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在 Anthropic 重新设计源代码管理。Anthropic 发布了一篇有趣的文章，阐述了他们为何以及如何基于过去六个月代码变更速率的显著提升，打造了一套自定义的 Git 替代方案。2026 年 5 月，Anthropic 每天平均合并约 2,500 条代码变更；截至 9 月，这一数字已超过每天 3 万条。文中还附有一张展示这些变更的图表。我让 Claude 对该图表进行了修改（代码在此），为其添加了 2026 年各模型公开发布的标记：此处显示的是公开发布日期，而非模型供 Anthropic 内部使用的日期。我推测，Fable/Mythos 等类模型的推出是导致编码速度大幅提升的关键因素之一。“Antique”是他们新推出的代码评审系统。源代码管理系统……
发布：llm-gemini 0.35 我为 llm-gemini 新增了对 Gemini 4 Argon 模型的支持，该模型今日已通过 Gemini API 推出，并面向其每月 100 美元订阅计划的客户开放。以下是低、中、高三个等级的鹈鹕图。正如预期，它们都非常出色：Gemini 向来以生成优质的 SVG 而闻名，而且据我所知，他们是唯一一家专门在 SVG 方面投入研发的实验室（此前他们还曾自豪地展示过自己的鹈鹕作品）。这是“高”档版本的插图，耗资 17.166 美分：定价颇为有趣。上线之初，Argon 的输入价格为每百万条 2 美元，输出价格为每百万条 10 美元——与 GPT-6 Sol 同价，是 Claude Opus 5.5 的一半，也是 Claude Fable 5.1 和 GPT-6 Astra 的五分之一。然而，自 12 月 1 日起，Argon 的价格将翻倍——届时其价格将与……相同
我们首个机器人舱与Dyna的首席科学家共同探讨其在世界行动模型及物理人工智能下一前沿领域的重大布局
Developing Gemini 4 Argon responsibly and safely
译文排队中，当前为原文 · enGitHub’s context-aware secret classifier improves detection quality, raises weighted acceptance by 11.5%, and reduces median scan execution time by 86%. The post GitHub’s model for secret detection: Finding more secrets with less noise appeared first on The GitHub Blog.
译文排队中，当前为原文 · enGemini 4 Argon for Robotics
译文排队中，当前为原文 · enGemini 4 Argon: Built to solve your hardest problems
译文排队中，当前为原文 · enIf the agent isn't using my diagnostics library, I could introspect and update AGENTS.md, but there's still a delta, and it'll drift. Instead, I write a pre-commit hook that checks the git diff for direct calls to observability modules that bypass my diagnostics module. Boom: fail the commit, inject a prompt telling the agent to use the diagnostics library. All of a sudden it's ten out of ten. — Geoffrey Huntley, nobody should type code by hand anymore. here's what's left. Tags: coding-agents, generative-ai, git, agentic-engineering, ai, geoffrey-huntley, llms
译文排队中，当前为原文 · enMSL作为前沿实验室的崛起终于宣告完成。
发布：web-edit 0.1a1 包含由 Claude Opus 5.5 编写的性能优化，是在我向其提供了由 GPT-6 Astra 生成的这份 Python 3.15 性能分析报告之后完成的。标签：性能分析，项目
快速运行, 使用 LFM2.5-24B- A2B 的私人代理设备
希格斯菲尔德用GPT-6 Astra带给创作者带去更长的视频,用GPT-Image-2.5带去精确的视觉,用GPT-Live带去语音电影制作的路径.
每天早上8点, 这个工作流程搜索当天的AI新闻, 没有真正新闻的话题.
双子座4 Argon刚刚下降,N8n是最早使用和整合该模型的公司之一. 请检查date=中的日期值 (帮助) 如何使用现有的 n8n 工作流程以及如何构建新工作流程 。 以下是Google发布的基准: Google 报告的奔驰. 资料来源:双子座4
Release: llm-meta-ai 0.2 Meta AI release Muse Wonder (and Muse Wonder Full), their new "most capable models". These look like they may be Meta's first Fable class models - Artificial Analysis scores Muse Wonder (Max) as equivalent to GPT-6.1 Sol. I updated my llm-meta-ai plugin for the new model. Here are pelicans for Muse Wonder for the six reasoning levels: minimal, low, medium, high, xhigh, max. This is the max one: For comparison, here are pelicans for Muse 1.3 (released September 2nd) and Muse 1.2 (released August 5th). Meta AI are putting out a new model a month, and the improvements between each model (at least in terms of drawing pelicans) are material. Tags: ai, generative-ai, llms, meta, pelican-riding-a-bicycle, llm-release
译文排队中，当前为原文 · en在“AI工程师纽约”活动中，Anthropic借鉴了Unix和Linux的经验，阐释了智能体如何在不令开发者不堪重负的情况下应对更大型的项目。
Python 3.15 is here! Here's the essential What's new in Python 3.15 guide, this year curated by 3.14 and 3.15 release manager Hugo van Kemenade. As someone who builds open source software that works in every supported Python version, I get to say goodbye to Python 3.10 and start embracing features from Python 3.11. First, some highlights from Python 3.15. Explicit lazy imports I'm most excited about Explicit lazy imports from PEP 810. These let you import modules like so: lazy import json lazy from pathlib import Path The module will then be imported the first time those symbols are used in the code that follows. My LLM CLI tool implements plugins. I had to recommend manual lazy imports in the plugin author documentation, because simply installing plugins that used heavy dependencies such
译文排队中，当前为原文 · enLearn how to add custom instructions and skills to your repository, use custom agents, and connect MCP servers like Playwright. The post GitHub Copilot app for Beginners: How to teach Copilot about your codebase appeared first on The GitHub Blog.
译文排队中，当前为原文 · enResearch: Profiling web-edit with Python 3.15: flame graphs and source heatmaps I put the new Python 3.15 profiler through its paces by having GPT-6 Astra Extra High in Codex run it against my new web-edit app: I need a Python 3.15 flame graph and heatmap demo of the "web-edit" app from PyPI - research first then make me those demos Here's the resulting example flame graph and heatmap. Tags: profiling, python, codex
译文排队中，当前为原文 · enStudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
译文排队中，当前为原文 · en