Thoughts on engineering, AI, architecture, and building software that lasts.
Most ML projects fail not because of bad models, but because of bad engineering. Here's what we've learned shipping ML systems that actually work.
OpenAI API vs fine-tuned open-source vs RAG: a practical framework for deciding which LLM approach fits your use case and budget.
Database-per-tenant vs shared database vs hybrid, and when to use each pattern plus the trade-offs that matter.
Consistency, predictability, and developer empathy: the principles behind APIs that developers actually enjoy using.
When investors ask us to evaluate a startup's technology, these are the signals that matter, plus the red flags that don't.