An Attempt to Improve Reverse Image Search

Hacker News

Hey guys, This is a demo of a bit of research I did that didn’t really go anywhere but I thought it was interesting so I made a demo out of it. I wanted to improve reverse image search on edited images and photobashed images. I thought it was neat that the algorithm only uses highschool math and the global minimum is found analytically. I did a demo a few years ago on similar work which made it to the hm front page here: https://news.ycombinator.com/item?id=14973741 This demo was hard to write. It was really hard to not to either over or under explain the algorithm. For some this will be a really simple algorithm that could be summed up in a few sentences and for others it’ll require remembering some math they haven’t seen since college or highschool. It gave me an even bigger appreciation for how well YouTubers like 3blue1brown can explain mathematical concepts. Hope you guys enjoy it

Visit product View on Hacker NewsJun 22, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

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.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, plain · Missing: mac, agents, macos

45%45% 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

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.

Incorrect prediction on native model

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