All of this runs at Zed's famously buttery-smooth 120 fps

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What he does have is a ChatGPT Pro subscription, which gives him access to the latest large language models from OpenAI.

I had the intuition that these problems were kind of clustered together and they had some kind of unifying feel to them. And this new method is really confirming that intuition.

I had the intuition that these problems were kind of clustered together and they had some kind of unifying feel to them. And this new method is really confirming that intuition.

run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025

Amazon is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future

committing more than $100 billion over the next ten years to AWS technologies

over one million Trainium2 chips to train and serve Claude

up to 5 gigawatts (GW) of capacity for training and deploying Claude

Amazon is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future

committing more than $100 billion over the next ten years to AWS technologies

over one million Trainium2 chips to train and serve Claude

over 100,000 customers now run Claude on Amazon Bedrock

up to 5 gigawatts (GW) of capacity for training and deploying Claude

over one million Trainium2 chips to train and serve Claude

run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025

Amazon is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future

over 100,000 customers now run Claude on Amazon Bedrock

up to 5 gigawatts (GW) of capacity for training and deploying Claude

This card was updated on April 24, 2026, to include additional information about safeguards for the deployment of GPT‑5.5 and GPT‑5.5 Pro in the API.

Our professionals are using Codex to move from static requirements to working solutions in hours, not weeks. It's enabling rapid prototyping, real-time workflow redesign, and faster iteration across the development lifecycle.

In early April, we shared that more than 3 million developers were using Codex every week. Just two weeks later, that number has grown to more than 4 million.

Accepted applicants and collaborators must have existing ChatGPT accounts to apply

Once selected, successful applicants will be onboarded to the bio bug bounty platform

🔹 **Rich World Knowledge:** Leads all current open models, trailing only Gemini-3.1-Pro.

🔹 **Enhanced Agentic Capabilities:** Open-source SOTA in Agentic Coding benchmarks.

⚠️ Note: deepseek-chat & deepseek-reasoner will be fully retired and inaccessible after Jul 24th, 2026, 15:59 (UTC Time).

Ubuntu 26.04 LTS is the first LTS to expand the number of memory safe system components. In practice, this means new kernel drivers and subsystems written in Rust, as well as `sudo-rs` and `uutils``coreutils` bringing memory-safe reimplementations of foundational system tools such as `sudo`, `ls`, `cp`, and `mv`.

IgH Master driver brings microsecond-level timing precision natively into the OS, removing a significant integration burden for engineers building motion control systems, robotics platforms, or complex factory automation.

Ubuntu powers millions of PCs and laptops around the world.

The 11th long-term supported release of Ubuntu delivers deep silicon optimization and state-of-the-art security for enterprise workloads.

_Self-reported score with custom Anthropic scaffold._ SWEPro were evaluated with the mini-swe-agent scaffold. However, we use the scores reported by Anthropic for Opus with the max thinking efforts due to frequent timeouts during our evaluation trials.

A core conviction at Sakana AI is that the most capable AI systems will not be monolithic models scaled in isolation, but collections of specialized agents working together.

Sakana Fugu coordinates pools of frontier foundation models to achieve state-of-the-art performance across coding, mathematics, scientific reasoning, etc.

We examine whether AI capabilities are accelerating by fitting statistical models to benchmark performance over time, and comparing their predictive accuracies.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

We have been calling this the 'reasoning' / 'non-reasoning' split, but this is not a perfectly clean dichotomy. Several correlated but not strictly identical changes happened over the same few months: scaling inference compute, heavier use of RL in post-training, and models producing reasoning tokens.

The best-performing model across these three metrics was a pair of independent linear trends: one for reasoning models and one for non-reasoning models.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

Several correlated but not strictly identical changes happened over the same few months: scaling inference compute, heavier use of RL in post-training, and models producing reasoning tokens.

The three metrics where we find acceleration are concentrated in programming and mathematics. These are areas that labs have explicitly targeted for improvement, and they share an important property: correctness is easy to verify automatically.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

We pre-selected the 6-month horizon as our primary metric, balancing genuine forecasting distance against the limited date range of our data.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

Our fourth metric, an index constructed from WeirdML V2 results, showed no sign of acceleration. A single global linear trend fit the data best.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

The three metrics where we find acceleration are concentrated in programming and mathematics.

We select the median-difficulty question from the set with maximum model coverage and standardize it to 0.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

Design decisions include the system prompt, tool choice, and execution flow.

The Prompt API for the web is still being developed. While we build this API, refer to our best practices on session management for optimal performance.

OpenAI can now serve all its products to customers across any cloud provider.

I would put venture capitalist in finite demand & open loop. There's only a certain amount of venture capital dollars entering the ecosystem in a year, & investment selection remains an open problem.

Closed Loop + Infinite Demand = Economic Engines. Software engineering lives here. AI writes the code. Tests verify correctness. More code enables more features. Companies will always need more software.

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 (spoiler: it won't.)

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

The question it forces is not which model is best. It is who owns the inference layer your organization depends on, what happens when the economics of that layer stop being subsidized, and whether the thing in your pocket turns out to matter more than the thing in the datacenter.

The structural cost problem in AI inference that makes Apple's on-device bet defensible, not just defensive.

The board looked at the AI race Apple was losing and, rather than try harder at the thing that was failing, changed which game the company plays.

For a company that spent fifteen years running a functional model where no single discipline owned a product, putting two hardware engineers at the top is not a personnel decision. It is a structural break.

benchmarks sourced from publicly available material carry contamination risk, where training-data exposure can silently inflate scores.

Tests reject correct solutions: We audited a 27.6% subset of the dataset that models often failed to solve and found that at least 59.4% of the audited problems have flawed test cases that reject functionally correct submissions

Kimi K2.6 demonstrates significant improvements over Kimi K2.5 in internal evaluations conducted by CodeBuddy: code generation accuracy increased by 12%, long-context stability improved by 18%, and tool invocation success rate reached 96.60%.

The layoffs will start on May 20, the company confirmed.

Meta plans to lay off roughly 8,000 employees, or 10% of its workforce

Claude packages everything into a handoff bundle that you can pass to Claude Code with a single instruction.

Claude Design gives designers room to explore widely and everyone else a way to produce visual work.

Our most complex pages, which took 20+ prompts to recreate in other tools, only required 2 prompts in Claude Design.

GPT‑5.5 found a proof of a longstanding asymptotic fact about off-diagonal Ramsey numbers, later verified in Lean. The result is a concrete example of GPT‑5.5 contributing not just code or explanation, but a surprising and useful mathematical argument in a core research area.

GPT‑5.5 found a proof of a longstanding asymptotic fact about off-diagonal Ramsey numbers, later verified in Lean. The result is a concrete example of GPT‑5.5 contributing not just code or explanation, but a surprising and useful mathematical argument in a core research area.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

Drug manufacturers pay pharmacy benefit managers rebates above 50% of list price for formulary access.

Interestingly, this means that prompts written for earlier models can sometimes now produce unexpected results: where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally.

In our own testing, the net effect is favorable—token usage across all effort levels is improved on an internal coding evaluation, as shown below—but we recommend measuring the difference on real traffic.

Claude Opus 4.7 feels like a real step up in intelligence. Code quality is noticeably improved, it's cutting out the meaningless wrapper functions and fallback scaffolding that used to pile up, and fixes its own code as it goes.

For the computer-use work that sits at the heart of XBOW's autonomous penetration testing, the new Claude Opus 4.7 is a step change: 98.5% on our visual-acuity benchmark versus 54.5% for Opus 4.6.

For complex multi-step workflows, Claude Opus 4.7 is a clear step up: plus 14% over Opus 4.6 at fewer tokens and a third of the tool errors. It's the first model to pass our implicit-need tests.

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.

lack of a well-defined user agent role in AI that's backed up by transparent, public standards... leaves a gap – it makes it harder for a marketplace to form.

Every time you use an Internet-connected computer, you're trusting someone (and most likely, a multitude) to act on your behalf.

The fix is not smarter prompts. It is software built to meet agents halfway.

memory-driven experience scaling represents a crucial new frontier for agent scaling

simple checklists evolved into memories with compositional, preventative logic structures

existing TTS methods often discard the exploration trajectory and treat the final answer as the only useful outcome

by over-emphasizing successful experiences, they miss out on a primary source of learning — their own failures

by recording detailed actions instead of tactical foresight, they fail to distill higher-level, transferable reasoning patterns

But plenty of categories survived through specialization or direct competition : cloud, travel, domain registration, social networking. Commoditizing complements doesn't always work because focus is scarce even for the largest, fastest growing businesses.

The intelligence lives in the static parameters, and the apparent capabilities change radically depending on what you feed into the window.

The irony is that the very mechanism that makes LLMs powerful during training (e.g. compressing raw data into compact, transferable representations) is exactly what we refuse to let them do after deployment.

Large language models live in a similar perpetual present. They emerge from training with vast knowledge frozen into their parameters but they cannot form new memories – cannot update their parameters in response to new experience.

if AI models can answer these questions well (that is, accurately and impartially), they can be a positive force for the democratic process.

Some attacks only fired after 50 prior calls. Others activated only in auto-approve mode.

Duplicate section heading | Error; reject the file

The DESIGN.md format is at version `alpha`. The spec, token schema, and CLI are under active development. Expect changes to the format as it matures.

Components map a name to a group of sub-token properties: ... Variants (hover, active, pressed) are expressed as separate component entries with a related key name.

Unknown section heading | Preserve; do not error

A DESIGN.md file combines machine-readable design tokens (YAML front matter) with human-readable design rationale (markdown prose). Tokens give agents exact values. Prose tells them _why_ those values exist and how to apply them.

其中 Pattern 是最容易被忽略也最关键的一层，它定义了'在具体业务场景下该怎么组合这些组件'，是 AI 时代设计系统真正的价值所在。

We have applied to list our Class A common stock on the Nasdaq Global Select Market under the symbol 'CBRS,' and this offering is contingent upon the listing of our Class A common stock on the Nasdaq Global Select Market.

It happens several times a year in the US alone, often unreported, and about 100 times a year worldwide.

Nor does it matter, given that the modifying strains for pathogens for research purposes is what every research lab does, because that is what virology is.

This is the part people miss about AI-native companies - the $113k is not a cost, it is your headcount budget allocated differently.

If the most effective way to communicate something were a single word, an illustration, or a photorealistic rendering, that's what you'd see.

The screen you're reading this on is already presenting you an image, it's just generated with rigid code and rules that makes it difficult to communicate complex and detailed ideas.

All text on the screen is rendered as pixels by the image model. There are no text overlays applied to the images.

The entire web is just generated pixels on your screen.

the initial access occurred after a Vercel employee's Google Workspace account was compromised via a breach at the AI platform Context.ai.

The structural failure is not the war: It is that the global memory supply chain has built itself around a conversion chokepoint with no redundancy and no fallback.

Emissions in advanced economies grew faster (+0.5%) than in emerging and developing economies (+0.3%) for the first time since the 1990s.

The time when we suffer from large amounts of AI slop is gone. Now we instead suffer under a massive load of good reports.

SWE-chat is a living dataset; our collection pipeline automatically and continually discovers and processes sessions from public repositories

The central question is not whether AI can imitate human conversation, but whether it can participate in the production of publishable scientific knowledge at a level comparable to a recognized human contributor.

The most effective pattern of human-AI cooperation may differ substantially across disciplines, and these patterns will likely be discovered through practice rather than designed in advance.

Its Self Evolution Protocol Layer (SEPL) specifies a closed loop operator interface for proposing, assessing, and committing improvements with auditable lineage and rollback.

We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs.

existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code.

The results demonstrate consistent improvements over strong baselines, supporting the effectiveness of agent resource management and closed loop self evolution.

Its Self Evolution Protocol Layer (SEPL) specifies a closed loop operator interface for proposing, assessing, and committing improvements with auditable lineage and rollback.

We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs.

However, existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code.

A chatbot responds in the moment or not at all. An agent thinks, acts, and communicates on its own timeline.

Email is the most accessible interface in the world. It is ubiquitous. There's no need for a custom chat application, no custom SDK for each channel.

Ebbinghaus Adaptive Forgetting with lifecycle-aware quantization -- the first mathematical forgetting curve in local agent memory coupled to progressive embedding compression, achieving 6.7x discriminative power.

For Max-plan users hitting rate limits instead of dollars: your 5-hour window ends sooner by roughly the same ratio on English-heavy work. A session that ran the full window on 4.6 probably doesn't on 4.7.

The real-world weighted ratio (1.325x) lands near the top of their range. Individual file types exceed it — CLAUDE.md at 1.445x, technical docs at 1.473x. That's the useful finding: the top of the documented range is where most Claude Code content sits, not the middle.

Same sticker price. Same quota. More tokens per prompt. Your Max window burns through faster. Your cached prefix costs more per turn. Your rate limit hits sooner.

The extra tokens bought something measurable. +5pp on strict instruction-following. Small. Real. So: is that worth 1.3–1.45x more tokens per prompt?

Training looped models is notoriously unstable. Two failure modes dominate: Residual explosion — the hidden state h_t grows unboundedly across loops; Loss spikes — training diverges suddenly due to large spectral norms in injection parameters.

The real bottleneck in AI right now is not compute but rather data quality

frontier AI models are not too big because the technology is complex and too big because the training data is garbage

See updates to the core system prompts on claude.ai and the Claude iOS and Android apps.

Think of multi-agent systems as the new assembly lines. Henry Ford's innovation upended entire industries last century. In theory, networks of AI agents could do to white-collar knowledge work what assembly lines did to manufacturing.

The real issue is not whether defenders can get access to another model. It is whether they can turn model capability into something a security team can trust and use every day.

The takeaway is not whether Mythos is better or more powerful. It is that public models can already achieve much the same results.

The real challenge is validating outputs, prioritizing what matters, and operationalizing them.

The immediate danger is not that machines will act without human oversight; it is that human overseers have no idea what the machines are actually 'thinking.'

If people ask Claude to give a simple yes or no answer... Claude can decline to offer the short response

Claude calls tool_search to check whether a relevant tool is available but deferred

the move from pattern matching to understanding cause and effect

transformers update their predictions in a precise, mathematically predictable way as they process new information

LLMs take knowledge from millions of people who have written web content or posted in places like Reddit and Wikipedia, interacted with chatbots, and generated other types of data, and make that available to individuals on demand.

Members have been using Mythos regularly since gaining access — providing screenshots and a live demonstration of the model as evidence to _Bloomberg_ — though reportedly not for cybersecurity purposes in an attempt to avoid detection by Anthropic.

TPU 8i is designed with more memory bandwidth to serve the most latency-sensitive inference workloads, which is critical because interactions between agents at scale magnify even small inefficiencies.

Both chips can run various workloads, but specialization unlocks significant efficiencies and gains.

By customizing and co-designing silicon with hardware, networking and software, including model architecture and application requirements, we can deliver dramatically more power efficiency and absolute performance.

TypeScript 7.0 now performs many steps in parallel, including parsing, type-checking, and emitting.

Meta is not alone in pursuing such a vision: Anthropic debuted tech capable of doing this [in 2024] and OpenAI last year announced [“Operator”] – a tool that can use a web browser on a human’s behalf.

Among the latest frontier models, GPT-5.4 over-edits the most.

Chinese labs, for their part, are not purely idealistic: Open-source is not only free advertising but also a shrewd workaround. Without access to cutting-edge chips restricted by US export controls, releasing models openly accelerates the cycle of external feedback and contributions that compensates for constrained compute.

Chinese open-weight models accounted for 17.1% of global AI model downloads over the year ending in August 2025. That narrowly surpassed the US share of 15.86%—the first time China had led in this metric.

Telling people to avoid using generative AI is increasingly telling them they must avoid taking part in society.

Is this what we signed up for? Is today the day? Did the drones wake up? Did it achieve consciousness? Is it alive?

This piece will primarily focus on data context that ties together traditional systems of record. An equally important and overlapping opportunity is also capturing an organization's decisions and workflow logic so truly multipurpose agents can be built that are properly grounded in all of an organization's data and decisioning context.

The modern data stack has undergone a decade+ transition from disparate data sources to consolidated data and cleaned definitions (which is good), but even then the consolidation is never perfect and a lot of messiness is introduced.

10

，必须要OCR文本辅助"。但图片模糊的话，OCR的识别结果也会差。

You start with $1,000 cash, 0 YES, and 0 NO. Minting one YES and one NO costs $1.

The diffusion term is fixed across all simulations. The regime-level variation comes entirely from the jump parameters - intensity, mean, and variance - which are randomized per simulation.

Performance was compared against 57 historical scores from human experts in the AI-bio field.

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

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.

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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

从视频生成器升级为导演工具套件

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

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

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.

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.