Rememberall
Open source solution for long term memory for LLMs
What it does
Rememberall is a secure, open-source solution that gives your Custom GPTs persistent memory across conversations. Simply mention @rememberall in your GPT to access past context and maintain continuous conversations.
Does the same job
all alternatives →- RRRem: Remember Everything (open source)2023 · github.com · ▲555
An open source approach to locally record everything you view on your Apple Silicon computer. Note: Relies on Apple Silicon, and configured to only produce Apple Silicon builds. I think the idea of recording everything you see has the potential to change how we interact with our computers, and believe it should be open source. Also, from a privacy / security perspective, this is like... pretty scary stuff, and I want the code open so we know for certain that nothing is leaving your laptop. Even logging to Sentry has the potential to leak private info.
- COCore – open source memory graph for LLMs – shareable, user owned2025 · github.com · ▲112
I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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