Cobalt – social media downloader with no bullshit

Hacker News

Hey, HN! I made my own social media downloader because I got annoyed by others that either shove 50 ad banners down your throat, or require you to do some extra steps. The frontend page is light and all processing is done on the server, so it runs speedily on pretty much anything. It also has a bunch of download/ui customization options, but none of them are forced on you. My main goal was to make something that's open, easy to use, and not annoying to deal with. Please take a look at it and let me know what you think, I'm thirsty for feedback and opinions. You can also take a look at its source code and judge my coding abilities: https://github.com/wukko/cobalt

Visit product View on Hacker NewsOct 12, 2022

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Actual performance

3points

1comments

Did not reach leaderboard

Launch Intel predictions

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

73%73% 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: coding, code, open · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% 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

45%45% 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

40%40% 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

20%20% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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