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AI memory reliability, agent engineering, security, and automation. Written by the operator who builds the tooling — production systems, real failure modes, working code.
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A production self-healing loop: error-signal gathering, LLM applicability review, isolated worktree patching, real smoke tests, gated merge, memory consolidation.
How AI agents commonly mishandle provider API keys, what encrypted-at-rest handling actually looks like, and the two deployment models (BYOK vs managed) we use in production.
Train a LoRA on your own photos, run on our GPU, and generate consistent on-brand images in any scene — without sending your data to a third-party cloud.
How to build real operations on Telegram: bots, approval flows, mini-apps, group automation, and always-on agents deployed on Fly.io.
The extract-then-store bug makes AI memory layers save user questions as facts. The four leaking speech acts and the one-conditional gate that fixes it.
Binary AI memory treats every row as true with confidence 1.0. Model facts as Beta beliefs instead: start unsure, earn trust through repetition, decay over time.
We wire the tooling we write about into real production systems. Start with a free audit.