I built a system where two AI agents pair-program with each other in real-time. One writes code, the other reviews and refactors. They built a complete CRUD app in 6 minutes. Video inside.
Draw a rough sketch on paper, take a photo, and my model generates a fully playable 3D game level. Uses a custom diffusion model + depth estimation. Here's a 2-minute demo.
![I made an AI that generates game levels from hand-drawn sketches [video demo]](https://images.unsplash.com/photo-1551103782-8ab07afd45c1?w=800&q=80)
After 3 months of fine-tuning GPT-4 and Claude, I created an agent that takes a natural language description and outputs a fully working React + Tailwind app. Here's how it works and the architecture behind it.
Sharing my exact VS Code config that maximizes AI productivity: extensions, keybindings, snippets, and settings. This took me a year to perfect. Screenshot of my daily workflow included.
I've been iterating on this prompt for 6 months across 200+ PRs. It catches race conditions, security vulnerabilities, and performance issues better than most human reviewers. Copy-paste ready.
I described a new programming language spec in plain English and asked Claude to build a compiler. It generated a lexer, parser, AST, and code generator in TypeScript. Everything works. I'm shook.
Pass a Figma file URL → get pixel-perfect Next.js + Tailwind components. Handles responsive layouts, dark mode, and even animations. Accuracy is around 92% compared to the design.
Hot take: I tried every AI coding tool for 2 months each. Here's my brutally honest comparison of Copilot, Cursor, Cody, Windsurf, and Aider. The results surprised me.
When CI tests fail, this bot reads the error, understands the codebase, generates a fix, and opens a PR. It fixed 73% of our test failures automatically last month.
Built an interactive visualization tool that shows attention patterns as LLMs process code. You can paste any code and see exactly which tokens the model focuses on. Really eye-opening for debugging prompts.
Built a terminal emulator with built-in AI that understands your codebase context. Auto-completes commands, explains errors, and suggests fixes. Written in Rust + TypeScript.
Everyone's building RAG pipelines but getting mediocre results. I switched to fine-tuning domain-specific 3B models and the quality difference is staggering. Here's my cost analysis.
Upload a PDF → AI extracts the schema → generates TypeScript types → creates REST endpoints → writes tests. All automated with a 4-step prompt chain. Works with Claude 3.5.
Fine-tuned Mistral 7B on 100k SQL query pairs with DPO. It now beats GPT-4 on Spider benchmark by 12%. Runs on a single RTX 4090. Model weights and training code included.
Controversial opinion: with o1 and Claude 3.5 Opus, elaborate prompt chains are becoming unnecessary. Simple instructions now outperform heavily engineered prompts. Here's my data from 500 experiments.
This meta-prompt transforms how LLMs approach coding tasks. It enforces error handling, edge cases, logging, types, and tests. Just prepend it to any coding request.