Practical notes on MVP development, AI integrations, and improving existing products.
Notes from the workbench—what we are learning while building useful products.
A practical roadmap for adding AI features to a SaaS product you already run: choosing the first feature, fitting an AI layer into your architecture, respecting tenant permissions, testing, rolling out safely, and controlling cost.
Read MoreWhat AI agent development really costs: realistic ranges by scope, what drives the price, how token and infrastructure costs add up at run time, the costs teams forget, and how to keep the budget under control.
Read MoreA practical framework for comparing managed, self-hosted, and embedded vector databases, and the criteria that actually matter once you go beyond a proof of concept.
Read MoreA clear explanation of what vector databases are, how similarity search works, and why they've become core infrastructure for RAG and AI search applications.
Read MoreA practical, code-level guide to instrumenting LLM calls, tracing multi-step RAG and agent pipelines, running async evaluation, and alerting on the right signals in production.
Read MoreWhat LLM observability means, why standard uptime monitoring misses AI failures, and the specific signals - logging, cost, latency, and quality checks - you need to track in production.
Read More