Practical writing for SaaS founders making technical decisions around AI products, LLM applications, MVP architecture, and production delivery.
Practical writing on LLM applications, AI product delivery, MVP architecture, and founder-led technical decisions. No generic agency content. No inflated case studies.
For hands-on delivery, review AI product engineering for LLM apps.
Startup plans help AI teams move fast, but production AI products need real infrastructure cost planning.
Read After the Startup Plan Ends: How to Think About LangChain, LangSmith, and AI Infrastructure CostsWhat production AI teams should learn about agent observability, traces, and product feedback loops.
Read SmithDB ExplainedLangChain helps teams prototype quickly. Production agents need state, memory, tool safety, observability, permissions, retries, recovery, and human review.
Read Why Most LangChain Apps Break After the DemoMost AI demos fail not because the model is bad, but because the product system around the model is weak.
Read Why most startup AI prototypes fail before productionCheap hourly engineering often looks affordable upfront. The real cost appears later as rework, unclear ownership, slow decisions, and product drift.
Read The hidden cost of hiring cheap engineering teams for your MVPAI tools can increase delivery speed, but they do not replace product judgment, architecture ownership, QA, or release discipline.
Read When Cursor, ChatGPT, and Claude accelerate delivery — and when they do notA practical checklist for SaaS founders deciding whether an AI prototype is ready for production or needs stabilization first.
Read AI Prototype Production Readiness ChecklistFounder-led product engineering
Talk through your AI product, MVP architecture, or production delivery path with the engineer who would lead the build.
