Gemma Tuner Multimodal vs Modo

Gemma Tuner Multimodal is an open-source fine-tuning toolkit for Google's Gemma 4 and Gemma 3n models that runs entirely on Apple Silicon using PyTorch with Metal Performance Shaders (MPS) backend — no NVIDIA GPU or cloud infrastructure required. It supports LoRA training on multimodal inputs: audio, images, and text simultaneously, using local CSV files or streamed from Google Cloud Storage or BigQuery. The tool targets the growing segment of developers who own M-series Macs but have been locked out of fine-tuning workflows that assume CUDA availability. Gemma 4's architecture is particularly well-suited to this use case: its 4B multimodal variant (designed for on-device deployment) trains efficiently on M3 Max and M4 Pro hardware within the available unified memory constraints. Primary use cases include medical transcription fine-tuning (audio → text with clinical terminology), visual QA systems (image + text → structured response), and private on-device pipelines where cloud API calls are prohibited by compliance requirements. The project fills a specific niche that Google's own fine-tuning documentation doesn't cover well for Apple hardware.

Modo is an open-source AI IDE built on the Void editor (a VS Code fork) that flips the script on how AI coding tools work. Instead of jumping straight to code generation, Modo forces a spec-first workflow: describe what you want, and the agent converts your prompt into structured requirements docs, design docs, and task breakdowns stored in a persistent `.modo/specs/` directory before writing a single line of code. The approach draws from the "vibe coding is bad actually" school of thought. Modo's steering files and agent hooks let developers set coding conventions, stack preferences, and project constraints that persist across sessions. Autopilot mode chains spec generation through implementation, while parallel chat lets you run multiple agent conversations simultaneously against the same codebase. Built by a solo developer and posted to Hacker News as a Show HN, Modo positions itself against Cursor, Windsurf, and Kiro. The bet: slowing down agents with structured planning up front produces fewer hallucinated architectures and rewrites. It's early — rough edges abound — but the spec-driven philosophy is increasingly mainstream as larger teams adopt AI coding tools.