LLM engineering spans the full stack of building with large language models: training, inference optimization, agent architecture, harness design, and the operational tradeoffs that determine whether model capability translates into reliable software.

Software engineering spans craft, process, and judgment — how code is structured, tested, reviewed, deployed, and maintained — and the sources collected here collectively interrogate each layer as AI tooling reshapes who does what and why.

The startup ecosystem is shaped by failure rates, infrastructure bets, pricing dynamics, and product philosophy — sources here trace how dead companies, AI cost shifts, and great-product thinking collectively define the terrain.