@NousResearch@Magnificode@openclaw Please check out the repo where I merge the 2 because I had the same question! https://t.co/FaBwr7IikR
Benchmark results have come out pretty good. And retrieval speed is as fast with no wasted tokens.
I just open sourced my first project!
Shiba Memory, persistent, self-improving memory for AI agents.
34ms hybrid retrieval. ACT-R scoring. Native Claude Code + Hermes support. Fully local, no cloud deps.
🔗 https://t.co/FaBwr7IikR
Thread 🧵
@bcherny@trq212@ClaudeCode@AnthropicAI Hermes users, Shiba ships as a native memory provider plugin. 🔌
shiba_recall, shiba_remember, shiba_forget tools available to the LLM out of the box. Auto memory on every turn. Session summaries. Prefetch before each response.
@NousResearch@teknium
What Shiba actually does:
• Remembers across ALL sessions & projects
• Hybrid semantic + full-text search
• Links related memories via a knowledge graph
• Instincts evolve into skills over time
• Ingests web, RSS, git, files
• HTTP gateway for any agent
@sudoingX Adding a 3090 to the rig this week! I have a 5070ti and it runs gemma4 26B pretty well but not enough context bc of the space. Qwen14B is terrible. Plan to try gemma4 31B first on the 3090
@Teknium pgvector on docker has worked really well. Do a combination of what karpathy talks about with the wiki LLM obsedian md files and then underlying it with core/long term memory in the db