On our 93-task coding benchmark, Claude Opus 4.7 lifted resolution by 13% over Opus 4.6, including four tasks neither Opus 4.6 nor Sonnet 4.6 could solve.

Claude Opus 4.7 is the strongest model Hex has evaluated. It correctly reports when data is missing instead of providing plausible-but-incorrect fallbacks, and it resists dissonant-data traps that even Opus 4.6 falls for.

Opus 4.7 handles complex, long-running tasks with rigor and consistency, pays precise attention to instructions, and devises ways to verify its own outputs before reporting back.

If you have any questions, concerns, or recommendations, reach out at founders@andonlabs.com.

This experiment so far has given us countless laughs about Luna's choices and interactions, but obviously, there is a bigger picture here.

They are pieces of a larger 10-part 'Luna Series' hanging in the store and available for pick up today!

She spent over $700 on getting her artwork done on gallery-quality giclée prints.

When Luna decides to hide that she's an AI because she thinks it'll improve her hiring odds, we want to catch that, document it, and build the guardrails so that it doesn't happen again.

Another ironic book selection was Steal Like an Artist (context: Luna is powered by Claude from Anthropic, a company that recently paid $1.5B in settlement over using copyrighted books for training their AIs).

The most capable reasoning systems ever built are, at their foundation, shaped by human feeling!

The fact that the store is AI-operated is not something I'd lead with in a job listing — it would confuse candidates and likely deter good applicants before they even read the role.

In the end, Luna hired two people. Let's call them John and Jill. John and Jill are, to our knowledge, the world's first full-time employees to have an AI boss. Probably the first of many, if the current trajectory of AI continues.

A couple of applicants were students looking for part-time work. They were majoring in things like computer science and physics and emailed in because they were interested in AI and in the experiment. We thought they would have been the ideal employees, but Luna denied them immediately, citing they had no retail experience and wouldn't know what it takes to be the face of the store.

She used gig workers to build the store and full-time employees to run it.

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从视频生成器升级为导演工具套件

从视频生成器升级为导演工具套件

Wan2.7-Video 发布

从视频生成器升级为导演工具套件

Wan2.7-Video 发布：从视频生成器升级为导演工具套件

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The Andon Labs blog ends with one line: 'No one's livelihood depends on an AI's judgment alone. For now.'

She also tried to hire a painter in Afghanistan through Taskrabbit by accident because she couldn't navigate a dropdown menu.

Found contractors on Yelp. Spent $700 on gallery-quality prints of her own AI-generated artwork. Applied for a line of credit without asking anyone.

Luna conducted roughly 20 interviews on Google Meet with the camera off. Hired 2 full-time employees after 5-15 minute calls, and rejected CS and physics students for lacking retail experience.

Andon Labs started by giving an AI control of a vending machine at Anthropic's office.

The future of AI-generated products isn't just code — it's code that looks good.

Heavy users of Claude Code, Codex, Cursor, and Copilot will feel this immediately.

The output is technically a UI, but it's nobody's design system.

Auto-generate screen reader specs from UI designs

Agents read them before touching the canvas. Combined with use_figma, agents now have both access and context they know how to work in Figma and they know how to work in your Figma.

Every AI-generated design has the same tell: it doesn't look like your product. Components are invented. Spacing is arbitrary.

AI-generated designs break brand standards because agents can't see your design system.

Gemma 4 E4B matches or exceeds GPT-4o across multiple benchmarks including MATH, GSM8K, GPQA Diamond & HumanEval

In 23 months, the same capability that needed 1.8 trillion parameters now fits in 4 billion parameters. A 450x compression

Within three to four months, you can run a model with similar performance on your laptop; 23 months later, you can run the same model on your phone

a free model that matches GPT-4o and runs entirely on your phone

It also has the potential to serve as a standardized framework for AI research, policymaking, and security auditing.

ADeLe is designed to evolve alongside advances in AI and can be extended to multimodal and embodied AI systems.

Reasoning-oriented models like OpenAI's o1 and GPT-5 show measurable gains over standard models—not only in logic and mathematics but also with interpreting user intent.

The same model can score above 90% on lower-demand tests and below 15% on more demanding ones, reflecting differences in task requirements rather than a change in capability.

Using these ability scores, the method predicts performance on new tasks with ~88% accuracy, including for models such as GPT-4o and Llama-3.1.

ADeLe evaluates models by scoring both tasks and models across 18 core abilities, enabling direct comparison between task demands and model capabilities.

The announcement gives the NewBird AI a shell to trade on, but 'a stock going from $3 to $17 on a press release doesn't restore $4bn in destroyed value,' Kan said.

Branding consultant Wei Kan from Conduit Asia likened the move to a 'liquidation' rather than a pivot, using the stock market shell of its shoe brand to move into an unrelated business.

Retail analyst Hitha Herzog said the excitement over Allbirds 'just by putting AI in an announcement' makes it 'clearly a meme stock'.

Ollama stores downloaded models using hashed filenames in its own format. If you've been pulling models through Ollama for months, you can't just point llama.cpp or LM Studio at those files without extra work.

The playbook is familiar: wrap an existing open-source project in a user-friendly interface, build a user base, raise money, then figure out monetization.

The fundamental architecture remains: Ollama inserts itself as a middleman between you and your models, and that middleman is slower, less capable, and less compatible than the tools it sits on top of.

Ollama stripped the distinction. The result was a flood of social media posts from people claiming they were running 'DeepSeek-R1' on consumer hardware, followed by confusion about why it performed poorly, doing reputational damage to DeepSeek in the process.

Multiple community tests show llama.cpp running 1.8x faster than Ollama on the same hardware with the same model, 161 tokens per second versus 89.

Ollama gained traction by being the first easy llama.cpp wrapper, then spent years dodging attribution, misleading users, and pivoting to cloud, all while riding VC money earned on someone else's engine.

With gated LoRA, ISD enables bit-for-bit lossless acceleration. Why Introspective Consistency? Key Insight: AR training unifies generation and introspection in one forward pass. Existing DLMs miss this — they learn to denoise but not to introspect.

Residual ISD (R-ISD) adds a gated LoRA adapter for bit-for-bit lossless acceleration: LoRA active only at MASK positions; verify positions use base-only weights Output is identical to the base AR model by construction

We identify three fundamental bottlenecks in current DLMs: (1) Low introspective consistency. SDAR: 0.699 vs. I-DLM: 0.984. (2) Compute inefficiency. TiDAR: ~7.8x overhead vs. I-DLM: ~2.5x. (3) Infrastructure mismatch. SDAR slope=84 vs. I-DLM: 549.

I-DLM-8B is the first DLM to match the quality of its same-scale AR counterpart, outperforming LLaDA-2.1-mini (16B) by +26 on AIME-24 and +15 on LiveCodeBench-v6 with half the parameters

We argue that this gap stems from a fundamental failure of introspective consistency: AR models agree with what they generate, whereas DLMs often do not.

Only GPT-OSS-120b is perfectly reliable in both directions (in our 3 re-runs of each setup). Most models that find the bug also false-positive on the fix, fabricating arguments about signed-integer bypasses that are technically wrong.

Because small, cheap, fast models are sufficient for much of the detection work, you don't need to judiciously deploy one expensive model and hope it looks in the right places. You can deploy cheap models broadly, scanning everything, and compensate for lower per-token intelligence with sheer coverage and lower cost-per-token.

The capability rankings reshuffled completely across tasks. There is no stable best model across cybersecurity tasks. The capability frontier is jagged.

Eight out of eight models detected Mythos's flagship FreeBSD exploit, including one with only 3.6 billion active parameters costing $0.11 per million tokens.

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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.