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Safely share and compare your Startup Engineer Salary

Hacker News

Safely share and compare your Startup Engineer Salary

Hi HN, This is a mixture between a Show and Ask HN. I really appreciate your time and thoughts on this. My name is Alexis and I would like to show you something I've been working on on the technical side: It's called Huddle Metrics. Huddle Metrics is basically a safe place for you to anonymously share and compare sensitive data with your community. Sort of like a very controlled collaborative spreadsheet, really. Or a survey. But hopefully better. Think engineering salary and your equity in the company. Information that you track yourself but that's too taboo to share. If a pool of the data existed, however, it would be beneficial to you contributing because you would get to see what's market. After running a pilot Huddle throughout this year with a bunch of startups, my friend Adrian and I created a system that allows you to create any sort of Huddle or "Data Community" for different topics. For demo-purposes I've created a Huddle around Startup Engineer Salaries because I've seen this discussion come up a bunch of times and I'm an engineer myself. To get an idea please check out http://demo.huddlemetrics.com/start/1040592633 (This is super raw and imperfect but I feel it works well enough) To the non-engineering public here at HN: I'd love to open the discussion further than the demo and hear what you think would be other immensely valuable metrics to share and compare with the HN community. Again, thank you for your time and consideration and please comment away with ideas, criticism and metrics you would like to see shared! With best wishes, Alexis

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: exist, lua, ide · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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: way · Missing: mobile apps, ios, personal
34%34% 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
11%11% 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.

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