Uploaded June 2026 | Updated September 2026, 2 weeks ago
👉 Get started: mem0.ai/?via=julien. Promo code JULIEN: $19 off all payments within the first 3 months!
I upgraded my local RAG chatbot to a 35B Qwen mixture-of-experts at 32K context, running entirely on a MacBook. The trick is TheTom's TurboQuant fork of llama.cpp, which compresses the KV cache by ~60% with no measurable quality loss. This time it does real work: RAG over 94 full IEA energy reports, plus persistent cross-session memory with mem0: the bot remembers who I am after a full restart.
mem0 is an open-source memory layer for AI apps. It works with your existing stack, local or hosted: run it on your own machine while you build, plug into managed memory when you ship.
The chatbot + mem0 integration & launch scripts: github.com/juliensimon/local-rag-chatbot/tree/feature/mem0
⭐️⭐️⭐️ More content on Substack at airealist.ai ⭐️⭐️⭐️
👉 Get started: mem0.ai/?via=julien. Promo code JULIEN: $19 off all payments within the first 3 months!
I upgraded my local RAG chatbot to a 35B Qwen mixture-of-experts at 32K context, running entirely on a MacBook. The trick is TheTom's TurboQuant fork of llama.cpp, which compresses the KV cache by ~60% with no measurable quality loss. This time it does real work: RAG over 94 full IEA energy reports, plus persistent cross-session memory with mem0: the bot remembers who I am after a full restart.
mem0 is an open-source memory layer for AI apps. It works with your existing stack, local or hosted: run it on your own machine while you build, plug into managed memory when you ship.
The chatbot + mem0 integration & launch scripts: github.com/juliensimon/local-rag-chatbot/tree/feature/mem0
⭐️⭐️⭐️ More content on Substack at airealist.ai ⭐️⭐️⭐️
