AI agents use our developer tools, but an error code often leaves them guessing. I explore how failure context, structured diagnostics, and recovery guidance could help them choose a safe next step.
Exploring whether larger AI models could help maintain smaller ones by debugging their weights instead of always retraining them.
LLMs inherit knowledge from human-generated data, while reinforcement learning helps AI learn from action and feedback. The future may combine both.
Production AI should not send every event to a reasoning model. Jev offers a cheap decision layer that can route, score, and escalate work first.
LLM caching isn’t response caching. It’s KV state caching: here’s how it works, what it costs, and how to structure prompts to actually get hits.
Are we losing depth in the age of short-form content ?