#编码

🚀 DeepSeek-V4.1-Flash-1017 来了！ 与上一版本相比，基准测试表现更强、任务完成更快、输出更短——在性能与成本上实现了帕累托改进。现已在我们的网站、应用和 API 上线。 🛠️ 针对 @DeepSeekHarness、Tencent CodeBuddy、WorkBuddy 等进行了优化。升级到 DeepSeek Harness v0.2.1-rc.1 或更高版本，以获得新模型的最佳体验。 1/4

Anthropic is on track to stop hiring technical juniors by 2027

Claude 编写了 Claude Code 约 90% 的代码。 从六月到九月，合并的 PR 数量翻了一倍多，而回退的占比下降了 40% 以上。 我们撰文介绍了我们如何改变代码审查来实现这一目标，以及如何采用它： https://claude.dev/blog/solving-code-review-at-anthropic/

Bolt is free for EVERYONE, all weekend long. And you could win $10k! Turn your idea into a product. Submit it by Oct 20 for your shot at the prize. Bolt Builders Week starts now 🏁 Details here 👉 https://bolt.new/builders-week

Claude 编写了 Claude Code 约 90% 的代码。 从六月到九月，合并的 PR 数量翻了一倍多，而回退的占比下降了 40% 以上。 我们撰文介绍了我们如何改变代码审查来实现这一目标，以及如何采用它： https://claude.dev/blog/solving-code-review-at-anthropic/

how do you trust your agents' work more? copy this post/link and give it to your @bot or agent! 1. codebase improvements: this is just good software engineering - better architecture, stronger types and invariants, clear boundaries. agents love to copy what exists and take the shortest path, so make the easy thing the right thing. categorically eliminate problems through code and you won't need to think about it again. 2. static analysis: lint rules, tests, CI, verification (eg TLA+). very token efficient ways to catch issues ahead of time. 3. prose: comments, rules, AGENTS.md, skills - anything that can't be enforced and requires agent judgment. use it minimally, but do use /create-verification-skill. 4. human review: the hardest to scale. at the rate agents land code, it's pretty much impossible to thoroughly review every PR, so spend that effort on the highest leverage point, designing an environment where agents do the right thing by default. this forms an inverse pyramid: you want most of your effort at the top, least at the bottom. codebase improvements are deterministic, making it more token efficient, and lets you sleep more soundly at night. install https://github.com/cursor/plugins/tree/main/pstack and create a new Project: 1. look through my slack/chat messages and code review comments where i've given feedback on PRs, code, architecture 2. use /recall on my past transcripts to learn how i steer agents 3. /poteto-mode use /correct and /architect on past commits to find the most common pitfalls agents fall into, then propose a plan to rearchitect the code i own so it's more agent friendly. answer open questions with prototyping rather than defer to me, and back the plan with real data. 4. before you begin, use /sharpen + /in-your-own-words and confirm this prompt with me

今天我们发布 Muse Spark Open，这是迄今为止能力最强的美国开放权重模型。 专为编程和智能体工作打造，它擅长长周期任务、复杂软件工程和多步推理。 关键模型规格： → 816B 总参数 / 42B 激活参数 → 1M token 上下文窗口 → 原生支持文本、图像、视频、音频多模态

Software maintenance is changing. A few months ago, I had to tell Codex exactly where to look for dead code. Now it can find candidates in Slack, check them against production telemetry, and draft cleanup PRs. Here’s how we do this at OpenAI. https://developers.openai.com/blog/maintaining-software-with-the-gpt-6-family

Claude 编写了 Claude Code 约 90% 的代码。 从六月到九月，合并的 PR 数量翻了一倍多，而回退的占比下降了 40% 以上。 我们撰文介绍了我们如何改变代码审查来实现这一目标，以及如何采用它： https://claude.dev/blog/solving-code-review-at-anthropic/

Ok can finally talk about what I’ve been working on at Reflection. Introducing Mirror, our own open source coding agent optimized for Beam. I started the mirror project when I first joined Reflection, and it has gone through many iterations since then. Install it today with: uv tool install reflection-mirror https://github.com/reflection-oss/mirror-beta

Claude 编写了 Claude Code 约 90% 的代码。 从六月到九月，合并的 PR 数量翻了一倍多，而回退的占比下降了 40% 以上。 我们撰文介绍了我们如何改变代码审查来实现这一目标，以及如何采用它： https://claude.dev/blog/solving-code-review-at-anthropic/

Code Review is now generally available for Claude Team and Enterprise plans. Reviews are now faster, catch more of the bugs that matter, and admins can pick from Quick, Standard, or Deep review levels for each repo.

Codex is one of the most important untold stories in the history of technology. A year ago, it was a scrappy effort in a quiet corner of @OpenAI. Its cloud product hadn’t found PMF, and the team was starting over from scratch. Less than 12 months later: - Codex swallowed the flagship ChatGPT desktop app from the inside (it literally runs as com.openai.codex on your Mac) - Codex and ChatGPT for Work hit 40M+ weekly users across engineering and knowledge work - Its leader @thsottiaux was elevated to run all of OpenAI’s core products, including ChatGPT, Codex, and the API How did they pull it off? They trained their own dedicated model line, separate from ChatGPT, with researchers and product builders sitting on the same team They built four different products in a single year (cloud → terminal → IDE sidebar → a desktop app with no code editor at all) as every model leap made a new form factor possible They used Codex to build Codex—so a handful of people could prove a new product in weeks instead of arguing for it in meetings I had a front-row seat to the whole arc—from their failed Cloud launch, to OpenAI’s offices the night before GPT-5 dropped, to the day Codex took over ChatGPT. And I spent months interviewing @gdb, @thsottiaux, @embirico, @mia_glaese, @aambrosino, and @edbayes to get inside how it all went down. Here is the full, behind-closed-doors story of the team that remade OpenAI: https://every.to/p/how-codex-happened We’re pushing the boundaries of how stories like this are told. This piece is completely agent-native—designed to be read alongside your agent inside ChatGPT’s in-app browser: - Every interview transcript is included in full: Click any quote and you (or your agent) can see the exact conversation it came from. - Built for your agent to explore: Ask your agent to pull out product lessons for your team, cross-examine the transcripts, or find insights I missed when writing the piece. It’s a story about the future of software—built to be read with an agent: https://every.to/p/how-codex-happened