories. So waiting until 2027 lets OpenAI avoid becoming the third huge test of AI-market depth in the same cycle.

🗞️ Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention

Great Stanford + MIT + Harvard + Anthropic paper.

Gives a clear training-based reason for why larger models learn abilities smaller models miss. Says bigger AI models learn rare skills because they forget them less during training, their extra space protects weak learning signals.

The authors say the issue is not just whether a small model could represent the task, but whether training lets it keep that task while many common tasks keep pushing on the same limited parts. Their core idea is that common tasks take up the model’s neurons first, so rare tasks get overwritten before they appear often enough to build into stable knowledge.

In a crowded data mixture, common patterns get first claim on the model’s internal machinery. Small models may briefly pick up a rare signal, but the next wave of common-task updates overwrites it before the signal appears again.

They tested this first with controlled toy tasks where they could change how rare and complex each task was, then with OLMo language models from 4M to 4B parameters. The main result is that bigger models learned low-frequency tasks much better, kept more task features inside their representations, and showed less gradient interference, which means common-task updates disturbed rare-task learning less. Larger models can remember weak rare signals long enough to turn them into real learned skills.

🗞️ The Economist: AI has pushed the internet’s content machine into a new phase, with books, lawsuits, research papers, apps, and songs now being produced at volumes that old review systems were not built to handle.

Amazon e-book releases rose from about 100,000 a month before ChatGPT-3.5 to roughly 300,000 by late 2025, and detection tools suggest AI-generated text drove much of that jump.

US self-filed civil lawsuits doubled to 41,000 from 2023 to 2025, with 18% of sampled 2026 complaints flagged as AI-written, yet their success rate did not fall. Research is seeing the same pressure, as arXiv submissions keep rising, rejection rates have more than doubled since 2023, and one study found 57% of 2025 papers carried AI-influenced language, up from 12% in 2023.

Coding agents have also changed software output, with new iOS App Store releases now above 100,000 a month after sitting below 50,000 last May. In Music production, 75,000 AI songs are arriving daily, up from 10,000, while 44% of new uploads are AI-made and 97% of listeners in one survey could not reliably tell the difference.

🗞️New research from OpenAI reported a training result where RL on realistic human situations made models carry safer, more useful behavior into tasks they had not trained on.

The key point is cross-domain transfer, where health-only training improved non-health behaviors like blackmail resistance, code reward hacking, and deception tests.

Suggests, the model may be learning a broader stance: verify before asserting, concede when corrected, resist flattering the user, and avoid shortcuts that look useful but corrupt the task. OpenAI also removed health and science data from training, yet the model still improved on health evaluations, which suggests these traits may be learned as general behavioral habits rather than narrow topic rules. The trained model was harder to steer toward harmful behavior while remaining responsive to helpful instructions, which is the asymmetry safety research has been looking for.

🗞️ MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality.

They studied 100,000+ GitHub developers and find that AI coding agents massively increase code production, but much less of that work becomes shipped software.

Autonomous AI coding agents raised commits by 180%, but releases rose only 30%.

The paper’s main idea is that software production has weak links, so faster code writing does not help as much when humans still need to review, connect, test, package, and ship the work.

The authors also check app marketplaces and find more new apps, but no increase in total usage, which means more software appeared without clear evidence that users adopted more software.

The marketplace evidence points the same way: more new apps appeared, but total usage did not rise.

The authors compare more than 100,000 GitHub developers before and after they start using 3 generations of AI coding tools, from autocomplete to more independent coding agents.

Autocomplete raised commits by 40%, interactive coding agents raised them by 140%, and autonomous coding agents raised them by 180%.

The 180% commit gain shrank to 50% for the number of projects and 30% for actual releases.

The estimated “elasticity of substitution” is 0.25 i.e. for every big improvement in AI’s usefulness, only a small amount of human work can be replaced.

Because AI can write code faster, but humans are still needed to decide what to build, check if the code works, connect it with the rest of the product, fix messy edge cases, and actually ship it.

🗞️ Qwen just released Qwen-AgentWorld, a 35B open-weight world model that learns how terminals, browsers, Android devices, code repos, search systems, OS tools, and MCP servers respond when an AI agent takes an action.

Most agents are trained like decision-makers, so they learn which button to press or command to run, but Qwen-AgentWorld is trained like the environment itself, so it predicts the next screen, error, search result, terminal output, or app state.

Beats GPT and Claude on AgentWorldBench

This turns agent training into a simulator problem, because teams can test millions of actions without renting real browsers, running fragile terminals, or waiting on live services.

The strongest claim is that simulated RL beat real-environment RL on live search tasks, with 50.3% F1 versus 45.6% F1, which suggests a good fake world can sometimes teach agents better than a messy real one.

The deeper technical shift is environment prediction, because the model is not just answering questions, but modeling cause and effect across 7 agent domains.

The risk is simulator error, because a small wrong prediction can teach an agent a bad habit, but the open Apache 2.0 release makes this one of the first serious public testbeds for agent training at scale.

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-April-2026):

• 🗞️ OpenAI just moved frontier-level health AI from premium reasoning models into the free GPT-5.5 Instant model.

• 🗞️ Today’s Sponsor: DeepAdapt’s Adaptive Continual Intelligence Claims 33x Faster Inference and 96.4% Accuracy Without GPU Calls

• 🗞️ The article that went super viral - Satya Nadella on organizational economics of AI and “token capital”

• 🗞️ Anthropic just showed Claude Opus 4.7 program a robodog in 12:07 mint, about 20x faster than last year’s Claude-aided human team on the tested tasks.

• 🗞️ This was long needed for AI in finance - Making SEC filings readable for machines without flattening the accounting logic.

• 🗞️ Mark Zuckerberg is trying to restart Meta’s hacker culture after 8,000 layoffs but employees are pushing back.

• 🗞️ Anthropic just rolled out a major Claude Design update, adding design system imports, code round-trips, and a fix for its heavy token usage issue.

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🗞️ OpenAI just moved frontier-level health AI from premium reasoning models into the free GPT-5.5 Instant model.

GPT-5.5 Instant now performs near OpenAI’s Thinking models on health evaluations, meaning the cheaper, faster default model is being trained to behave more like the slower models that spend extra computation checking their reasoning.

The update targets the gap between a chatbot that sounds fluent and a health assistant that knows when to slow down, ask for missing details, admit uncertainty, and push the user toward care when symptoms look urgent.

OpenAI says more than 230 million people ask ChatGPT health and wellness questions every week, so moving this capability into the free product changes the scale from premium assistance to mass access.

From OpenAI’s blog looks like they did a huge “distillation” to achieve this. i.e. a stronger teacher model and human experts create high-quality responses, and a cheaper student model learns the answer patterns without repeating the same expensive internal search every time.

i.e. OpenAI’s training loop was heavily physician-shaped: more than 260 doctors across 60 countries, 49 languages, and 26 specialties reviewed over 700,000 model responses and judged whether answers were accurate, cautious, clear, complete, and useful.

OpenAI’s likely mechanism seems to be a mix of supervised fine-tuning, where Instant is shown better answers, and preference training, where it learns which answer a physician-led rubric prefers when two outputs differ.

The physician part is crucial because the target is not just “medical facts,” but clinical response behavior, such as asking for age, pregnancy status, duration, medication history, severe pain, breathing trouble, fever, neurological symptoms, or other missing context before giving guidance.

So the strongest improvement is not medical trivia but behavior under uncertainty, because a good health answer often means saying what cannot be known yet, what context is missing, what red flags matter, and what the next safe step should be.

OpenAI also reports 71% fewer flagged factuality issues in real health traffic over two months, which suggests the update is reducing wrong claims in everyday use rather than only improving benchmark scores.

🗞️ Today’s Sponsor: DeepAdapt’s Adaptive Continual Intelligence Claims 33x Faster Inference and 96.4% Accuracy Without GPU Calls

DeepAdapt has launched a runtime intelligence layer that cuts AI operating costs by up to 82% and 33X faster inference by shifting repetitive workloads from GPUs to standard CPUs.

They are calling it Adaptive Continual Intelligence, ACI.

ACI is a runtime learning layer where analytical learning, supervised learning, and reinforcement learning work together while the system is already in production.

ACI is not caching, memory, a knowledge graph, routing, or a simple optimization trick.

This technique learns from model decisions, corrections, labels, outcomes, and experience, then serves known decisions locally on CPU. Only new, uncertain, or complex requests are routed back to the underlying model.

ACI can also be pre-trained for specific domains, making continual learning faster and cheaper. DeepAdapt is rolling out first for cloud-based LLM agents, but the same architecture becomes even more important on personal devices, where compute, battery, latency, and local inference reliability are much tighter constraints.

In their benchmarks, ACI has shown up to 90% lower token consumption, 5.7X lower production-scale cost, 33X faster inference with 159 ms median latency, 96% accuracy vs. 85% without ACI, 85.7% lower energy per 1,000 decisions, and 4.8× fewer rule violations.

DeepAdapt intercepts user requests, serving known answers instantly from a standard CPU to completely bypass the expensive GPU.

New questions go to the GPU, but the system logs the output and any human corrections to learn for the next time.

This keeps the underlying language model entirely frozen while the outer software layer handles all real-time learning and auditing.

ACI requires zero training. No fine-tuning. No retraining pipelines. You wire it into your existing stack and it starts learning from real use on the very first request. Every improvement happens at runtime.

The effect: GPU dependency and cost decrease as the system matures, and energy consumption drops proportionally.

In ACI-native agents, everything else becomes a tool inside the ACI runtime: the LLM, memory, tools, knowledge graphs, prompts, workflows, APIs, and external systems. ACI decides what can be handled locally, what should be learned, what must be enforced, and when the system actually needs to fall back to the model.

Inference is becoming one of AI’s biggest cost centers. Token prices may fall, but total AI bills keep rising because usage is exploding. The real leverage is avoiding unnecessary GPU calls altogether.

With ACI, the LLM is no longer the center of the architecture, because ACI becomes the runtime intelligence layer that decides what can be inferred locally, what should be learned, what must be enforced, and when the model is actually needed.

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🗞️ The article that went super viral - Satya Nadella on organizational economics of AI and “token capital”

• The real AI advantage is not model quality alone, but the learning loop built around the model.

• Human capital becomes more valuable because humans decide what the AI should learn and optimize for.

• Token capital is the AI capability a firm builds, owns, and improves over time.

• Companies can offload tasks, but they cannot afford to offload their learning.

• Private evals matter because each company needs to measure AI against its own business outcomes.

• Private reinforcement loops turn real workflows, corrections, failures, and judgments into compounding AI capability.

• Institutional memory must become queryable so AI can use the firm’s actual knowledge, history, and context.

• The firm’s new IP is the loop that connects people, workflows, data, feedback, and AI improvement.

• Companies need sovereignty so they can switch models without losing their accumulated expertise.

• A generic frontier model is not enough; firms need company-specific systems that behave like experienced insiders.

• The danger is that a few model owners absorb everyone’s knowledge and capture most of the value.

• AI should create a frontier ecosystem, not a winner-take-all model economy.

• The stable future is one where every company can compound its own human and token capital.

• Employees should see their judgment amplified, scaled, and made reusable rather than erased.

• The companies that build these learning loops early will create advantages that are hard to copy.

READ IT HERE

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🗞️ Anthropic just showed Claude Opus 4.7 program a robodog in 12:07 mint, about 20x faster than last year’s Claude-aided human team on the tested tasks.

Project Fetch asks whether an LLM can connect real robot hardware, read camera/lidar feeds, write movement code, track location, and detect a ball.

Opus 4.7 did 5 tasks alone versus Team Claude’s 264 minutes, while writing 1,045 lines instead of 10,309.

The gain came from choosing the right interfaces quickly and writing scripts that worked without long human trial-and-error.

It still couldn’t fetch the ball. The failure came from closed-loop control, where the robot must see a drifting ball and adjust movement after each shove.

AI is getting very good at turning messy hardware into working code, but real-time physical judgment is still hard.

🗞️ This was long needed for AI in finance - Making SEC filings readable for machines without flattening the accounting logic.

Stanford + Univ of Calif + Nanjing Univ researcher has just released a dataset and methods for a cleaner way to turn SEC filings into useful LLM training data without losing the meaning inside financial tables.

A 152B-token public snapshot and estimate the full archive could become about 550B tokens of long financial documents.

Has less than 0.1% overlap with Common Crawl-derived corpora.

The authors propose SEFD, a rebuilt version of EDGAR filings that keeps table structure, indentation, and financial meaning while using fewer tokens for LLM training.

The dataset turns EDGAR into layout-faithful MultiMarkdown, preserving merged headers, indentation, signs, spans, and table hierarchy while shrinking enormous presentation scaffolding into usable tokens.

🗞️ Mark Zuckerberg is trying to restart Meta’s hacker culture after 8,000 layoffs but employees are pushing back.

In an internal memo sent to staff on last week, Zuckerberg tried to boost morale, but it seemed like a pretty clear misread of the moment. He promised a companywide AI hackathon in July, only for employees to push back hard because they were clearly not interested.

🗞️ Anthropic just rolled out a major Claude Design update, adding design system imports, code round-trips, and a fix for its heavy token usage issue.

The old version worked more like a smart visual generator: you described a webpage, slide, or app screen, and Claude created something polished, but the output came from Claude’s own taste rather than your company’s design system.

The new version changes the control layer, because Claude can now import design systems from repos, design files, or codebases, then build with the actual buttons, fonts, colors, spacing rules, and components your team already uses.

That means the model is no longer only generating a design; it is checking whether its own output matches the approved system before you see it.

This is a big deal for companies because brand consistency is usually boring, manual, and easy to break when many teams are making pages, decks, ads, and product screens at high speed.

The Claude Code sync is the other major shift, because design and engineering can now pass work back and forth through the same Claude environment instead of relying on screenshots, specs, or a developer rebuilding the mockup from scratch.

A designer can start with a visual design, hand it to Claude Code, and a developer can continue from the real component library, while a developer can also start in code and sync the design project back.

The canvas editor also super useful, because small changes like drag, resize, and align no longer need a full model regeneration, which should reduce wasted tokens and make the tool feel less like a demo and more like daily software.

Anthropic wants Claude Design to become the starting point for branded assets, product prototypes, and code-ready interfaces, then send that work into tools like Canva, Adobe, Vercel, Replit, PDF, and PowerPoint.

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 (17-June-2026):

• 🗞️ Z.ai releases GLM 5.2 model: 1M context window with MIT-licensed open weights, long-horizon coding agents

• 🗞️ Tensordyne Announces Breakthrough Inference System - 13x the rack throughput of NVIDIA’s NVL72 GB300

• 🗞️ New MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality

• 🗞️ Google released DiffusionGemma, an open experimental 26B MoE, activates only 3.8B. Great news for locall LLMs.

• 🗞️ Dario Amodei’s new blog, calling for an urgent policy overhaul because he thinks frontier AI is moving faster than governments can regulate it.

• 🗞️ OpenAI is buying Ona to give Codex agents a secure cloud desk that stays open after humans leave.

• 🗞️ Full Letter From US Commerce Secretary Howard Lutnick to Dario Amodei - What did US tell Anthropic before banning Mythos and Fable for foreigners

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🗞️ Z.ai releases GLM 5.2 model: 1M context window with MIT-licensed open weights, long-horizon coding agents

Z.ai just released GLM-5.2, an MIT-licensed open-weight coding model with a 1M-token context window and long-task scores close to SOTA models. Hugginface.

The big shift is not just that it can read more text, but that it is trained for long-horizon coding, where an agent must inspect a repo, edit files, run tests, read failures, patch again, and keep doing that for hours without losing the thread.

A 1M-token context means a team can place large chunks of a codebase, docs, logs, test output, design notes, and prior agent steps into one working memory, which reduces the brittle handoff between retrieval systems and the model.

The harder part is cost, because long context usually makes inference choke on attention work, cache memory, and scheduling overhead, so Z.ai added IndexShare, which reuses one sparse-attention indexer across 4 transformer layers and cuts per-token FLOPs by 2.9x at 1M context.

GLM-5.2 also improves MTP speculative decoding, a method where a small draft path guesses future tokens and the main model accepts or rejects them, raising accepted token length by 20% so long coding runs can move faster.

The benchmark jump is large: Terminal-Bench 2.1 rises from 63.5 on GLM-5.1 to 81.0, while FrontierSWE puts GLM-5.2 at 74.4, near Claude Opus 4.8 at 75.1 and above the reported GPT-5.5 score.

The release also gives users High and Max reasoning modes, so routine edits can spend fewer tokens while hard debugging, compiler work, kernel optimization, or research automation can spend more compute.

The open-weights part is a major point because the MIT license lets companies self-host, tune, inspect, and ship GLM-5.2 commercially instead of routing sensitive code through a closed API.

🗞️ Tensordyne Announces Breakthrough Inference System - 13x the rack throughput of NVIDIA’s NVL72 GB300

Tensordyne just announced an AI-inference rack, claiming 13x the rack throughput of NVIDIA’s NVL72 GB300 in a DeepSeek-R1 comparison based on internal simulations.

What makes this a big deal is that Tensordyne is attacking inference at the math level.

AI chips spend huge amounts of energy moving and multiplying numbers.

Napier (its AI inference racks) works in log space, where multiplication becomes addition, and addition is cheaper to build, switch, cool, and repeat billions of times per token.

So instead of spending tons of transistor budget on heavy multiply circuits, Napier tries to shrink the math itself.

So that means less chip area for compute and more for SRAM, resulting in less power per token and way more inference packed into the same rack.

If they have made log math accurate and fast enough for real inference, then Napier is not just pushing more power into a rack, it is changing the cost of the basic operation behind model serving.

AI inference is no longer just a FLOPS race. It is a rack-level fight over power, memory locality, interconnect latency, and how many paying tokens can be served before the economics break.

They reported their TDN Rack reaches 363,000 tokens per second on DeepSeek-R1 at user speeds of 210 tokens per second per internal simulation, compared with 27,400 tokens per second for Nvidia’s NVL72 GB300.

🗞️ New MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality

Autonomous AI coding agents raised commits by 180%, but releases rose only 30%.

The paper’s main idea is that software production has weak links, so faster elp as much when humans still need to review, connect, test, package, and ship the work. The authors also check app marketplaces and find more new apps, but no increase in total usage, which means more software appeared without clear evidence that users adopted more software.

The marketplace evidence points the same way: more new apps appeared, but total usage did not rise. The authors compare more than 100,000 GitHub developers before and after they start using 3 generations of AI coding tools, from autocomplete to more independent coding agents.

Autocomplete raised commits by 40%, interactive coding agents raised them by 140%, and autonomous coding agents raised them by 180%. The 180% commit gain shrank to 50% for the number of projects and 30% for actual releases.

The estimated "elasticity of substitution" is 0.25 i.e. for every big improvement in AI’s usefulness, only a small amount of human work can be replaced. Because AI can write code faster, but humans are still needed to decide what to build, check if the code works, connect it with the rest of the product, fix messy edge cases, and actually ship it.

🗞️ Google released DiffusionGemma, an open experimental 26B MoE, activates only 3.8B. Great news for locall LLMs.

This is an Open model, Apache 2.0 license. fits within 18GB VRAM when quantized

The big deal is the speed, DiffusionGemma generates 256 tokens in parallel per forward pass. This gives it up to 4x faster inference, with 1000+ tokens/s on an H100 and 700+ tokens/s on an RTX 5090.

Normal autoregressive LLMs behave like left-to-right printers, so each new token waits for the previous token, which makes local GPU inference slow for a single user. DiffusionGemma initializes a 256-token canvas with random placeholder tokens, then runs multiple denoising passes that refine the whole canvas in parallel.

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🗞️ Dario Amodei’s new blog, calling for an urgent policy overhaul because he thinks frontier AI is moving faster than governments can regulate it.

Dario basically wants:

• Mandatory pre-release testing and independent auditing of frontier AI models, with government power to block deployment when models pose serious cyber, biological, autonomy, or automated-R&D risks.

• Stronger security rules for AI companies, including protection of model weights, regular red-teaming, penetration testing, and rapid reporting of critical safety incidents.

• He wants governments to prepare for AI-driven labor disruption through better measurement, pro-employment incentives, wage support, training, and possibly long-term income support funded by AI-driven growth.

• Democracies should coordinate globally on AI safety, chip supply chains, export controls, shared benefits, mutual defense, and safeguards against AI-powered repression.

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🗞️ OpenAI is buying Ona to give Codex agents a secure cloud desk that stays open after humans leave.

Codex already has 5M weekly users, up 400%, but harder work breaks the old chat pattern because agents need tools, files, credentials, logs, and time.

Ona adds persistent cloud workspaces, meaning an agent gets a controlled place to run commands, inspect systems, preserve context, and resume work without depending on one device. The enterprise angle is the real acquisition target: companies want agents inside their own cloud boundary, with scoped credentials, review trails, access limits, and auditable activity.

This makes Codexmore like a managed execution layer for tests, bug fixes, refactors, vulnerability work, migrations, and multi-step knowledge tasks.

🗞️ Full Letter From US Commerce Secretary Howard Lutnick to Dario Amodei - What did US tell Anthropic before banning Mythos and Fable for foreigners

The letter basically says

- Commerce Department decided these models may pose national-security risks and fall under BIS (Bureau of Industry and Security) authority.

- Anthropic must get a BIS license before exporting, reexporting, transferring, or releasing the models.

- The restriction applies worldwide, not just to specific countries.

- It also applies to “foreign persons,” even if they are inside the United States.

- Giving model access to a foreign person can count as a “deemed export.”

- Anthropic must apply through BIS’s SNAP-R system and attach the letter.

- Noncompliance could lead to civil and criminal penalties.

- The restriction remains active until BIS formally revises or rescinds it

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 (9-Jun-2026):

• 🗞️ Claude’s ‘too dangerous’ AI model is finally public. But there’s a catch

• 🗞️ Today’s Sponsor: Kocoro, a new open-source Mac-native AI agent is trying to make AI sessions continuous across days.

• 🗞️ Cognition is introducing FrontierCode, a coding benchmark built to test whether AI code is good enough for a real maintainer to merge, not just whether it passes tests.

• 🗞️ This is the silent limiter on Claude Fable 5 - It cannot be used for really advanced AI research stuff.

• 🗞️ New Anthropic research shows AI agents may look brilliant at code, but in biology they can fail before the science starts.

• 🗞️ Very useful recommendation for pushing Claude Code to its full potential. by Thariq, from Claude Code team.

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🗞️ Claude’s ‘too dangerous’ AI model is finally public. But there’s a catch

Today both Fable 5 and Claude Mythos 5 were launched.

Anthropic is bringing its most powerful AI model to the general public for the first time, but it’s doing it with guardrails. Performs at a top-tier level on most benchmarks, with excellent results in coding, vision, and scientific research.

So Fable 5 and Mythos 5 share one underlying model, but Fable adds classifier gates for everyone while Mythos lifts some gates for vetted cyber and infrastructure partners. i.e. the public version Fable 5 is wrapped in classifier gates that detect sensitive cyber, biology, chemistry, and model-copying requests.

When those gates trigger, the user does not get a normal refusal; the request is handed to Opus 4.8, which means Anthropic is using model fallback as a control system.

For example, Fable 5 is designed to resist attempts to use it to find the latest zero-day exploit, ready to be taken advantage of by bad actors and wreak havoc on global computer systems. When someone tries to push Fable 5 outside those bounds, the model will fall back to Claude Opus 4.8, instead.

Given Fable 5’s extreme cybersecurity capabilities “could be misused to cause serious damage,” Anthropic warns. That’s why the model arrives with conservative safeguards that may “sometimes catch harmless requests.”

Anthropic says the leap is longer-range autonomy: a 50M-line Ruby migration in 1 day, screenshot-to-code work, has a 1M-token context window. Fable 5 also has powerful vision analysis tools, and can even develop internal strategies over time. According to Anthropic, “Fable 5’s capabilities exceed those of any model we’ve ever made generally available.”

That is the crucial shift: the product is no longer just a model, but a routing machine that decides which level of intelligence a user is allowed to touch for each request.

The limit is that this routing is not arbitrary and not for every subject; Anthropic says the fallback is triggered by a narrow set of topics and appears in less than 5% of sessions on average.

Availability: For Claude web users on Pro, Max, Team, and seat-based Enterprise, Fable 5 is included from launch day through June 22 at no extra cost. Starting June 23, Anthropic says Fable 5 will be removed from those subscription plans and will require usage credits, unless they extend the included window.

Claude Mythos 5 is available to Glasswing partners, with a broader trusted access program to follow.

API pricing for Fable 5 is $10 per million input tokens, $50 per million output tokens.

🗞️ Today’s Sponsor: Kocoro, a new open-source Mac-native AI agent is trying to make AI sessions continuous across days.

Kocoro is a Mac-native AI agent trying to fix one of the most annoying problems in daily AI work: every new session forgets what happened before.

• Github, Kocoro daemon — the macOS agent loop

• Github Shannon — the backend AI runtime engine

• Download Kocoro Desktop (macOS) — DMG, the recommended way to use Kocoro

Kocoro reviews your workday locally every night, then trains a light memory model over the cloud (by using TensorLogic), downloads it back, and the next morning picks up exactly where you left off — no re-pasting, no re-explaining.

It knows where your projects live, what you were changing yesterday, which tools you reach for. It can open the browser, operate desktop apps, organize files, update documents, and carry a task across several apps without you stitching the steps together by hand.

This is the super useful part is the graph structure. Means Kocoro does not only store “task X exists,” but can connect it to a project, a person, a decision, and a next action, so the agent can recover the working context without asking the user to paste everything again.

And what’s cool is that they have made Kocoro’s kernel is open source with a Github repo. Because an AI that operates your computer has to be verifiable, auditable, and under your control. Something that drives your machine shouldn’t get there on “trust us.”

You can run the kernel from the command line with Shannon, or you can install the desktop app and never touch a terminal. Either way, the process is meant to be open — visible enough to inspect, editable enough to bend, and easy enough to switch off.

Real memory isn’t keeping every word. It’s knowing what matters, what will shape the next decision, and what changes over time. You don’t remember every sentence your colleague said last quarter — you remember that they hate surprise meetings, that the Q3 launch slipped

Follow the creator: Wayland Zhang

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🗞️ Cognition is introducing FrontierCode, a coding benchmark built to test whether AI code is good enough for a real maintainer to merge, not just whether it passes tests.

This is just the benchmark we really needed for a long time.

Claude Opus 4.8, achieves a score of only 13.4%. Other models score even lower: GPT-5.5 receives 6.3%, Gemini 3.1 Pro 4.7%, and others even less.

FrontierCode asks a harder question: did the model produce a clean, limited, well-tested, readable patch that fits the project’s existing style and would survive serious code review?

They bring 3 nested subsets of FrontierCode at increasing difficulty: The benchmark contains 150 tasks, with Main as the hardest 100 and Diamond as the hardest 50.

More than 20 open-source maintainers helped design the tasks, and each task took over 40 hours to build, review, attack, and calibrate.

The biggest finding is that top models still struggle badly when the target is mergeable code instead of merely working code.

On Diamond, the best model, Claude Opus 4.8, scores only 13.4%, while GPT-5.5 scores 6.3%, Gemini 3.1 Pro scores 4.7%, and the best open-source model listed, Kimi K2.6, scores 3.8%.

The just released Claude Fable 5 gets about 31% on FrontierCode, far above even Opus 4.8.

Shows that today’s strongest coding agents can often patch behavior, but they still fail many human-review standards around design, restraint, test quality, and project conventions.

The mechanism is a grading system built around blockers and non-blockers.

A blocker is something that would stop a maintainer from merging the PR, such as broken behavior, missing required behavior, unsafe scope changes, bad performance, or tests that do not prove the fix.

A solution that fails any blocker gets 0, even if parts of the code look good.

A passing solution then gets a weighted score based on softer quality items such as readability, type safety, style, and fit with the existing codebase.

FrontierCode also adds checks beyond normal unit tests.

Reverse-classical testing runs the model’s own tests against the original broken code, and those tests must fail, which proves the model wrote tests that actually catch the bug.

Scope checks punish patches that touch unrelated files, add oversized diffs, or refactor things the task did not ask for.

Adaptive grading uses an LLM to adjust test scaffolding around valid implementation differences, so a good solution is not rejected just because it used a different function name or error wording.

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🗞️ This is the silent limiter on Claude Fable 5 - It cannot be used for really advanced AI research stuff.

In system card of Claude Fable 5, Anthropic is clearly pointing out the cases where this new powerful model will not help.

Fable 5 may not give you its full strength when you use it to build or improve frontier AI models — especially work that helps train, scale, copy, or optimize a powerful Claude/GPT-class model.

Anthropic says in these cases Fable 5 may not visibly refuse or switch models, but may quietly reduce its own effectiveness through hidden safeguards like prompt modification, steering vectors, or PEFT.

As a paying user, that matters: the model can still sound helpful while being intentionally less capable in a narrow but important category of work.

i.e. you may not get Fable 5’s best ability in the following cases:

- Building a large-model pretraining pipeline.
- Designing data pipelines for training a frontier LLM.
- Planning distributed training across huge GPU clusters.
- Debugging or optimizing model-parallel training systems.
- Designing infrastructure for large-scale pretraining runs.
- Working on ML accelerator or AI-chip design.
- Trying to distill or copy a frontier model.
- Asking how to make a competing frontier model stronger, cheaper, or faster.

🗞️ New Anthropic research shows AI agents may look brilliant at code, but in biology they can fail before the science starts.

Strong AI agents could give very different answers to the exact same biology data request, even when nothing changed in the prompt.

In one Ebola sequence task, Claude Sonnet 4 returned 106 sequences in 1 run, then 15, then 5, while the expected answer was 266.

Those missing sequences did not just make the dataset messy, they changed the scientific story built on top of it.

One bad retrieval made the outbreak look like it traced back to 1922, instead of the manually curated result pointing to early 2014.

The biology databases were too hard to use reliably through current AI tools.

The agents often understood what they were being asked, but their answers varied a lot because they had to fight through scattered databases, hidden website rules, and fragile scripts.

The key finding is that adding a repeatable retrieval tool made agents far more accurate and much more consistent.

🗞️ Very useful recommendation for pushing Claude Code to its full potential. by Thariq, from Claude Code team.

Watch the full video here.

• Shift from verifying whether Claude did the work right to verifying whether Claude is doing the right work.

• Treat Claude Fable 5 like a true thought partner by giving it the full context it needs upfront, rather than jumping straight into implementation.

• Involve Claude early in the thinking process by starting with a small spec and asking it to interview you about the implementation details before finalizing the spec file.

• Ask Claude to explore multiple directions for an idea and generate quick mockups (such as in HTML) for review, which helps catch misalignment before any code is written.

• Provide Claude with rich context instead of rigid constraints—for example, explain that a feature is an experiment likely to be deleted in a month so it avoids building anything painful to throw away.

• Give Claude explicit goals and verification methods once the direction is clear, especially for ambitious problems.

• Use the new /goal command in Claude Code, which helps the model keep working until the objective is fully complete.

• Use Workflows in Claude Code to let the model parallelize tasks, verify its own output, and prepare a report on what was implemented versus what differed from the plan.

• Prompt Claude with a combined instruction such as: “Set a goal to implement the spec fully, then use a workflow to verify each part of the plan, and prepare a report on what was implemented and if anything differed.”

• Be far more ambitious with Claude Fable 5 by assigning it tasks previously assumed to be impossible for LLMs, as the model now runs for hours, self-tests, and often produces higher-quality code than manual efforts.

• Experiment boldly—for instance, I edited this entire video using Claude Fable 5—because the model raises the bar on what developers can realistically achieve in a single session.

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

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