My

My new personal site

Hacker News

My new personal site

I was browsing through Awwwards and a number of articles related to web design this morning and it inspired me to re-make my personal/portfolio site. I have never made a decent looking theme before, from scratch. I've been primarily in backend developer roles while working professionally and freelancing. I'm looking for any and all feedback! What do you like? What do you dislike? Any tips? Comments? etc... I am excited to hear back from the community. Please note that I haven't even tried to make this responsive on mobile devices, yet. I plan to do that tomorrow or later tonight if I feel up to it. Anyways, enjoy! https://jesseh.xyz https://github.com/jessehorne/personalsite

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Actual performance

2points
3comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
46%46% 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
13%13% 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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