I guess that’s the world we live in now, Anthropic did similar with Mythos and even Fable if I recall correctly.

But worse.

> Gas Town is for “write-only code”, … If you adopt Gas Town, you will lose the speed benefit of multiple agents if you read the code it produces. The first time you stare at an 8,500 line Git diff and try and parse what happened, you’ve lost. Go back to a single Claude Code session. Gas Town isn’t for you.

So none of the code has even been read by the author, it’s purposely overly verbose. “Touched by LLMs” seems to downplay just how vibe coded this is. I won’t use this database for anything.

They’ve had this weird phrasing on this landing page for at least 6 months if not longer. It’s clearly a landing page that receives no love.

www.memoryplugin.com

Long term memory for dozens of AI tools, designed for power users who want more control and flexibility than native memory systems and who do not want to be locked into any one platform. You can also have the system remember your entire chat history going back years and use this information to help you better in new chats, it sometimes makes chats 10x more useful when I say something like: “Using recall tool, do 10+ calls for 1000 tokens context each to learn about my interests, strengths, curiosities, what I’ve tried in the past, what worked, what didn’t, etc and suggest a new hobby I would enjoy”.

Without long term recall, AI is a super intelligence in your hands that uses the knowledge of the world to give you generic, nearly useless advice because of how generic it is. With long term memory, you have a super-intelligence that knows YOU. This is what MemoryPlugin solves for.

Comments URL: https://news.ycombinator.com/item?id=45889039

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Lately I’ve worked on a chat history based memory feature that can recall information from every conversation you’ve ever had with ChatGPT and Claude. It’s been particularly useful and also technically fun to implement. Speed has been very important as I do just in time summarisation and a multi stage RAG pipeline, and most LLMs have unacceptable performance. I ended up going with GPT-OSS on Groq due to its ultra low latency often completing full generations before Gemini or ChatGPT APIs return even the first token.

The ability to recall details from conversations going back years makes tasks where I want personalised plans or feedback like 10x more useful, at times I get the AI to ingest tens of thousands of tokens of context to help me better.

Over time, the project has grown to now support more than 17 platforms, thousands of users, and has been growing organically.

As of most recently a major feature I've been working on is full chat history based memory. Being able to remember and recall every conversation you've ever had across multiple supported AI tools, similar to the reference past conversations feature in various AI apps. This has been pretty intense and fun. Ingesting tens of millions of tokens per user, and doing complex multi-stage RAG on-the-fly across this vast dataset with a tight latency target for UX.

The project is MemoryPlugin: https://www.memoryplugin.com

Another project is a RAG app that's built specifically for books. No "we work with your receipts, and legal documents, instruction manuals, product documentation, lecture transcripts, your dogs novel, the script for a play and everything else possible". I wanted something tailored for books, specifically, non-fiction books. When you try to work everywhere, you can not deliver an amazing experience for any one specific use case. AskLibrary is tailored for non-fiction books, so everything from the answer generation pipelines, to the ingestion pipeline, and various other features are all designed for this specific use case. https://www.asklibrary.ai