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I built open-source Apple Intelligence-like Writing Tools for Windows

Hacker News

I built open-source Apple Intelligence-like Writing Tools for Windows

I posted this a while back, but there's been a major update — it now supports local LLMs, multiple cloud LLMs, code editing, chat mode, themes, dark mode, and more! As a high school student, I love LLMs and the execution of Apple's Appel Intelligence Writing Tools, but was disappointed that nothing like that existed for Windows. So, I created Writing Tools, a better than Apple Intelligence open-source alternative that works system-wide on Windows! It can use the free Gemini API, or a multitude of local LLMs via Ollama, llama.cpp, KoboldCPP, TabbyAPI, vLLM, etc. It's much better than the tiny 2B parameter Apple Intelligence model! It works in any application with a customizable hotkey - Proofreads, rewrites, summarizes, and more - Free and privacy-focused (your API key stays local, no logging, no tracking, local model options, etc.) It's built with Python and PySide6, and I've made it easy to install with a pre-compiled exe. For the technically inclined, the well-documented source is available to run or modify. I'd love feedback from the HN community on both the concept and the implementation. Are there features you'd like to see? Any thoughts on making it more robust or user-friendly? GitHub: https://github.com/theJayTea/WritingTools P.S. This is my first major Windows coding project, so I'm especially keen on advice for best practices and potential improvements! Thanks so much for your time :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, user · Missing: mac, agents, macos
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: supports, created, para · Missing: reddit linkedin, podcasting, latex
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Hacker NewsStrong engagement from HN community · Strong signals: exist, llama, ide · Missing: https docs, excited, just released
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AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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