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🔥 microsoft / qlib - Qlib is an AI-oriented Quant investment platform that aims t

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

2026-06-18 原文 →
AI 资讯

Collection of Claude Skills for Indie Developers - Here's What I Learned

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

2026-06-18 原文 →
AI 资讯

Testing GLM-5.2 on OpenCode: I'm impressed!

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

2026-06-18 原文 →
AI 资讯

One command turns Claude Code into a full dev team

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

2026-06-18 原文 →
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