Discover Top Tech Industry Bloggers (TechStarsDigest.com)

Hacker News

I have a list of about 40 of my favourite tech bloggers who I follow (mainly entrepreneurs, founders of successful startups, VCs). I created a script around this list, which is pulling the newest posts and letting users to vote for them and comment. The idea has to parts: (1). A website HN-like list of posts with voting and comments. (2). A weekly newsletter [1] with summaries of the best articles from a given week. Most of the posts are very long, and if you are crazy busy (aren’t you?), having weekly summaries allows you to stay on top of the tech stories without spending hours on reading. What do you think? On the bottom of the page there is a link to Suggest a Blogger Form. http://www.TechStarsDigest.com/ ----- [1] http://webdesigndigest.us5.list-manage.com/subscribe?u=9a5d5e2de300c8782ff8758a6&id=90e77955b5

Visit product View on Hacker NewsAug 29, 2014

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

57%57% 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.

TrustMRRFits verified-revenue profile · Strong signals: entrepreneurs, users · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · 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.

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

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

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

24%24% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

11%11% 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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