Pattern Recognition Engine

Hacker News

This is a video of a pattern recognition engine I've been working on. https://www.youtube.com/watch?v=WHNdIuBJHTo&feature=youtu.be It is able to learn new patterns even if there is a lot of noise. Currently because of the lack of computer power it is only able to learn very simple patterns. With enough computer power I think I could use video to train it. About the architecture: The pattern recognition engine is a hierarchy of nodes. Each node is responsible for a small field of view, similar to a neuron. Because the input image can be broken up into smaller pieces for processing it is very easy to process it in parallel with multiple computers, which should make it quite fast. Unfortunately I do not have the resources to build a cluster of computers to test the speed so right now this part is just a theory. I was going to submit this as part of my application to y-combinator but unfortunately I did not complete this prototype on time. I may try to apply for the next one or even do a kickstarter if I can drive enough interest. Please let me know what you guys think.

Visit product View on Hacker NewsApr 7, 2017

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

93%93% 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.

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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

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

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

TrustMRRLess likely to generate early MRR · Strong signals: video, para · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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.

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