第100期 | Fireside chat at Sequoia Ascent 2026 from a ~week ago. S...
今日摘要
OpenAI Blog：Learn how ChatGPT safeguards your privacy, reduces personal data in training, and gives you control over whether your conversation…
OpenAI Blog：Uber uses OpenAI to power AI assistants and voice features that help drivers earn smarter and riders book faster across a global r…
OpenAI Blog：OpenAI’s B2B Signals research shows how frontier enterprises deepen AI adoption, scale Codex-powered agentic workflows, and build…
X Andrej Karpathy：Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about…
OpenAI Blog：Introducing Advanced Account Security: phishing-resistant login, stronger recovery, and enhanced protections to safeguard sensitiv…
总结 + 观点：How goblin outputs spread in AI models: timeline…｜中文观点：Where the goblins came from 更值得从实际采用价值来判断，而不是…
总结 + 观点：OpenAI scales Stargate to build the compute infr…｜中文观点：Building the compute infrastructure for the I…
总结 + 观点：openai/realtime-voice-component recently updated…｜中文观点：openai/realtime-voice-component 的核心不在新鲜感，而在它是…
总结 + 观点：Code for the paper "Jukebox: A Generative Model…｜中文观点：比起表面参数，openai/jukebox 更需要观察它是否在推理质量、检索效果或可用性上…
总结 + 观点：Quick illustration of how one can easily read bo…｜中文观点：karpathy/reader3 更值得从实际采用价值来判断，而不是只看它有没有制造新的讨…
How ChatGPT learns about the world while protecting privacy
标签：#ai_engineering_blogs #core
作者：
原文：Learn how ChatGPT safeguards your privacy, reduces personal data in training, and gives you control over whether your conversations improve AI models.
Uber uses OpenAI to help people earn smarter and book faster
标签：#ai_engineering_blogs #core
作者：
原文：Uber uses OpenAI to power AI assistants and voice features that help drivers earn smarter and riders book faster across a global real-time marketplace.
How frontier firms are pulling ahead
标签：#ai_engineering_blogs #core
作者：
原文：OpenAI’s B2B Signals research shows how frontier enterprises deepen AI adoption, scale Codex-powered agentic workflows, and build durable competitive advantage.
Fireside chat at Sequoia Ascent 2026 from a ~week ago.
标签：#x_profiles #extended
作者：
原文：Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons: 1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing. 2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc. 3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc. I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3). The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to... Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors. Stephanie Zhan (@stephzhan) @karpathy and I are back! At @sequoia AI Ascent 2026. And a lot has changed. Last year, he coined “vibe coding”. This year, he’s never felt more behind as a programmer. The big shift: vibe coding raised the floor. Agentic engineering raises the ceiling. We talk about what it means to build seriously in the agent era. Not just moving faster. Building new things, with new tools, while preserving the parts that still require human taste, judgment, and understanding. Video https://nitter.net/stephzhan/status/2049518659513852109#m
Introducing Advanced Account Security
标签：#ai_engineering_blogs #core
作者：
原文：Introducing Advanced Account Security: phishing-resistant login, stronger recovery, and enhanced protections to safeguard sensitive data and prevent account takeover.
Where the goblins came from
标签：#ai_engineering_blogs #core
作者：
原文：How goblin outputs spread in AI models: timeline, root cause, and fixes behind personality-driven quirks in GPT-5 behavior.
Building the compute infrastructure for the Intelligence Age
标签：#ai_engineering_blogs #core
作者：
原文：OpenAI scales Stargate to build the compute infrastructure powering AGI, adding new data center capacity to meet growing AI demand.
链接：https://openai.com/index/building-the-compute-infrastructure-for-the-intelligence-age
openai/realtime-voice-component
标签：#github_orgs #extended
作者：
原文：openai/realtime-voice-component recently updated repository.
openai/jukebox
标签：#github_orgs #extended
作者：
原文：Code for the paper "Jukebox: A Generative Model for Music"
karpathy/reader3
标签：#github_orgs #extended
作者：
原文：Quick illustration of how one can easily read books together with LLMs. It's great and I highly recommend it.
anthropics/claude-quickstarts
标签：#github_orgs #extended
作者：
原文：A collection of projects designed to help developers quickly get started with building deployable applications using the Claude API
Cybersecurity in the Intelligence Age
标签：#ai_engineering_blogs #core
作者：
原文：OpenAI outlines a five-part action plan for strengthening cybersecurity in the Intelligence Age, focused on democratizing AI-powered cyber defense and protecting critical systems.
链接：https://openai.com/index/cybersecurity-in-the-intelligence-age
anthropics/claude-agent-sdk-typescript
标签：#github_orgs #extended
作者：
原文：anthropics/claude-agent-sdk-typescript recently updated repository.
链接：https://github.com/anthropics/claude-agent-sdk-typescript
openai/whisper
标签：#github_orgs #extended
作者：
原文：Robust Speech Recognition via Large-Scale Weak Supervision
anthropics/anthropic-sdk-python
标签：#github_orgs #extended
作者：
原文：anthropics/anthropic-sdk-python recently updated repository.
Initial impressions of Claude Fable 5
标签：#ai_engineering_blogs #core
作者：
原文：I didn't have early access to today's Claude Fable 5 release, but I've spent the past ~5.5 hours putting it through its paces. My initial impressions are that this is something of a beast It's slow, expensive and has been quite happily churning through everything I've thrown at it so far. As is frequently the case with current frontier models the challenge is finding tasks that it can't do. First, let's review the key characteristics. Anthropic claim that Claude Fable 5 offers the same performance as Claude Mythos 5, except with much more strict guardrails in place to prevent it being used for harmful things. Those guardrails trigger often enough that the Claude API has new mechanisms for letting you know when you hit them, and even has a new option to request it falls back to another model automatically if something gets rejected. Claude Mythos 5 is out today as well, Anthropic say it "Shares Claude Fable 5's capabilities without the safety classifiers". The models have a 1 million token context window, 128,000 maximum output tokens and a knowledge cut-off date of January 2026. They are priced at twice the price of Claude Opus 4.5/4.6/4.7/4.8: $10/million input tokens and $50/million output tokens. There's no increase in price for longer context usage. Other than that the upgrade guide is substantially thinner than the similar guide for Opus 4.8 The big model smell The best way to describe Fable is that it feels big Not just in terms of speed and cost, but also in how much it knows. Here's an example prompt, which I used to compare Fable's knowledge with Opus 4.8 (typo included): List all of Simon Willion's open source projects, most recent first, each with a rough date of when they were first released Opus 4.8's reply started like this I don't have a reliable, comprehensive, and date-verified list of all of Simon Willison's open source projects, and I want to be honest about that rather than risk giving you inaccurate dates or fabricated entries. Here's what I can tell you with reasonable confidence: Well-known projects by Simon Willison: LLM (~2023) A command-line tool and Python library for interacting with large language models. Datasette (~2017) A tool for exploring and publishing data, probably his most prominent recent project. sqlite-utils (~2018-2019) A Python library and CLI tool for manipulating SQLite databases, often used alongside Datasette. Django (2005) He is one of the co-creators of the Django web framework, along with Adrian Holovaty, originally developed at the Lawrence Journal-World newspaper. He also maintains a large ecosystem of Datasette plugins and many smaller utilities (often with names like shot-scraper various -to-sqlite tools for importing data, etc.). That's not bad. Here's the start of the response for the exact same prompt from Fable 5 - both models were running without access to search: I think you mean Simon Willison the prolific open source developer, co-creator of Django, and creator of Datasette. An important caveat upfront: a truly complete list isn't feasible. Simon Willison is famously prolific he has published hundreds of repositories (Datasette alone has 100+ plugins, many written by him), and my knowledge has a cutoff date, so I'll be missing his most recent work. Below are his notable projects with approximate initial release dates, most recent first: Notable projects (approximate dates) files-to-prompt (April 2024) concatenates files into a single prompt for LLMs datasette-extract (2024) plugin for extracting structured data using LLMs LLM (May–June 2023) CLI tool and Python library for interacting with large language models, plus a large ecosystem of plugins (llm-gpt4all, llm-mistral, llm-claude, etc., 2023 onward) symbex (June 2023) search Python code for symbols ttok and strip-tags (May 2023) CLI tools for token counting and HTML cleanup for LLM pipelines datasette-lite (May 2022) Datasette running entirely in the browser via WebAssembly/Pyodide shot-scraper (March 2022) automated website screenshots via Playwright s3-credentials (November 2021) CLI for creating scoped AWS S3 credentials django-sql-dashboard (2021) SQL reporting dashboards for Django Dogsheep suite (2019) personal analytics tools: twitter-to-sqlite, github-to-sqlite, healthkit-to-sqlite, dogsheep-beta, etc. sqlite-utils (2018) CLI and Python library for manipulating SQLite databases Datasette (November 2017) his flagship project; tool for exploring and publishing data csvs-to-sqlite (2017) convert CSV files to SQLite Various early tools (~2007–2010) soupselect, json-head, geocoders, and others Django (developed 2003–2005, open-sourced July 2005) co-created with Adrian Holovaty at the Lawrence Journal-World (Here's GPT-5.5 for good measure It listed even more projects than Fable did!) In the past I've stated that I don't care about how much models know - I want them to be able to manipulate text and code in useful ways and actively look up the information they need via search tools, not bake it into their weights. But knowledge like this is a reasonably good proxy for model size - you can cram a whole lot more details about the world into a larger number of parameters. Does knowing more stuff mean the model is better at the tasks we pose to it? I can certainly imagine how a coding model with deeper knowledge of modern libraries and patterns could crunch through coding tasks more effectively. Is Fable really bigger than Opus? Anthropic haven't said anything about model size, so all we have are tea-leaves, but the speed, pricing and my own poking at its knowledge make me think that it's a large model. Maybe the largest yet from any vendor. Using Fable in Claude.ai Anthropic made Fable 5 available across all of their surfaces - the Claude.ai chat interface, Claude Code for web, Claude Code CLI and Claude Cowork as well. The model is available "until June 22nd" on the subscription plans (I'm on $100/month Max at the moment), after which it will be billed extra. Claude.ai is often under-estimated. Since September 2025 every chat has had access to a full container environment to run code, including the ability to install additional packages and even clone repositories directly from GitHub. Last week I released micropython-wasm a Python library that uses wasmtime to run a custom build of MicroPython in WebAssembly to act as a sandbox for untrusted Python code. I decided to see if Fable could upgrade that to running full Python instead. I started with this prompt: Clone simonw/micropython-wasm from GitHub and research how this could use a full Python as opposed to MicroPython Fable identified that it could use Brett Cannon's cpython-wasi-build builds for this, but was unable to download them itself due to environment restrictions. So I grabbed the two zip files from that page and uploaded them to Claude: Here's the Brett Cannon builds python-3.zip _build-python-3.zip as attachments) And that was that. It churned away for a few minutes and got the entire thing working. Part of the response included: I tried the cleaner single-zip-stdlib approach to shrink the filesystem surface, but CPython's getpath bootstrap fails to find encodings from inside a zip without more prefix finessing the directory-preopen approach works reliably, so that's what the PoC uses. The zip path is solvable but needs _PYTHONHOME /frozen-getpath work. So I said: Try a bit more at the single-zip-stdlib problem Then a little later: I want a wheel that has the whole system in it, the Python wrappers and the WASM files and the stdlibrary, so I can do uv run --with path-to-whl python -c "demo code" and it gave me this 13.9MB cpython_wasm-0.1.0-py3-none-any.whl file. You can try running Python code in a sandbox using that wheel URL and uv like this: uv run --with https://static.simonwillison.net/static/cors-allow/2026/cpython_wasm-0.1.0-py3-none-any.whl cpython-wasm -c print(45 56) Here's the full chat transcript This was a very strong start. Adding features to Datasette Agent and LLM using Claude Code Before I'd realized it was Fable day, my stretch goal for today was to add a new feature to Datasette Agent I wanted tool calls within that agent software to gain the ability to pause mid-execution and request approval directly from the user. This felt like a suitably meaty task to throw at the new model. Over the course of the day Fable not only solved that problem it also identified and then implemented four issues in my underlying LLM library that would help support this kind of advanced pause-resume mechanism in tool calls. It got everything working first using somewhat gnarly hacks, but the moment I told it that changes to LLM itself were in scope it set to work unraveling the hacks and turning them into supported features of LLM instead. My stretch goal turned into LLM 0.32a3 almost entirely written by Fable. Here are the release notes: Driven by the needs of Datasette Agent 's human-in-the-loop ask_user() feature, made the following improvements to how tool calls work: Tool implementations can declare a parameter named llm_tool_call in order to be passed the llm.ToolCall object for the current invocation. This allows them to access the current llm_tool_call.tool_call_id See Accessing the tool call from inside a tool #1480 Every tool call is now guaranteed a unique tool_call_id - providers that do not supply one get a synthesized tc_ -prefixed ULID. #1481 Tools can raise a llm.PauseChain exception to cleanly pause the tool chain, useful for things like waiting for human approval. The exception propagates to the caller with .tool_call and .tool_results (completed sibling results) attached, and no model call is made with a placeholder result. See Pausing a chain from inside a tool #1482 Failure semantics for concurrent tool execution: async sibling tool calls always run to completion before a pause or hook exception propagates. #1482 Chains can now resume from a messages= history ending in unresolved tool calls: the calls are executed through the normal before_call after_call machinery before the first model call, skipping any that already have results. The execute_tool_calls() method also accepts a new optional tool_calls_list= argument for executing an explicit list of ToolCall objects in place of the calls requested by the response. See Resuming a chain with pending tool calls #1482 Fixed a bug where the async tool executor silently dropped calls to tools not present in tools= - these now return Error: tool does not exist results, matching the sync executor. #1483 I'm really impressed with the quality of API design, tests, code and documentation that Fable put together for this. I spent several hours on it today, but it feels like several days' worth of work. How much I've spent I recently started using AgentsView to help track my local LLM usage across all of the different coding agents. I published a TIL today about adding custom Fable pricing to that tool, which I expect will not be necessary in the very near future. After setting the price, I ran this command to start a localhost web server to explore my usage: uvx agentsview serve Here's the treemap showing the breakdown of my Fable usage across various projects today: I used $110.42 worth of tokens today, all as part of my $100/month subscription. And some pelicans I ran "Generate an SVG of a pelican riding a bicycle" against all five thinking effort levels with Fable. Here are the results including the token cost for each one: low: 1,929 out, 9.67c medium: 2,290 out, 11.475c high: 2,057 out, 10.31c xhigh: 5,992 out, 29.985c max: 14,430 out, 72.175c It's interesting that high ended up using fewer tokens than medium for this particular run. Here are the Opus 4.8 pelicans for comparison. Tags: ai generative-ai llms anthropic claude llm-pricing pelican-riding-a-bicycle llm-release claude-mythos
链接：https://simonwillison.net/2026/Jun/9/claude-fable-5/#atom-everything
It's death
标签：#research_community #core
作者：
原文：Article URL: https://jesseduffield.com/ITS-DEATH/ Comments URL: https://news.ycombinator.com/item?id=48469347 Points: 144 Comments: 46
Claude Fable 5 and new AI safety fables
标签：#hidden_high_value #hidden_high_value
作者：
原文：One step further into the power politics of frontier AI systems.
链接：https://www.interconnects.ai/p/claude-fable-5-and-new-ai-safety
RIP software hackathons. Long live the hardware hackathon
标签：#research_community #core
作者：
原文：Article URL: https://blog.oscars.dev/posts/rip-software-hackathons-long-live-the-hardware-hackathon/ Comments URL: https://news.ycombinator.com/item?id=48468766 Points: 103 Comments: 38
链接：https://blog.oscars.dev/posts/rip-software-hackathons-long-live-the-hardware-hackathon/
llm 0.32a3
标签：#ai_engineering_blogs #core
作者：
原文：Release: llm 0.32a3 Almost entirely written by the new Claude Fable 5, see my write-up for more details Tags: projects ai generative-ai llms llm claude-mythos
链接：https://simonwillison.net/2026/Jun/9/llm/#atom-everything