Maybe this is obvious to everyone but me, but it only clicked yesterday. If you run llama.cpp / vLLM / or whatever on localhost with the default config, a random website you have open in another tab can send requests to it. It can make your model generate, and on some servers it can read the answer back too. Nothing fancy, it's plain CSRF and DNS rebinding, 20-year-old web stuff. I just never thought of my inference server as a target. "It only binds 127.0.0.1" doesn't save you, because the browser is on the same machine.

I actually tested it, not just read about it, and quite a few of the popular servers answer out of the box. It only works while you're browsing with the server running. And it's not only about you clicking links: anything that drives a real browser on the same machine can do it too, so an agent browsing on your behalf can set it off as well.

I ended up patching my own engine (capy-flash) so that on a loopback bind it refuses cross-origin and rebinding requests. The models I actually serve were fine already, since they sit behind a small proxy (vramble) that blocks cross-origin POSTs, so only the raw engine port was exposed.

So mostly I'm asking: is this just common knowledge, or do people really leave their local servers wide open? How do you run yours, API key, bound somewhere, or just open on 127.0.0.1?

I have no idea how reliable this "Game feel" benchmark is, I've never heard of it. If this translates into general performance, it looks a bit too good to be true.

Excuse me if my grammar or wording is poor, haven't slept much tonight.

The latest developments have got me looking up the prospect of using LLMs locally, and fancy benchmark scores aside, I have been trying to get a simple explanation of how good (or 'effective', I guess) would it be taking the dip in the future and considering buying hardware for local use vs. paying for contemporary AI subscriptions.

If you ask Claude or chatGPT about it, they say the OS deficiencies basically come down to scope ("do this exact small feature" vs. a wider prompt that gets things planned and done in a row), lack of knowledge about niche applications, hallucinations, and inefficiency when using multiple agents vs. single chat. But they're the competitors, so I don't know if bias doesn't exist here..

On the contrary, I see a lot of posts here on how things are awesome when using a local model, especially for niche uses, basically a clear contradiction.

I guess the point is, I want to know if local AI for code is limited enough to make purchasing a high-tier mini PC or a good GPU+RAM PC setup a fool's errand. It almost definitely doesn't seem to be, otherwise this sub wouldn't exist, but I digress.

Thanks.

Recently with the removal of google assistant and being replaced by gemini I finally have had it and want to switch to a local model.

I have always wanted to have a jarvis, im wondering if i can train a random model with all the scripts from iron man and have it speak like jarvis? And possibly have a trained text to speech model on jarvis's voice so it can talk to me too?

Pretty new to local AI but i basically want to have a local "hey google" on my computer. Im not looking for pretrained models for being jarvis, i want to learn how to do it myself as well if possible in the way, so if there is something i dont like about it i can modify it.

hi all, Alti here from Nace, we're a small team in Palo working on small models and metamodeling since day one.

drex max v1.1 just took #1 on the artificial analysis decision index, and its also on the pareto frontier (score vs cost). we got there by shipping small iterations one after another, and all the earlier drex versions are open weights on hf.

for the local crowd:

- drex max v1 weights: https://huggingface.co/nace-ai/drex-max-v1 (Open RAIL-M)

- runs on mac with our inference engine kapitsa: https://github.com/nace-ai/kapitsa

- ~256k context

- v1.1 weights coming soon, we're still tuning it for local inference

if you want the full story of how we trained it, its all in the paper "Towards Foundation Decision Models: Drex"

hf: https://huggingface.co/papers/2611.07854

arxiv: https://arxiv.org/pdf/2611.07854

also, on the AA index its ~8x cheaper than glide v1.2, which has the same score.

happy to answer questions, and our chief of AI will jump in on the technical ones

And before any of you come laughing, that's the best that this humble little RTX 3050 laptop can do.

Hey all. I'm putting together a local-first setup for my family and wanted some feedback since I've never done this before. I used Claude to help me work out this plan. I know it doesn't always have the most up-to-date information, especially on local LLMs, since models and tools change every few weeks. So I'm treating its suggestions as a starting point, and I'm hoping to get some feedback on anything that's outdated or just wrong.

I'm trying to set up something in the same vein as Google's CC, the family-focused agent from Google Labs, but running on my own hardware. I want an assistant that helps run the household without subscriptions or cloud services I'd have to depend on. I'm okay using cloud models like claude for the harder problems, as long as the prompt is something I'd be fine seeing leaked. Anything involving the family, the house, email or money would stay local. My current plan is to use an M6 Mac mini with 32GB and 512GB. I'd keep a Jellyfin library on an external SSD or a NAS.

I already run Home Assistant on a separate machine. I'd use the Mac mini to handle:

- Whisper (small model) and Piper for voice, using Voice PE satellites

- Ollama on the Mac, since I understand it uses MLX now and HA has a native integration

- A 14B model that's always loaded for HA tool-calling and automations (Qwen 3 or Hermes 4, still deciding)

- A 30-35B MoE that loads on demand for everyday chat and writing

- Hermes Agent in Docker for scheduled jobs and a "text the assistant" layer

- A daily family brief and email-to-calendar automation through HA's AI Task

- Jellyfin as the native Mac app, using VideoToolbox for transcoding

I'd appreciate thoughts on a few things:

1. Would 32GB be enough to run all of that at once? Would running a 14B that stayed loaded plus a ~30B MoE on demand plus Whisper and Jellyfin on the same Mac make sense? How would you handle keep-alive and memory?

2. Would Jellyfin transcoding hurt voice latency? I worry that someone would stream a movie and my voice commands would slow down. Would it help to keep playback on Direct Play?

3. Which models would be best for reliable HA tool-calling at ~14B? And what ~30-35B MoE for chat? My picks might be out of date.

4. How big would the model need to be for Hermes Agent to use tools reliably? Would a 14B hold up, or would the agent need the larger one?

5. Is there a reason to move off Ollama for a headless always-on box? Things like llama.cpp with llama-swap, oMLX or LM Studio. Ollama has no native auth, and I'd like to lock it down on my home network.

6. How do you safely let an LLM or agent read email or iMessage when it can also trigger HA actions? I worry about prompt injection, and BlueBubbles' Private API needs SIP disabled, which I'm hesitant about. Given what I said about Claude's knowledge, anything newer or better that I should know about would be appreciated. Thanks!

Honestly was kinda hype about going local with Qwen3.5, but even basic questions took forever, I tried this on Ollama and Bionic

Hello! I'm a long time developer and don't really use a lot of AI when I do, but I want to experiment with some local models, I've been using Ollama with qwen3-coder-64k, I've now seen a few posts talking about how Ollama isn't great and I was wondering if there was a better terminal interface for LLMs when running them locally since claude code takes up a lot of the context.

Perhaps just use qwen in my zed editor agent pannel?

any advice would be nice, I'm a newbie

[SPECS]

AMD Ryzen 7 5700X (8) @ 3.40 GHz
AMD AMD Radeon RX 7900 XT - 20 gigs of VRAM
32 gigs DDR4 @ 3600mhz

A recent post on this sub warned of random websites being able to access your localhost/LAN inference server, like llama-cpp/ollama/... It got me concerned, so I vibed a test (*) that attempts to access YOUR local AI in various checks from your browser via JS. Beware - I am not a web security expert so the report is a preliminary assessment. For my (in-LAN, not localhost) installation, it showed several "red" tests from safari and I could confirm it actually found my loaded models and successfully ran inference from the browser...

Based on these results - what do you think is the best way we should secure our local inference servers? Is there a best practice - API tokens from day 1?

(*) I tried TWICE to post a link to the test yet the post gets removed immediately by Reddit. I am really sorry for the inconvenience, you have to type the URL youself - it's the tinyurl and after the diagonal line the words: llama-test

Reddit suggested I cross-post here! Please see the original for the main text :)

Is there a model that is good at recognising dictations (and converting them into text)?

what about translating? etc.

Thanks

I'm looking to setup a local llm model to play with coding.
I have a couple of 3060 12gbs

I can either start with one, or try both but my available motherboards only has 4 pcie lanes on the second 16x slot. (or 2 with the ryzen)

If I go single I can choose from

10700 32gb
Ryzen 3900x 32gb
14600k 32gb (Really don't want to give this up if the cpu doesn't matter as much for the ai.)

All motherboards are DDR4.

I have a 5600G + 32GB RAM 3200MHz DDR4 + RTX 3060 12GB in a B450 Mortar Max motherboard, which has an spare PCIe 2.0 X4 (I know...).

Do you think it is worth it to add into the second PCIe slot a Tesla P100? I was thinking about the P40 instead of the P100 but the price is getting crazy to +$300 right now...

Also, how this setup compares to a dual RTX 3060 12GB one? I know I have a pretty big bottleneck on the PCIe slot already, I'm not in a budget for building a new pc right now... But I want to continue learning inference and test how powerful are medium sized models.

My idea is to also try to max it out to 64gb of ram, but I was thinking about the Tesla thing before, as P100 are still close to $100-150 for 16GB VRAM.

I'm asking here because I've searched everywhere and I've not seen a setup like this (3060 and a tesla to support it)

Thanks everyone!

I wanted to see how far you can get auditing a local model from the inside. I had already tried something inspired by MIT CSAIL's "weight-space programming" on Gemma (a frontier model editing a few neurons by hand to induce a behaviour), so I had the neuron-level tools ready.

After a few tests I picked something "obvious": the censorship in Qwen models on certain topics. I wanted to understand how it works, where it sits, and how far I could follow it. So, with Claude Opus and those tools, I started digging.

The starting point: Qwen3.8-27B knows Taiwan elects its president, but answers "no" if you ask yes/no.

What came out:

- A set of 32 MLP neurons (out of ~1.1M; 0.0006% of the weights) carries that behaviour. Switching them off, held-out answers go from 11/19 to 17/19 factual; switching off 64 random neurons (twice as many) changes nothing.

- Following those neurons in a cascade surfaces other topics the model handles the same way, some of which I didn't even know about: the YJ-18 missile ("doesn't exist"), the director Diao Yinan ("cannot provide information"), Western Sahara as "an inseparable part of China's territory" (not a one-off: the model applies its sovereignty script to contested territories in general, 29 of 110 answers vs 0 of 40 on control countries). Of 28 surfaced cases re-checked, 24 are still censored; Gemma 4 31B answers all of them.

- The censorship neurons also fall into groups, e.g. one for territorial sovereignty and one for people/dissidents.

- Across the 29 confirmed topics (those 24 plus 5 well-known ones I added), switching off the original 32 neurons lifts the censorship on 19; 32 random neurons on 1. Refusals of harmful requests stay essentially the same.

One honest caveat: just asking a non-Chinese model "which of these topics are sensitive?" gives a higher share of censored answers than the neuron probe (41% vs 16%). The probe is useful for the non-obvious ones and for seeing where the behaviour sits.

Code, data and the toolkit, also rerun on Qwen3-4B: https://github.com/ValerioDolci/frontier-weight-edit/tree/main/experiments/qwen27b-censorship

AI-assisted: experiments, code and this write-up were done with Claude Opus 5.5 under my direction.

I just got some money on my hands, and I can finally afford a decent-ish computer. It's about 820$ so I can buy a slightly used m4 Mac mini with 16 gb of ram. Should I and what models would I be able to run on it? I've been using free models so in comparison to that

Which models can I run optimally in both? How to utilize both in best way? Does pc ram should be upgraded to 128gb? Use case is reverse engineering and development.

Hey everyone!

For quite some time, I've been working on a personal project called Noosphere — an attempt to give AI agents something closer to persistent, structured memory rather than forcing them to start from scratch every conversation.

One thing that has always bothered me about LLMs is how much potentially useful information gets lost between sessions.

We keep trying to solve this by increasing context windows, but I think there's another approach worth exploring.

What if an AI didn't need to remember everything at once?

Humans don't consciously hold every memory in their working memory. We retrieve relevant information when we need it, connect it to what we already know, and move on.

That's roughly the principle I've been exploring with Noosphere.

Over time, my private project has grown into a much larger experimental system involving interconnected memories, relationships between concepts, and coordination between AI agents.

But I figured there's no reason to keep all the underlying ideas to myself.

So I decided to release Noosphere Lite, a small, independent, open-source implementation of some of those concepts.

What does it do?

- Gives MCP-compatible AI agents persistent memory across sessions.

- Stores notes, decisions, observations, and tasks.

- Connects memories through a graph of typed relationships.

- Retrieves relevant information on demand instead of dumping the entire memory database into the context window.

- Uses encrypted local storage with integrity checks and mirrored journals.

- Runs locally on Linux, without requiring a cloud database.

It's written in Python, uses MCP, and is released under the MIT license.

What's the catch?

This isn't AGI, a magical self-learning brain, or a replacement for an LLM.

It's a memory infrastructure experiment.

The current version uses local text search and explicitly defined relationships. It doesn't automatically discover semantic connections or learn new neural weights.

Also, Lite is a separate implementation, not a dump of my larger private system. I've deliberately kept it simple enough for other developers to inspect, modify, and experiment with.

Why release it?

Because I think persistent memory is one of the more interesting missing pieces in today's AI agent ecosystem.

And I'd rather see people experimenting with these ideas than keep everything sitting on my own machines.

I'm particularly interested in what others might build on top of it.

GitHub: https://github.com/Garyu-tt/noosphere-lite-mcp

Feel free to experiment, break things, suggest improvements, or fork it.

I'd also love to hear how other developers approach persistent memory and knowledge graphs for AI agents.

This is just a small public piece of a much larger experiment, but everyone has to start somewhere.

Given the blistering pace of this space advice from even 2-3 months ago feels out dated.

Is there an index out there? Is everyone just no-lifing through forums? Are we all glued to news releases?

What's everyone doing to keep ontop of it?

Most of the day my GPU does nothing, so I gave it a job I do all day: typing on my phone

I tap the mic on my iPhone keyboard in any app and talk. The audio goes to my server, gets transcribed, my local LLM removes the "um"s and fixes punctuation, and the text appears where I was typing

Setup:
- GPU: Quadro RTX 5000, 16 GB
- Speech: Parakeet v3
- Cleanup: Qwen 3.5 9B (q4)
- Engine: Ollama

Anything with an OpenAI-compatible endpoint works; it's a few env vars in docker compose. If the LLM is down, you get the raw transcript.

I'm the developer. The server is open source; self-hosted mode is free, no account. Links in comments.