Notes from the foundry
Engineering essays on generative AI, retrieval systems, and what it takes to ship intelligent software to production.
Build a Multi-Agent Workflow with LangGraph
A Python tutorial: model a researcher, writer, and reviewer as a LangGraph state machine, with shared state, conditional edges, and a review loop.
Build an AI Code Review Bot for Your Pull Requests
A TypeScript tutorial: a GitHub Action that sends your PR diff to Claude and posts a focused, useful review comment, without the noise.
RAG Is Not a Silver Bullet · It's a Retrieval Problem
Why most retrieval-augmented generation systems underperform, and the unglamorous fixes that actually move the needle.
How to Evaluate an AI Development Partner
A practical checklist for CTOs vetting an AI development agency, covering evals, IP, security, cost, and the questions that separate builders from demo shops.
Vendor Lock-In with LLMs: How to Keep Providers Swappable
Prices change, models get deprecated, and providers face export limits overnight. How to architect LLM features so you can switch providers without a rewrite.
Cutting LLM Cost 50% Without Wrecking Quality
A practical playbook for bringing down LLM spend: model routing, prompt caching, context discipline, and batching, with the quality tradeoffs spelled out.
What It Really Costs to Run an LLM Feature in Production
Token bills are only part of it. The true cost of an LLM feature: inference, retrieval infra, evals, monitoring, and the engineering time nobody budgets for.
Should We Self-Host an LLM? A Cost and Control Framework
Self-hosting an open-weight model can save money or quietly cost more. A CTO's framework for deciding based on volume, privacy, control, and the true total cost.
RAG vs Fine-Tuning vs Long Context: Which, and When
Three ways to get a model to use your data, often confused. When retrieval, fine-tuning, or a big context window is the right call, and when to combine them.
When AI Is the Wrong Tool (and Cheaper Options Win)
Not every problem needs an LLM. A candid look at where rules, search, or plain software beat AI, and how to tell before you spend the budget.
The AI Readiness Checklist Before You Ship to Customers
A pre-launch checklist for AI features: evals, guardrails, cost controls, monitoring, fallbacks, and the failure modes that embarrass you in production.
How to Scope an AI MVP That Won't Get Thrown Away
Most AI MVPs die because they were scoped wrong, not built wrong. A founder's guide to picking the first use case, setting a quality bar, and avoiding the demo trap.
Estimating Timeline and Budget for an AI Build
Why AI projects blow past estimates, and a framework for scoping timeline and budget that accounts for evals, data work, and the iteration AI actually needs.
Measuring AI Quality: Evals Your Board Will Trust
How to measure AI feature quality in numbers leadership can act on, covering what to measure, how to avoid vanity metrics, and how to report quality over time.