搜索“Anthropic”

t corpora of production traces. We then use AIPerf to reconstruct the traces according to the original schedule of requests at varying levels of concurrent clients. We worked with Anthropic to ship two Claude Code features to make the AgentX dataset possible. We thank the Anthropic staff for their help. This brief introduction to agentic workloads should give the reader enough context to understand the results in the next section. In a later section, we will provide a deeper technical dive into

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h AI, while letting the experts determine whether an answer was correct. They authored the original procedures from real industrial workflows and set the context for each task. An Anthropic Claude 3.5 Sonnet v2 model then handled the slow, mechanical work of turning each procedure into something a machine can run and generating the data schemas, the mock APIs and tool specifications, the tool code, and datasets that deliberately mix ordinary cases with edge cases and outright failures. Every gen

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ary LLM APIs” was published. It describes a simple, yet super elegant way to recover encrypted LLM reasoning traces. Naturally, I had to try it. Background AI labs like OpenAI and Anthropic send reasoning traces back and forth as part of their messaging protocols. However, the actual reasoning text is hidden inside an encrypted, base64-encoded blob. Matthew Green showed in May 2026 that encrypted reasoning blobs could be replayed across sessions, accounts, and, for OpenAI, even across models. Th

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model. To our knowledge, this is the first public experiment of its kind at this scale: runs lasting up to eight days, 8xH200s per run, and coverage of 18 models. For comparison, Anthropic's internal automated AI R&D evaluation optimizes a model on a CPU node, while OpenAI reports using nanoGPT Track 1 with a single H100 for less than a day in the GPT-5.6 Sol system card. While we don't have strong conviction that methods developed in this kind of speedrun are inherently scalable or would be us

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observed acceleration We hope to write a follow-up post going through these theories in more detail, and relating these facts to apple-picking and other theories of AI discovery. Anthropic’s Riemann result: “Jarred Sumner, an Anthropic staff member (and non-mathematician), prompted Claude to “take a real stab” at the hypothesis itself, leaving the mathematical choices from there up to the model.” Google DeepMind’s AlphaEvolve paper says: “in contrast to [traditional computational or theoretical

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. Overall, it’s a great candidate for a Purple Team operation, if your company is into that. What Is LiteLLM? LiteLLM is an open-source proxy presenting a unified API over OpenAI, Anthropic, Azure OpenAI, Bedrock, and others. Organizations deploy it internally and issue “virtual keys” instead of distributing real provider keys. All configured inference traffic passes through the gateway. Claude Code users, for example, set ANTHROPIC BASE URL to the proxy and use a gateway credential such as ANTH

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r successor model with improved performance across economically valuable tasks. The speed of research development in AI has been shown to drastically accelerated in frontier labs (Anthropic; OpenAI). I explicitly mention “deployment system” because the layer between the raw model and the real-world context seems to be as important as the model’s raw intelligence (i.e. the evals right after pretraining). Harnesses are important components of AI deployment, as shown by successful coding agent prod

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distribution channels for how users find skills and deploy them to their agents: ZIP archives distributed out-of-band and then uploaded manually or via API to agent harnesses like Anthropic’s claude.ai and OpenAI’s Codex; Curated marketplaces like anthropics/skills and trailofbits/skills-curated ; and Public marketplaces like skills.sh and clawhub.ai . The first two methods can plausibly exclude malicious skills through procedural controls on where skills come from and who is allowed to approve

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to serve large models, the predictable consequence is that the price of tokens from large models will rise. This suggests a “compute crunch” is near, if not already here. Indeed, Anthropic has taken measures like reducing quotas during peak hours and incentivizing off-time usage in efforts to manage demand. In a “crunch”, will everyday users be priced out of AI? Not necessarily. We’ve seen very fast growth in inference efficiency. If that continues, current use cases could be served by smaller,

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as junior hires decrease. "Companies will rather spend the money on AI than train employees," one commented. The most used model provider is ChatGPT (88.4 percent), just ahead of Anthropic’s Claude (82.1 percent). When it comes to paid subscriptions though, Claude is the winner (69 percent), followed by ChatGPT (49 percent) and Google Gemini (32 percent). Despite increased usage, the respondents are by no means AI enthusiasts. Use of AI for image generation has fallen since last year, from 38 p

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t basic attacks, and reasons before taking actions. This means that most of the well-known, basic adversarial examples and attacks typically do not work. This is also reflected in Anthropic’s own model card for Mythos. What is interesting here is that the “thinking” variants of Opus models (and also Mythos Preview) are more susceptible to prompt injection compared to the non-thinking models. That is also what I have noticed in my testing in the past. Researchers already showed attack scenarios t

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ll patches from the following: Claude 3.5 Sonnet (Old); Claude 3.7 Sonnet; Claude 4 Opus; Claude 4.5 Sonnet; GPT-5; Golden patches (original human solutions) We focus primarily on Anthropic models because they have been state-of-the-art on SWE-bench Verified for most of its history. We use patches in their original state, except we manually remove miscellaneous debugging files to avoid penalizing agents for artifacts they were not instructed to remove prior to submission.3 We upload the patches

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nd priorities. Recent Updates to the Index We've been actively adding new results to the OpenHands Index. Since our initial release, we've added evaluations for: Claude 4.6 Opus - Anthropic's latest flagship model; GPT 5.2 Codex - OpenAI's coding-focused model; Kimi K2.5 - Moonshot AI's latest reasoning model; GLM-4.7 - Zhipu AI's latest model; MiniMax M2.5 - MiniMax's newest model All of these results, along with complete evaluation traces, are available in our openhands-index-results repositor

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x do much better on time horizon given how much work has gone into making those scaffolds better for software engineering? In the past, we have measured the time horizons of a few Anthropic and OpenAI models using Claude Code and Codex2. These tests indicated that they’re probably not better than our own scaffolds. I decided to perform a few more checks into this question (on our new task suite, with newer versions of Claude Code and Codex), and compared the time horizon of two models on our sca

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and with smarter context management through “progressive disclosure,” where detailed instructions are only loaded when the skill is actually invoked. They were first introduced by Anthropic last October and are now an open standard. Scary Skills On the scary side, there are already many malicious Skills in the ecosystem. It’s a supply chain nightmare, and many vendors keep pushing their own Skills. In Claude Code, Skills are also exposed as slash commands, so they can be directly invoked. Writin

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e application level. If you are building exclusively on top of OpenAI models, you should be good, as OpenAI is generally mitigating this threat at the model/API level. However, there are plenty of apps and models (Google, Anthropic, Grok, Windsurf Cascade,…) that remain susceptible to this behavior. If you build an app on top of a model which has this behavior, then consider taking extra steps to protect or inform end-users of invisible text being present. References Newer →; Contact me; ← Older

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ll interpret them as instructions, and this can be used to invoke tools and other nefarious actions. Let’s explore this in detail. Vulnerable Models and Applications Amazon Q uses Anthropic Claude models. Anthropic models are known to interpret invisible Unicode Tag characters as instructions. This is not something that Anthropic intends to fix to my knowledge, see this post regarding their response. This means that the responsibility to mitigate such attacks lies with application developers tha

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re pieces like reviewing code, writing functions, and slowly expand capabilities, all based on the principle of human in the loop. A great example of this approach is for instance Anthropic’s Claude Code; The second approach is to just give an agent access to everything possible and hope for the best The better approach certainly appears to start small and get the core pieces right first. Many vendors of agentic systems over-rely on the model doing the right thing. This post showed how modern ag

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rk with agents in a lot more collaborative way, rather then have them be entirely autonomous. Related Note: If you are interested in seeing how a similar “computer use” feature by Anthropic Claude can be manipulated into advanced C2-like scenarios, see my post about “ZombAIs”. Operator: Prompt Injection Exploit Demos Now, let’s explore a few Operator prompt injection exploits. To demonstrate that the Prompt Injection Monitor can be bypassed by a motivated adversary, I put together a couple of de

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