Image Processing Platform

Hacker News

I’ve invested quite a bit in creating a downloadable software that integrates some of the amazing things neural networks have introduced to image processing, make it accisible to a less computer-savvy audience, and be able to grow with the fast pace of new advances in this space. From the start I was aware of how saturated the image processing space is and the risk of investing in what I find interesting before doing some deep market research. However, now is the point that I’d like to check whether there is enough of a target audience or a pivoting strategy that can make this commercially interesting, and what would be even better, whether there exists a model where it can be open sourced (in full or in part) while still creating enough revenue to support its continued development. Any opinions, constructive criticism or advice on how to move forward will be greatly appreciated.

Visit product View on Hacker NewsJun 18, 2023

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

88%88% 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.

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

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

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

Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, io · 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.

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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