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.
Context rot: as you fill a model's window, facts get buried in the middle and quality drops. The fix isn't a bigger window — it's retrieving fewer, right facts.
Persistent agent memory is a new attack surface. Defend it by governing the write path, keeping memory readable and versioned, and auditing every change.
Workforce 2.0 explains how professionals can turn accumulated judgment into AI agents they own, supervise, and sell as leverage instead of hours.
Why AI assistants forget context, how context windows and compaction work, and what memory layers actually fix the problem across sessions.
How to switch between Claude Code, Codex, and ChatGPT without repeating context by keeping one shared memory layer outside the tools.
Why AI usage limits disappear faster than expected, and how retrieval-backed memory cuts token waste better than re-reading full context.
A case study on how a psychologist uses a memory-backed AI agent to pre-qualify leads, reduce wasted calls, and keep human judgment in the loop.
How to onboard an AI agent like a new hire with context, standards, and memory so it becomes useful faster and needs fewer repeated corrections.
How to make OpenClaw smarter by giving it persistent memory, so it can reuse decisions, preferences, and context across sessions.
