GPT-3 (2020) Paper Notes
Paper notes on GPT-3 covering its core ideas: 175B scaling, in-context learning (zero/one/few-shot), weighted-sampling training data, headline benchmark numbers, and the data-contamination and bias limitations.
All Posts
Why I Made Markdown the Source of Truth — Building a Dashboard with an AI Agent
A retrospective on building a macOS desktop app (Tauri + Rust + React) with an AI agent. The decision to make markdown files — not the database — the source of truth, the research I did before writing any code, the features I chose not to build, and the macOS permission bug that took the longest to crack. A story about judgment more than code.
Semble Architecture Analysis: How an Agent-Oriented RAG Solves Code Search with Static Embeddings
Semble is a Python library that splits code into chunks with tree-sitter, fuses Model2Vec static embeddings with BM25 via RRF, and applies code-aware reranking — delivering millisecond code search on CPU alone. Where CodeGraph solves the same problem with an AST knowledge graph, Semble solves it through retrieval. This post analyzes the architecture by contrasting the two approaches.
CodeGraph Architecture Analysis: How Is the Code Intelligence Layer Beneath Coding Agents Built?
CodeGraph is a TypeScript tool that uses tree-sitter to parse source code, builds a local SQLite knowledge graph of symbols, edges, and files (with FTS5), and exposes that graph via an MCP server to coding agents like Claude Code, Cursor, Codex, and Hermes Agent. Instead of burning tokens navigating a codebase with grep/Read calls, agents get answers from a single codegraph_explore invocation. This post analyzes the architecture that makes that possible.
spec-kit vs superpowers: Two Ways to Give a Coding Agent a "Process"
GitHub's spec-kit and Anthropic's superpowers plugin both force a workflow onto coding agents so they never drift into vibe coding. But one is a spec-first file system that leaves the specification behind as an artifact in a .specify/ directory, while the other is a collection of discipline prompts lazily loaded through the Skill tool. We compare the two projects across distribution model, artifact philosophy, token cost, and extensibility.