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GitHub热门项目 | Extensions for the Zed editor | Stars: 1,758 | 3 stars today | 语言: JavaScript

GitHub热门项目 | GitHub 汉化插件，GitHub 中文化界面。 (GitHub Translation To Chinese) | Stars: 27,319 | 63 stars today | 语言: JavaScript

GitHub热门项目 | A Git-based CMS for Static Site Generators | Stars: 19,157 | 5 stars today | 语言: JavaScript

GitHub热门项目 | Common recipes to run vLLM | Stars: 864 | 9 stars today | 语言: JavaScript

GitHub热门项目 | The open curriculum for learning web development | Stars: 12,664 | 10 stars today | 语言: JavaScript

GitHub热门项目 | AI builders digest — monitors top AI builders on X and YouTube podcasts, remixes their content into digestible summaries. Follow builders, not influencers. | Stars: 5,277 | 25 stars today | 语言: JavaScript

GitHub热门项目 | Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process. | Stars: 44,719 | 144 stars today | 语言: Python

GitHub热门项目 | | Stars: 31,754 | 480 stars today | 语言: Python

GitHub热门项目 | Free and Open Source Machine Translation API. Self-hosted, offline capable and easy to setup. | Stars: 14,817 | 179 stars today | 语言: Python

GitHub热门项目 | Official Python inference and LoRA trainer package for the LTX-2 audio–video generative model. | Stars: 7,389 | 47 stars today | 语言: Python

GitHub热门项目 | Kilo is the all-in-one agentic engineering platform. Build, ship, and iterate faster with the most popular open source coding agent. | Stars: 21,484 | 1,339 stars today | 语言: TypeScript

GitHub热门项目 | A lightweight, lightning-fast, in-process vector database | Stars: 11,033 | 435 stars today | 语言: C++

GitHub热门项目 | GLM-5: From Vibe Coding to Agentic Engineering | Stars: 3,890 | 286 stars today | 语言:

A few months ago I started building small tools as single HTML files - no npm, no React, no backend. Just one file that opens in a browser and works offline. I built 4 real products this way: DarkenAmber IT Tools - 17+ developer tools in 194KB ZeroOffice - PDF, image, AI tools in one file PrivacyKit - Photo privacy tools, no upload required ElectroKit - Electrical calculator + cost estimates for CIS market Every single one: one .html file. Works offline. Opens instantly. No server. The problem with AI coding assistants Every time I asked Claude or Copilot to build something simple, I got: A React project with src/ folder package.json with 12 dependencies webpack config TypeScript setup ...before writing a single line of actual logic. I kept manually correcting it. "No, one file. No npm. Vanilla JS." Then I realized - I should just teach it once and reuse that knowledge. What is a Claude Skill? A skill is a Markdown file with YAML frontmatter that changes how Claude thinks for a specific context. It is not a prompt. It is not a system message. It is a reusable set of rules that shapes how Claude reasons, what it prioritizes, and what it avoids. yaml--- name: single-file-app description: "Build complete web tools as a single HTML file - vanilla JS, inline CSS, localStorage, offline-first." tags: html vanilla-js offline version: 1.2 --- The two skills I built single-file-app Teaches Claude to build complete web tools in one HTML file. What changes: No React, no npm, no build tools unless truly justified Vanilla JS first, always localStorage for data persistence Dark/light theme with system preference detection Accessibility built in (labels, aria, keyboard nav) XSS prevention for user input Export/import for user data Anti-patterns it prevents: ❌ "Let me set up a React project" ❌ Creating src/ folder for a simple tool ❌ Suggesting npm install for a calculator ✅ "Here is your complete HTML file" ship-it Teaches Claude to bias toward shipping over planning for early-stag

I have a confession: I roll my eyes at AI benchmarks. Every other week someone on Twitter posts a chart where a brand new model is suddenly beating Opus and GPT, the replies go crazy, and then you actually use the thing and it falls apart on the first real task. Beautiful numbers, ugly code. So when z.ai shipped GLM 5.2 and the timeline started shouting that an open-weights model was now nipping at the heels of the frontier labs, my instinct was the usual one. Sure. Prove it. This post is me proving it. I gave GLM 5.2 a real feature to build on my actual production website, with almost no hand-holding, and watched what happened. Spoiler: I was not expecting to write the sentence I ended up writing. If you'd rather watch me run the whole thing live (including the part where my tools crashed on camera), the video is right here: The claims I was here to test Let's get the hype out of the way first, because the claims are genuinely big. // Detect dark theme var iframe = document.getElementById('tweet-2066938937344495629-390'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2066938937344495629&theme=dark" } GLM 5.2 is the same physical size as GLM 5.1 (744B total parameters, 40B active), but on the Artificial Analysis Intelligence Index it jumped 11 points, from 40 to 51. That score makes it the leading open-weights model , ahead of MiniMax-M3 (44), DeepSeek V4 Pro (44) and Kimi K2.6 (43). On the overall leaderboard it sits behind only Claude Fable 5 (60), Claude Opus 4.8 (56) and GPT-5.5 (55). For an open, MIT-licensed model you can download the weights for, that is a wild place to be. Here is the upgrade at a glance: GLM 5.1 GLM 5.2 Intelligence Index (v4.1) 40 51 Context window 200K 1M Total / active params 744B / 40B 744B / 40B Output tokens per task 26k 43k Cost per task ~$0.25 ~$0.46 Price (in / cache / out per 1M) $1.4 / $0.26 / $4.4 $1.4 / $0.26 / $4.4 License MIT MIT Two things jump out. First

I love Claude Code's subagents. But I kept noticing the same chore: every new project, I'd hand-write the same crew again — a builder, a reviewer, someone to keep the stack conventions straight. Good setups, but they lived in one repo and never got reused. So I built ccteams — a package manager for agent teams. One command drops a ready-made team of Claude Code subagents into your project. npm install -g ccteams ccteams use go-api // apply your favourite team That applies a Go builder + reviewer, tuned for net/http , to the current project. Switch when the work changes: ccteams use next-ts # Next.js App Router + TypeScript + Tailwind ccteams use generalist # scope -> design -> build -> QA -> ship, any stack Are you not sure which team you need? Don't worry, you can use /ccteams:choose-team and AI will choose the best team for you! /plugin marketplace add toffyui/ccteams /plugin install ccteams@ccteams /ccteams:choose-team I want to create a todo app. What's a "team"? A team is just a curated bundle of Claude Code subagents — each a markdown file with the usual name / description / tools frontmatter and a system prompt — plus an orchestration.md that gets merged into your project's CLAUDE.md . Nothing magic, nothing proprietary. It's the setup you'd build by hand, except already built and ready to reuse. ccteams ships with 8 teams: generalist — stack-agnostic, takes a feature scope → design → build → QA → ship next-ts — Next.js App Router + TypeScript + Tailwind frontend — framework-agnostic UI/UX and accessibility go-api — idiomatic Go HTTP APIs python-fastapi — FastAPI + Pydantic v2 rails — Ruby on Rails debug — reproduce → root-cause → fix → regression test research — compares options and recommends; writes no code What use actually does No black box. ccteams use <team> : Copies the team's agents into .claude/agents/ Writes .claude/active-team.md and adds an @.claude/active-team.md import to your CLAUDE.md Tracks everything in .claude/.ccteams-manifest.json so swi

Cybersecurity firm Chainguard has announced the launch of Athena, an industry coalition to use artificial intelligence to find and fix vulnerabilities in widely-used open-source software before attackers can exploit them. The coalition focuses on libraries, containers and other components that underpin web browsers, data centres, smartphones and payment systems. By Matt Saunders

There is a dangerous trap in the local AI world: picking the smallest quantization that fits into your VRAM just because it "runs." We see developers doing this all the time, completely unaware that they’ve crippled their agent's ability to reason. It’s easy to look at a leaderboard, see a model rank high, and assume it’s good to go. But leaderboard scores are a poor proxy for real-world agent behavior. A model might pass a static benchmark at a lower quantization, but when you put it in an agentic loop, its tool-calling accuracy can fall off a cliff. We built the "Quant Audit" feature in QuantaMind because we were tired of this silent failure. It systematically measures the performance drop-off as you move through different compression levels. The goal shouldn’t be to find the smallest quant that loads; it should be to identify the largest quant that actually retains the reasoning integrity your app requires. Stop guessing, start measuring, and stop letting leaderboard hype dictate your architecture.

Epic Games just dropped something that could reshape how game studios handle code and assets. They've open-sourced Lore — a centralized version control system built from the ground up to solve one painful problem: managing enormous binary files alongside source code. This isn't another Git wrapper. It's a completely new VCS, written in Rust, MIT-licensed, and battle-tested behind Fortnite's UEFN (Unreal Editor for Fortnite) toolkit. Why Does the World Need Another VCS? Git is brilliant for text-based code. But game development isn't just text. It's 4K textures, uncompressed audio, rigged 3D models, animation sequences, and massive world maps. These files can be hundreds of megabytes each. Git wasn't designed for this. Git LFS (Large File Storage) helps, but it's a patch on top of a fundamentally text-oriented system. Perforce Helix Core has been the industry standard for game studios for decades — but it's proprietary, expensive, and closed-source. Epic Games looked at this landscape and said: we can do better. What Is Lore? Lore is a centralized, content-addressed version control system optimized for: Large binary assets — textures, meshes, audio, video Massive teams — hundreds of developers working simultaneously Hybrid projects — code + binaries in the same repository Sparse checkouts — developers only download what they need Think of it as Perforce's philosophy (centralized, binary-friendly) combined with Git's content-addressed storage model, wrapped in Rust's performance guarantees. How It Works Under the Hood Lore's architecture is built around a few key technical decisions: Content-Addressed Storage Every piece of data is stored and referenced by its content hash. This means: Automatic deduplication — identical content is stored once Integrity verification — any tampering changes the hash Efficient caching — content can be cached anywhere in the pipeline Merkle Trees & Immutable Revision Chain Revision hashes are cryptographically derived from parent hashes

你知道吗？GitHub 上有一个 17,889 Stars 的开源项目，能让你的 AI Agent 拥有跨会话的持久记忆——不是简单的向量检索，而是一个会自动进化的知识图谱。但大多数开发者只用它来做基础的文档搜索，完全忽略了它真正的能力。 Cognee 是一个开源 AI 记忆平台，它把知识图谱、向量搜索和认知本体论生成统一到一个记忆层中。在 2026 年，AI Agent 正从单轮对话机器人向长时间运行的自主系统演进，而瓶颈不再是模型能力，而是上下文管理。以下是大多数人不知道的五个隐藏用法。 隐藏用法 #1：自动图谱同步的会话记忆 大多数人的做法：把对话历史存在简单的列表或向量数据库里，上下文长了就塞进 prompt。这在前几轮还行，但会话一长就迅速退化。 隐藏技巧：Cognee 的会话记忆充当快速缓存，会在后台自动同步到持久化知识图谱。你既能获得内存上下文的速度，又能拥有图数据库的持久性——而且同步过程完全不需要手动编排。 import cognee import asyncio async def agent_session (): # 会话记忆——快速、临时、按会话隔离 await cognee . remember ( " 用户询问了 Q3 收入趋势并请求导出 CSV。 " , session_id = " support_ticket_4421 " ) # 后续查询会话记忆（快速路径） results = await cognee . recall ( " 用户问了什么收入相关的问题？ " , session_id = " support_ticket_4421 " ) # 会话结束时，会话记忆自动同步到永久图谱 # 无需手动导出，不会丢失任何数据 asyncio . run ( agent_session ()) 效果：你的 Agent 在会话内部保持对话上下文以实现快速响应，但会话结束后不会丢失任何信息。知识图谱会自动跨所有会话积累洞察。 数据来源：Cognee GitHub 17,889 Stars，README 文档中 session_id 参数和自动同步行为在"Use with AI Agents"章节有详细说明。 隐藏用法 #2：面向领域推理的本体论 grounding 大多数人的做法：把文档灌入向量数据库，依赖语义相似性做检索。模糊匹配还行，但当你需要结构化的、领域感知的推理时就不行了。 隐藏技巧：Cognee 的 cognify 流水线不只是嵌入文档——它会从你的数据中生成认知本体论，创建带有类型化关系的结构化知识图谱。这意味着你的 Agent 可以对实体及其连接进行推理，而不仅仅是找到相似的文本。 import cognee import asyncio async def build_domain_memory (): # 摄入领域文档 await cognee . remember ( """ 客户 Acme Corp 有 3 个活跃订阅。 订阅 A：企业计划，到期日 2026-09-15。 订阅 B：入门计划，已于 2026-03-01 到期。 客户经理是 Sarah Chen。 升级路径：Sarah -> 销售副总裁 -> CRO。 """ ) # Cognee 自动提取实体和关系： # (Acme Corp) --拥有--> (订阅 A) # (订阅 A) --类型--> (企业计划) # (订阅 A) --到期--> (2026-09-15) # (Sarah Chen) --管理--> (Acme Corp) # 现在进行结构化精确查询 results = await cognee . recall ( " 哪些客户在未来 90 天内订阅到期？ " ) # 返回 Acme Corp 及具体订阅和日期—— # 而不仅仅是"关于订阅的相似文本" asyncio . run ( build_domain_memory ()) 效果：你的 Agent 不再依赖向量相似性碰运气，而是从理解实体类型、关系和时间约束的本体论中获得结构化答案。 数据来源：Cognee README "Product Features"章节描述了"ontology grounding"和"cognitive-science-grounded ontology generation"；ArXiv 论文 2505.24478 关于优化知识图谱与 LLM 的接口。 隐藏用法 #3：通过共享图谱实现跨 Agent 知识共享 大多数人的做法：每个 Agent 维护自己独立的记忆。客服 Agent 无法受益于销售 Agent 昨天学到的东西。知识按设计被隔离。 隐藏技巧：Cognee 的知识图谱是一个共享基础设施层。多个基于不同框