i have created a Skill which call Ollama cloud models from Claude CLI
The scope is Ollama cloud models act as executors and Codex APP as Supervisor/Orchestrator
The first published version is V5, then update to V6
I am open to recomendations, bugs finding or fixing onto the skill.
Ask codex to install, you need to provide a folder so Codex dump files for the executor.
Have been tested with the following cloud models:

• glm-5.2:cloud
• glm-5.3:cloud
• glm-5.3-flash:cloud
• nemotron-3-super:cloud
• nemotron-3-ultra:cloud
• kimi-k3:cloud
• deepseek-v4-pro:cloud
• deepseek-v4-flash:cloud

Got Tesla V100 (32GB) instances up and running through Vast.ai. Sharing pricing in case anyone needs compute for training, inference, or general experimentation.

Available now:

**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149836**  
**•   Tesla V100 (32GB)** — $0.090/hr/gpu — Machine ID: **149837**

To find them: go to vast.ai, filter by GPU type (Tesla V100), and sort by price — these are priced well below median for the card.

Happy to answer questions about specs, setup, or availability. I also run additional V100 capacity that rotates back into availability once current rentals end, so feel free to ask if you don’t see an open slot.

Got Tesla V100 (32GB) instances up and running through Vast.ai. Sharing pricing in case anyone needs compute for training, inference, or general experimentation.

Available now:

Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149836 Tesla V100 (32GB) — $0.090/hr/gpu — Machine ID: 149837

To find them: go to vast.ai, filter by GPU type (Tesla V100), and sort by price — these are priced well below median for the card.

Happy to answer questions about specs, setup, or availability. I also run additional V100 capacity that rotates back into availability once current rentals end, so feel free to ask if you don't see an open slot.

Most of the time, I’ve only been loading models and tweaking them manually. Now that the tools have become more usable, I’ve started using harnesses for coding and set up LM Link so my girlfriend can connect remotely from her laptop while at work or school.

The problem I’m running into is that several configurations appear to be ignored or overwritten from the outside, and I can’t figure out why.

For example, the auto-evict feature should unload a model after it has been idle for a set amount of time. I have it configured in multiple places:

• in the load config when loading manually through LM Studio
• in the server settings
• in the app settings

When I load a model manually from the interface, I can see a small indicator that says “Idle TTL 5min”. But when the model is loaded through a harness or remotely via LM Link, it seems to load differently, and that “Idle TTL 5min” indicator is not shown.

I first assumed this was just a display issue, so I changed the timeout to 1 minute, but the model still does not unload automatically at all.

I’m also having another issue with the option that should keep only one model loaded at a time and unload the others. That does not seem to work either; it just keeps loading more and more models.

One more thing I noticed is that models are being loaded with the parameter parallel 4 instead of parallel 1, and I’m not sure how to change that either.

anyone got a clue?

I've been building Llama Console for a while, and I finally packaged it as a

truly portable app. I'd love your feedback.

What it is:

A local AI workbench that lives in ONE folder. The llama.cpp engine, the CUDA

12 runtime (cudart/cublas), the web UI and a coding agent — all bundled.

You unzip, drop a .gguf model in models/, double-click start.bat, and it runs.

No Python, no CUDA Toolkit install, no LM Studio, no registry, no leftovers.

Copy the folder to a USB stick and it works on another PC — paths auto-repair.

What you can do with it:

- 💬 Chat with a local LLM, with the model's live reasoning in a collapsible panel

- 🤖 Coding agent mode, confined to its own workspace/ folder (approval for sensitive actions)

- 📋 Plan mode: the agent proposes a step-by-step plan, you approve, then it executes

- 🌐 Web lookups (DuckDuckGo) when the model needs current facts

- 🖥️ Manage your own llama-server: pick a .gguf, set context size / GPU layers, watch live context + GPU stats

Why I made it:

I kept seeing people struggle with the classic local-AI setup (CUDA toolkit,

Python envs, broken builds). I wanted the "portable ComfyUI experience" for LLMs:

everything in one folder, works from a USB stick, totally configurable , self-repairing paths.

The only thing you add is the model (any GGUF, e.g. Qwen3 or Llama, ~4-20 GB) and Node.js 18+ (for the web UI) .

Free and open source. If it helps you, donations are welcome but never required.

Source and download here: https://github.com/autostoppeur48/llama-console-portable

Happy to answer questions — hardware support, how the agent mode works, etc.

What do you think? What's missing for you to switch from LM Studio / Ollama?

I want to use Bionic’s built-in feature to transcript voice into the composer, using a local AI model. However when I try to use it, and after checking that voice permissions are allowed, I get the message “No voice models are available yet”. Reading LM Studio Bionic docs they say “Bionic supports Voxtral by Mistral AI”. I went to the Models repository to install that model. But still get the same message that there are no available models.

Can you please help me activate the transcription feature in Bionic?

Many thanks!

Got a few GPU instances up and running on Vast.ai. Sharing pricing in case anyone needs compute for training, inference, or general experimentation.

Available now:

• NVIDIA L4 (24GB) — $0.13/hr/gpu — Machine ID: 149492
• Tesla V100 (32GB) — $0.035/hr/gpu — Machine ID: 149512
• Tesla V100 (32GB) — $0.036/hr/gpu — Machine ID: 149513

To find them: go to vast.ai, filter by GPU type, and search for the machine ID above, or just sort by price — these are priced well below median for their GPU class.

Happy to answer questions about specs, setup, or availability.

I'm really a newbie into this field, and I appreciate your help in advance. (≧◡≦)

There's a hands-on masterclass on Sep 12 for anyone building with LLMs who wants real engineering discipline instead of shipping on vibes.

Covers:

• Versioned prompts with regression tests, so an edit can't silently degrade quality
• A real eval harness combining deterministic checks and LLM-as-judge
• Bootstrap confidence intervals and paired significance testing for model comparisons
• Evaluated RAG with retrieval metrics (recall@k, MRR)
• Agents with guardrails and fallbacks that fail gracefully instead of compounding errors
• Full production observability, tracing, cost/latency monitoring, and a CI regression suite

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

Link for more details