P.ai.os – A local, modular AI "operating" system for macOS (M4/MLX)

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Hello HN, I’m sharing a personal project I’ve been working on: a modular, "headless" operating system for my digital life, hosted locally on a Mac Mini M4. The source code is available here: https://github.com/vag-mac-mini/PAIOS_Public I travel frequently and wanted a "sovereign" system that doesn't rely on SaaS subscriptions or cloud data. The stack is: - Hardware: Headless Mac Mini M4 (16GB). - AI Core: Qwen 3 VL 8B (Abliterated) running via MLX. - Network: Tailscale (for remote access via PWA). - Agentic Layer: Custom Python scripts for tool use (API calls, system control). The constraint for this project was unique: I did not write the code manually. I utilized Google Gemini to architect and generate the Python files with Antigravity IDE, acting as the "product manager". The code structure is admittedly messy in places, but it is fully functional. The OS currently runs 10 active modules, categorized by function: -- Intelligence & Memory -- * Digital Diary: Uses Vision AI to analyze screen activity/productivity and logs daily summaries to Apple Notes. * Voice & Memory: Indexes voice transcriptions into a searchable "Second Brain." * Ghostwriter: Remixes rough voice notes into structured essays or book chapters. -- Global Logistics -- * Travel Command: Aggregates flight/visa data and summarizes local security risks via Tavily API.Gives recommendations for packing based on country/city/period/weather. * Aero Intel: Audits flight paths and generates deep links for travel logistics. * Chronos Calendar: A master schedule that integrates financial timelines with travel itineraries. -- Network & Security -- * Network Sentry: Monitors the local ARP table for unknown devices. * Secure Dead Drop: An encrypted P2P file tunnel between my devices. * CRM & Network: A relationship database that parses raw notes into structured contacts. Latest update that is not on GitHub, created a python script and added it on cron where it uses an api to check all the bank holidays across the globe(excluding the major ones) checks my contacts, which could be from all over the world, see's who is from the country with the bank holiday and I receive an iMessage in the morning in order to wish them happy whatever holiday. This is not a SaaS or a commercial product. I am open to feedback on the architecture or suggestions for other "sovereign" modules I could add. Thanks.

Visit product View on Hacker NewsJan 27, 2026

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

95%95% 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 · Strong signals: created, ios, gemini · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% 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: ios, personal, google · Missing: mobile apps, entrepreneurs, apps

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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.

Incorrect prediction on native model

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