College application essay inspiration and brainstorm

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I collected thousands of outstanding college application essays to train a specialized AI model designed to inspire and coach students throughout the essay-writing process. Unlike general-purpose AI, each essay is highly personalized based on the applicant's background, experiences, and personal story. The AI also adapts its writing to emphasize the qualities and values that different universities are looking for. You can browse our essay library for free, explore each applicant's profile, and see exactly why the AI made specific writing choices. By opening the "black box," you'll learn not just what to write, but how and why an effective essay is structured. Everyone can generate up to three application essays for free. I wish I could offer unlimited generations at no cost. However, this project is fully bootstrapped, with no outside investment, and every AI-generated essay incurs computing costs. If you find the tool helpful, your support helps keep the service running and allows us to continue improving it. Hope to release the pressure of application season. Glad to hear you guys feedback. thank you.

Visit product View on Hacker NewsJul 29, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% 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: personal · Missing: mobile apps, ios, entrepreneurs

64%64% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, open · Missing: mac, agents, macos

37%37% predicted probability of success on Product Hunt, 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

30%30% 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 · Strong signals: bootstrapped · Missing: arr, mrr, revenue

13%13% 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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