Platform self-evolution: how a Level-5 AI platform-engineering system (LLMGen) can generate a substrate that improves itself from within - internal defects + external EARS/free-text requirements → verified, deployed self-changes. Position + feasibility + reference architecture.

Real-time feature store for startups — YAML feature defs, online+offline dual-write, Python SDK. Feast/Tecton alternative you can deploy in a day.

Production-grade AI system (multi-agent + RAG + LLM + lakehouse) for intelligent healthcare decision support

Open-source LLM evaluation framework — drop-in SDK + CI plugin. LLM-as-judge, regression detection, free + self-hostable. Braintrust alternative.

Datadog for LLM apps, for indie devs. Drop-in Python SDK + free self-host. Latency, cost, hallucination patterns out of the box.

Reference architectures for designing, deploying, and operating enterprise AI systems on AWS and Azure — Well-Architected, production-grade, IaC-first.