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janhq
jan
Rust2024–2025
janhq / jan

jan

Jan is an open source alternative to ChatGPT that runs 100% offline on your computer.

12Distinct people
17Mentions
0Explicit recommenders
45.7KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Jan is an open source alternative to ChatGPT that runs 100% offline on your computer.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month17 dated mentions

Observed mentions: 2024-01-12 01:24:52 UTC to 2025-11-03 15:30:49 UTC.

01232024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJan 20242024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-09-01 00:00:00 UTC to 2024-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsDec 20242025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsNov 2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC110
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC000
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC101
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC220
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC101
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC202
Sep 20242024-09-01 00:00:00 UTC2024-10-01 00:00:00 UTC101
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC101
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC000
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC101
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC101
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC312
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC211
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC101

What Hacker News says

17 sources
If you're on Mac you can download LM Studio and get the MLX version (edit - links below). I am running it on 64 gb M1 and it takes about ~30 gb ram. I've been on the hunt for a local orchestrator model that interprets input with speech to text (STT) from WhisperX, then can decide what to do. I have only been running it for a day, but it may be overkill for my setup. For simple tasks it can quickly respond and then understand to use MCP servers for tasks and other things, but offloading all the heavy lifting to claude code via sdk and cli, then bringing the results back in a summary or with clarifying questions as text to speech (TTS). I'm playing with Kyutai TTs b/c have great models that sound real and can do conversational streaming with VAD (though my mbp is too slow with it for now but see https://unmute.sh/ for demo). I am looking fo
Read the rest
r an orchestrator model that runs on 10-15 gb of ram and can do really good tool calling and model routing. I'm will likely move to something even smaller designed specifically for this, like Jan Nano and then spin up an intermediate model like Qwen if needed, or try a smaller Qwen. https://github.com/menloresearch/jan?tab=readme-ov-file Ultimately, I want something that can see my screen and know what is going on and have full context and be live, so I was excited about Gemma 3N multi-modal, but its not really available yet fully with vision at least for MLX. https://deepmind.google/models/gemma/gemma-3n/ Next 6 months in this area is going to be pretty wild though. edit: Fixed links below, thanks https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3... https://huggingface.co/mlx-community/Qwen3-Coder-30B-A3B-Ins...
In Qwen3-Coder-30B-A3B-Instruct
Given how much data (personal and otherwise) that you're likely going to feeding into it, I would HIGHLY recommend using an open-source chat interface like Jan. It's cross platform, works with most LLMs, and has extension support. https://github.com/janhq/jan Another alternative is LibreChat, it's more fully featured but its heavier weight and spins up quite a few docker instances. https://github.com/danny-avila/LibreChat If you're going through the trouble of running models locally, it doesn't make sense to couple that with a proprietary closed source interface (like LM Studio).
In How I run LLMs locally
Jan [0] is MacOS native, open source, similar feel to the ChatGPT frontend, very polished, and offers Anthropic integration (all Claude models). It also features one-click installation, OpenAI integration, a hub for downloading and running local models, a spec-compatible API server, global "quick answer" shortcut, and more. Really can't recommend it enough! [0] https://github.com/janhq/jan
In GitHub cuts AI deals with Google, Anthropic
LibreChat is also one of the few LLM chat interfaces that works with both external APIs (OpenAI, Anthropic, etc) and local ones (hosted via Ollama, etc) out of the box. If it has a downside, I'd say its a little more involved to get setup, lots of docker containers, etc then a more batteries-included approach like Jan. https://github.com/janhq/jan
In Kagi Assistant
A better question would be over something like Jan or LibreChat. Ollama's is CLI/API/backend for easily downloading and running models. https://github.com/janhq/jan https://github.com/danny-avila/LibreChat Jan's probably the closest thing to a open-source LLM chat interface that is relatively easy to get started with. I personally prefer Librechat (which supports integration with image generation) but it does have to spin up some docker stuff and that can make it a bit more complicated.
In LM Studio 0.3 – Discover, download, and run local LLMs

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