tonipotato

3 ms·

1. 1.▲
   It's hard for solo developers to gain attentions

   5 points by tonipotato 7mo ago|3 comments
2. 2.▲
   by tonipotato 7mo ago

   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
3. 3.▲
   by tonipotato 7mo ago

   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 f
4. 4.▲
   by tonipotato 7mo ago

   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
5. 5.▲
   by tonipotato 7mo ago

   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 pra
6. 6.▲
   by tonipotato 7mo ago

   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 ge