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The most Y Combinator YC application: it applied itself

Hacker News

The most Y Combinator YC application: it applied itself

So I applied to YC a few times...this time I thought I'd use assistance. And I went all out. The thing I'm applying with did my application. I didn't touch a knob or a dial...didn't type a key. I just prompted the agent and it used the tool. You can see the insane 3 min video - sped up 21x here (link in its description to the full slow video too): https://www.youtube.com/watch?v=udwuE1PrM_0&feature=youtu.be To me this is the ultimate YC application - one i didn't have to stress over, had someone to help me with, and was a showcase of itself. This Show is for the receipts. Also, the most Y Combinator thing a YC application could do was recursively submit itself. If you want to know what this dank magic is that crafted this insanity it lives here: https://fuckui.com Good luck out there

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Actual performance

5points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, 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: video · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, 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
45%45% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: agent · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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.

Correct prediction on native model

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