Right now we're processing a lot of data from various government sources and other free datasets to get salary information across all of the major US metros. Plus ingesting a bunch of data from some unnamed paid job posting APIs (respecting ToS) and pulling out reported salary information, if it exists. It's why the landing page is marked as "US-only" right now, I wanted a tight scope for an MVP launch. I have a number of tech friends in Canada so that'll probably be the next country I support.

Right now I'm basing the user's salary data on the metro where they currently live. Not perfect and not the long term solution but cracking it fell to the cutting room floor to get an MVP shipped. That was a difficult cut but I gave myself a mid-May deadline to get something shipped.

Re: Uneven sample distribution. Sample size is a first-class concept in scoring. For each user's metro+role+level slice, n is computed over a trailing 60-day window. Below a threshold (currently 30), I aggregate to a broader peer group and explicitly flag lower confidence on the score. Bayesian priors derived from the nationwide distribution for that role help fill in thin slices, so a senior Rust dev in Boise still gets a number but they also see "this is computed from a small local sample plus regional inference" rather than a false-precision point estimate. It's a lot and is still being fine-tuned.

Pay transparency laws in CA/NYC/CO/WA are helping but coverage is patchy.

Right now I'm not using any user-provided data in calculations as my user-base is too small and there's too much risk for identification. Eventually I want to add opt-in data submission so we can run real-time metro-aware compensation surveys based on consented and anonymized peer data.

Also is this fully automated or is someone sitting in an office with a VR headset? How monitored is the whole process?

Lastly, specs on the robot you used in the landing page? I'm genuinely curious.

When I get back to SF in a few weeks I may hit you up. My place could use a deep cleaning.

Founder here. I recently left a job and had a lot of time on my hands so I decided to build an app that will help people navigate the current job market and ensure they are being paid fairly.

I wanted to build something that gives the career benefits of LinkedIn without the social feed or self-promotion. Let people privately post about their work, keep track of their brags, and have an idea of where their career gaps are. Maybe help them to realize they are being underpaid so they can make a bit more.

Applied to YC on a whim but I'm thinking that's more of a lottery ticket. Mostly hoping to help people, build something worthwhile, and make enough on the side to cover hosting costs.

We have public profiles too for people who want to show off their work but not deal with social engagement. Here's mine: https://proving.app/@jondaniel.

Feedback is welcome. Thanks everyone!

Comments URL: https://news.ycombinator.com/item?id=48145125

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A number of people's lives have been forever changed by the recently surfaced actions and instead of focusing on the "what ifs" and the hypothetical situations we focus on fixing the underlying problems within our community.