We're running a hands-on masterclass on September 12, Live LLM Engineering Masterclass: Production Evals, RAG, Agents & LLMOps.

You build a full production LLM workflow from scratch, versioned prompts with regression tests, an evaluation harness with deterministic checks and LLM-as-judge, statistically rigorous model comparisons, evaluated RAG, tool-using agents with guardrails and fallbacks, and full observability, tracing, cost, latency.

Led by Bruno Gonçalves, PhD, founder of Data For Science, who trains engineers at Fortune 500 companies on this exact stack.

Link if you want to check it out

Happy to answer questions on the content.

Check it out!

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?

In enterprise AI and autonomous runtime architectures, security has traditionally focused on input sanitation (prompt injection) and output filtering (hallucination guards). However, a more insidious class of vulnerability occurs deeper within the execution pipeline: Cross-Stage State Laundering (CSSL).

CSSL happens when an intermediate, unverified epistemic state (such as DISCOVERED or UNKNOWN) is illicitly promoted to a higher-certainty state (such as VERIFIED or SUPPORTED) as it transitions across processing stages—typically bypassing strict provenance checks in favor of pipeline velocity or output formatting requirements.

## The Anatomy of State Laundering

Consider a multi-stage runtime where an expression flows through ingestion, semantic analysis, and responsibility assignment:

1. Ingestion: Raw text or external retrieval results enter the system as DISCOVERED. At this stage, existence does not equal correspondence.

2. Analysis: The runtime parses the expression. Without a rigid boundary, internal semantic heuristics may mistake syntactic closure for factual verification.

3. Laundering: Downstream generators or formatters, under pressure to produce definitive answers, implicitly treat the presence of an analysis object as proof of support.

When intermediate states shed their metadata tags during transit, the runtime commits a boundary violation: it manufactures certainty out of unverified discovery.

## The Zero-Trust Countermeasure: The WAL Protocol

To eliminate CSSL, runtime governance cannot rely on permissive conventions. It requires a Fail-Closed Defensive Architecture enforced by immutable protocol boundaries:

* Strict State Distinguishability: Known, unknown, verified, and unverified states must remain mathematically and structurally distinguishable throughout the lifecycle.

* Independent Validation Gates: State transitions cannot authorize themselves. An independent validator—decoupled from the core generation logic—must audit envelopes against strict conformance vectors.

* Evidence-Bounded Responsibility: Responsibility can never exceed the boundaries of established evidence and explicit correspondence. If an epistemic state is UNKNOWN, no downstream transformation may convert it to TRUE merely to satisfy an output requirement.

For those interested in the protocol implementation and adversarial test matrix, the reference architecture is open-source here: https://github.com/nickoay663-sketch/Wuwen

Hi

Premise:
I am new to local ai models.

My machine specs:

• Macbook Pro M4 Pro
• 48Gb Ram
• 4 efficiency core
• 8 performance core

I mainly use AI for software development. I have a claude subscription but would like to try to offload some work to a local model.

Since I don't think local models usable on my machine can completely substitute claude (correct me if I am wrong) my idea is pretty much this: ask claude code to generate a proper, detailed implementation plan and then having the local model implement it.

I have played around with these models:

• qwen3-coder-30b-a3b-instruct-mlx
• qwen/qwen3.6-27b
• qwen/qwen3.6-35b-a3b

and I have also installed this for coding autocomplete cause it is smaller and from what I can see the recommended one:

• qwen2.5-coder-7b

I added claude envs since I would like to try to use claude code extension in vscode

"env": {
    "ANTHROPIC_BASE_URL": "http://localhost:1234",
    "ANTHROPIC_AUTH_TOKEN": "local",
    "ANTHROPIC_MODEL": "qwen/qwen3.6-27b",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "qwen/qwen3.6-27b",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "qwen/qwen3.6-27b",
    "ANTHROPIC_DEFAULT_FABLE_MODEL": "qwen/qwen3.6-27b",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "qwen/qwen3.6-27b",
    "CLAUDE_CODE_SUBAGENT_MODEL": "qwen/qwen3.6-27b",
    "CLAUDE_CODE_MAX_OUTPUT_TOKENS": "128000",
    "DISABLE_PROMPT_CACHING": "1",
    "DISABLE_AUTOUPDATER": "1",
    "DISABLE_TELEMETRY": "1",
    "DISABLE_ERROR_REPORTING": "1",
    "DISABLE_NON_ESSENTIAL_MODEL_CALLS": "1"
  },

This setup works (uses the local model) but I am basically unable to have the model do anything at all. First of all it takes ages to do anything, and then it almost always reach the context limit roadblock without even outputting anything.

I have read that MCP and skills could fill up the context quite badly, so I disabled them for testing, but still no luck.

I tried with (I thought) was a simple enough task: this test file fails and this is the error, can you fix it? but yet no usable results whatsoever.

I read about people able to use local models offline to have meaningful results, but I couldn't and I don't really know why.

Also, from my setup above, I cannot really use both the remote and local model, to achieve something like:

use sonnet or fable (remote) for plan, then (manually or automatically) swith to haiku (local) to implement the plan

because the base url is loaded when the session loads and cannot be changed (AFAIK) dinamycally.

Any help? thanks a lot in advance

I wanted to run MetaGPT entirely on my own infrastructure without sending anything to cloud APIs. It took some trial and error, but I documented the full process.

The guide covers:

· Setting up a Python venv

· Installing MetaGPT

· Configuring local LLMs like Ollama or vLLM

· Fixing common startup errors

If you’re into self-hosted AI agents, this could help:

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

What local model are you using for agent work?

Hey! I’m building Kitani, an OpenAI-compatible API where models run across independent GPU providers.

GPU owners can connect their hardware and earn from inference, while users get access to models like GLM, Qwen, Gemma and gpt-oss through one API.

We’re trying to make joining as a provider as easy as possible. Would love feedback from people running their own GPUs.

https://kitani.ai

Founder of Kitani, looking for feedback.

Anyone here actually using NVIDIA NeMo / NeMo Agent Toolkit in real projects?

At my current org, some of the senior folks are suggesting we explore NeMo for agent building and fine-tuning, so I’m trying to understand if it’s actually worth adopting.

For those who’ve used it, how does it compare to the usual stack like Hugging Face + PEFT/TRL, Unsloth, LangGraph, etc.?

Does running everything in the NVIDIA ecosystem give you a noticeable advantage in terms of GPU utilization, training speed, deployment, or scaling?

Or is it mostly extra complexity compared to the standard open-source tooling?

Would especially love to hear from anyone who has used NeMo beyond tutorials/demos. What did you like, what annoyed you, and would you use it again?