A new take on social networks

Hacker News

Hey everyone. Akachik is a social network that is derived from Twitter and Reddit, with a couple of extra features (like multiquoting). It started as an idea I had about three weeks ago so I worked to get something together and see if people might be interested in something like it. I have a lot of other features I would like to add but want to iterate on the base conversation flow first. The goal is to have content that is viral like Twitter, but more focused on long-form threaded discussions like Reddit. If you try it out, you might notice there are a couple of bugs, because you're some of the first people seeing it. Anyways, let me know what you think, good and bad both.

Visit product View on Hacker NewsJul 25, 2024

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

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

33%33% 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

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

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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