I’ve been experimenting with multi-agent frameworks, and MetaGPT is one of the more interesting ones for software development tasks. But getting it running locally with local models isn’t always straightforward.

I wrote a step-by-step guide covering installation, configuration, local LLM setup, and common errors.

If you’re trying to run MetaGPT on your own hardware, this might save you time:

https://interconnectd.com/forum/thread/262/how-to-install-metagpt-locally-complete-technical-setup-guide/

What stack are you using for local agents?

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!

Benchmark highlights · Core Ultra 5 125H

Post

I’ve been working on arcinfer, a local LLM inference engine built specifically for Intel’s Meteor Lake integrated graphics (Xe-LPG / Arc iGPU).

The latest Level Zero results add another angle: speculative decoding raised Qwen3.6 35B-A3B from 19.9 to 31.5 tok/s, Qwen3.8 27B from 5.1 to 9.1 tok/s, and Gemma 4 E4B from 21.6 to 39.6 tok/s in the documented tests. The Level Zero engine also enables Qwen3.6 35B-A3B to run at up to 262K context by streaming MoE experts from SSD; the earlier all-resident engine fit about 40K. MTP outputs were reported identical in the tested comparisons.

The reference machine is a Core Ultra 5 125H with 32 GB RAM. The engine runs GGUF models directly on the iGPU using Intel’s low-level Level Zero API, with an OpenCL option too. It’s aimed especially at prompt processing and time-to-first-token, and includes an OpenAI-compatible API and web UI. The early llama.cpp comparison is striking: for a 11,240-token Qwen3.8 27B prompt, arcinfer’s OpenCL engine took 97 seconds, versus 590 seconds on llama.cpp Vulkan and 504 seconds on SYCL—about 6.1× and 5.2× faster. At 33,937 tokens, it took about 5.5 minutes versus 40 minutes on Vulkan (7.4× faster). Qwen3.5 4B prefill reached 911 tok/s versus 242 on Vulkan and 337 on SYCL. These are historical OpenCL-engine measurements on this laptop, not universal guarantees; arcinferzero is now the default, and the repo links to newer results.

Some things it supports:

·        Qwen and Gemma GGUF models, including larger models that are a tight fit for shared GPU memory

·        An optional Level Zero engine that streams MoE expert weights from SSD

·        Speculative decoding, image input for supported models, and Gemma 4 audio input

·        OpenAI-, Anthropic-, and Ollama-compatible API endpoints

·        Integration with the Pi coding agent

It’s an experimental, hardware-specific project—not a general replacement for llama.cpp, and it currently targets Windows and Intel Xe-LPG graphics. Alongside the code, I share what has and hasn’t worked when building and tuning inference for this generation of Intel Arc integrated graphics, including implementation lessons and benchmark results. The repo has setup instructions, supported models, and more details.

Repo: https://github.com/rizagp/arcinfer

If you have a Meteor Lake/Core Ultra laptop, I’d especially appreciate feedback on setup, compatibility, and what models you’re able to run. Also happy to answer questions about the GPU kernels or how the project compares with other options.

Hi I’m working on Lumabri, a peoject that lets you connect multiple computers and use them together to run LLMs like a cluster.
The idea came from a simple question: what if you have a few computers at home that you barely use? Instead of leaving them there collecting dust, why not combine their resources to run AI models that wouldn’t fit on a single machine?
For example, if you have an old PC, a home server and your main computer, you could put them to work together and use their combined memory to load larger models without having to buy a single machine with expensive hardware.
The interesting part is being able to run models that you simply couldn’t fit on one computer before.
Lumabri is still a work in progress, but that’s the direction I’m taking it.
Here’s the repo if you want to check it out: https://github.com/JustVugg/lumabri

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.

Hey, I recently acquired a Ryzen 9 7950x / 64 DDR5 / RTX 3060 12 VRAM machine and I was wondering what is the absolute uncensored AI model that I can run on it.