AI does benefit from learning to use skills, and being instructed with good structures (like “save [specific important kind of detail] in memories” and “plan before implementing anything”). But none of these memory solutions seem better than any other, they’re basically just different folders. Your CLAUDE.md can say “see AGENTS.md” and AGENTS.md can say “write memories to .agent/memories/“, and I’d be interested in a measurement, but I assume it performs almost the exact same as native CLAUDE.md and .claude/memories.
One thing I’ve noticed AI does much better than me, is not have OCD over the “perfect” solution.
[–] bee_rider 3 hours ago
[–] reactordev 7 hours ago
[–] soraminazuki 6 hours ago
[–] yladiz 5 hours ago
[–] soraminazuki 4 hours ago
It's even worse when applied to LLMs, which are more unpredictable.
[–] ares623 2 hours ago
[–] reactordev 1 hour ago
[–] alberto467 2 hours ago
In reality many of them have not needed to look at a data structure or do any real computer science for years. They were just attending bullshit meetings and approving some pr with some basic sql query or some new react code.
Common people knew very little about computers and so they thought they were running on magic and that the software engineers were the magicians.
Memories are lossy things that live in our brains, I’d expect they to be stored in some native LLM encoded representation (which would make the lack of standardization understandable).
This probably seems like a dumb distinction, but I think some of the problems in the article come from calling these things memories. Like, the prompt injection/provenience issue. The model has received some notes from someone about a topic, if it they were treated as “notes from <whoever sent them> about <topic>” instead of “my memories about <topic>” that should put things on the right footing to start at least, right?