Simple self-hosted LLM assistant with user-steered compounding context
I built a personal LLM assistant on Cloudflare Workers + Durable Objects. You specify a category and topic when starting a new conversation, so the backend maintains a summary for each category/topic - building up as more conversations happen under the same one. There is no complicated RAG, embeddings, or agentic magic, but the category/topic summaries system just works, and I've genuinely found it super handy and have been using it daily for my life and work. I started it to get familiar with Durable Objects and to test out this idea draft I had buried on my board. After recently…
Simple self-hosted LLM assistant with user-steered compounding context launched on August 4, 2026 with 5 votes, #1,809 of 23,274 launches that month and more than 75% of that year's launches.
What it does
Pawmc: Private Assistant with Managed Context. Contribute to kol3x/pawmc development by creating an account on GitHub.
A personal LLM assistant that runs on Cloudflare Workers and maintains a user-guided compounding knowledge base across conversations. When starting a new conversation, choose which historical context you want it to have by choosing a category and topic. Additional context from new chats is compounded daily or on request. It's simple and lightweight, and you own your data, pick your model, and control the prompts. Pawmc asks for a little effort from a user, but the result is predictability of compounding context. Every time you start a chat, you must select a "category" and "topic" (existing or new) and that's exactly where the context will compound. Over time, when you gather context about…from github.com
In the maker’s words, at launch
I built a personal LLM assistant on Cloudflare Workers + Durable Objects. You specify a category and topic when starting a new conversation, so the backend maintains a summary for each category/topic - building up as more conversations happen under the same one. There is no complicated RAG, embeddings, or…
Does a similar job
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Twigg2025 · twigg.ai · ▲157Hi HN, we’re Jamie and Matti, co-founders of Twigg. During our master’s we continually found the same pain points cropping up when using LLMs. The linear nature of typical LLMs interfaces - like ChatGPT and Claude - made it really easy to get lost without any easy way to visualise or navigate your project. Worst of all, none of them are well suited for long term projects. We found ourselves spending days using the same chat, only for it to eventually break. Transferring context from one chat to another is also cumbersome. We decided to build something more intuitive to the ways humans think.…
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Object database for LLMs that persists across chats (MCP server)2025 · dry.ai · ▲9I’d like to use LLMs for remembering all kinds of things: fitness, to-do lists, contacts, bug reports, research links, whatever. But there is no way to do that now. For example, if I find a great coding tutorial in chat, or tell it how much I ran yesterday, it forgets that when I close the chat. Even if I keep the chat history, I still need to scour through lots of messages to find the data I want. Ideally, Claude would remember all this, and I’d be able to find it later with ease. This is what my team built. It is a collaborative database you add to any LLM that supports MCP. (Claude Code,…
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I trained a 65B LLM on my texts to talk to myself (details inside)2023 · airic.serveo.net · ▲13I trained the 65b model on my texts so I can talk to myself. It's pretty useless as an assistant, and will only do stuff you convince it to, but I guess it's technically uncensored? I'll leave it up for a bit if you want to chat with it. I posted this to Reddit and had several hundred people talking to it. Salient points from that discussion: LLAMA 1 65b Rank 128 5 epochs Batch size 1, 256 cutoff Trained in the Oobabooga suite using bitsandbytes 4-bit quantization for the lora Loss around 1.5 seems to give the most coherent results Trained on raw text dumps that is then parsed by a crappy…
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