I built "TeamViewer with AI" with GPT-4o for my dad's computer troubles

Hacker News

Hey HN, My dad often struggles with using his computer and calls me multiple times a week to ask for help. We used to try to diagnose things via FaceTime (which was always a struggle), until a friend told me about TeamViewer, which made things much easier. However, at this point in my life, I am either too busy working or outside the house and can’t access TeamViewer, which is why I have started to help my dad less and less. When the chatGPT iOS app was released, I was completely blown away by its speech abilities. When my dad called me the very same day for another computer problem, I thought “Why not automate this using AI?” So I created helpmee.ai, which I believe can solve most of the problems my dad typically calls me for. It uses openAI’s Whisper for transcription, GPT-4o for reasoning and analyzing the computer screen, and openAI’s Text-To-Speech. If you are interested, give it a try and I would love to hear your feedback. Tim

Visit product View on Hacker NewsMay 16, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: computer, chatgpt, openai · Missing: mac, agents, macos

93%93% 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, started, ios · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% 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

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

21%21% 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 · 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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