Writekin – fine-tune a local LLM on your own writing, on your Mac

Hacker News

Hey Hacker News! I built Writekin over the past week because I was tired of AI writing that didn't sound like me, even though I had just used AI to clean it up, rather than wholesale write it. The usual fixes I found online for this were: - Some sort of SKILL.md, or - A system prompt full of rules to strip the generic AI tells (e.g. no em-dashes, none of the stock phrases, varying the sentence length, etc). While those cleaned up the surface a bit, Pangram still came back as ~100% AI written, which was frustrating, as again it was mainly taking my sloppy copy and tweaking it. So when building Writekin I took a different route: Writekin fine-tunes a local model on your own writing. It reads what you've already written (Apple Mail, iMessage, local documents, chat exports), curates it into a training corpus, and runs a QLoRA fine-tuning on-device via Apple's MLX. A Compose screen then drafts and rewrites in that voice. Everything runs on your Mac. Ingestion, training, and generation are all local. The only network calls are: (1) When you download the model weights from Hugging Face and (2) The Sparkle update check. Training on your own Mail/Messages only felt okay to ship if the end user could verify that, so the source is public — so you can read exactly what it does! Quick gut check: It's v0.9 and the output is uneven. Honestly, sometimes it nails your voice, and sometimes it's just completely off. This is more a "this is possible and kind of works" than a finished product. Would genuinely love feedback! Source: https://github.com/scouttyg/writekin

Visit product View on Hacker NewsJul 28, 2026

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

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

21%21% 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.

Correct prediction on native model

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