OppenheimerGPT – use Bard and ChatGPT side-by-side (macOS menubar app)

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I built this app over the weekend, let me know if you guys find it useful! * What is it?: Very simple to use. Type your prompt once, and app will mirror your prompt into both Bard and ChatGPT browser windows sitting side-by-side. Hit send! * Why did I built it? 1) I found myself consistently pasting the same question into both Bard and ChatGPT to get answers from each. This became even more frequent after ChatGPT disabled its internet browser plugin. 2) I wanted an elegant app that sits in macOS menubar, but I found no existing solutions. 3) I'm fascinated by the current AI race. I find it somewhat captivating to see which model does better for my question and see how each model is improving over time. Why is the app called OppenheimerGPT? :P Like many of you, I watched Oppenheimer movie over the weekend and drew numerous parallels between the movie and the ongoing AI race, hence the app's name.

Visit product View on Hacker NewsJul 25, 2023

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

86%86% 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: para · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: answers, para · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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, existing, ide · Missing: https docs, excited, just released

27%27% 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

17%17% 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.

Incorrect prediction on native model

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