Your AI Wrote the Code. Who Understands It Now?
Generated code keeps the artefact and throws away the argument. What to record while the reason is still in someone's head, so the next change is safe.
Product updates, implementation details, and field notes on getting an app running, who may reach it, and the record the next change starts from.
Articles, shipping notes, and the thinking behind how aidrop.it works.
Generated code keeps the artefact and throws away the argument. What to record while the reason is still in someone's head, so the next change is safe.
ProductOps gives AI teams one operating system for customer signals, product decisions, releases, evaluation cases, and ownership.
AI makes it easy to build a working app. This guide covers the deployment decisions that turn a localhost demo into a service people can use.
Skills give AI agents a repeatable way to work: the right context, the right sequence, evidence checks, and clear points for human judgment.
Case study: a consultant delegates first drafts to an agent built on her recorded standards — it drafts in her format, she keeps judgment. Drafts start at 70%.
Your AI can't read your Obsidian or Notion notes. Distill the durable decisions out of them into a memory your agent can query across every tool you use.
Facts change, so AI memory needs versioning — history, audit, and rollback, not one overwritten value. Why agent knowledge should work like git, not a notepad.
Choosing an AI memory layer: build on a vector DB, adopt a framework (Mem0, Zep, Letta), or connect a hosted MCP memory. Decide on curation and portability.
Multiple AI agents coordinate best through one shared, persistent memory they all read and write — not lossy message-passing. How shared memory scales a swarm.