Practical notes on MVP development, AI integrations, and improving existing products.
Notes from the workbench—what we are learning while building useful products.
What a custom internal business tool costs to build in 2026: realistic ranges by scope, what drives the price, the ongoing costs teams forget, and how to get an estimate you can trust.
Read MoreWhat to actually look for when hiring an AI development team: the gap between demo experience and production experience, the interview questions that reveal it, red flags, and how to vet before committing.
Read MoreHow to test and evaluate LLM outputs before shipping: building a real evaluation set, choosing the right scoring method, what to actually measure, and running evaluation on every prompt, model, or retrieval change.
Read MorePractical ways to cut LLM API costs without hurting quality: right-sizing models by task, reducing token count, caching aggressively, fixing retry behavior, and what not to cut when trimming spend.
Read MoreBuild or buy for your next internal business tool? A practical framework covering when vendor software wins, when custom code earns its cost, the real maintenance burden of building, and how AI has shifted the calculation.
Read MoreCustom AI development or an off-the-shelf model API? A practical framework for deciding, what "custom" really means once you look past model training, and a path that works for most teams.
Read More