MVP development · September 18, 202602
How to Validate Your MVP Idea Before Writing Code
A practical framework for testing whether your MVP idea deserves engineering resources before you build a single feature.
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Practical notes on MVP development, AI integrations, and improving existing products.
Notes from the workbench—what we are learning while building useful products.
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6 notesAI integrations · September 17, 202603
How to Choose a Vector Database for Your AI Project
A 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 MoreAI integrations · September 17, 202604
What Are Vector Databases? A Practical Guide
A 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 MoreAI integrations · September 16, 202605
How to Set Up LLM Observability: A Step-by-Step Guide
A 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 MoreAI integrations · September 16, 202606
LLM Observability: What to Monitor in Production AI Systems
What 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 MoreAI integrations · September 15, 202607
Building Your First AI Agent: A Practical Guide
A hands-on guide to building your first AI agent: choosing a framework, defining tools, designing the control loop, and avoiding the mistakes that make agents unreliable.
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