Hey Product Hunt! I’m Navin Mishra, an enterprise delivery manager who has spent over 20 years leading large-scale portfolios and building AI products. Over the decades, I noticed a universal law of tech culture: by 4:30 PM, after surviving hours of huddles to "align on cross-functional synergy matrices," every developer's brain reaches its critical limit. We don't want more corporate networking; we want a cold craft beer with people who understand our pain. To solve this afternoon burnout, I built DrinkedIn (https://drinkedin.me) — an anonymous sanctuary and hyper-local spatial radar designed to let tech workers clock out early and find local watering holes. Core Architecture Under the Hood: 1. Spatial Proximity Grid: Uses a client-side HTML5 geolocation Haversine formula to anonymously cluster nearby professionals within specific tech-park radii without tracking or storing raw, pinpoint geographic data. 2. The "Broetry" Engine: A fine-tuned LLM micro-service that instantly transforms your standard, dry corporate complaints into hyper-cringe LinkedIn influencer parodies with one click. 3. Real-Time Vibe Boards: Leverages live WebSocket channels to crowd-source taproom noise levels, crowd densities, and geofenced venue check-ins. Built for Absolute Corporate Privacy: Because HR boundaries are real, DrinkedIn does NOT request or verify corporate email domains. Authentication relies entirely on personal sign-ins, and users choose a self-declared corporate mask to stay completely incognito. The stack is designed lightweight—React, Tailwind CSS, and a serverless Supabase real-time backend ensuring sub-50ms render cycles on mobile. We are currently live-testing the telemetry in major tech hubs, and I would love to hear your feedback on the spatial clustering logic, UI layout, or your favorite corporate coping strategies. Let's take this offline... at the nearest pub!

DrinkedIn

The anonymous 4:30 PM office escape radar & meme engine

The anonymous 4:30 PM office escape radar & meme engine