Local Ai
I’ve spent months building an AI memory system meant to outlast the models using it.
I’ve put months of work into PCS - Personal Continuity System. Through hundreds of revisions, failed approaches, rebuilds and long sessions trying to understand why something wasn’t working the way it
I’ve put months of work into PCS - Personal Continuity System. Through hundreds of revisions, failed approaches, rebuilds and long sessions trying to understand why something wasn’t working the way it should. I’ve been building it with AI coding tools. A lot of my time has gone into directing the design, trying the results, finding problems, and working through what needed to change. Getting something to work once and getting its different parts to behave consistently have been very different challenges. The reason I’ve stayed with it is that I want continuity that can survive a change of model. The model I use today probably won’t be the model I use forever. My needs will change. New models will become available. I might want a local model for one purpose and a cloud model for another. I want the conversations, decisions, corrections and understanding I’ve chosen to keep to remain useful through those changes. That’s what PCS is being built around. Retrieval, including RAG, is part of it. The larger job is maintaining a history that can be inspected, corrected and carried forward. An original conversation, a model’s interpretation of it, and my later correction are different things. PCS keeps those distinctions visible. For example, if I change a project plan, I want to preserve the earlier plan, why I changed it, and what I decided afterward. When I return to that project with another supported model, I want it to have access to the relevant history—including what changed. A lot of the work has gone into those details: keeping corrections attached to the accounts they change, preserving sources and revisions, distinguishing older information from the current account, and respecting what the user has allowed a model to read. PCS stores saved continuity in an encrypted vault on your PC, independently of the connected model. You can use supported local models or Google, OpenAI and Meta connections, with selected context brought into the conversation according to your sharing choices. The latest release, 0.9.114, adds explicit links between memories, named lessons with their stories and takeaways, and ways to follow how a decision evolved. It also expands the Continuity Map and improves Local conversations, setup and reading notes. It’s a free, open-source Windows application, and it’s still in active development. I’d like people to try it with something they actually want to return to—a project, something they’re learning, or an idea they’ve been working through—and tell me where the continuity helps and where it breaks down. I’ve put a lot of myself into getting it this far. One of the greatest outcomes I can imagine is someone looking at it and deciding there’s something here worth taking further. I’d be proud to have built something useful enough that another person wants to improve on it. Website and overview Downloads, source and release notes PCS has no subscription or paid feature tier. Cloud API usage is billed separately by the provider; Local use requires suitable hardware. submitted by /u/MightyMouse420 [link] [comments]
Related
- How are you feeding personal context to your local models?
- Built a personal memory system using Ollama + qwen2.5:7b - queries your entire life history via RAG (AetherMind)
- Construí um sistema de IA com estado persistente (4B como roteador + 9B principal + 9B “subconsciente”) rodando em 2x RTX 3060 — e ele não se comporta como stateless
- How would you improve reasoning + memory in a local AI companion?
Source: r/ollama | 2026-10-21