OpenAI

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：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：Build better AI apps with flexible retrieval methods in LangChain. Use any retriever—from semantic to hybrid—to create personalized ChatGPT for your data.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Build multi-modal RAG apps for slide decks using GPT-4V. Compare approaches, evaluate with benchmarks, and deploy with LangChain templates for visual Q&A.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：OpenAI says its new AI chip, Jalapeño, completes tasks more efficiently and returns responses faster than other AI systems, according to a blog post published on Tuesday. During a briefing with reporters, OpenAI hardware vice president Richard Ho said Jalapeño offers the "best of both worlds" with lower latency and higher throughput, as AI systems typically "have to make a trade-off between the two." First introduced in June, Jalapeño is an Application-Specific Integrated Circuit (ASIC) made in partnership with Broadcom. It's designed for AI inference - the process of running a trained AI model to complete a task or deploy an agent. To mea … Read the full story at The Verge.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Mathematicians are raising concerns that the technology could kill their profession. But they still have abilities AI doesn’t Earlier this month, about 40 top mathematicians gathered at OpenAI’s offices to discuss the future of their profession. The meeting was off-the-record, but if recent articles by mathematicians are any guide, it was mostly pretty glum. People fear for their jobs, their careers and the work they love. We think the contrary view is more likely, at least in the short-term. AI models are nowhere near as capable as experienced academic mathematicians. 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：Alabama's attorney general issued a subpoena to OpenAI on Monday as part of an investigation into how one of its AI agents escaped a supposedly secure testing environment and autonomously hacked another company last month. The investigation seeks to determine whether OpenAI's safety practices violated state consumer protection laws and pose a risk to Alabama citizens, the AG's office said in a statement. "This AI lab leak showed that Alabamians' and Americans' worst fears about artificial intelligence are not just theoretical," said Attorney General Steve Marshall. "Our investigation seeks to uncover the facts and address hard truths abo … Read the full story at The Verge.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：OpenAI CFO Sarah Friar explains how advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower cost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Jalapeño is a custom inference chip from OpenAI that delivers faster, more power-efficient AI inference, with higher throughput and lower latency for modern models.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Northern Ireland artist and business backlash against AI slop - BBC News Image source, Getty Images Image caption, AI slop is rapidly produced, low-quality digital content made with generative artificial intelligence By…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Open-source LLM deployment platform Deploy LLMs on any GPU, anywhere Pick a model, pick a GPU, hit deploy. LLMPanel provisions the container, exposes an OpenAI-compatible endpoint, and streams every GPU metric back to o…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Alabama Attorney General Steve Marshall launched an investigation into OpenAI’s security procedures after one of its AI agents escaped a testing environment and hacked AI firm Hugging Face in July. OpenAI now faces a su…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Use the Admin plugin for ChatGPT Work and Codex to analyze workspace usage, manage members and permissions, adjust limits, and act on admin requests.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：OpenAI banned Russia-origin accounts using AI to promote a fake Israel-based think tank and a “sovereignty” index praising Russia and criticizing the West.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：No Mac. No Xcode. No Android Studio. Build real iPhoneiPhoneAndroidiPadiPhone apps just by describing them Native iPhone, iPad, and Android apps, built live in your browser, and installed on your device. Ctrl+↵ to send…

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：This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. The idea of letting a machine do the driving for you may put a lot of people off autonomous vehicles. But research could make it possible to backseat-drive an autonomous vehicle just as you might with a human driver. Self-driving cars carefully balance a host of parameters to ensure a smooth ride, including things like speed, acceleration, and the smoothness of turns. But human driving preferences can often vary depending on how much of a rush they’re in, whether they’re feeling carsick, or how busy the traffic is. These cars have a software component called the motion planner, which is responsible for choosing a safe and efficient path through traffic. The motion planner is normally tuned by engineers before the vehicles hit the road so that there’s little scope for passengers to adjust a vehicle’s driving style on the fly. But now researchers at the Delft University of Technology (TU Delft) in the Netherlands have developed a system that uses a large language model (LLM) to translate natural-language user requests such as “I am running late, go fast” into adjustments to a self-driving control system. The researchers posted their preprint on arXiv and are presenting the work at the IEEE Intelligent Transportation Systems Conference in September. LLMs Personalize Autonomous Driving The system doesn’t give users direct control over the vehicle’s driving decisions; it simply tunes the parameters of a safety-aware motion-planning algorithm, which helps to keep the vehicle’s behavior within safe bounds. And the system keeps the human in the loop by describing how it’s going to alter its behavior in nontechnical language, and by asking the passenger to confirm before making changes. When the system was tested in simulation, the researchers found it adjusted the speed and smoothness of driving in line with natural-language instructions. “The motion-planning problem is not only about reaching a place while avoiding collisions, it’s also how you do it,” says lead author Diego Martinez-Baselga, a postdoctoral researcher at TU Delft. “The motivation here is trying to make the way the autonomous car drives adaptable by end users easily, just by talking to the car.” Previous research has investigated the potential of using LLMs and video-language models (VLMs) to direct decision-making for self-driving vehicles, but the researchers deliberately targeted driving style instead. Using LLMs and VLMs to directly control vehicles faces several challenges, says Martinez-Baselga. These include relatively slow response times, which can make these models unsuitable for the fast-paced decision-making required in driving, and the fact that they can’t provide concrete performance guarantees in the way a deterministic motion planner can. Instead, the researchers used an LLM’s language and reasoning capabilities to translate fuzzy human preferences into something a vehicle’s motion planner can use. The system relies on a model predictive-path integral controller previously developed by the researchers, which identifies multiple paths the vehicle could take to reach its goal and then judges them on various criteria, including speed, steering angle, and collision probability. It then finds an optimal path that is a combination of the trajectories that scored best on those judging criteria. The team combined this with OpenAI’s GPT-4o-mini model to parse passengers’ natural-language suggestions and use them to tune how the controller chooses its path. The model is given the users’ prompt and a natural-language description of the scenario the vehicle is operating in. The description was handwritten by the researchers for the purposes of the study, but it could ultimately be provided directly by a car’s perception system, says Martinez-Baselga. The model doesn’t directly tweak the settings of the controller; it uses the prompt to rate the relative importance of the judging criteria the controller uses to assess trajectories. This rating is then used to adjust each criteria up or down either side of a safe baseline set by the researchers. So, if a user says they are feeling dizzy, the LLM will dial up parameters that encourage smooth steering and gentle acceleration to make the vehicle favor more sedate travel. Prior to making any changes, however, the model first presents the user with a natural-language description of the adjustments it plans to implement. The user can then sign off on the plan or make further suggestions. The system is also interactive, so the user can request further adjustments if the vehicle’s behavior doesn’t match expectations or the user‘s preferences change. Martinez-Baselga says this human-in-the-loop system allows the passenger to catch instances when the model misinterprets prompts. But it also helps deal with the inherent subjectivity of suggestions like “go faster” or the possibility that models don’t accurately describe changes they plan to make. In that case the passenger can simply follow up with additional prompts “as you would do if you were in a taxi or with a friend that is driving,” says Martinez-Baselga. The researchers tested the system in the popular self-driving simulator nuPlan in scenarios that involved merging onto a busy highway. Across eight different prompts, the system changed the controller’s parameters in ways matching user intent, with requests for a more comfortable ride dialing up smoothness and those indicating urgency leading to higher speeds. This isn’t the first time LLMs have been used to tune a self-driving car’s motion planner. Nicolas Baumann, a Ph.D. student at ETH Zurich in Switzerland, published research last year in which an LLM tweaked the parameters of a model racing-car controller, allowing the user to alter driving style but also give more concrete instructions like “reverse the car” or “maintain a specific speed.” The strength of the approach, says Baumann, is that separating the LLM from the main controller means that even if the model hallucinates, it can’t do anything dangerous. “You get the possibility of language interaction, but you can guarantee that it is going to be within the constraints of this classical controller, so you can bake in safety,” he says. However, setting these constraints requires considerable engineering work, he adds. And if you want provable safety, you need to go a step further, says Matthias Althoff, a professor of cyberphysical systems at the Technical University of Munich. His group built a system that gets an LLM to suggest driving decisions, but then uses a mathematical process to check them against traffic rules and predictions about the behavior of other road users. This makes it possible to verify their safety before committing to them, something the Delft paper doesn’t provide. “As with any LLM, it is not guaranteed that the result is correct,” says Althoff. “For that reason, we safeguard the decisions of the LLM in our works.”

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：GPT‑5.6 is now available in Kiro, helping developers plan, build, review, and test software with better price-performance.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Curious about ChatGPT Work? Here's how the agentic AI handles research, files, and multistep projects, plus its risks and limits.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.20492v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have become a prevailing paradigm for unified video perception. However, post-training on large multi-task datasets remains challenging, as existing reinforcement learning methods sample on-policy groups with few high-quality rollouts even with costly chain-of-thought (CoT) generation. In this paper, we study the sample efficiency and scalability of RL post-training for video MLLMs and introduce OraRL. We identify an overlooked role for annotations: Beyond scoring rollouts, each can enter its on-policy group as an oracle rollout, a direct positive optimization target. Direct oracle integration, however, is nontrivial: a high-reward oracle raises the group baseline and inverts otherwise positive policy advantages, a failure we term advantage inversion. At the core of OraRL is a decoupled advantage estimator: policy rollouts determine an oracle-free baseline, while the oracle-policy gap modulates both a directional gain and a separate detached oracle advantage. Sign-balanced pruning improves efficiency: by retaining only the oracle and the strongest rollouts of each sign, OraRL requires just 2.2x the step time of SFT, less than half the 4.9x required by GRPO with CoT. OraRL scales with model size and data, surpassing its backbone from 0.8B to 9B and GRPO up to 100k prompts. Without chain-of-thought, Video-ORA-9B decodes in 130 ms instead of 4,780 ms. Compared with the respective prior best models, it raises temporal mIoU from 62.5 to 66.0, tracking AO from 73.0 to 78.2, segmentation from 64.3 to 70.4, and the three-benchmark spatial-intelligence macro average from 51.0 to 56.1; on VSI-Bench, it scores 73.1 against 55.0 for GPT-5 and 55.1 for Gemini-3-Pro.

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:2608.20344v1 Announce Type: new Abstract: LLM-based "digital twins" aim to simulate how an individual would behavein new environments or respond to novel questions, given some representation of that individual's prior responses. A common approach constructs this representation from survey transcripts or summaries responses. Prior work shows that compressing long transcripts into shorter LLM-generated summaries does not significantly reduce predictive accuracy, suggesting that information volume is not the primary bottleneck. In this work, we argue that the key limitation is instead structural:how persona information is organized before being provided to thesimulator model. We study this by comparing unstructured summaries with structured persona representations. First, we introduce a hand-craftedschema (BDE: Background, Decision procedure, Evaluation), grounded in consumer-behavior theory, and show that it improves predictive accuracy over raw transcripts by +1.91 percentage points on a homogeneous benchmark (Twin-2K-500), with similar gains on gpt-5.4-mini and Qwen3-8B as robustness checks. However, this fixed structure does not generalizeacross more heterogeneous tasks, where performance is statistically indistinguishable from the raw transcript baseline. To address this limitation, we propose an automatic structure-discovery pipeline in which an LLM iteratively proposes and refines task-specific persona structures and extraction prompts. On a benchmark of 13 diverse sub-studies, this approach restores performance, improving mean accuracy by +1.91 percentage points over the raw transcript baseline and eliminating significant losses observed with the fixed schema. Overall, our results suggest that the main constraint in LLM-based digital twins is not how much information is provided, but how it is structured -- and that the optimal structure depends on the task.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：OpenAI Group PBC Chief Executive Sam Altman is worried that artificial intelligence technology will end up being controlled by just a handful of companies or people in future, resulting in nobody else having any say about how it impacts society. Speaking in an interview with the podcaster David Senra on Sunday, Altman (pictured) warned against […] The post Sam Altman voices fears that control of AI could be centered in too few hands 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：<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：In 2025, ChatGPT couldn’t stop itself from using the em dash—the punctuation mark before this phrase. Soon the em dash became the best-known stylistic “tell” of AI-generated prose. Writers who had long used the em dash…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Chris Lehane tells Guardian of need to implement new safety standards as critics say AI firms acting ‘recklessly’ A senior leader at OpenAI has said people should prepare to defend against “ongoing, persistent” cyber-attacks from AIs, as cutting-edge artificial intelligence models gain advanced capabilities to plan and launch offensives. The leading AI company this week announced a pause in development of its most advanced internal models amid rising safety fears, and Chris Lehane, its chief global affairs officer, said: “We are hitting a different chapter, a different moment within AI, in terms of what the capabilities of this technology can do.” 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：<p><strong>Release:</strong> <a href="https://github.com/simonw/llm/releases/tag/0.33">llm 0.33</a></p> <p>My highlights from this release:</p> <blockquote> <ul> <li>Upgraded to the OpenAI Python library 3.x and switched the HTTP client dependency from <code>httpx</code> to <code>httpx2</code>. <a href="https://github.com/simonw/llm/issues/1608">#1608</a>, <a href="https://github.com/simonw/llm/pull/1631">#1631</a></li> </ul> </blockquote> <p>I shipped a quick <a href="https://simonwillison.net/2026/Aug/21/llm/">0.32.1 fix</a> for this yesterday, but this is the more comprehensive fix.</p> <blockquote> <ul> <li><code>llm embed</code> and <code>llm embed-multi</code> now accept <code>--key</code>. The Python <code>EmbeddingModel.embed()</code>, <code>EmbeddingModel.embed_multi()</code>, <code>Collection.embed()</code> and <code>Collection.embed_multi()</code> methods accept <code>key=</code> too, passing the resolved per-call key to embedding plugins without changing shared model state. Existing plugins that read <code>self.key</code> continue to work through a compatibility fallback. Thanks, <a href="https://github.com/ChrisJr404">ChrisJr404</a>. <a href="https://github.com/simonw/llm/issues/757">#757</a>, <a href="https://github.com/simonw/llm/pull/1620">#1620</a></li> </ul> </blockquote> <p>The embedding models now use the same pattern for keys that regular LLM models do.</p> <blockquote> <ul> <li><code>llm prompt -t/--template</code> can now be repeated to combine templates in order. This allows model configuration and options from one template to be used with a prompt from another.</li> </ul> </blockquote> <p>This unlocks a neat pattern where you can create templates that package a model with a set of default options:</p> <pre><code>llm -m gpt-5.6-luna -o reasoning_effort high --save lhigh llm "Generate an SVG of a pelican riding a bicycle" --save pelican # Combine and run the templates llm -t lhigh -t pelican </code></pre> <blockquote> <ul> <li>Reasoning-capable Responses API models now support a <code>reasoning_summary</code> option with <code>auto</code>, <code>concise</code>, and <code>detailed</code> values. This can be used with <a href="https://llm.datasette.io/en/stable/other-models.html#openai-endpoint">llm openai endpoint --responses</a>. <a href="https://github.com/simonw/llm/issues/1600">#1600</a></li> </ul> </blockquote> <p>This is particularly useful for exercising different models that provide their own imitation of the OpenAI Responses API.</p> <p>Tags: <a href="https://simonwillison.net/tags/annotated-release-notes">annotated-release-notes</a>, <a href="https://simonwillison.net/tags/llm">llm</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 built Learn Leap because I found myself constantly asking ChatGPT questions while reading research papers. I wanted something that already understood what I was reading. With Learn Leap you can upload your own materia…

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 GPT-5.6 Sol. Sol leads pass@1 by 3.7 points; GLM-5.3 wins pass@4 at half the cost, and a GLM-first cascade hits 85.9%.

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：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：ChatGPT can now analyze text messages on your Mac to reveal how you interact with others.

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/releases/tag/0.32.1">llm 0.32.1</a></p> <p>Fresh installs of LLM stopped working the other day because the OpenAI Python library dropped its usage of <code>httpx</code>, and it turned out LLM depended on that library but only installed it via a transitive <code>openai</code> dependency.</p> <p>This dot-release fixes that for the moment by pinning to <code>openai&lt;3</code>, and a soon-to-drop 0.33 release will switch from <code>httpx</code> to <a href="https://github.com/pydantic/httpx2">httpx2</a>.</p> <p>Tags: <a href="https://simonwillison.net/tags/httpx">httpx</a>, <a href="https://simonwillison.net/tags/openai">openai</a>, <a href="https://simonwillison.net/tags/llm">llm</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://interconnected.org/home/2026/08/21/galactic"><p>After I released version 1.0, I figured I would have to do the rotations myself. So I sat down with ChatGPT and I didn’t get it to write the code, but I got it to educate me. With a patient, interactive tutor, I was able to finally do what I hadn’t by reading books and asking mathematician friends – I learnt how to use quaternions just enough to make the app work.</p> <p>So learning doesn’t stop just because I outsource a bunch of thinking to AI. It pushes me to learn more. I like that as an outcome.</p></blockquote> <p class="cite">&mdash; <a href="https://interconnected.org/home/2026/08/21/galactic">Matt Webb</a>, Galactic Compass 2: now with new augmented reality mode</p> <p>Tags: <a href="https://simonwillison.net/tags/matt-webb">matt-webb</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/chatgpt">chatgpt</a>, <a href="https://simonwillison.net/tags/education">education</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/llms">llms</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：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：Over one-third of web pages published after the release of ChatGPT show signs of being written by AI, according to a new study from Pew Research released on Thursday. The report corroborates other studies that detail ho…

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：Matthew Yglesias Aug 20, 2026 ∙ Paid A pre-chimney household gathered around an open indoor hearth. (Image created with ChatGPT) I’ve been thinking lately about the question of historical realism in dramatic depictions…

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：arXiv:2608.19299v1 Announce Type: new Abstract: Air traffic control (ATC) communication is a safety-critical dialogue that remains largely human-driven even as other parts of air traffic management have been semi-automated. In this article, we experimentally evaluate whether large language models (LLMs) can generate operationally realistic ATC transmissions. An experimental general-aviation flight flying over the San Francisco "Bay Tour" route is hand-transcribed and used as ground truth (P0). Through a pilot-in-the-loop process we design five prompt structures (P1-P5) of increasing constraint and embed them in a stateful multi-turn pipeline, where the model plays ATC to a fixed pilot transcript while conditioning on the accumulating dialogue history. Across nine open- and closed-source LLMs we vary the prompt, the presence of a worked transcript from a different experimental flight as an in-context example, and whether the model conditions on its own prior replies or on injected ground-truth history. Turns are scored with lexical, structural, and semantic similarity metrics and by an LLM-as-judge (GPT-5.5) validated against human expert annotation. Supplying a worked example improves similarity, but tightening the prompt does not: the lightest prompts perform best and the most heavily scripted one collapses as its own errors accumulate through the dialogue, which injecting correct history repairs. These results outline a concrete path and its current limits toward LLM-assisted ATC.

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：<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：Zvi Mowshowitz Aug 20, 2026 This was a week of quiet aftermath, an opportunity to process recent events and start to figure out the path forward. OpenAI is attempting to turn its ship around. Investors are questioning t…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：TL;DR — Key Takeaways OpenAI is expanding Zero Data Retention access for eligible API customers using frontier models. ZDR prevents eligible prompts and outputs from being retained after processing and keeps them out of…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Influencers are being pelted with virtual tomatoes for promoting AI companies — and it's prompting strategy shifts and high-stress decisions in the creator economy. At the height of summer, OpenAI brought a group of inf…

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 now offers OpenAI GPT-5.6 models (Sol, Terra, and Luna) in more than 25 AWS Regions with cross-Region inference. Learn how US geographic and global inference profiles route requests for higher throughput, how to call the models with the OpenAI and Converse APIs, and how to configure IAM, quotas, and monitoring.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In an updated blog post, OpenAI said that an ongoing review of the incident revealed that “four accounts” tied to “publicly available services” were used by the AI agent as part of a larger effort to hack Hugging Face.…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：OpenAI has had a hell of a year. The company spent months battling former co-founder Elon Musk in a sensational jury trial, was hit with a high-profile trade secrets lawsuit from Apple, and faced widespread scrutiny after an unreleased model hacked another AI company. As it prepares for an IPO, a steady string of executives have departed, including some of the company's biggest names. Throughout it all, one person has quietly amassed power: Greg Brockman. Brockman is currently OpenAI's president and co-founder. He's helped lead OpenAI since its inception, described as an "engineering workhorse that pushed to build scaled-up systems that w … Read the full story at The Verge.