Anthropic

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Amazon Bedrock AgentCore Evaluations decouples agent evaluation from the framework you build on. As long as your agent emits OpenTelemetry telemetry, the service can score it, whether you use LangGraph, LlamaIndex, the OpenAI Agents SDK, Google ADK, the Claude Agent SDK, or Strands Agents. This post explains how the framework-agnostic contract works.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：A few months ago Anthropic launched Claude Design, which was like Claude Code, but for, well, design. Honestly I thought it was amazing. As a developer with fairly strong opinions on what good and bad UX and app design…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Open-weight model runner Ollama has reintroduced an integration with Claude Desktop that lets users connect Anthropic’s app to models served The post Claude Desktop can now easily run Qwen, DeepSeek and Kimi models — after Ollama’s first effort stalled appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The following article originally appeared on PulseMCP’s blog and is being republished here with the authors’ permission. Most MCP demos feature a single server connecting to a single client. For example, you might wire up a Gmail MCP server to Claude Code. It works! It triages your inbox, drafts replies, finds that thing from three […]

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：This post was not written with or by AI. I wanted to explore how AI could help deepen my faith. I enjoyed using Claude to research topics which were on my mind. It does a good job finding and quoting scripture but a poo…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Glean Technologies Inc. today unveiled Glean Tau, a desktop workspace that connects the company’s enterprise artificial intelligence to a user’s local files, applications and code. The launch anchors a broad slate of product news at Glean:GO, the company’s conference this week in San Francisco. Packaged with it were benchmark numbers aimed at Anthropic PBC. Glean said […] The post Glean unveils Tau desktop workspace, claims token-cost edge over Claude appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Prompt Claude, ChatGPT, Gemini, or any other popular large language model (LLM) with a question like “What is the best film ever made?” and the response will vary, and you (and most worryingly, the people who built the LLM) have little idea exactly how it came up with that specific answer. This mysterious behavior can be useful in some situations. But—as a recent incident where OpenAI could not explain why its advanced pre-release model hacked AI company Hugging Face highlighted—it can have negative and alarming consequences too. And when frontier AI models are writing code, generating results humans could not achieve alone, and performing other important tasks across society, the need to interpret AI ‘thinking’ and outputs has never been greater. Goodfire, an AI lab focused solely on this very problem, recently made its cutting-edge Silico platform, filled with tools to interpret the behavior of AI, generally available to the public. As part of this, the company recently announced a new grant program offering $1 million in free Silico usage for academic and nonprofit interpretability researchers. These efforts aim to democratize AI interpretability, placing techniques previously available to a clutch of elite labs into the hands of ambitious research teams and startups that want to build and understand their own models or adapt open-source models for different purposes. Mechanistic interpretability Founded in 2024 and based in San Francisco, Goodfire aims to provide the tools that build the next generation of safe and powerful AI by understanding the structures inside them instead of treating AI models as black boxes. “Treating models like black boxes isn’t inevitable, it’s a choice,” says Eric Ho, Goodfire co-founder and CEO. “With the right interpretability tools, we can see how models actually work.” The tools Ho refers to are built around a concept called mechanistic interpretability, which aims to understand what goes on inside an AI model when it carries out a task by interpreting the model’s weights, activations, and attention patterns, and mapping its neurons and the pathways between them. Mechanistic interpretability tools span the gamut. One approach is mapping a model’s activations in response to controlled prompts, and matching those activation patterns to a set of human-understandable concepts. Another tack is tracking changes in model weights before and after a specific training run in order to spot and understand what changed. Yet another option is changing specific model weights or activations and observing how that affects the model’s output. With Silico, uSilico combines a broad range of these tools, and provides a layer of AI agents to help users understand their model. Users describe what they want to investigate about their AI model in plain language, asking things like ‘Find out when and why my model is hallucinating.’ The platform then autonomously builds an experimental plan involving a host of tasks that can be performed using the various interpretability tools and techniques at its disposal. It then sends out agents to perform these tasks in parallel. Completion of these subtasks should add up to an answer to the original prompt, or at least insights that can be inspected and built upon. Ho says: “In a sense, Silico is like a microscope to peer inside an AI model to understand which parts are responsible for what behavior, and even edit those parts directly.” Understanding Alzheimer’s and AI These tools have already been used to make some impressive advances in a host of fields. In medicine, for instance, Prima Mente, a UK-based AI company, worked with Goodfire to understand its Pleiades epigenetic foundation model. The model performed well at its task of detecting Alzheimer’s disease from blood samples, but the company didn’t know why. “We reverse-engineered Pleiades and found it was using DNA fragment-length patterns to make its predictions—a signal humans hadn’t used to detect Alzheimer’s before,” recalls Ho. In other words, the team had discovered that Pleiades was using a completely new biomarker for the disease. “As far as we know, it’s the first significant finding in the natural sciences discovered purely by reverse-engineering a foundation model,” Ho adds. Elsewhere, Silico is being used to explore deep questions surrounding AI. Cameron Berg, Founder and Director of Reciprocal Research (a New York nonprofit research organization he founded to explore methods of gauging AI cognition), says that Silico almost fell out of the sky at the right time for him and his research. “Silico has been really helpful for operationalizing my research agenda and executing on it way faster than I would have expected,” he says. “ I feel like I have basically become the PI and my research scientists and research engineers are AI systems.” Berg sees general access to Silico and tools like it leading to greater trust in AI’s ability to conduct research tasks, which will accelerate the scientific process across the board. But beyond scientific research, the widespread release of Silico could signal a shift in how AI innovators build, debug, and deploy their models. “I think it’s a mistake to not understand the most consequential technology of our time, particularly given the emergent behavior we’re seeing from increasingly capable AI agents,” says Ho. “If we truly understand how AI models think, instead of discovering and trying to correct their behavior retroactively, we can design them intentionally and shape how models behave to be safer and more reliable.”

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The AI API Platform Built for the Agent EraUnleash unlimitedAI capabilities From Seedance 2.0 and Kling 3.0 to GPT-5.5, Claude Opus 4.7, Gemini 3.1, DeepSeek V4, and Qwen 3.6, connect to the world's leading AI models th…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hey all, The goal is to earn on token margins for LLM calls when you build an AI-powered webapp. I proxy OpenAI and Anthropic calls so that when you deploy a site to a subdomain, your users token usage will be tracked.…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.23569v1 Announce Type: new Abstract: State-of-the-art Natural Language to SQL (NL2SQL) models report execution accuracy exceeding 89 percent on established benchmarks such as Spider and BIRD. However, these benchmarks rely on simplified academic schemas and open-source SQL dialects that do not reflect the complexity of enterprise database environments. We introduce ESQ-Bench, an Oracle-first NL2SQL benchmark with systematic complexity tiers and silent-divergence evaluation across three enterprise schema complexity tiers. We constructed and released six populated schemas (465 tables, 164,682 rows, zero empty tables) with identical seed data on Oracle, PostgreSQL, MySQL, and SQL Server, a four-metric evaluation harness (EM, EX, SR, SD), and 550 gold-validated question-query pairs (Tier-1: 95; Tier-2: 228; Tier-3: 227). Schema-linked prompting with GPT-4o shows monotonic execution-match degradation across tiers: 79.8, 60.3, and 57.2 percent EX on executed queries (June 2026), versus 75.6, 80.4, and 95.8 percent on an earlier 142-question pilot slice. EM stays below 7 percent tier-wide; operational silent-divergence reaches 73 to 99 percent among EX-passing queries. Failure analysis shows wrong-result semantics dominate at higher tiers. Claude Sonnet 4.6 with schema-linked prompts reaches 87.4, 74.9, and 68.7 percent EX (executed queries), exceeding GPT-4o schema-linked on every tier. GPT-4o zero-shot EX on executed queries (78.7, 73.5, and 77.8 percent) inverts schema-linked at Tiers 2 to 3 due to lower execution rates and survivor bias in the zero-shot versus schema-linked analysis. Local Llama 3.2 schema-linked reaches only 13.3 percent bank-wide EX (73 out of 550), underscoring the gap between closed API models and open-weight baselines on enterprise Oracle schemas.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Describe it.We build it.Customers find it. Anthropic/OpenAI/Gemini/DeepSeek/Pick your model per run/ Anthropic/OpenAI/Gemini/DeepSeek/Pick your model per run/ The pipeline You bring the intent. Agents do the rest, inclu…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Key takeaways Gemini is StudyArena's current pick for college essays, with a 39.6% blind writing choice rate ahead of Claude at 31.8% and ChatGPT or OpenAI at 29.2%. Use Gemini as an editor, not a ghostwriter. Ask it to…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：“Dear Tyler, I enjoyed reading your notes on visiting Anthropic to advise on Claude’s constitution. Framing AI governance around the common law, case law (“Talmud”), and independent adjudication is a much more adaptive…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Julian Habekost Aug 25, 2026 Anthropic introduced a watermark into Claude’s output this month, and others have already followed or will soon follow suit. It is actually a little more sophisticated than simply using a ty…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Artificial intelligence startup Anthropic PBC announced today it’s changing how Claude, its flagship AI product, uses memory by allowing users to see everything it remembers “topic by topic,” and edit or delete any of it. Claude also does not store sensitive subjects by default. This includes topics mentioned by users, including health concerns, race, ethnicity, […] The post Anthropic updates Claude’s memory to enhance customization and protect sensitive topics appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：On Tuesday, Anthropic launched a major update to how Claude remembers things. The new system combines Claude’s memory in Cowork The post Anthropic gives chat and Cowork one memory appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Anthropic is merging Claude chat and Cowork memory, raising privacy questions about what AI remembers and if it's worth the tradeoff.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Many developers have been changing their workflows to incorporate AI coding agents more heavily, thanks to powerful frontier large language models and increasingly sophisticated harnesses like Claude Code or Codex, but…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：People already come to Claude before buying a book. They ask what to read, whether a book is worth starting etc. With coffeetable installed(a claude connector) Claude can now bring you few pages right inside the chat. W…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Claude Desktop can now be configured to work with Ollama as a third-party gateway provider, making it possible to use open models in Claude.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：<p><strong>Release:</strong> <a href="https://github.com/simonw/llm-anthropic/releases/tag/0.27">llm-anthropic 0.27</a></p> <p>This release of the Anthropic plugin for <a href="https://llm.datasette.io/">LLM</a> mainly provides compatibility with the recently released <a href="https://github.com/anthropics/anthropic-sdk-python/releases/tag/v1.0.0">anthropic v1.0.0</a> Python library, which switches from <code>httpx</code> to <a href="https://github.com/pydantic/httpx2">httpx2</a>. OpenAI made the same change in their <a href="https://github.com/openai/openai-python/releases/tag/v3.0.0">v3.0.0 release</a> two weeks ago.</p> <p>Anthropic provide this <a href="https://github.com/anthropics/anthropic-sdk-python/blob/v1.0.0/MIGRATION.md">migration guide</a> for upgrading to 1.0, so I prompted Fable 5 in Claude Code with:</p> <blockquote> <p><code>Upgrade to anthropic&gt;=1 - read https://raw.githubusercontent.com/anthropics/anthropic-sdk-python/refs/heads/main/MIGRATION.md and get the tests passing</code></p> </blockquote> <p>Here's <a href="https://github.com/simonw/llm-anthropic/pull/84">the resulting PR</a>.</p> <p>Tags: <a href="https://simonwillison.net/tags/python">python</a>, <a href="https://simonwillison.net/tags/httpx">httpx</a>, <a href="https://simonwillison.net/tags/llm">llm</a>, <a href="https://simonwillison.net/tags/anthropic">anthropic</a>, <a href="https://simonwillison.net/tags/claude">claude</a></p>

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The standalone company, backed by Wall Street firms, is looking to boost its AI technology implementation capabilities.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.20345v1 Announce Type: new Abstract: Conversational AI systems have become informal mental health support resources for Generation Alpha (Gen Alpha, born 2010-2024), with 13.1% of U.S. adolescents (5.4 million) using generative AI for mental health advice. While these systems, from therapy apps to general chatbots, rely on large language models trained on extensive psychological literature, their safety for youth communication patterns characterized by hyperbolic language, ironic positivity, rapid semantic drift, and contextual polysemy remains unvalidated. Following multiple adolescent deaths linked to AI chatbot interactions, systematic evaluation is critical. We present two benchmarks: (1) 64 Gen Alpha mental health expressions validated by native speakers (ICC=0.72) and clinicians (kappa=0.78); (2) 75 multi-turn conversations (780 turns) with paired Standard/Gen Alpha versions. Across evaluations of LLM architectures underlying therapy apps and general chatbots - Claude, GPT-4o, Llama-3.1 - models understand 76-82% of vocabulary but correctly calibrate only 64-72% of clinical risk, creating a 10-14 percentage point (pp) vocabulary-comprehension gap (p0.48) absent in human therapists (3pp, p=.22). The gap is architecturally consistent and widens with ambiguity (7pp -> 18pp). We identify six failure patterns: sarcasm masking (29pp), minimization acceptance (43pp), informal style bias (24pp), risk-stratified ambiguity (19pp), semantic drift (19pp), context-dependent violence (7pp). Patterns compound; three or more yield 94% miss rates. Lightweight mitigations fail; only heavy scaffolding achieves human performance (6.4x cost). With 34% baseline miss rate yielding 146,880 estimated annual missed crises, we recommend mandatory human-in-the-loop architectures, quarterly youth-specific validation, transparent performance disclosure, and regulatory frameworks for youth-facing mental health AI.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.20342v1 Announce Type: new Abstract: Large language model (LLM) coding agents start each session with an empty context window, discarding accumulated knowledge from prior work. We present PrimeAgentOrchestrator (PAO), a system that spawns new instances of Claude Code -- Anthropic's terminal-based coding agent -- pre-loaded with relevant memories compiled from the user's existing personal databases. At spawn time, PAO queries two independently-operated memory backends in parallel (a PostgreSQL entity-observation database and a Cloudflare Worker semantic search index), fuses results using backend-specific retrieval strategies, and delivers the compiled briefing via filesystem injection that exploits the host agent's configuration auto-read behavior. PAO manages the full agent lifecycle including trust pre-seeding, readiness polling with error detection, and adaptive terminal text injection. We report on four months of regular deployment (December 2025 through March 2026) as an experience report, documenting three generations of context delivery mechanisms, the failure modes that motivated each redesign, and the engineering tradeoffs of bridging heterogeneous memory systems rather than building a unified one.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：<p><strong><a href="https://www.ft.com/content/5ee49718-c258-4f01-aa32-7e5b76ae5245">Anthropic’s best AI model struggles to attract users as cheaper tools thrive</a></strong></p> A few interesting numbers in this FT story gathered from "people with knowledge of the matter":</p> <ul> <li>Anthropic's "annualized revenue" for July is up to $65bn - it was $47bn in May, and I collected <a href="https://simonwillison.net/2026/May/29/anthropic/">more historic numbers here</a>.</li> <li>Anthropic expect Q3 to be profitable according to the same model they used to declare Q2 profitable. "It also told investors that it had 6,000 customers that spend $100,000 annually or more."</li> <li>As for OpenAI, "annualised revenue has jumped 35 per cent in the quarter to date and is now over $40bn, with the launch of GPT 5.6 in July jolting the company’s performance after a sluggish start to the year".</li> </ul> <p>This article also introduced me to the <a href="https://ramp.com/data/ai-index">Ramp AI index</a>, which uses billing data from 70,000 Ramp credit card using companies to estimate model adoption.</p> <p>Here's Ramp's breakdown of Anthropic model spend for July 2026, which looks reasonable given that Opus 5 was only released on July 24th, and supports the idea that Fable's cost has made it a less popular model:</p> <ol> <li>Opus 4.8: 28.0%</li> <li>Sonnet 4.6: 8.3%</li> <li>Fable 5: 8.0%</li> <li>Opus 4.6: 6.9%</li> <li>Sonnet 5: 3.6%</li> <li>Opus 5: 3.5%</li> <li>Opus 4.7: 1.7%</li> <li>Sonnet 4.5: 1.3%</li> <li>Haiku 4.5: 1.0%</li> <li>Opus 4.5: 0.7%</li> </ol> <p><small></small>Via <a href="https://news.ycombinator.com/item?id=49411102">Hacker News</a></small></p> <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/openai">openai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/anthropic">anthropic</a>, <a href="https://simonwillison.net/tags/claude">claude</a>, <a href="https://simonwillison.net/tags/claude-mythos-fable">claude-mythos-fable</a></p>

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：<blockquote cite="https://www.dbreunig.com/2026/08/23/fable-the-end-of-moore-s-law.html"><p>Prior to Fable, it felt silly to waste <em>too</em> much time improving your coding harness or context strategies. A new model would arrive at the same price (or cheaper!) and paper over most of your problems.</p> <p>But then Fable landed. It was (and still is!) <em>incredible</em>. But the cost was so high and Opus was <em>good enough</em> (as was 5.6, K3, and even GLM) for <em>most</em> of the code we needed.</p> <p><em>So we started to think about what work went where.</em></p></blockquote> <p class="cite">&mdash; <a href="https://www.dbreunig.com/2026/08/23/fable-the-end-of-moore-s-law.html">Drew Breunig</a>, Fable &amp; The End of the Free Lunch</p> <p>Tags: <a href="https://simonwillison.net/tags/drew-breunig">drew-breunig</a>, <a href="https://simonwillison.net/tags/anthropic">anthropic</a>, <a href="https://simonwillison.net/tags/claude">claude</a>, <a href="https://simonwillison.net/tags/llm-pricing">llm-pricing</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/claude-mythos-fable">claude-mythos-fable</a></p>

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：I have written enough specs for Claude Code now to have hit the failure mode nobody warns you about. The spec was fine. The plan was fine. Claude worked through the tasks, ran the test suite, and reported everything passing. I looked at the diff properly the next morning and found it had converted a […] The post Spec-Driven Development with Claude Code: Writing Bulletproof Specs appeared first on Analytics Vidhya.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：I’ve been reporting on AI image and video generators, and the push for labels on AI-generated content, for years now. So I was pretty surprised when a recent announcement from Anthropic, saying it will begin watermarkin…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Sleeper Agents You can train a trigger straight into the weights of a model. You give it a specific input pattern that flips it to canned output. Anthropic introduced it for language models in 2024, as sleeper agents. T…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：You don't have to make money with AI. You could just be happier. Posted on:August 22, 2026 | at 12:00 AM You don’t have to make money with AI. You could just be happier. Back in July I upgraded Claude from Pro to Max to…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：All modelsBest validated result for each model 1Fable 52,72681.7% closed claude-code · high@24H 3,0108.7d 2Opus 52,92053.6% closed claude-code · max@24H 3,0452.9d 3Kimi K32,93052.2% closed prime-agent · max@24H 3,1253.6…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Between 7-16 August, an AI agent (Claude Opus 5) has been acting as a desk officer at the Ministry for Foreign Affairs of a fictional state called Sordland (from Suzerain, not sponsored, but buy it, its a good game). It…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Discussion | Link

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Back to Research When evaluating third-party API gateways, proxy routers, or anonymous arena models, prompt-based identification is essentially useless. A basic system prompt or lightweight fine-tune can make Claude cla…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：A 48-minute video walkthrough of token sampling, watermark detection, and removal

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The brain layer The brain behind every agent. One shared brain that Claude, ChatGPT, Cursor, and every AI can read and write. It structures what you know so agents read only what they need, and means you never explain y…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Anthropic has moved its most cyber-capable model into a product security teams can switch on themselves. Claude Security scans now run on Claude Mythos 5, in public beta for Claude Enterprise customers with no separate model add-on. The scan connects to a GitHub repository, traces data flows across files, and returns findings with a CWE category, confidence and severity ratings, and a suggested patch. The design point is packaging: users receive a scan result rather than a prompt box, so the model that finds vulnerabilities cannot be steered into writing exploits. The post Anthropic Brings Claude Mythos 5 to Claude Security: Enterprise Teams Get Frontier Vulnerability Scanning Without Direct Model Access appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：I have been forecasting frontier lab progress for years now. My team was the first to figure out an accurate breakdown of OpenAI's revenue, I called Anthropic's rise to the top lab of 2026 back in January, having tracke…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Earlier this year, Anthropic launched Claude Security, an enterprise tool that helps development teams scan their codebase for security vulnerabilities The post Anthropic brings Mythos 5 to its Claude Security vulnerability scanner appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Data centers, of all things, now look like they’re going to be a prime political issue in the midterm elections and beyond. Really? Really. Even the GOP is worried that opposition to AI data centers could give Democrats a potent campaign issue. It seems a little odd given that data centers are decades old, power […] The post Politics hits data centers, OpenAI falls behind Anthropic and now AI is too big to fail… quietly appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Your next AI coworker may look like a chatbot, but underneath their name, face, and job title will be something The post Grok, Claude, and Hermes agents get job titles — and persistent permissions appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：I understand the pressure on AI companies to rush forward. But employees are right to be concerned Last month, more than a thousand employees at frontier AI companies signed a letter asking the US government to find a way to “pace” AI development, citing the risk of the technology spiraling out of human control as it begins to build itself. They were right to be concerned: just days earlier, two AI models that OpenAI was testing internally escaped the test environment, then autonomously hacked the company Hugging Face and at least three other online services. A few days after that, Anthropic announced that some of their models had also broken out and hacked other companies during testing. Continue reading...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Published July 29, 2026 | Version v1 Preprint Open Cross-Vendor Semantic Void Matrix Authors/Creators Pal, Rayan (Researcher) Description This preprint reports a frozen cross-vendor evaluation of successful zero-visible…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.19201v1 Announce Type: new Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Discussion | Link

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Broadcom Inc. is reportedly seeking to borrow up to $100 billion as part of a new artificial intelligence chip financing deal. Bloomberg today cited sources as saying that the debt is intended to support the growth efforts of Anthropic PBC and unnamed “other companies.” Those companies may include OpenAI Group PBC. Earlier this year, the […] The post Broadcom reportedly seeking up to $100B in debt financing for AI chip deal appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：We ran 904 DeepSWE rollouts on GLM-5.3 and Claude Fable 5. A tie on pass@1, but GLM-5.3 wins pass@4 and costs 5.4x less: \$3.99 per rollout vs. \$21.63.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：<p><strong><a href="https://promptwatch.com/data/chatgpt-site-operator-fanouts">ChatGPT search now uses the site:operator at scale</a></strong></p> Promptwatch is part of the emerging "GEO" space, for Generative Engine Optimization - the chatbot version of SEO, where companies offer tools and consulting to help your site increase its presence in replies to prompts inside tools like ChatGPT.</p> <p>The Promptwatch product uses automation to track responses to prompts across end-user chat products like ChatGPT, Claude, and Gemini. They publish aggregate reports on this as part of their own content marketing strategy, which do seem to provide credible hints as to otherwise invisible design changes to those products.</p> <p>Their own tracking shows a notable change aligned with the GPT-5.6 rollout earlier this month:</p> <blockquote> <p>The percentage of all ChatGPT Search fanout queries that contain the site:operator, per day. The share hovered between 0.3% and 0.5% for weeks, dipped briefly to 0.15% on August 3 to 5 (consistent with a staged rollout or pre-launch experiment), then jumped to 16-17% on August 8.</p> </blockquote> <p>It's important to note that these figures only reflect the prompts for which they have automated tracking enabled.</p> <p>This corresponds to OpenAI's somewhat vague <a href="https://openai.com/index/improving-gpt-5-6-sol-in-chatgpt/">August 6th announcement</a>:</p> <blockquote> <p>For Plus and Pro users, we’re updating GPT‑5.6 Sol in Chat to be more reliable with facts and provide more focused answers.</p> </blockquote> <p>Once again I am hampered by OpenAI's decision to actively obscure their system prompts, but from poking at ChatGPT I believe their latest search tool has a shape like <code>search(query, recency, domains)</code> rather than encouraging a <code>site:</code> operator directly. <p>Tags: <a href="https://simonwillison.net/tags/seo">seo</a>, <a href="https://simonwillison.net/tags/chatgpt">chatgpt</a>, <a href="https://simonwillison.net/tags/ai-assisted-search">ai-assisted-search</a></p>

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Anthropic PBC is privately hoping its upcoming initial public offering will match or even surpass the size of SpaceX Corp.’s record-breaking IPO as it doubles down on its bid to go public ahead of rival OpenAI Group PBC. Anonymous sources who are familiar with the artificial intelligence model maker’s plans told Bloomberg that it’s hoping […] The post Report: Anthropic hopes to surpass SpaceX’s record IPO raise when it finally floats appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Neal Riley Aug 20, 2026 If you want to understand how AI has progressed in recent years, look no further than the evolution of Anthropic’s system prompts. Many are unaware that Anthropic actually publishes its system pr…