Make/Hack Web with URL. Experiment

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Hi, I've developed a simple webpage that responds to a URL-encoded GET request in the form of "?makeme=string". When you provide a string as input, it appends it to existing content and displays the result on the page. Essentially, this allows you to modify the webpage but also compete with everyone else. Everyone visiting the page sees the same content, but the cache is reset once a day. While I'm uncertain of its specific utility, it seems like a funny place where people could create innovative ideas. However, be careful, as it means that anyone accessing the page can potentially insert their JavaScript, which might be harmful. Nonetheless, I emphasize that this is purely an experimental endeavor.

Visit product View on Hacker NewsNov 10, 2023

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

70%70% 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.

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61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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