I joined a company where after joining I was shocked to be demeaned, belittled, publicly insulted in front of peers and my reporting team of AI engineers. The manager even crossed personal boundaries where he would even question my homemaker spouse as being irresponsible in taking care of our kids when they fell ill and I had to take a Wfh and leaves a couple of times. He used vulgar actions and language like "Why don't you hold and squeeze the b*lls of people who don't cooperate with us for sharing data?" This was even when HR (a lady) was sitting in the same room. I had crushing headaches dealing with this guy, was eating saridons like peanuts, had to take psychological counselling from the company provided professional seeking company counseling, only to be told to 'be stronger' while my health collapsed. I wondered if there was somewhere to look up the reputation of this person and the treatment he meted out to reportees, I'd never have joined this company.

This is where I thought we have glassdoor and ambitionbox for companies, but are companies inherently bad or good? No, they aren't. Glassdoor tracks companies, but companies aren't inherently good or bad; they are just legal abstractions. People don't leave companies; they leave bad managers. I have experienced this time and again in my career.

Hence, I started Professional-Health-Ledger as an immutable record of professional conduct. I want the world to at least be net neutral in good vs bad conduct. Hence, you get 1 flag credit for every one vouch you do on PHL. You have to start with a vouch / positive review before you can flag / negatively vote someone.

PHL is non-anonymous; I cannot afford a battery of lawyers to defend fake profiles. To withstand legal scrutiny, the core metric is anchored to a binary, subjective question: 'Would you work with them again?' This is a protected expression of opinion for the public good, not a factual allegation of misconduct. Optional text reviews go through a moderation queue before being committed.

I've structured the platform to respect GDPR/DPDP guidelines, allowing flagged individuals to apply for removal, backed by a transparent audit trail.

Transparency protects everyone. When professional conduct is publicly visible, it creates accountability. Good actors are recognized. Bad patterns become visible.

The stack is simple: JavaScript, CSS, and Vercel, with GitHub Issues acting as the database backend. It's 'vibe coded' and fully open source.

I’m looking for feedback from the HN community on our credit allocation logic, identity-verification graph hardening, and database schema. I'm also looking at how do we verify that an individual 'x' has actually worked with / for an individual 'y' they are vouching or flagging on PHL using some kind of Linkedin based logic.

Live Utility: https://ProHealthLedger.org

GitHub Repository: https://github.com/muglikar/ProHealthLedger

I'll be hanging out in the comments to discuss the architecture, design trade-offs, and edge cases.

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Comments

AI writes x86_64 asm and eBPF for fractals on /dev/fb0, in a browser VM

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25 years, 793,199 fashion records: non-White models 4.5× more likely plus-size

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