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A kneecapped Wayback Machine isn't just bad news for accountability journalism—it will also be a blow to the legal system, as pages archived by the tool are frequently cited as evidence in litigation across the United States.

If a similar situation arose today, watchdog media reporters may struggle to track older versions of Times articles in the same way.

the Internet Archive has been an 'essential tool' throughout my career, playing an instrumental role in fact checking and surfing audioclips.

They also help avoid a patchwork of state-by-state rules and move toward clearer, more consistent national standards

Several family members of children that died by suicide after allegedly developing unhealthy relationships with ChatGPT have sued OpenAI in the last year

If an AI model engages in conduct on its own that, if committed by a human, would constitute a criminal offense and leads to those extreme outcomes, that would also be a critical harm

We believe the North Star for frontier regulation should be the safe deployment of the most advanced models in a way that also preserves US leadership in innovation

90 percent of people oppose it. There's no reason existing AI companies should be facing reduced liability

The bill would shield frontier AI developers from liability for 'critical harms' caused by their frontier models as long as they did not intentionally or recklessly cause such an incident

Legacy platforms get worse over time : static detections degrade with changing data & behaviors. Artemis gets better : with each incident or proactive threat hunt, the system identifies new patterns.

Legacy platforms rely on brittle, hand-written rules. An engineer writes a detection rule : 'if events A, B, & C happen in sequence, fire an alert.' It works for a couple months.

Artemis turns raw logs into a living model of the customer's environment : users, assets, relationships, & security posture.

Deepfake scams have stolen tens of millions. AI-generated phishing bypasses legacy filters.

Architected before AI, these SIEM systems are wooden shields in an era of autonomous attackers.

Open Loop + Infinite Demand = Creative Amplifiers.

I would put venture capitalist in finite demand & open loop.

Some problems are open loop today but will close over time.

AI writes the code. Tests verify correctness. More code enables more features.

Closed Loop + Infinite Demand = Economic Engines. Software engineering lives here.

There were 1 billion commits in 2025. Now, it's 275 million per week, on pace for 14 billion this year if growth remains linear

The age of abundant AI is over, & it will remain so for years.

Five hallmarks define this era: Relationship Based Selling, AI to the Highest Bidder, Available but Slow, Inflationary Commodity, Forced Diversification

Anthropic has limited its newest model to roughly forty organizations.

As the cost of software development falls, trusted partners with broad adoption can expand faster than anyone else.

Each of these companies recognized the cognitive burden of unbundling. They're not selling features. They're selling trust.

Foundation model companies are doing the same. OpenAI launched a dedicated Healthcare & Life Sciences vertical... They're not selling APIs. They're becoming platforms.

They want a platform they can trust for three to five years.

When models change every 42 days, buyers can't assemble a best-of-breed stack.

The SaaS era was defined by unbundling: find a workflow, optimize it, own it.

The model can reverse-engineer compiled software to detect malware and vulnerabilities without needing source code, aiming to help analysts inspect and secure systems more efficiently.

OpenAI has introduced GPT-5.4-Cyber, a more permissive version of its flagship model built for defensive security work, expanding access to thousands of verified users through its Trusted Access for Cyber initiative.

The interest comes as Anthropic's annual revenue run rate has surged to about $30 billion, driven by strong demand from enterprise customers using its AI tools for coding, cybersecurity, and automation.

Anthropic has received investor offers that could value the company at around $800 billion, more than double the $350 billion valuation tied to its $30 billion raise in February

Anthropic is expected to release Claude Opus 4.7 alongside a new AI-powered design tool for building websites and presentations, with both potentially launching as soon as this week.

The system is designed to handle multi-step workflows like booking trips, clearing inboxes, or running research without constant input, bringing it closer to emerging agent platforms from OpenAI and Anthropic.

Google is expanding Gemini with a new agent system that can take a single goal and execute it across apps like Gmail, Drive, Calendar, and the web, shifting from chat-based prompts to full task execution.

Meta is reportedly developing an AI version of Mark Zuckerberg that can interact with employees, trained on his voice, mannerisms, and internal thinking as part of the company's broader push into AI.

Unitree is preparing to sell its R1 humanoid robot globally through AliExpress for around $4,000 to $4,370, making it one of the most affordable humanoid systems released so far.

Luna could observe the shop through security camera screenshots, but still made basic mistakes, including selecting the wrong country when hiring a contractor and mismanaging staff schedules during opening weekend.

The integration also connects to Upwork's AI agent Uma, which helps automate parts of the hiring and execution process once a project is underway.

An AI agent just hired humans and ran a store Andon Labs deployed an AI agent called Luna into a physical boutique with a $100,000 budget, giving it full control to create, staff, and run the business as what may be the first real-world AI employer.

Long term, it has to understand Cross-Reaper context, not just isolated Reapers. A lot of real work leaves independence between services, so that's definitely part of our direction.

The messy context and old ticket ambiguity are exactly the hard part, so we are building toward that step by step.

FE handles UI features, component refactors, and visual bugs; BE handles APIs, services, migrations, and tests; QA is coming next.

In Messi Legacy repos, low confidence should be flagged early. Better to be transparent than open a bad pull request.

bug fixes and cleanup are the 'death by a thousand cuts' for most dev teams. i usually have to beg my engineers to prioritize tech debt over new features.

Ovren puts AI frontend and backend engineers on it - they work inside your real codebase, execute scoped tasks, and deliver reviewable code updates.

M2.7 demonstrates excellent identity preservation and emotional intelligence. Beyond productivity use cases, it also opens space for innovation in interactive entertainment scenarios.

On the SWE-Pro benchmark, M2.7 scores 56.22%, nearly matching Opus's best level.

M2.7 shows significant improvement in complex editing capabilities for Office Suite (Excel/PPT/Word), better handling multi-turn modifications and high-fidelity edits.

On GDPval-AA, M2.7 achieves an ELO score of 1495, the highest among open-source models.

M2.7 demonstrates excellent performance in real-world software engineering, including end-to-end project delivery, log analysis for bug hunting, code security, and machine learning tasks.

The only fundamentally scarce thing is the synchronous human attention of my team

The obsession with fast build times: why one minute became the upper bound for the inner loop

humans became the bottleneck, and how Ryan's team shifted from reviewing code directly to building systems, observability, and context that let agents review, fix, and merge work autonomously

building and shipping an internal beta product with zero manually written code

We shed light on OpenAI's first Dark Factory for the first time.

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ChatGPT has 900 million weekly users, which means employees already know how to work with it. For enterprises, that reduces rollout friction and accelerates the point where every employee can delegate tedious tasks.

The shift started with agentic tools like Codex, which has grown more than 5X since the start of the year. This includes customers like GitHub, Nextdoor, Notion, and Wonderful that are building multi-agent systems that can execute engineering work end-to-end.

Codex just hit 3 million weekly active users, our APIs process more than 15 billion tokens per minute, and GPT‑5.4 is driving record engagement across agentic workflows.

Enterprise now makes up more than 40% of our revenue, and is on track to reach parity with consumer by the end of 2026.

The goal is to build the trust, verification, and accountability needed to make these tools available to the many defenders whose work keeps people, institutions, and critical systems safe.

We have also provided access to GPT-5.4-Cyber to the U.S. Center for AI Standards and Innovation (CAISI) and the UK AI Security Institute (UK AISI) so that they can conduct evaluations focused on the model's cyber capabilities and safeguards.

Not every organization has the benefit of a 24x7 security team who is able to respond to incidents when they are disclosed on a Friday night.

Cybersecurity is a team sport, and the systems people rely on are protected by organizations of many kinds, from major enterprises and security vendors to researchers, maintainers, public institutions, nonprofits, and smaller teams with limited security resources.

或许需要某种「第三方评测、审计机构」来评估 Skills 的数据使用方式、检测潜在安全风险等等。

未来的评估体系，必须同时考虑：成功率、成本、延迟。这有点类似于对于云计算的考核标准，而不是传统软件。

信任从「平台」转移到了个体。其中一部分原因在于，Skills 的工作机制不透明，像个「黑箱」，用户只知输入输出，不知其分析指令、调用工具、做出决策的过程。

Skills 的传播没有像 App 那样可以靠搜索、靠排名。用户更加追求结果导向，而不是过程导向。

一个本该由「应用商店」承载的分发体系，现在却被内容平台接管了。

对视频生成来说，这种文字密集、变化快、带闪烁、又几乎没有自然动态的场景，本来就是最难的一类。

用户输入不再只是触发一次性行为，而会逐渐安装、调用、组合并保留可复用的 neural routines。

未来的 CNC 也许不是一团越来越大的连续表征，而会更像一套可路由、可组合、局部更容易检查的机器底座。

Neural Computer 真正成形，大概还要三年。

模型能不能承担一部分原本属于机器运行本身的职责。

It maintains 97% skill compliance across 40 complex skills on MM Claw, each skill exceeding 2,000 tokens.

The 66.6% medal rate on MLE Bench Lite, achieved autonomously over 24 hour windows, tells you something real about how this model behaves when you give it a hard problem and step back.

The license looks MIT at first glance but it is not MIT. Non commercial use is free with no restrictions. Commercial use requires prior written authorization from MiniMax.

MiniMax claims it has reduced live production incident recovery time to under three minutes on multiple occasions using M2.7.

The model kept finding better approaches the longer it ran, which connects directly to the long horizon behavior that makes agentic models actually useful in production.

MiniMax handed an internal version of M2.7 a programming scaffold and let it run unsupervised. Over 100 rounds it analyzed its own failures, modified its own code, ran evaluations, and decided what to keep and what to revert.

focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama

We document a clear trend where Chinese models overtook their counterparts built in the U.S.

that are the foundation of an ecosystem crucial to researchers, entrepreneurs, and policy advisors.

We study a mix of Hugging Face downloads and model derivatives, inference market share, performance metrics and more to make a comprehensive picture of the ecosystem.

We present a comprehensive adoption snapshot of the leading open language models and who is building them, focusing on the ~1.5K mainline open models

Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts.

scaling Muse Spark with multi-agent thinking enables superior performance with comparable latency.

Muse Spark compresses its reasoning to solve problems using significantly fewer tokens. After compressing, the model again extends its solutions to achieve stronger performance.

The model frequently identified scenarios as 'alignment traps' and reasoned that it should behave honestly because it was being evaluated.

we rebuilt our pretraining stack with improvements to model architecture, optimization, and data curation.

Contemplating mode provides significant capability improvements in challenging tasks, achieving 58% in Humanity's Last Exam and 38% in FrontierScience Research.

we can reach the same capabilities with over an order of magnitude less compute than our previous model, Llama 4 Maverick.

Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.

Open-source development is starting to redistribute participation, with contributions from the rest of the world now outpacing Europe and approaching the United States on GitHub.

73% of experts expect a positive impact on how people do their jobs, compared with just 23% of the public, a 50-point gap.

The United States leads in AI investment, but its ability to attract global talent is declining.

Responsible AI is not keeping pace with AI capability, with safety benchmarks lagging and incidents rising sharply.

AI models can win a gold medal at the International Mathematical Olympiad but cannot reliably tell time—an example of what researchers call the jagged frontier of AI.

The U.S.-China AI model performance gap has effectively closed.

AI capability is not plateauing. It is accelerating and reaching more people than ever.

Website: add animated workflow demos

Add GCP WebVoyager benchmark runner and worktree tooling

Don't destroy cloud sessions on transient CDP failures

Add screenshot-based LLM judge evaluator, screenshot collector, and --parallelize flag

Simplify benchmarks to webVoyager-only with Pi SDK runner

Add cloud browser provider system (Kernel + Browserbase)

The AI toolkit for building and maintaining browser automations

Lightweight Agent Detection & Response (ADR) layer for AI agents — guards commands, files, and web requests.

Sage sends URLs and package hashes to Gen Digital reputation APIs. File content, commands, and source code stay local.

Sage intercepts tool calls (Bash commands, URL fetches, file writes) via hook systems in Claude Code, Cursor / VS Code, OpenClaw, and OpenCode, and checks them against:

The organizations that get this right won't be the ones that just automated the most tasks. They'll be the ones that figured out when the human should act, when the agent should act, and how the handoff between them works.

They have pride in what they do... They won't let some AI bot take over, and they will always find and show the flaws in that tool compared to them.

Workers lose the equivalent of 51 working days per year to technology friction — nearly two full months — up 42% from 2025.

Only 9% of workers trust AI for complex, business-critical decisions, compared to 61% of executives — a 52-point trust chasm.

White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates

Academic publishers, documentary archives, game studios, and companies sitting on years of enterprise data have all been courted for the seeds of intelligence needed to train the next generation of models.

Mercor, which provides data to AI labs for training, became one of the fastest-growing companies in history before losing four terabytes of data to hackers last week.

While some experts have speculated that general models will win out in performance over specialized models—that scale and compute will beat curation—the success of these companies shows that the market is making a more nuanced bet.

A small model trained on fewer than 2,000 examples from real lawyers, bankers, and consultants recently beat all but the best frontier models on corporate legal work, at a fraction of the price.

Reddit, Shutterstock, and News Corp are making hundreds of millions a year licensing their high-quality data to companies training AI, and those contracts are growing about 20 percent annually, according to their quarterly filings.

We calculate the aggregate amount of compute (in H100-equivalents) held by Amazon, Google, Meta, Microsoft, and Oracle, as a share of the global total each quarter.

Five hyperscalers now own over two-thirds of global AI compute

Our Chip Ownership data does not capture all global chip ownership, and has weaker coverage prior to 2023.

The H100-equivalent unit uses a chip's highest 8-bit operation/second specifications to convert between chips. The actual utility of a particular chip depend on workload assumptions, so H100e does not perfectly reflect real-world performance differences across chip types.

Many AI labs (including OpenAI and Anthropic) largely depend on these hyperscalers for access to R&D and inference compute.

Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world's cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power.

Four researchers and software engineers estimated that a skilled human engineer would take 2 to 17 weeks to reimplement gotree, as AI successfully did in this work.

We found weak evidence that Opus 4.0 and 4.1 had partially memorized cal, but no evidence Opus 4.6 had memorized it, despite performing best of all models considered.

It is not common for real software to be developed the way MirrorCode tasks are structured — against a precise, programmatically checkable specification.

Older models were more prone to submitting prematurely, even when test cases weren't passing.

We see continued gains from inference scaling on larger projects, suggesting they may be solvable given enough tokens.

Claude Opus 4.6 autonomously reimplemented a 16,000-line bioinformatics toolkit — a task we believe would take a human engineer weeks.

We did not collect detailed examples of specific tasks, but these results provide an early, nationally representative snapshot of how AI is reshaping work at the task level.

While most people still use AI mainly for personal tasks, about half of employed users use it at least as much for work. This share is even higher among those with paid tools, particularly when provided by employers.

Microsoft Copilot, which leads paid AI usage among both work-oriented and personal-oriented users, illustrates this dynamic: its prevalence likely reflects bundling with Microsoft 365, a product widely deployed in workplaces through enterprise licensing.

Among employed AI users, 38% of free-tier users reported using AI at least as much for work as for personal tasks. The share rises to 58% among self-paying subscribers and 76% among users with employer-provided subscriptions.

It has replaced existing tasks for 27% of employed AI work users and created new ones for 21%.

Half of employed Americans who used AI in the past week reported using AI tools at least as much for work as for personal tasks.

By default, keys generated in Google AI Studio are restricted to just the Gemini API, no other services are enabled.

In many cases, we can automatically detect when a key is visible on the public web and shut down those keys automatically for security reasons

We just started the prepaid billing rollout which means you have to pay ahead of time to use the Gemini API, this is rolled out to all new US billing accounts as of yesterday

We are moving to disable the usage of unrestricted API keys in the Gemini API, should have more updates there soon.

We had a budget alert (€80) and a cost anomaly alert, both of which triggered with a delay of a few hours

We experienced a sudden and extreme spike in Gemini API usage. The traffic was not correlated with our actual users and appeared to be automated.

Google spent over a decade telling developers that Google API keys (like those used in Maps, Firebase, etc.) are not secrets. But that's no longer true.

Gemini Robotics-ER 1.6 can use points as intermediate steps to reason about more complex tasks. For example, it can use points to count items in an image, or to identify salient points on an image to help the model perform mathematical operations to improve its metric estimations.

Capabilities like instrument reading and more reliable task reasoning will enable Spot to see, understand, and react to real-world challenges completely autonomously.

On these tasks, our Gemini Robotics-ER models improve over baseline Gemini 3.0 Flash performance (+6% in text, +10% in video) in perceiving injury risks accurately.

Safety is integrated into every level of our embodied reasoning models. Gemini Robotics-ER 1.6 is our safest robotics model to date, demonstrating superior compliance with Gemini safety policies on adversarial spatial reasoning tasks compared to all previous generations.

Gemini Robotics-ER 1.6 achieves its highly accurate instrument readings by using agentic vision, which combines visual reasoning with code execution. The model takes intermediate steps: first zooming into an image to get a better read of small details in a gauge, then using pointing and code execution to estimate proportions and intervals and get an accurate reading.

We are also unlocking a new capability: instrument reading, enabling robots to read complex gauges and sight glasses — a use case we discovered through close collaboration with our partner, Boston Dynamics.

Gemini Robotics-ER 1.6 shows significant improvement over both Gemini Robotics-ER 1.5 and Gemini 3.0 Flash, specifically enhancing spatial and physical reasoning capabilities such as pointing, counting, and success detection.

I just hope the industry doesn't abandon the Model Context Protocol. The dream of seamless AI integration relies on standardized interfaces, not a fractured landscape of hacky CLIs.

The result is a Skill that acts as a cheat sheet for the MCP, not a replacement for it. The MCP still handles the actual connection and tool execution. The Skill just makes sure the LLM doesn't waste tokens stumbling through the same pitfalls I already solved.

Shower thought: Maybe the terminology is the problem. Skills should just be called `LLM_MANUAL.md`, and MCPs should be called `Connectors`.

Most skills require you to install a dedicated CLI. But what if you aren't in a local terminal? ChatGPT can't run CLIs. Neither can Perplexity or the standard web version of Claude.

The core philosophy of MCP is simple: it's an API abstraction. The LLM doesn't need to understand the _how_; it just needs to know the _what_.

CSS Studio uses Motion to generate real CSS springs with the perfect feel.

Add elements, rename tags, reorder with drag-and-drop, duplicate and delete. Double-click text to edit inline.

CSS Studio detects the CSS variables available on an element. Edit a variable and watch it propagate across the site.

Scrub through CSS `@keyframes` animations on a visual timeline.

Send your changes to a local AI agent that finds the right files and applies your edits, no matter how your site was built.

Your AI agent writes every change into source code.

Design by hand. Code by agent.

Each run creates a new session alongside your other sessions, where you can see what Claude did, review changes, and create a pull request.

The prompt is the most important part: the routine runs autonomously, so the prompt must be self-contained and explicit about what to do and what success looks like.

Routines run autonomously as full Claude Code cloud sessions: there is no permission-mode picker and no approval prompts during a run.

A single routine can combine triggers. For example, a PR review routine can run nightly, trigger from a deploy script, and also react to every new PR.

Routines execute on Anthropic-managed cloud infrastructure, so they keep working when your laptop is closed.

A routine is a saved Claude Code configuration: a prompt, one or more repositories, and a set of connectors, packaged once and run automatically.

Routines are in research preview. Behavior, limits, and the API surface may change.

While our production codebase has significantly diverged, including major rewrites of core systems like authentication and data handling, we want to ensure there is still a truly open version available.

Each platform surfaces different vulnerabilities, making it difficult to establish a single, reliable source of truth for what is actually secure.

We hope that one day we can return to open source as the security landscape evolves. But for now, we have to put our customers first.

The risk landscape is accelerating quickly. Advanced AI models are now capable of identifying and exploiting vulnerabilities at unprecedented speed.

AI uncovered a 27-year-old vulnerability in the BSD kernel, one of the most widely used and security-focused open source projects, and generated working exploits in a matter of hours.

Being open source is increasingly like giving attackers the blueprints to the vault. When the structure is fully visible, it becomes much easier to identify weaknesses and exploit them.

AI can be pointed at an open source codebase and systematically scan it for vulnerabilities.

The standard autoresearch loop (brainstorm from code, run experiments, check metrics) works when the optimization surface is visible in the source. The Liquid results prove that. But for problems where the codebase doesn't contain enough information to generate good hypotheses, giving the agent access to papers and competing implementations changes what it tries.

The variance is also worth noting: baseline+FA TG has ±19 t/s of noise, while optimized+FA has ±0.59 t/s on x86. The fusions eliminate intermediate writes that pollute the cache, making the hot paths more predictable.

Without experience with compiler behavior, the agent couldn't have predicted which 'optimizations' the compiler would already handle.

A 606 MiB model at ~49 tokens/s consumes ~30 GB/s of memory bandwidth, close to the c6i.2xlarge's DRAM limit. No amount of SIMD tricks will help when the CPU is stalled waiting for model weights to arrive from DRAM.

Studying forks and other backends was more productive than searching arxiv. ik_llama.cpp and the CUDA backend directly informed two of the five final optimizations.

Coding agents working from code alone generate shallow hypotheses. Adding a research phase — arxiv papers, competing forks, other backends — produced 5 kernel fusions that made llama.cpp CPU inference 15% faster.

The macOS app is available to Gemini users ages 13+

We're building the foundation for a truly personal, proactive and powerful desktop assistant, with more news to share in the coming months.