LocalModelSlimmer (mac)

Who it's for

Indie Mac/Electron app developers who today cope by either (a) calling hosted LLM APIs (OpenAI/Anthropic) for features, or (b) manually packaging ggml/ONNX weights and hand-tuning CoreML/Metal exports (ad-hoc scripts).

The problem

Time and money: developers waste days manually converting models, endure high cloud inference bills, or ship bloated apps that users delete; also App-Store/Notarization friction because heavy runtimes trigger review/installer issues.

How to build it

CLI + small hosted optional dashboard: a command like `slimmy convert model.bin --target=coreml-mps --quant=4` that outputs a signed macOS-friendly .mlmodel/.framework, a 1‑page PDF latency/size report for M1–M6 ranges, and a tiny JS/Swift integration shim (3 files). Optional web UI shows batch jobs and consented benchmarks.

How it makes money

Who pays: indie devs / small teams and agencies; pricing: $49 one-off per-conversion bundle for non-commercial hobby use, $249 per-app-year for commercial apps (or $19/mo for indie teams) — they pay because packaging is a blocker (days of engineer time or recurring cloud cost) and the free options are either all-manual scripts (time-costly) or cloud inference (ongoing bill + privacy concerns).

Why it doesn't exist yet

Incumbents (CoreML tools, Apple sample code, ONNX exporters) provide low-level primitives but not the full thin product: they don't automate quantized conversion tuned for MPS on the latest M5/M6 silicon, don't produce developer-friendly integration shims, and don't surface per‑machine latency/size tradeoffs in one page. Big cloud vendors prefer to lock devs into online inference and don't optimize for offline Mac bundles. An indie can fill the UX gap with focused conversion recipes and reproducible, testable artifacts in weeks.

First users

1) Indie Mac app authors building offline features will try it to drop cloud bills; 2) Electron devs shipping 'local-first' features will adopt to reduce app size; 3) open-source model maintainers will link it as an easy Mac runtime; 4) Hacker-news/Product-Hunt driven Mac devs will try the simple CLI; 5) Early adopters will share benchmark reports for M5/M6; these groups get immediate value (smaller bundles, lower latency, lower cost).

Build size

2 people x 8 weeks: includes CLI converter for ggml/ONNX -> CoreML/MLProgram with 4/8-bit quantization presets, per-device benchmark harness (M1/M2/M5 emulation/measurement), sample Swift & Node integration shims, and a minimal web dashboard for job history; excludes custom model training, notarization automation (only docs + checklist), and mobile (iOS) packaging.

Biggest risk

Apple could ship first-party one-click CoreML quantization/export tooling bundled with macOS dev tools (Xcode) or a new CoreML conversion that matches the product's convenience before adoption spreads.

Conditions for a hit (all 3 required)

• Produces a single .mlmodel/.framework from a supplied ggml/ONNX/pt checkpoint with selectable 4-bit and 8-bit quantization that reduces raw model size by ≥4x (measured bytes on disk).
• Generates a 1‑page PDF report showing per-device median tokens/sec and 95th-percentile latency for 1–32-token runs on M1, M2, M5 (or local machine if present), and flags incompatibilities (missing ops) in conversion logs.
• Outputs a 3-file integration shim (Node JS + Swift example) that runs the model as a local process or in-process with <5 lines of glue and a sample benchmark script that reproduces the PDF numbers in under 60s on a developer machine.

How it's judged (in 6 months)

GitHub repository hits 1,000 stars OR Product Hunt maker-launch in top 5 daily (either qualifies)(judgment date 2027-02-26)

AI self-confidence 55/100 — self-reported likelihood of meeting the criterion, not a business success rate