Practical notes on MVP development, AI integrations, and improving existing products.
Notes from the workbench—what we are learning while building useful products.
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.
Read MoreA practical breakdown of how AI agents plan multi-step tasks, call tools, and retain context - and how that differs from a single-shot AI assistant.
Read MoreA step-by-step guide to building a retrieval-augmented generation pipeline that survives production: chunking, hybrid retrieval, reranking, evaluation, and the mistakes that sink most RAG systems.
Read MoreA clear, practical explanation of retrieval-augmented generation: how it grounds LLM answers in real data, the core pipeline, and when it's the right architecture choice.
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