LinkedIn sucks, so I built a better one

Hacker News

LinkedIn feels more like Facebook every day — noisy feeds, fake engagement, and everyone shouting into the void. Thats why I used to built a personal microsite on Squarespace and uploaded a video resume to YouTube to stand out - it helped me land interviews and get into Big Tech. But I always wondered: why isn’t there a platform designed to help you stand out like that? So I built OpenSpot: a public, curated platform where you can showcase who you are — with video, audio, and proof of your work. No endless feeds. No humblebrags. Just real people open to new opportunities. We’ve already onboarded a few companies, so recruiters can reach out to you directly. But you can also connect with other standout folks and supercharge your network. Just upload your resume and we´ll automatically generate your profile in under 1 minute. It’s early, but feels like something people actually need. Would love your thoughts.

Visit product View on Hacker NewsMar 23, 2025

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

87%87% 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: new, open · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

46%46% 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, video, way · Missing: mobile apps, ios, entrepreneurs

35%35% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio, real people · 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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