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Products that do what I built an open-source answer engine that runs local LLMs does

hey hn, I built an open-source Perplexity clone that can run local LLMs and cloud LLMs. It's fully self-hostable through Docker and uses ollama to support local LLMs. The demo video in the repository shows me running it locally with llama3 on my M1 Macbook Pro. I'm open to any suggestions or feedback, thanks!

  1. 1OR

    Hi HN A few folks and I have been working on this project for a couple weeks now. After previously working on the Docker project for a number of years (both on the container runtime and image registry side), the recent rise in open source language models made us think something similar needed to exist for large language models too. While not exactly the same as running linux containers, running LLMs shares quite a few of the same challenges. There are "base layers" (e.g. models like Llama 2), specific configuration to run correctly (parameters, temperature, context window sizes etc). There's…

    2023 · github.com

  2. 2IV

    The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)

    2024 · github.com

  3. 3
    Ollama235

    The easiest way to run large language models locally

    2023

  4. 4IB

    I spent the last few days building out a nicer ChatGPT-like interface to use Mistral 7B and Llama 3 fully within a browser (no deps and installs). I’ve used the WebLLM project by MLC AI for a while to interact with LLMs in the browser when handling sensitive data but I found their UI quite lacking for serious use so I built a much better interface around WebLLM. I’ve been using it as a therapist and coach. And it’s wonderful knowing that my personal information never leaves my local computer. Should work on Desktop with Chrome or Edge. Other browsers are adding WebGPU support as well - see…

    2024 · github.com

  5. 5IM

    Hi Hackers, Excited to share a macOS app I've been working on: https://recurse.chat/ for chatting with local AI. While it's amazing that you can run AI models locally quite easily these days (through llama.cpp / llamafile / ollama / llm CLI etc.), I missed feature complete chat interfaces. Tools like LMStudio are super powerful, but there's a learning curve to it. I'd like to hit a middleground of simplicity and customizability for advanced users. Here's what separates RecurseChat out from similar apps: - UX designed for you to use local AI as a daily driver.…

    2024 · recurse.chat

  6. 6
    ChattyUI149

    Run open-source LLMs locally in the browser using WebGPU

    2024

  7. 7MG

    Hello HN, I've been working on this project for a while, and it has been in an "open" beta for some time. I finally believe it's ready for its first release. I hope you like it. Here are some potential questions that may arise: 1. How does it compare to LM Studio? It's likely that if you're already using LM Studio, you'll continue to do so. This project is designed to be more user-friendly. 2. Is it open-source? No, it is not. 3. Does it use any open-source libraries? Yes, it uses llama.cpp and a few others, as indicated in the license information included with the application. 4. Why is not…

    2023 · avapls.com

  8. 8
    LM Studio209

    Discover, download, and run local LLMs (incl. DeepSeek R1)

    2025

  9. 9
    Perssua61

    Real-time guidance from any LLM (including local ones)

    Nov 2025

  10. 10NI

    This lets you talk to local LLMs in Apple Notes. I saw Obsidian Ollama (https://github.com/hinterdupfinger/obsidian-ollama) and thought it was handy, but I'm too lazy to migrate away from the Apple ecosystem, so I quickly hacked this together. I tend to use Notes as a scratchpad for prompts, so it's nice to do some quick inference without leaving the app. Notes doesn't really support plugins so I'm using the macOS accessibility API for reading selections and then stream responses using the clipboard (not ideal but it works).

    2024 · smallest.app

  11. 11IB

    Hey HN, I am proud to show you guys that I have built an open source alternative to Azure OpenAI services. Azure OpenAI services was born out of companies needing enhanced security and access control for using different GPT models. I want to build an OSS version of Azure OpenAI services that people could self host in their own infrastructure. "How can I track LLM spend per API key?" "Can I create a development OpenAI API key with limited access for Bob?" "Can I see my LLM spend breakdown by models and endpoints?" "Can I create 100 OpenAI API keys that my students could use in a classroom…

    2023 · github.com

  12. 12OS

    Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.

    2025 · github.com

  13. 13CI

    One of the most frequent questions one faces while running LLMs locally is: I have xx RAM and yy GPU, Can I run zz LLM model ? I have vibe coded a simple application to help you with just that. Update: A lot of great feedback for me to improve the app. Thank you all.

    2025 · can-i-run-this-llm-blue.vercel.app

  14. 14LL

    Hey Folks! I've been building an open source benchmark for measuring local LLM performance on your own hardware. The benchmarking tool is a CLI written on top of Llamafile to allow for portability across different hardware setups and operating systems. The website is a database of results from the benchmark, allowing you to explore the performance of different models and hardware configurations. Please give it a try! Any feedback and contribution is much appreciated. I'd love for this to serve as a helpful resource for the local AI community. For more check out: - Website:…

    2025 · localscore.ai

  15. 15AO

    I've built an airgapped Retrieval-Augmented Generation (RAG) system for question-answering on documents, running entirely offline with local inference. Using Llama 3, Mistral, and Gemini, this setup allows secure, private NLP on your own machine. Perfect for researchers, data scientists, and developers who need to process sensitive data without cloud dependencies. Built with Llama C++, LangChain, and Streamlit, it supports quantized models and provides a sleek UI for document processing. Check it out, contribute, or suggest new features!

    2024 · github.com

  16. 16PU

    After seeing a cool demo of a hack on Twitter, I built a cross platform version of it that works well and uses streaming. From anywhere on Mac and Linux, trigger Ollama and optionally feed it your clipboard. I built it yesterday and it's already very useful to me. I'm pretty excited about it and wanted to share!

    2024 · github.com

  17. 17IB

    I’ve been playing around with local LLMs for the past couple of months and decided to build something that can run on an iPhone. It’s a universal app built with SwiftUI and the excellent ggml library. The model is an SFT fine tuned and 4 bit quantised version of the RedPajama-INCITE-Chat-3B-v1 OSS LLM. It works reasonably well on recent-ish (~3 year old) iPhones, iPads and Macs. It was launched on the App Store yesterday[1] and Product Hunt today[2]. It seems to be reasonably ok at natural language interactions, but given its size, does pretty badly at coding and reasoning. Also, it…

    2023

  18. 18AT

    I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…

    2025 · llmapitest.com

  19. 19RM
  20. 20IB

    Hey HN, I built a website where you can train Llama 3.1 8b & 70b (4bit) on your data. I use unsloth in the backend and the training is done on H100s which I rent programmatically from Runpod. I'd love some feedback. If you would be interested in using it feel free to book a chat with me: cal.com/hamada/tunellama-intro Happy to give you free credits :) P.S. I'm also looking for a co-founder as I have big plans for this.

    2024 · tunellama.com

  21. 21IB

    Built a simple web app that tells you which open-source LLMs will work on your hardware. It auto-detects your specs, shows compatible models from Hugging Face, gives realistic performance estimates (tokens/sec), and recommends quantization settings. You can also manually input specs to see "what if I upgraded my RAM?" Made this after wasting time downloading giant models only to find they crawled on my hardware. Hope it saves you some frustration!

    2025 · caniusellm.com

  22. 22AW

    Lately, I've been tinkering with llama.cpp and the ollama server. The speed of these tools caught my attention, even on my modest 4060 setup. I was quite impressed with the generation quality of models like Mistral. But I was a bit unhappy at the same time because whenever I explore a topic, there is a lot of typing involved when using the chat interface. So I needed a tool to not only give a response but also generate a set of "suggestions" which can be explored further just by clicking. My experience in front-end development is limited. Nonetheless, I tinkered together a small web app to…

    2023 · github.com

  23. 23TF

    I’d originally launched my app: Private LLM[1][2] on HN around 10 months ago, with a single RedPajama Chat 3B model. The app has come a long way since then. About a month ago, I added support for 4-bit OmniQuant quantized Mixtral 8x7B Instruct model, and it seems to outperform Q4 models at inference speed and Q8 models at text generation quality, while consuming only about 24GB of RAM[3] at 8k context length. The trick is: a) to use a better quantization algorithm and b) to use unquantized embeddings and the MoE gates (the overhead is quite small). Other notable features include many more…

    2024

  24. 24BA

    Hi HN, I'm excited to share Bodhi App, a tool designed to simplify running open-source Large Language Models (LLMs) locally on your laptops. While we currently support M2 Macs, we plan to support other platforms as our community grows. # Problem To use LLMs, you typically need to purchase a subscription from providers like OpenAI or Anthropic, or use OpenAI API credits with compatible Chat UIs. These options can not only burden you financially, but also raise data security and privacy concerns. Many laptops are capable of running powerful open-source LLMs, but for non-tech users, setting…

    2024 · github.com

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