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Encode and decode messages using animated GIFs of QR codes

Hacker News

Encode and decode messages using animated GIFs of QR codes

Hey HN - Qraft is something I made for fun after having a conversation with a co-worker about how it would be cool to embed long-form messages as QR codes in GIF format. There's a generator for creating the GIF and a scanner for reading them back. A sharable HTML page is created for each message with decoding instructions. A direct link to the image is also provided for embedding.

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Actual performance

1points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
64%64% 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: scanner · Missing: mobile apps, ios, personal
50%50% 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
47%47% predicted probability of success on AppSumo, 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
46%46% 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 · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using, coding, code · Missing: mac, agents, macos
15%15% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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