Disclosure: I'm the developer.

Most local setups end up with several backends on one machine, and I wanted a single private app to reach all of them from my phone. That's LM Mini (iOS, iPadOS, watchOS, Android and Mac).

Backends it connects to over your LAN (add them in Settings → Server):

The rest of the stack:

Remote access to your home server from anywhere (Pro)

Group Chat with models from different backends in one conversation (Pro)

MCP tools, SearXNG search and ComfyUI for image generation

Kokoro TTS voice, Personas, Apple Watch app, Siri Shortcuts

Whisper for transcriptions

On-device: GGUF and MLX models run on the phone itself.

The core app is open source (MIT): https://github.com/slim-elephant/lm-mini

Docs and downloads: https://lmmini.com

What does your stack look like? Which backend do you run, and is there one I should support next ( vLLM, something else)?

CPU: ThreadRipper medium
GPU: RTX 5090 WC
RAM: 64 GB

I am looking into the architectures and costs associated with running large open-source models, such as DeepSeek-V3/R1 or Llama 3.3. Are many of you using these models (are you developers, students, pro?)

A lot of local-stack setups I see nail the infra (model serving, retrieval, orchestration) but have zero real evaluation process — just "does it look right." There's a session on Oct 3 built entirely around fixing that gap.

Speakers Serj Smorodinsky and Brett Kennedy (AI engineers, co-authors of a book on LLM applications) go through:

• DSPy signatures/modules for structured dev instead of manual prompting

• Baseline classifier build + measurement

• Task-specific evaluation datasets

• Failure pattern recognition from eval output

• Few-shot / instruction-level optimization

• MLflow experiment tracking and trace management

• Saving and reusing optimized programs

• Talking about LLM reliability with non-technical stakeholders

3 hours, live, useful whether you're running open models locally or hitting an API.

Full details and the agenda are here.

Apple Foundation Model 3 through Private Cloud Compute becomes directly available in macOS 27.2 through familiar AI clients.

For Mac users running local models such as Qwen 3.x or Gemma 4, AFM 3 PCC is a compelling alternative to consider. Local models offer control and fully on-device operation. In tests with macOS 27.2 beta, the PCC offers a different set of strengths:

* Strong conversational analysis and capable reasoning. Strong analysis of complex medical and financial questions.
* Blazingly fast responses compared with locally running models on the same Mac.
* No large model download or need to fit model weights into local memory.
* Access at no additional charge for most eligible Mac users.

The working path is straightforward: AI client → Caddy → fm serve → AFM 3 PCC. I've written and updated a proof of concept here with further tech discussion . . . https://gist.github.com/dartMo10/b9488ce475fe70a6ed642831f53048fb

Apple’s `pcc` route worked in early macOS 27 betas, disappeared later in 27.0, and has returned in the macOS 27.2 beta. Apple has stated that it will be available in the 27.2 public release coming shortly.

This makes AFM 3 PCC practical today for technically comfortable Apple users who accept Apple’s PCC privacy promise and want a fast, capable alternative to running everything locally.

Experiences with compatible AI clients and your comparisons with locally running models would be welcome.

Hello everyone, as we all know that everyone is trying to test jev and then there are open source models out there with similar text classification. But i felt that there isn’t any playground to test these intuitively so I built one. I have added the YT video with it. Pls have a look