MacGaiver, a free GPT-V powered macOS assistant (BYO API key)

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Howdy. A month ago I posted on HN about a macOS assistant I built and published to GH ( https://news.ycombinator.com/item?id=38611700 ) The repo got 1k stars, there was a few suggestions, and people asked if I could distribute it as an .app. So I went to work, learnt a lot, and today I "launch" MacGaiver as a free macOS app (BYO OpenAI API key) at www.macgaiver.app There's a 2-minute demo video on the website and linked below [0], but the app is pretty simple: MacGaiver runs in the background and can be used from within any application. - Press the keyboard shortcut to activate MacGaiver - Ask a question about the application you have open, using voice or keyboard - Get an the answer in context, without leaving your active application Behind the scenes, MacGaiver takes a screenshot of your active application when you press the keyboard shortcut, and sends that off together with your question to OpenAI GPT-V (Vision). This is my first time taking solo project from 0 to 1 with a website and all, I hope you enjoy the product as much as I enjoyed building it! :) [0] 2-minute demo video on YouTube: https://www.youtube.com/watch?v=rFUPqK264Xg

Visit product View on Hacker NewsJan 12, 2024

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

90%90% 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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, month · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, 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

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

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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