MindHalo – macOS study assistant using on-device Foundation Models

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I’ve been building a macOS study assistant called MindHalo, and I’m looking for feedback from people who work with study tools, macOS utilities, or Apple’s new Foundation Models. The app is built with SwiftUI and uses Apple’s Foundation Models API, so all inference happens on-device. No cloud calls, no data sent to servers, and performance is fully local to Apple Silicon. Features AI Study Tutor Responds to questions with follow-up reasoning Minimal, focused chat interface Study Guide Generator Converts pasted notes into structured study outlines Includes explanations and short examples Saves guides locally (no backend) Flashcards Generates flashcards from any text Simple flip-card UI with local progress tracking Licensing During the beta, anyone can generate a free lifetime license. Licenses are hardware-bound and remain valid even if the app becomes paid later. License generation page: https://mindhalo.techfixpro.net/ (one per IP) Requirements macOS 26+ Apple Silicon (no Intel support) If anyone is interested, the project page has screenshots, details, and the current build: https://mindhalo.techfixpro.net/ I’d appreciate feedback on interface decisions, workflow, and overall performance — especially from people with experience in Apple Silicon optimization or local model integration.

Visit product View on Hacker NewsNov 20, 2025

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

90%90% 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 · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

43%43% 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 · Missing: mobile apps, ios, personal

43%43% 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

16%16% 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, paid · Missing: web3, crypto, cryptocurrency

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

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