Courtyard – Open-source macOS app for local MLX fine-tuning Text

Hacker News

I've been building Courtyard, a macOS desktop app designed to make local LLM workflows on Apple Silicon less tedious. The motivation: I was tired of juggling multiple Python CLI scripts, JSONL formatting, and environment issues just to run a simple LoRA fine-tune on my Mac. Courtyard is essentially a UI wrapper around mlx-lm combined with data preparation tools. It handles: Dataset formatting and cleaning (privacy filtering, deduplication). Local LoRA fine-tuning via MLX on Apple Silicon. An integrated chat UI for A/B testing the base model vs. the fine-tuned adapter. Exporting to GGUF or directly to an Ollama runtime. The stack is Tauri 2.x + React + Rust + Python (mlx-lm). It's fully open-source (AGPL). Repo: https://github.com/Mcourtyard/m-courtyard I'd love to hear your thoughts on the architecture, MLX implementation, or any edge cases you run into. Happy to answer technical questions.

Visit product View on Hacker NewsFeb 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, model · Missing: agents, agent, cursor

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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