FeedSubscription – Uncomplicated content distribution for Bloggers

Hacker News

Dear fellow hackers, I’ve built this system for a blogger friend of mine, and hope it’s useful for other blogger people too. Any product feedback is welcome, and if you find it useful, I’d greatly appreciate if you’d share it with anyone you think could use it. Specifically for the HN community, I’ve created a Demo Account that you can use to see the system from the inside. Here is the magic link that you can use: https://feedsubscription.com/user/authentication.html?demo=1 (I’m sure you’ll be able to find a way to see the auto-filled password. ;-)

Visit product View on Hacker NewsAug 16, 2023

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Launch Intel predictions

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

74%74% 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.

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

49%49% 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 · Strong signals: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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.

Correct prediction on native model

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