Hi

HiDimensional – senior engineers recommend candidates to startups

Hacker News

HiDimensional – senior engineers recommend candidates to startups

Hi HN! We’re excited to share with you the public beta launch of our startup, HiDimensional (www.hidimensional.com). We are a platform consisting of senior engineering leaders who interview candidates and provide recommendations of the best candidates and their strengths. We then use the recommendations to refer these candidates to founders of startups that match their strengths and interests. The recommendation serves as a personal referral from the interviewer, so for candidates, it amplifies their application and fast-tracks them through the process. And for companies, they are receiving pre-vetted candidates endorsed by someone they can trust. That last point is key - our interviewers are established senior engineers (e.g., former Head of ML @ Quora, former VP @ Addepar, former Head of Newsfeed @ Facebook, etc.) and technical founders and hiring managers, so we believe their word carries weight. We have over 30 such interviewers today, and they cover a variety of engineering disciplines from full-stack/product engineers to data scientists to backend/data engineers, and everything in between. There’s more detail about the platform, how it works here: https://blog.hidimensional.com/2017/09/07/introducing-hidime... We’d love to hear your feedback or if you have any questions about this or generally about technical hiring, feel free to drop us a message at hello@hidimensional.com! And of course, if you’re interested in trying out the service as a candidate, sign up on our website: www.hidimensional.com/signup Thanks! Pradeep & Nikhil P.S. If you’d like to interview on our platform or hire our candidates, we’d love to hear from you. Send us a note at hello@hidimensional.com.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% 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 · Missing: mac, agents, macos
63%63% 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: excited, ide, io · Missing: https docs, just released, exist
49%49% 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 · Strong signals: platform · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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.

Incorrect prediction on native model

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