The problem with formal prompting languages is they assume the bottleneck is ambiguity in the prompt. In my experience building agents, the bottleneck is actually the model's context understanding. Same precise prompt, wildly different results depending on what else is in the context window. Formalizing the prompt doesn't help if the model builds the wrong internal representation of your codebase. That said curious to see where this goes.
Working on Engram, a cognitive memory system for AI agents. Instead of vector DB + semantic search, it uses models from cognitive science (ACT-R activation decay, Hebbian learning, forgetting curves) to decide what to remember and what to forget. Been running it in production for a month, 230K+ recalls. Just shipped v2 with multi-agent shared memory.
https://github.com/tonitangpotato/engram-aihttps://github.com/tonitangpotato/engram-ai-rust
I feel the same! they are raising the bar higher and higher. I wrote a bot and pass the swe bench lite for 67% and can not get a chance to show. I also tried to submit for swe bench full but they limit it to organization only. where can us independent developers post our stuff, can we have an open bench mark for everyone and we just use merit to rank?
Crypto receipts for agent state is cool, especially for compliance stuff where you need to prove what an agent knew at some point. But the thing I keep running into，most agent memory is just append-only. Store everything forever. And in practice long-running agents just drown in thier own noise. The harder problem imo isn't reliable storage, it's deciding what to keep active vs what to let fade.
Cool project. The deterministic layer first → LLM only for edge cases is the right call, keeps it fast for the obvious stuff.
One thing I'm curious about: when the LLM does kick in to resolve an "ask", what context does it get? Just the command itself, or also what happened before it? Like curl right after the agent read .env feels very different from curl after reading docs — does nah pick up on that?