The GitHub Repository Every Claude Code User Needs to Bookmark

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There's a moment every Claude Code user eventually hits. You've gotten comfortable with the basics, you've written a few solid prompts, maybe you've even set up a CLAUDE.md file. And then you realize: I have no idea what I'm missing.

That's where awesome-claude-code comes in.

Created and maintained by GitHub user hesreallyhim, this repository has become the de facto community hub for Claude Code power users – a hand-curated collection of skills, hooks, slash commands, agent orchestrators, workflows, tooling, and plugins that extend what Claude Code can do. At the time of writing, it sits at ~47,000 stars and nearly 4,000 forks, and it's growing fast.

This is not an auto-scraped list of everything on GitHub. It's a judgment-driven curation. Tools that don't work get cut. Tools that do work get tagged with what they're actually useful for. That distinction matters more than it sounds.

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Important Claude Code Permission Modes Every Developer Should Know

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When Anthropic shipped Claude Code, they weren't just releasing a coding assistant. They were releasing an agent framework – and with that comes a fundamental question every agentic system must answer: who decides what the AI is allowed to do?

The answer in Claude Code is a layered permission system with seven distinct modes. Each mode represents a different stance on the autonomy-safety tradeoff. Most are user-selectable. Two are not – and for good reason.

claude-code-permission-modes-feature

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MVCC Explained: Help Your Database NOT Freeze When Someone Else Hits “Update”

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Ever wondered how thousands of users can read and write to the same database table at the same time without everything grinding to a halt? The answer, in most modern databases (PostgreSQL, MySQL/InnoDB, Oracle, and others), is a clever mechanism called MVCC – Multiversion Concurrency Control.

Let’s break it down in plain language, with a simple example you’ll never forget.

mvcc-feature
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RAG vs. Agentic RAG vs. Graph RAG: Which One Actually Fits Your Use Case?

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If you’ve built anything with LLMs in the last couple of years, you’ve built a RAG pipeline. Embed the query, search a vector store, stuff the top chunks into a prompt, let the model talk. It’s the “Hello World” of grounding LLMs in real data – and for a long time, it was enough.

It isn’t anymore.

The moment your use case involves multi-hop reasoning, tool calls, or relationships between entities scattered across thousands of documents, naive RAG starts cracking. That’s given rise to two evolutions worth understanding deeply: Agentic RAG and Graph RAG. They solve different problems, and confusing them will cost you weeks of rebuilding. Let’s walk through all three, step by step.

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The Self-Hosted Obsidian Web App I Didn’t Know I Needed

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If you’re anything like me, your “second brain” lives in a folder of Markdown files managed by Obsidian. It’s fast, local-first, and endlessly extensible – until you’re on a machine that isn’t yours. A work laptop. A friend’s desktop. A Chromebook you grabbed in a pinch. Suddenly your years of linked notes are one app-install-away from being useless.

That’s the itch WebObsidian scratches.

It’s an open-source project by developer xnohat, and the pitch is refreshingly simple: point it at a folder of Markdown files and edit your notes from any browser – while staying 100% compatible with your existing Obsidian vault, .obsidian/ config and all.

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