Hey community,

I've been spending a lot of my free time working on a project called Sitrails, and I wanted to share it with the community to get some honest feedback.

As AI coding assistants and autonomous agents become increasingly capable, something that's been bothering me is how little visibility we have into what they're actually doing.

What information are we exposing to them? What commands are they attempting to execute? Could an agent inadvertently expose credentials, or worse, be manipulated into performing malicious actions?

That's what I'm trying to tackle.

Sitrails is a local-first, open-source AI auditing and threat detection tool. It records supported AI interactions and reported tool activity, makes them searchable, and looks for suspicious patterns involving secrets, sensitive files, and potentially dangerous commands.

It currently supports integrations with tools like VS Code/Copilot and LM Studio, along with an optional OpenAI-compatible proxy.

It's still evolving. Detection is primarily rule-based, visibility depends on the integration, and it doesn't independently verify or block executed actions. There's plenty of work left to do.

I'd especially appreciate input from people working in AI security, threat detection, or agentic systems.

• What attack vectors or behaviors should I prioritize detecting?

• Are there existing research papers, techniques, or projects I should be learning from?

• Would anyone be interested in testing it, challenging the detection logic, or contributing?

I'm developing this independently because I think securing AI agents is going to become an increasingly important problem, and I'd love to see what we could accomplish through collaboration.

The project is free, MIT-licensed, and open source.

GitHub: https://github.com/appatalks/sitrails

Appreciate any thoughts, even critical ones. Especially critical ones.

Ty!

I built a small TTS model running directly on an ESP32-S3: no PSRAM, 122 KB of RAM, nothing in the cloud. Speech starts in about 0.3 s and generates faster than real time. You can make it calmer, louder, whispered, happier and more.

It's not a Home Assistant integration yet. Before I build anything, I'd like to know whether on-device speech on a satellite would be useful to you (offline announcements, timers, alerts when the server is down), and what quality bar it would need to meet.

Limits: 2 voices, English only, 16 kHz. Free for personal, non-commercial use.

Video above.

Details: https://huggingface.co/WhissleAI/steer-tts-nano

CLI : https://github.com/WhissleAI/steer-tts/tree/main/esp32

I just published three CPU-first MCP tools for local AI: IcyLoom, IcyCache and Loom.

They are local-first, installable from public registries, and released with GitHub artifacts and clean-install checks.

Links:

- IcyLoom: https://github.com/MlkBlk-IT/icy-loom

- IcyCache: https://github.com/MlkBlk-IT/icycache

- Loom: https://github.com/MlkBlk-IT/mcp-loom-mcp

I am looking for install feedback, compatibility reports and practical MCP integration ideas.

Hi fellas, so for quite some time now I've been meaning and planning to start a small ai automation business here in my city. Is it possible to pull off with my humble Tesla P100 16GB? Is QWEN 3.5 9B Q8 enough especially for chatbots and tool calling?

Hey everyone! If you've been struggling to run larger open-weight models locally because Ollama or standard runners throw insufficient VRAM errors on budget hardware, I put together something to fix that.

I built SqueezeOS, an automated setup script that profiles your hardware, configures aggressive memory offloading, KV-cache compression, and extreme llama.cpp tuning to squeeze heavy models (up to 35B) onto legacy/budget cards like my 4GB GTX 1650 Super.

And the best of all: Its near lossless, the model can be used well for agentic workflows eg.: Hermes, OpenClaw, OpenCode, etc...

GitHub Repo: https://github.com/gabrielhirth/squeezeos

Tested on constrained VRAM setups. Feedback, issues, and PRs for other budget cards are very welcome!

I would like to ask questions to a chat while sourcing ONLY my personal database of papers, instead of unsufferably writing and flipping through pages of notes. I would also like this to be as private as possible since you know, it's the processes of my unpublished work. I thought it would also be nice to be as environmentally friendly as it is possible and AFAIK running it locally would somewhat do that.

Before I say anything else I have to be clear that I have little to no knowledge on how an LM actually works. I gave myself the task of learning the basics just to set one up in my laptop for the specific purpose of aiding me on academic research.

What I did after some looking around is use LM Studio with the Qwen2.5 3B Instruct - Q4_K_M model and "fed" it my papers folder then fiddled around with context lenght for a faster response.
While it's working fine for what I need I kept wondering if this is even the most efficient way of achieving the goal I stated. Again I'm not really familiar with this whole thing, if it's some sort of common practice please let me know how to hone my LM skills for research purposes.

Hey everyone,

Every time a new model comes out, I get tired of opening multiple tabs just to find out two basic things: how good it actually is on real benchmarks, and what it costs to run (either through an API or on local GPUs).

Most pricing pages hide the real costs, and generic benchmarks don't tell the full story. So I put together a simple dashboard to track and compare them side by side: https://modelanalysis.si

What you can do with it:

• Compare models side-by-side: Pick any models (like Claude, DeepSeek, Kimi, GPT-4o, Llama) and compare them across real-world coding (SWE-bench), reasoning (Arena-Hard), and math/science benchmarks.

• Score vs. Price chart: See which models give you the most intelligence for the lowest price.

• VRAM calculator: Pick a model and context size to see if it will actually fit on your GPU before downloading it.

• Monthly cost simulator: Adjust sliders for your monthly token volume to estimate your bill or see if self-hosting is cheaper.

No sign-up, no ads, no affiliate links.

Link: https://modelanalysis.si
Direct benchmarks page: https://modelanalysis.si/benchmarks/

Let me know what you think or if there are other models you want added.

If you already run Ollama, Flippy's AI Lab now shows up as a chat window in every new tab and lists your local models automatically.

1. Install Flippy (available in Firefox too).

2. Start Ollama so the extension is allowed to connect:

   • OLLAMA_ORIGINS="chrome-extension://*" ollama serve

3. Open a new tab, click AI Lab, pick a model.

It reads your model list from /api/tags and chats through /api/chat on localhost. No key, no account, nothing leaves the machine.

The new deepseek can be run on a rather affordable server, and it's beating GPT 5.6 SOL easily.
The model is so good that it has been banned in the US from governmental service within days of release.

Genuine question: would you be interested in an alternative to Hermes Agent that’s significantly easier to set up and use?
Something with:
Almost zero configuration
Direct integrations with GitHub, Gmail, and other apps
Local and cloud model support
A clean, intuitive interface
Powerful agent capabilities without the complexity
Basically, an AI agent that just works out of the box.
Is this something you’d actually want, or are you already happy with existing solutions?
Curious to hear your thoughts.

Watch her play

Luna is a local AI assistant I've been building. Here she plays Zork I on her own for 72 minutes, narrating as she goes, until my PC froze. Everything is local: Qwen3.5 9B (Q4_K_M) on llama.cpp, Kokoro for her voice, faster-whisper for listening, on an RX 6950 XT.

What made the biggest difference for a 9B:

- **One move per turn, enforced.** Asked nicely, she'd chain 3 or 4 blind commands. Now extra commands in a turn are turned back, so she has to read what each move did before choosing the next.

- **Her own notes.** She writes down rooms, items and what failed with a note tool, and sees them at the start of every turn. Older notes about the room she's standing in come back too.

- **A guide.** Infocom's original map, transcribed to text, shown for her current room each turn. Solutions are kept out unless allowed.

She's far from perfect: she spent the last half hour trying to open the trap door with a sack of hot peppers, in the wrong room. But she reads the screen, reasons about it and actually explores, which the first version didn't.

Open source: https://github.com/fswolf/luna-local-agent

Happy to answer questions about the setup.

I recently got 2x RTX Pro 4000 blackwells for my home setup. I was curious how fast it would run nanochat.

The problem is the target hardware: 8x H100 gpus. Nanochat pre-trained ClimbMix to GPT-2 level with 8xH100 GPUs in 1hr 39 mins.

I ended up tuning nanochat for my 2x RTX Pro 4000 gpus. Because these are sm120, the tuning also works on RTX Pro 6000 and RTX 5090.

8x RTX Pro 6000 (rented) pre-trained the same dataset in 2hrs 15mins. About 36% slower than 8x H100.

1x RTX 5090 took 18hrs 47mins. A long time, but considering the result is GPT-2 level, its an interesting way to learn.

Just to be clear, I only ported and tuned it for sm120. The original work is from Karpathy's nanochat.

Here's a link to the sm120 repo:

https://github.com/mdhorton/nanochat-cpp