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OpenAI Apps Handbook

Hacker News

OpenAI Apps Handbook

I went swimming in the ocean of OpenAI's Apps SDK… and came back with a handbook! Over the past few weeks, I’ve been diving deep into the ChatGPT App SDK: exploring its APIs, tools, and hidden gems. Along the way, I built, broke, fixed, and reimagined a bunch of little experiments. P.S: Indeed OAIs official docs is the source of truth, this is just a rough notebook Maybe, I can create a CLI tool to scaffold app?

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Actual performance

10points
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, way · Missing: mobile apps, ios, personal
62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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