I built an open-source Apple Intelligence-like Writing Tool for Windows

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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! Key features: - Uses Google's Gemini 1.5 Flash model for superior AI-assisted writing (much better than the tiny 2B parameter Apple Intelligence model) - 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, 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 :)

Visit product View on Hacker NewsOct 16, 2024

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

93%93% 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: created, para, gemini · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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: google, 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.

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

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

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