Handpicked Front-End Dev news from HN

Hacker News

Hi HN, I created a personal site ( nej.cc ) as a collection of hand picked articles surrounding the web/mobile front-end development world. My main source of the links is from HN itself. I'm planning on using this site as my consulting site as well. Wanted to see what you guys thought of the whole site (design/performance/etc). tl;dr HN has a lot of information on a daily basis and becomes overwhelming, this site, curated by me, filters through the daily dose and adds them on the site with a focus on Front-End Development. Setup: - Jekyll - Amazon S3 - Amazon Route 53 - Disqus

Visit product View on Hacker NewsMar 19, 2013

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Actual performance

3points

1comments

Did not reach leaderboard

Launch Intel predictions

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

62%62% 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: created · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% 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: personal · Missing: mobile apps, ios, entrepreneurs

49%49% 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

39%39% 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

16%16% 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.

Incorrect prediction on native model

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