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AI 资讯

We Made Our AI-vs-Human PR Stats a Public Live Dashboard

A while back I wrote about 64% of our merged PRs being written by AI . A few people (reasonably) asked: "nice story, but can I verify any of that?" So we put it on a public, auto-updating dashboard: 👉 www.codens.ai/stats/en It shows, for our GitHub organization: What % of merged PRs are authored by AI agents (currently 65%) Median time from PR-open to merge (2 minutes) Who merged what — task-execution agents vs maintenance bots vs auto-fix vs humans Weekly AI-merge counts and a per-repository breakdown Every number is tallied straight from the GitHub API by a small collector script, and the page regenerates itself weekly. We can't inflate it — it's measured, and the down weeks show up too (that's kind of the point). Why bother making it public Two reasons. 1. "Trust me bro" doesn't scale. When you claim most of your code is AI-written, the only honest move is to expose the raw counts so anyone can sanity-check them. The dashboard is that receipt. 2. It's a live dogfooding test. The whole thing — the PRD, the implementation, the review, the auto-fix on production errors — runs on Codens , our own AI dev-automation suite. If our own numbers ever tanked, the dashboard would be the first place it'd show. Public accountability is a good forcing function. Funny footnote: the dashboard itself was code-reviewed by our own AI reviewer before it shipped, and it caught two real bugs — an OG-image percentage that had drifted out of sync with the live number, and a broken error-fallback that would have blanked the page on malformed data. The tool built to sell "AI reviews your PRs" reviewed the PR that announces it. We'll take it. If you want to see the same machinery on your repos, there's a 14-day free trial (no card): codens.ai . Japanese version of the dashboard is here .

2026-07-07 原文 →
AI 资讯

How to Set Up Claude Code for a Project with Skills, Agents, Hooks, and a Secure GitHub Repository

How to Set Up Claude Code for a Project with Skills, Agents, Hooks, and a Secure GitHub Repository AI coding tools work best when they understand the project around the code. A fresh Claude Code session can answer questions and edit files, but it does not automatically know your architecture decisions, coding standards, security expectations, testing rules, pull request format, or operational constraints. That context needs to live somewhere predictable. This guide walks through a full Claude Code project setup using a reusable repository owned by desertfox33 : Reference repository: https://github.com/desertfox33/claude-code-project-template The goal is not just to create folders. The goal is to make Claude Code behave consistently across real project work: reviewing code, writing tests, preparing pull requests, checking security concerns, and following project-specific rules. Before using this setup in a production environment, verify current Claude Code behavior against the latest official documentation. Tooling, configuration names, and feature behavior can change. What We Are Building The repository uses this structure: claude-code-project-template/ ├── CLAUDE.md ├── CLAUDE.local.md.example ├── AGENTS.md ├── .mcp.example.json ├── .gitignore ├── SECURITY.md ├── CONTRIBUTING.md ├── .github/ │ ├── CODEOWNERS │ ├── dependabot.yml │ └── pull_request_template.md ├── .claude/ │ ├── settings.json │ ├── settings.local.json.example │ ├── rules/ │ │ ├── code-style.md │ │ ├── api-conventions.md │ │ ├── testing-standard.md │ │ └── pr.md │ ├── commands/ │ │ ├── review.md │ │ ├── deploy.md │ │ ├── scaffold.md │ │ ├── test.md │ │ └── pr.md │ ├── skills/ │ │ ├── code-review/ │ │ │ ├── SKILL.md │ │ │ └── review-checklist.md │ │ ├── testing-patterns/ │ │ │ ├── SKILL.md │ │ │ └── test-strategy.md │ │ ├── pr-description/ │ │ │ ├── SKILL.md │ │ │ └── template.md │ │ └── security-review/ │ │ └── SKILL.md │ ├── agents/ │ │ ├── security-reviewer.md │ │ ├── test-writer.md │ │ └── researc

2026-07-06 原文 →
AI 资讯

Why Developers Don't Read READMEs

Developers are the world's most efficient skimmers. When someone lands on your repo, they're running a rapid mental triage: What does this do? (5 seconds) Can I run it? (10 seconds) Should I trust it? (5 seconds) If they can't answer all three within 20 seconds, they close the tab and move on. They don't owe you a careful read. They're choosing between your project and ten others. Most READMEs fail the triage test because they're written from the author's perspective, not the reader's. The author knows how it works, so they explain how it works. The reader doesn't know if it works at all, so they need to know what it does first. That's the gap. Let's close it. The README That Passes the 20-Second Test Every high-performing README follows a version of the same structure. The order is not arbitrary — it mirrors the reader's decision-making process. 1. One-Line Description (Not Your Project's Name) The name is already in the repo title. The first line of your README should be a plain-language sentence of what this thing does . ❌ SuperCache v2.0 ✅ A zero-config in-memory cache for Node.js that cuts database eat time by 60%. If your one-liner doesn't tell me what problem you're solving, I'm already skimming toward the exit. 2. A 30-Second "Why This Exists" Paragraph Two to four sentences. What problem does this solve? Who is it for? Why this over the alternatives? This is not a marketing pitch. It's a fast filter. You want the right people to know immediately that this is for them — and the wrong people to know it's not. 3. Demo / Screenshot First — Before Installation This is the most skipped section in most READMEs. It shouldn't be. A GIF, screenshot, or three-line code output does more work than five paragraphs of description. Show me what success looks like before you tell me how to get there. If I can see that your output solves my problem, I'll read every word of your installation guide. 4. Installation — Zero Assumptions Assume your reader is smart but unfamiliar

2026-07-06 原文 →
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