Alternatives
Products that do what Txtos for LLMs – 60 SEC setup, long memory, boundary guard, MIT does
i built TXTOS because my models kept forgetting and bluffing. i wanted a portable fix that works across providers without code or setup. TXTOS is a single .txt you paste into any LLM chat. it boots a small reasoning OS that gives you two things by default: a semantic tree memory that survives long threads, and a knowledge boundary guard that pushes back when the model is out of scope. what it is plain text. no scripts, no trackers, no api calls. MIT. the file encodes a protocol for reasoning, memory, and safety. you can diff it and fork it. it is not “a clever prompt”. it behaves like a tiny…
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- 2ZL
Zep is a long-term memory store designed for conversational AI applications built using modern LLMs. It handles the storage, summarization, embedding, indexing, and enrichment of chat histories, and offers developers a simple, low-latency API to this data. Chat history storage is an infrastructure challenge all developers and enterprises face as they look to move from prototypes to deploying conversational AI applications that provide rich and intimate experiences to users. Key features include long-term memory persistence, auto-summarization, vector search, auto-token counting, and Python…
2023
- 3OD
I’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,…
2025 · dry.ai
- 4TL
Little tool that I made to understand how (un)reasonable my prompts are.
Jan 2026 · github.com
- 5MC
Hey everyone! Many of you might have come across the Mamba paper a few days ago, which introduced an LLM based on a state space model architecture. The Mamba architecture is quite useful as its complexity scales subquadratically with input length and is therefore way more efficient than transformer models: https://github.com/state-spaces/mamba I got really excited about the paper, so I decided to fine-tune the model on a chat dataset. It turns that this actually worked quite well! The model is quite suitable for casual chatting, which honestly surprised me given that it…
2023 · github.com
- 6AA
An all-in-one blog for learning LLM ins and outs: tokenize, attention, PE, and more Project I've been diving deep into the internals of Large Language Models (LLMs) and started documenting my findings. My blog covers topics like: Tokenization techniques (e.g., BBPE) Attention mechanism (e.g. MHA, MQA, MLA) Positional encoding and extrapolation (e.g. RoPE, NTK-aware interpolation, YaRN) Architecture details of models like QWen, LLaMA Training methods including SFT and Reinforcement Learning If you're interested in the nuts and bolts of LLMs, feel free to check it out:…
2025 · comfyai.app
- 7AT
We kept shipping “simple” LLM features that were fluent-but-wrong. After too many postmortems we wrote down the failure patterns and added a small reasoning layer in front of the model. It’s model-agnostic, sits beside your existing stack, and you can implement it from a single PDF (MIT). What’s inside the PDF A problem map of 16 failure modes we kept hitting in real systems (OCR/layout drift, table-to-question mismatches, embedding≠meaning, pre-deploy collapse, etc.). Four lightweight gates you can add today: Knowledge-boundary canaries (empty/adversarial/known-fact probes).…
2025 · github.com
- 8LS
Hi HN, I built llm.sql, an LLM inference framework that reimagines the LLM execution pipeline as a series of structured SQL queries atop SQLite. The motivation: Edge LLMs are getting better, but hardware remains a bottleneck, especially RAM (size and bandwidth). When available memory is less than the model size and KV cache, the OS incurs page faults and swaps pages using LRU-like strategies, resulting in throughput degradation that's hard to notice and even harder to debug. In fact, the memory access pattern during LLM inference is deterministic - we know exactly which weights are needed…
Apr 2026
- 9YP
It's an biological inspired decay system for our memories with extended support of temporal reasoning. Created a CLI command to infer knowledge from the context stored in memory system without any token utilization or llm call. It comes with a memory dashboard to monitor and manage your memories it can be extended as audit trail for agents as well !
May 2026
- 10CR
hi everyone. how does moving llm call prompts and output structure definitions away from code into configuration land sound? would you use something like this if it was stable and well documented enough? please don't hold back the criticism. i appreciate all feedback (constructive & otherwise).
2024 · github.com
- 11AD
Hi all, I threw together a small prototype I am calling “Notepad.ai”. A new take on UIs for interacting with LLMs. While I enjoy using LLM’s in the chat format I wanted to see what it would be like to do it in a more long form style. It let’s you write in a pretty free form, much like Window’s Notepad, but you can choose to hit ctrl+[ to analyze the text with a preset prompt of your choosing. It has a few other small features. It’s WIP and very experimental. I would appreciate any feedback or thoughts. Video: https://youtu.be/ntdlgFmSxQY Live Demo:…
2024 · github.com
- 12AI
Hi I am Jan, CTO @ Pathway. A use case we have been working on with LLMs is to let people know when an answer to their query changes due to revisions of source documents. Obviously, we want to avoid periodically re-computing all queries for the LLM. Why I think it’s cool? - We don’t spin in a loop to repeat with the LLM. - Alerts are LLM-deduplicated - no spamming users with typo fixes - And the best - our framework, Pathway takes care of handling the updates, the example looks nearly like a regular, static RAG chatbot. More context + GIF of how it works for Google Drive document alerts:…
2023 · github.com
- 13CF
Over this past month I had the idea to build a 100% open-source MIT-licensed tool to simplify sharing code with LLMs, without the vendor lock-in you get from most SDKs. Right now, it’s way too hard to export your data or work freely with models like o1 PRO or Grok 3, especially since they don’t even have API access. So I built OpenRepoPrompt, an open-source tool from wildberry-source that serializes files and folders into XML for LLMs. I coded/designed from 12PM -> 11PM on Saturday and 8AM -> 11PM on Sunday. There are still tons of features missing (I'm working on better file filtering…
2025 · github.com
- 14UA
I've been using LLMs for long discovery and research chats (papers, repos, best practices), then distilling that into phased markdown (build plan + tests), then handing those phases to Codex/Claude to implement and test phase by phase. The annoying part was always the distillation and keeping docs and architecture current, so I built Unpack: a lightweight GitHub template plus docs structure and a few commands that turns conversations into phases/specs and keeps project docs up to date as the agent builds. It can also generate Mintlify-friendly end-user docs. There are other…
Feb 2026 · github.com
- 15LP
I was not getting good cache utilization when including dynamic context in agent threads. After a lot of experimentation, I found a good pattern that minimizes how often long lived conversation history gets modified while still supporting dynamic context. It has flexible hooks for doing things like truncating or summarizing tool outputs when transitioning messages to the long term history. And I'm seeing >>90% of tokens hitting the cache for my agents despite including a lot of dynamic user context. There are a wide range of agent prompting strategies so I'd love to hear where this library…
Jun 2026 · github.com
- 16OM
Hey HN, we’re launching OpenMemory (https://github.com/mem0ai/mem0/tree/main/openmemory), an open source tool that lets you run a personal, portable memory layer for LLMs. Fully self-hosted and under your control. It uses standard MCP protocol and plugs into any MCP client (like Cursor, Windsurf, Claude, etc.) over Server-Sent Events (SSE). https://mem0.ai/blog/how-to-make-your-clients-more-context-a... is a complete tutorial that shows how to set it up locally, the underlying components involved, complete overview of architecture and…
2025 · github.com
- 17LS
LLMStack is a low-code platform that can be used to build LLM apps, chatbots and integrate AI experiences into existing products/workflows. It comes with everything out of the box that one needs to build LLM apps locally. It can also be used in a multi-tenant setting, making it available for everyone to use in an enterprise. Some highlights of the platform: - Chain multiple LLM models allowing for complex pipelines - Includes a vector database and necessary connectors to help enrich LLM responses with private data - App templates tailored to specific use cases to quickly build LLM apps…
2023 · github.com
- 18LM
I built a super easy to integrate memory storage and retrieval system for NodeJS projects because I saw a need for information to be shared and persisted across LLM chat sessions (and many other LLM feature interactions). I tried to keep the barrier to use as low as possible so I included built-in support for major LLMs (GPT, Gemini, and Claude) as well as major vector store providers (Weaviate and Pinecone). The memory store works by ingesting and automatically extracting “memories” (summarized single bits of information) from LLM interactions and vectorizing those. When you want to provide…
Mar 2026 · github.com
- 19CA
Hi HN, I've been working with LLMs in production for a while both as a solo dev building apps for clients and working at an AI startup. The one thing that always was a pain was to pay OpenAI/Gemini/Anthropic a few dollars a month just for me to say "test" or have a CI runner validate some UI code. So I built this server called ChunkBack, that mocks the popular llm provider's functionality but allows you to type in a deterministic language: `SAY "cheese"` or `TOOLCALL "tool_name" {} "tool response"` I've had to work in some test environments and give good results for experimenting…
Nov 2025 · github.com
- 20MA
Hello everyone! I have a hobby project that has become fairly full featured that I figured I would share. The idea of MinimalChat has been to create a project that is a lightweight and dead simple application that can be deployed locally in a few seconds (with docker). While of course also having most of the nice to have features and looking pretty nice. A nice bonus is it a Progressive Web Application so it can be installed like a normal application to your mobile device. It has a full mobile UI. For those using Chrome and Edge you can also locally download, load and host entirely via your…
2024 · github.com
- 21MC
Hi HN, I'm excited to introduce Mixlayer, a platform I've been working on over the past 6 months that allows you to code and deploy prompts using simple JavaScript functions. Mixlayer recreates the developer experience of using LLMs locally without having to do all of the local setup yourself. I originally came up with this idea when using LLMs on my MacBook and thought it’d be cool to build a product that makes it easy for everyone. It compiles your code to a WASM binary and runs it alongside a custom inference stack I wrote in Rust. When you integrate LLMs in this way, your code and the…
2024 · mixlayer.com
- 22SD
Hi! Been working on DialtoneApp, a free domain scanning tool to see how your site does with all the new rules for AI SEO. Also known as AEO (Answer Engine Optimization) or GEO (Generative Engine Optimization) the A can also stand for "Agent"! It's a whole new world out there and we haven't even gotten to agents.json files and the new "b2b" (bot to bot) commerce part. But there are some standards starting to take shape with llms.txt and using things like: on all your html pages to have this other markdown version. We list the top 300 sites in terms of how well they follow all the new rules.…
Apr 2026
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- 24AE
Anchor Engine is ground truth for personal and business AI. A lightweight, local-first memory layer that lets LLMs retrieve answers from your actual data—not hallucinations. Every response is traceable, every policy enforced. Runs in <3GB RAM. No cloud, no drift, no guessing. Your AI's anchor to reality. We built Anchor Engine because LLMs have no persistent memory. Every conversation is a fresh start—yesterday's discussion, last week's project notes, even context from another tab—all gone. Context windows help, but they're ephemeral and expensive. The STAR algorithm (Semantic Traversal And…
Mar 2026 · github.com
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