All-in-One Profile for Devs

Hacker News

Hey HN! First post btw I want to share a product we are developing right now. It's a thing to gather your data from LinkedIn, GitHub, StackOverflow (and a couple more things in the future) and we obviously use LLMs under the hood :D So the idea of using it is that you don't need to share 69 links and docs and can use one link (or download PDF) now. When you connect all the stuff you will see your profile with a radar chart, which represents your "domains". The next thing will be a section with ai-generated-summary-bio + section with things you're open to. Then a section with work experience and skills, tight together. So you can click on a skill and check which job is related to this skill, or you can click on the job and see the skills gathered from it. Really want to hear your feedback and thoughts! P.S. Here is my profile, btw https://hackerpulse.io/hacker//*paravozz

Visit product View on Hacker NewsJul 31, 2024

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6points

2comments

Did not reach leaderboard

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

69%69% 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: using, open · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, 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.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

15%15% 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.

Correct prediction on native model

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