A source-level teardown of one well-known project.
A source-level teardown of how five motion-design skills separate vocabulary, principles, review, planning, and execution—and where two important workflow exceptions keep the architecture honest.
CodexBar looks like a tiny macOS utility for AI coding limits. Read at v0.41.0, the real architecture is a curated provider catalog: compile-time provider IDs, descriptors, strategy pipelines, and separate app hooks that turn uneven vendor quota signals into one usage surface.
LazyCodex is marketed as an agent harness for complex codebases. Read at v4.15.1, the source says something narrower and more useful: the root package is a small Codex install alias, the concrete product is the bundled omo plugin, and the deeper harness ownership points back to OmO.
Aider is pitched as AI that reads your whole codebase. Read at v0.86.3.dev, the repo map is a ranking problem, not an AI one: tree-sitter pulls the symbols, a PageRank over the reference graph ranks them, and a binary search packs the top ones into a token budget. No embeddings, no vector store, no network call.
Continue is the embedding-first counterpart to Aider. Read at v2.1.0, the answer to the question everyone actually asks — does my proprietary code get shipped to an embeddings API? — is: not by default. It embeds locally with a bundled all-MiniLM-L6-v2, stores vectors in a local LanceDB, and only sends code out if you configure a remote embeddings provider.
LanceDB is a library and a file format, not a server: connect() opens a local path or object-store URI in-process, and only URIs starting with the literal prefix `db` route to the Cloud HTTP client. Storage is the Lance columnar format with append-only manifest versioning, and the index family is IVF-first with PQ compression — not an HNSW graph. Read at lance v8.0.0 / lancedb v0.30.0.
LiteLLM is sold as a smart load balancer across your model deployments. Read at v1.92.0-dev.1, the default is a weighted coin flip: simple-shuffle picks a random healthy deployment, weighted by rpm/tpm if you set it. The intelligence — least-busy, latency, usage-based routing — is opt-in, and it works only because LiteLLM meters every key. On failure it runs a retry-cooldown-fallback ladder.
Everyone assumes Ollama estimates your model's memory and computes how many layers to put on the GPU. Read at v0.31.1, that's not what happens: Ollama is a scheduler that spawns an upstream llama.cpp llama-server, lets it auto-detect the GPU split by default, and reads the offloaded layer count back from its logs. Ollama's own memory math is a deliberately rough proxy used only to decide what fits and what to evict.
pgvector is the 'just use Postgres' answer to vector search, and the contrast with a dedicated engine like Qdrant is the home, not the algorithm. Both are HNSW; pgvector implements it as a Postgres index access method. Read at v0.8.4 — the graph lives in WAL-logged Postgres pages, a search is plain SQL, and filtering is Postgres's job, patched by a new iterative scan.
Adding a metadata filter is where naive vector search breaks — post-filtering wrecks recall, pre-filtering can't use the graph. Read at v1.18.2, Qdrant's answer is two moves: it estimates the filter's cardinality to choose between an exact scan and a graph search, and at build time it adds payload-aware links so a filtered subgraph stays connected — a fix it justifies with percolation theory.
vLLM's throughput comes from two linked ideas, read here at v0.24.0rc2 (the v1 engine). PagedAttention pages each request's KV cache into fixed 16-token blocks from a shared pool, so memory is non-contiguous, tightly packed, and shareable across identical prefixes. Continuous batching rebuilds the batch every forward step from a token budget, mixing prefills and decodes and preempting when blocks run out.
CLIProxyAPI is sold as an OpenAI-compatible proxy that round-robins across your Claude, ChatGPT/Codex, and Gemini subscriptions. Read the source and it's a reactive cooldown scheduler: it doesn't track your quota, it waits for a 429 — and only for Codex does it actually know when your limit resets.
