Two Skills to Fix the Context Gap in Claude Code
They cover what CLAUDE.md never will.
They cover what CLAUDE.md never will.
We need RSS for sharing abundant vibe-coded apps Matt Webb: I would love an RSS web feed for all those various tools and apps pages, each item with an “Install” button. (But install to where?) The lesson here is that when vibe-coding accelerates app development, apps become more personal, more situated, and more frequent. Shipping a tool or a micro-app is less like launching a website and more like posting on a blog. This inspired me to have Claude add an Atom feed (and icon) to my…
Cat Wu leads product for Claude Code and Cowork at Anthropic, so she’s well-versed in building reliable, interpretable, and steerable AI systems. And since 90% of Anthropic’s code is now written by Claude Code, she’s also deeply familiar with fitting them into routine day-to-day work. Last month, Cat joined Addy Osmani at AI Codecon for […]
This is the fifth article in a series on agentic engineering and AI-driven development. Read part one here, part two here, part three here, and part four here. I recently had a taste of humility with my AI-generated code. I live in Park Slope, Brooklyn, and recently I needed to get to the other side of the neighborhood. […]
Zig has one of the most stringent anti-LLM policies of any major open source project: No LLMs for issues. No LLMs for pull requests. No LLMs for comments on the bug tracker, including translation. English is encouraged, but not required. You are welcome to post in your native language and rely on others to have their own translation tools of choice to interpret your words. The most prominent project written in Zig may be the Bun JavaScript runtime, which was acquired by Anthropic in December…
Release: llm 0.32a1 Fixed a bug in 0.32a0 where tool-calling conversations were not correctly reinflated from SQLite. #1426 Tags: llm
...using 100% open-source stack!
I just released LLM 0.32a0, an alpha release of my LLM Python library and CLI tool for accessing LLMs, with some consequential changes that I've been working towards for quite a while. Previous versions of LLM modeled the world in terms of prompts and responses. Send the model a text prompt, get back a text response. import llm model = llm.get_model("gpt-5.5") response = model.prompt("Capital of France?") print(response.text()) This made sense when I started working on the library back in April…
Release: llm 0.32a0 See the annotated release notes. Tags: llm
It’s hard to root for either side, but Musk has a point.
We all need a break so: What is the most important chart in the world?
I was talking to a senior engineer at a well-funded company not long ago. I asked him to walk me through a critical algorithm at the heart of their product, something that ran hundreds of times a second and directly affected customer outcomes. He paused and said, “Honestly, I’m not totally sure how it works. […]
Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query. — OpenAI Codex base_instructions, for GPT-5.5 Tags: openai, ai, llms, system-prompts, prompt-engineering, codex-cli, generative-ai, gpt