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Play rock paper scissors remotely over chat

Hacker News

Play rock paper scissors remotely over chat

Just came up with it. It needs weakness analysis though. 0. Alice creates alice.txt file with 'scissors' text in it 1. Alice zips alice.txt with password Apass as alice.zip 2. Alice sends alice.zip to Bob 3. Bob creates bob.txt file with 'paper' text in it 4. Bob zips bob.txt and alice.zip with password Bpass as bob.zip 5. Bob sends bob.zip back to Alice 6. They share their passwords over chat and unzip both zip files 7. Alice wins.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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BetaListSuited for BetaList early-adopters · Strong signals: chat · Missing: web3, crypto, cryptocurrency
55%55% predicted probability of success on BetaList, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
17%17% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Correct prediction on native model

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