Project ideas from Hacker News discussions.

DoorDash Spent $1.4M Trying to Stop Mamdani from Becoming Mayor. Now We Know Why

📝 Discussion Summary (Click to expand)

1. Worker exploitation fuels Big Tech valuations
Commenters repeatedly argue that platforms like DoorDash profit by underpaying gig workers, skimming tips, and classifying labor as contractors to avoid benefits.

“More evidence Big Tech valuations are driven primarily by worker exploitation.” – toomuchtodo
“The problem is they use exploitative labor practices … and price‑dumping funded by infinite amounts of venture capital.” – mschuster91

2. Rent‑extraction / middleman tactics
Many see these companies as inserting themselves into existing supply chains, squeezing both sides, and monetizing the data they collect.

“the common unifying theme of most tech companies over the past 15 years has been rent extraction.” – ausbah
“inject yourself in the middle of a supply chain, and then once established squeeze the living shit out of both sides.” – peezd

3. Convenience outweighs the cost for users
Despite criticisms, users often defend the services as worthwhile time‑savers, valuing the convenience enough to tolerate higher fees or questionable labor practices.

“Lyfted to the office in 10 minutes. Cost $10. That $10 was worth 30 minutes of my day.” – shadowgovt
“I know I’m getting ripped off by DoorDash … but the harm of getting ripped off just a bit is way lesser than the time it’d take me to get or prepare food myself.” – malfist


🚀 Project Ideas

FeeLens: Transparent Delivery & Ride Fee Explorer

Summary

  • Browser extension that reveals hidden fees, tip allocations, and estimated worker earnings for DoorDash, Uber Eats, Lyft, etc., addressing frustration over opaque pricing and tip deductions.
  • Core value: empowers users to see true cost distribution and make informed decisions about convenience vs. exploitation.

Details

Key Value
Target Audience frequent users of food delivery and ride‑hailing apps who care about fairness and cost transparency
Core Feature injects a side‑panel on order/checkout pages showing breakdown: menu price, service fee, delivery fee, tip, estimated driver pay after platform cut, using publicly disclosed rates and user‑provided tip
Tech Stack JavaScript/TypeScript, WebExtensions API, optional backend Node.js for fee‑data updates, hosted on Cloudflare Workers
Difficulty Medium
Monetization Revenue-ready: freemium (free basic breakdown, premium for historical analytics and alerts)

Notes

  • HN commenters complained about hidden markups and tip theft (e.g., “they hide the fact that the food prices are marked up…”, “they didn’t just reduce pay, they hid what they were doing”).
  • Could spark discussion on platform accountability and help users shift to fairer alternatives or advocate for regulation.

CoopDeliver: Open‑Source Worker‑Owned Delivery Stack

Summary

  • Provides a ready‑to‑deploy microservice suite (order management, dispatcher, driver app, payout smart contract) for launching a cooperative food‑delivery or ride‑hailing platform that returns >80% of revenue to workers.
  • Core value: lowers the technical barrier for communities to build ethically owned alternatives to DoorDash/Uber, addressing the desire for worker‑owned coop solutions.

Details

Key Value
Target Audience local cooperatives, activist developers, municipal agencies wanting to run a community delivery service
Core Feature modular architecture with real‑time matching, transparent profit‑sharing wallet (e.g., using ERC‑20 or stablecoin), and governance UI for voting on fees and rules
Tech Stack Go or Rust for backend services, React Native for driver/customer apps, PostgreSQL, Docker/Kubernetes, optional blockchain layer (Polygon) for payouts
Difficulty High
Monetization Hobby (open‑source; optional paid support/hosting tiers)

Notes

  • Commenters asked: “How can we… allow Uber/Doordash to go bankrupt and instead a smaller worker owned or nonprofit cooperative take its place…”
  • Offers concrete code that can be forked, lowering the risk of “copying Uber/Doordash” while enabling local value capture.

TransitBlob: Real‑Time Bus Crowding & Wait Predictor

Summary

  • Aggregates live GTFS‑realtime feeds with crowd‑sourced passenger reports to detect bus “blobs” (multiple buses arriving together) and predict actual wait times, helping users decide whether to wait or take a rideshare.
  • Core value: reduces frustration from unpredictable long waits and enables smarter time‑vs‑cost decisions, addressing the bus‑blob anecdote.

Details

Key Value
Target Audience commuters in cities with irregular bus schedules who experience long waits due to bunching
Core Feature mobile/web app showing next‑bus ETA, variance, and a “blob score”; integrates with ride‑share APIs to show cost‑time trade‑off for alternatives
Tech Stack Python backend (FastAPI) consuming GTFS‑realtime, PostGIS for spatial data, React/React Native frontend, optionally TensorFlow Lite for prediction models
Difficulty Medium
Monetization Revenue-ready: subscription ($3/mo) for premium features like historical analytics and ad‑free experience

Notes

  • HN users described watching three buses arrive back‑to‑back, picking up zero passengers, leading to 30‑minute waits; they wished for better info to avoid wasted time.
  • Could foster discussion on transit inefficiencies and motivate cities to improve headway management.

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