I built a minimal website for Los Angeles

Hacker News

I built https://veryla.io - a minimal website for Los Angeles. After growing tired of the chaotic, fast paced, attention-economy focused state of the web, I decided to build something simple and quiet. A site with no "real" database (just a json file I update by hand), no email signups, no ads, no pop ups, no data collection (besides google analytics), and no comment section filled with highly polarized politics. A site updated only 1-2 times per week. It's just unique information in a simple and accessible format. The goal is for people to not check veryLA every day. I don't want people hitting refresh for emotionally charged SEO packed news to react to. My goal is for people to check out veryLA 1-2 per week and then go into the real world and have fun with the information they found. Hope you enjoy if you live in Los Angeles or are planning to visit!

Visit product View on Hacker NewsMay 16, 2022

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

82%82% 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.

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

62%62% predicted probability of success on Product Hunt, 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

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

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real world · 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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