Technical essays, architectural deep-dives, and practical guides at the intersection of code and design.
Page 3 of 30
A practical guide to market data for algo trading: the levels (OHLCV bars, L1 ticks, L2 depth, L3 full book), free sources you can start with today (yfinance, Stooq, crypto exchange APIs, NSE/BSE bhavcopy), paid sources for real money (Polygon, Databento, Kite Connect), and the two biases that quietly wreck backtests — with Python to pull data.
A one-page vocabulary for algorithmic and HFT trading: the order book (bid, ask, spread, depth, L1/L2/L3), the order types you actually send (market, limit, stop, IOC/FOK/GTC, maker/taker), and the rest of the words — slippage, VWAP, Sharpe, drawdown — explained plainly.
Why small, focused, self-reviewed pull requests matter more than ever now that AI can generate a 500-line diff in seconds. The anatomy of a PR that's easy to say yes to — clear description, green CI, one concern — and the research-backed reason to keep it under ~400 lines.
How to write commits that explain themselves — atomic changes, a subject that says what and a body that says why, conventional commits — and the new discipline the AI era demands: reviewing and splitting AI-generated diffs before you commit, because you are still the author of record.
How to review code well when a machine wrote it and the CI is already green: the layered review (let automated gates and AI bots catch the mechanical; save humans for judgment), and the new failure mode — automation complacency — where reviewers rubber-stamp fluent, confident, subtly-wrong AI code.
The real difference between a message queue (RabbitMQ, SQS) and an event stream (Kafka): consume-and-delete vs append-and-keep, how each scales consumers, delivery and ordering guarantees, and a simple test for choosing the right one.
A practical guide to idempotency: why retries cause double charges, how idempotency keys and dedup stores fix it, delivery semantics (at-least-once vs exactly-once), and how to make non-idempotent operations like payments safe.
A clear guide to embeddings and vector databases: what an embedding really is, how approximate nearest-neighbour (ANN) search and HNSW make similarity search fast at scale, and when you actually need a dedicated vector DB versus pgvector or plain brute force.
A comparison of AI models from Anthropic, OpenAI, Google, Meta, Mistral, and DeepSeek — pricing, capabilities, and the best fit per use case.
A guide to AI video models — Sora, Veo 2, Kling, Runway, Pika — per-second pricing, quality tradeoffs, and pipelines that cut costs by 80%.
A guide to AI image generation models — DALL-E 3, Imagen 3, Stable Diffusion, FLUX, Midjourney — pricing, quality, and how to cut costs 10-20x.
A deep comparison of AI coding assistants — Copilot, Cursor, Claude Code, Aider, Windsurf, and more — features, pricing, and which wins per workflow.