21 stories
Most CONTRIBUTING.md files are either a code of conduct with a link to the issue tracker or a wall of process nobody reads. A practical guide to writing one that gets a first pull request merged: what goes in it, what to leave out, how it fits next to AGENTS.md, and how to keep it true.
Most pull request descriptions restate the title or the prompt. A practical guide to writing PR descriptions that speed up review and survive in git history: what to include, a copy-paste GitHub template, examples, and how to keep agent-written descriptions honest.
Coding agents resolve nested instruction files differently: AGENTS.md uses the closest file, Claude Code stacks every CLAUDE.md from the root down. How to lay out instruction files in a monorepo, what goes at each level, and how to stop them contradicting each other.
AI agents now read your API docs before developers do. What OpenAPI, llms.txt, Markdown pages, and an MCP server each give an agent, which one to build first, how to write descriptions an agent can act on, and how to keep all of them in sync with the code.
llms.txt is a small Markdown file that tells AI agents which pages on your site matter. What the v2 spec requires, a worked example, the mistakes validators catch, how to serve it, and why the file quietly goes stale.
Agents write code faster than teams can review it, and agent PRs fail differently than human ones. A practical guide to reviewing AI-generated pull requests: the failure modes worth your attention, a review order that catches them, and how to move the work earlier so less of it lands on a human.
Coding agents fail on context, not capability. A practical guide to context engineering for a real repository: the four layers, a token budget you can reason about, how to diagnose which layer is failing, and how to keep it from going stale.
Skills, MCP servers, and CLAUDE.md all load context into a coding agent, but they are not interchangeable. A decision guide to picking the right one - static procedure vs. live data - without bloating the context window or shipping stale answers.
AGENTS.md is the open standard for giving AI coding agents project context. Learn what it is, how it differs from CLAUDE.md and .cursorrules, and how to write one with a free generator.
Struggling to keep docs up-to-date with a tiny team? Compare MoxieDocs and DeepDocs to see which AI docs generator saves your engineers time without sacrificing clarity—so you can ship faster.
GitHub documentation tools matter—especially for teams using docs as code. This comparison cuts through the noise to show which tool, MoxieDocs or DocuWriter.ai, keeps your docs in sync with your codebase *without* the hassle.
Outdated docs slow teams down. Discover how **living documentation tools** help engineering teams sync docs with code, reduce friction, and keep knowledge current—without the manual busywork.
Discover the best AI documentation platforms tailored for software teams, technical writers, and AI developers. Find the ideal tool to improve clarity and collaboration in your projects.
Moxie Docs now has a permanent Free plan: connect one repository, no credit card required ever, and see real generated docs, search, and live MCP context on your own code.
Discover essential AI code conventions that ensure models and development teams stay aligned, improving efficiency and consistency across AI projects. Practical strategies for engineering leaders and teams adopting AI.
MCP servers introduce a new attack surface: tool poisoning, rug pulls, confused deputy, and the lethal trifecta. What is real, and how to mitigate it.
Learn how to build an enhanced MCP feedback loop for coding agents. Practical guide to integrating automated testing, linting, and self-correction via MCP servers.
Static .cursorrules files grow bloated, fall out of sync, and go stale. Learn how Model Context Protocol (MCP) servers replace static custom rules files with live, queryable repository context.
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