LM Studio

Sovereign Escape Velocity: Ownership w Open Models — Gus Martins, & Ian Ballantyne, Google DeepMind

Jun 10, 2026 · 20:52

Gus Martins and Ian Ballantyne of Google DeepMind introduce Gemma 4, a family of open-weight models that deliver high quality per parameter, enabling deployment on a single GPU or even a phone. They argue that the models' efficiency — a 31B model rivals those twenty times larger — and the shift to Apache 2.0 licensing remove barriers for sovereign institutions like those in Ukraine, Bulgaria, and Brazil. Ian demonstrates multi-agent translation running locally on an M4 Mac, showcasing ownership and control over agentic workloads.

Prompt to Pipeline: Building with Google's Gen Media Stack — Paige & Guillaume, Google DeepMind

May 23, 2026 · 1:54:35

Paige Bailey, Guillaume Vernade, and Ian Valentine from Google DeepMind demonstrate the company's full generative media stack—from Gemini 3.1 Flash Light's video analysis at $0.25 per million tokens to Genie 3's playable world models and Gemma 4's on-device agentic coding—showing developers how to build multimodal apps without cloud APIs. Paige shows AI Studio's Build feature creating a bookshelf scanning app with Firestore and OAuth. Guillaume walks through a workshop using Nano Banana 2 for character portraits, VO 3.1 Lite for video generation at $0.05 per image, LIA 3 for chapter scores, and text-to-speech with distinct voices. Ian runs Gemma 4's 26B mixture-of-experts model on a MacBook to generate 10 SVGs in parallel and build a game from a spec, all locally. The episode argues that Google's models absorb common agent patterns, making custom fine-tunes less necessary.

Self-Training Agents: Hermes Agent, HF Traces, Skills, MCP & Finetuning — Merve Noyan, Hugging Face

May 13, 2026 · 19:11

Merve Noyan from Hugging Face argues that open-weight and open-source models have caught up with closed models, citing GLM 5.1 topping the Artificial Analysis Intelligence index. She walks through Hugging Face’s ecosystem for agentic AI: benchmark datasets on the Hub to filter models by SWE-bench or AIME scores; inference providers that route to the cheapest or fastest option per model; a traces repository type for storing and exploring agent sessions; and skills that plug into coding agents (e.g., Claude Code) to fine-tune vision-language models on a dataset by name—calculating VRAM, selecting an instance, and launching the job. She demos an agent-driven fine-tuning of Qwen2-VL on a vision-language dataset, and a case study where an LLM agent orchestrated OCR of 30,000 AI papers using open OCR models and Hugging Face Jobs, eliminating napkin math. The MCP server also enables querying Hub models, datasets, and spaces from agents.

Why MLX — Prince Canuma, Neywa Labs

May 11, 2026 · 23:10

Prince Canuma argues that on-device AI, powered by Apple's MLX framework, is a viable alternative to cloud-dependent services, especially for users in regions with unreliable internet. MLX, an array framework for Apple Silicon, has reached 1.5 million downloads and 4000 ported models, including day-zero support for Gemma 4 and Qwen 3 Omni. Canuma demonstrates real-time vision models (e.g., RF Deter for object detection), sub-100ms text-to-speech via Marvis TTS, and modular speech-to-speech pipelines that run entirely on-device. Community projects showcased include a native voice app (Locally), a robot with real-time voice cloning, and a video generation system that chains coherent stories on 16GB VRAM. A recent breakthrough, Turbo Quant, reduces KV cache by 4x, enabling 1 million context windows on-device. Canuma emphasizes that these capabilities provide accessibility for his blind father and enable agents that hear, see, and respond without phoning home.

Running LLMs on your iPhone: 40 tok/s Gemma 4 with MLX — Adrien Grondin, Locally AI

Apr 20, 2026 · 10:51

Adrien Grondin, developer of Locally AI, demonstrates how to run Gemma 4 and other LLMs on iPhone using Apple's MLX framework, achieving 40 tokens per second on the latest devices. He explains that MLX is optimized for Apple Silicon and that the open-source mlx-swift-lm GitHub repo enables easy integration into iOS, macOS, and iPadOS apps in under 10 minutes. Grondin recommends quantized models from the Hugging Face MLX community—typically 4-bit to 8-bit—and shows a live demo of Gemma 4 generating text offline. He confirms that mlx-swift-lm supports tool calling, though structured generation is not yet available. He also notes that Locally AI has been acquired by LM Studio, which now lets users run models via MLX or llama.cpp and connect them through OpenAI or Anthropic-compatible APIs.

10x Development: LLMs For the working Programmer - Manuel Odendahl

Aug 21, 2024 · 1:14:46

Manuel Odendahl presents a workshop on using LLMs as a translation engine to achieve 10x development productivity, arguing that treating models as cultural rather than purely technological artifacts enables novel techniques like world simulation for code reviews and domain-specific language creation. He demonstrates concrete methods: regenerating responses, editing model outputs, clearing context frequently, and summarizing transcripts into actionable formats. Odendahl introduces his tool Prompto for managing code fragments and emphasizes that fundamental programming knowledge remains essential while API-specific details become less critical. The episode focuses on decomposing problems into language translation steps, using examples such as transforming meeting transcripts into GitHub issues and building applications by instructing the LLM to simulate itself as the app. Odendahl advocates for divergent thinking and human-centered output, noting that all generated artifacts should ultimately serve human understanding.