AI Engineering From Scratch: A Python Course With 58K Stars
AI Engineering From Scratch is an MIT-licensed Python repository that walks learners from deep learning basics to agents. 58K stars and 47 open issues.
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Browse 12 posts tagged Tutorial. Setup notes, reviews, comparisons, and workflow patterns.
AI Engineering From Scratch is an MIT-licensed Python repository that walks learners from deep learning basics to agents. 58K stars and 47 open issues.
A web and API data pipeline tool with a SQL-like pipe syntax. Open any URL, filter rows, rename fields, vectorize content, and chain GPT prompts in one command.
Self-hosted digital game distribution platform for LAN parties. No internet required — host your own game library and let players browse and install from a polished desktop launcher.
Free browser tool that marks points and polygon regions on any image. Export to JSON or YAML, no upload required.
Open-source VS Code extension + runtime library for building TypeScript backend logic as visual node graphs. Prototyping AI agents and prompt chains directly in your codebase.
A Google Sheets-based finance tracker that shows your whole month on one screen. One-time payment, no subscription, tracks income, expenses, and budgets.
Intraview is a VS Code extension that lets AI agents build interactive, step-by-step code walkthroughs from your actual codebase. Local-first, git-stored, works with any AI.
A free browser-based tool to connect to any remote MCP server, explore its tools and resources, execute them live, and debug with real-time JSON-RPC logs.
TextFrame converts screen recordings into scroll-driven animated tutorials. Embed them anywhere with minimal code.
Learn systems internals by rebuilding Redis, Git, SQLite and more from scratch. Advanced challenges used by engineers at Google, OpenAI, and Vercel.
Awesome Architecture is a bilingual architecture knowledge base with 26 tutorials, 25 system maps, and 6 end-to-end cases for developers designing AI and distributed systems.
raiyanyahya/how-to-train-your-gpt teaches modern decoder-only LLM internals with a 12-chapter walkthrough, runnable notebooks, a commented `main.py`, and fine-tuning guides aimed at Python developers.