Project ideas from Hacker News discussions.

Seattle City Council votes to ban surveillance pricing in sale of groceries

📝 Discussion Summary (Click to expand)

Theme 1 – Fairness and impact of algorithmic price discrimination
Commenters debated whether personalized pricing helps or harms consumers, especially low‑income shoppers.
- “If you disallow that and have everyone pay the average, the richer students would pay less, but the poorer students would be priced out entirely.” – valleyer
- “Wealth redistribution at point‑of‑sale is simply an insane policy. If you want to have transfers to the poor, provide transfers to the poor.” – ndriscoll
- “It can never be better for consumers. The only way a business would adopt this practice is if it leads to greater revenues. On average, that necessarily means worse prices for the average consumer.” – jsrozner
- “If they were only allowed to offer one type of ticket at one price, it would mostly benefit richer passengers while pricing the poorest passengers out of air travel.” – Legend2440

Theme 2 – Privacy and limits on corporate use of personal data
Many argued that the core problem is surveillance‑based data collection and called for stronger privacy protections.
- “The best solution is a constitutional amendment that actually enshrines a right to privacy… the retention, aggregation, correlation of any personal data should be illegal (including for commercial purposes).” – jsrozner
- “Does showing you less ads for things you actually want to buy instead of more random adds serve you directly?” – consensus1
- “The bill also permits a vast array of discounting practices while requiring increased transparency around discounts and placing some limitations on how consumers can be profiled.” – apparent

Theme 3 – Scope, applicability, and risk of overreach
Participants questioned how broad the ban should be, warned of unintended consequences, and stressed the need for clear limits and enforceable thresholds.
- “Because passing laws without limits is what leads to unintended consequences.” – valleyer
- “I wonder if it would apply to other more general online storefronts such as amazon.” – brasspepperbrea
- “Why is this limited to groceries? I don't want to pay a different price than others for any category of item…” – flowerlad
- “If regulation … they should include thresholds for scale … equating an individual seller on Craigslist with Albertsons buying petabytes of data is not valid.” – jbombadil


🚀 Project Ideas

Generating project ideas…

PriceCheck: Real‑Time Algorithmic Price Discrimination Detector

Summary

  • Browser extension that creates multiple synthetic shopper profiles (different IPs, cookies, login states) to fetch the same product page and compares prices in real time.
  • Alerts the user when they are being shown a personalized premium price, exposing hidden surveillance pricing.
  • Core value: gives shoppers immediate transparency into whether they’re being charged more based on their data profile.

Details

Key Value
Target Audience Privacy‑conscious online shoppers, especially grocery and e‑commerce users
Core Feature Automated price comparison across varied user profiles to detect personalized pricing
Tech Stack JavaScript/TypeScript (WebExtension), Node.js/Express backend, Puppeteer/Playwright for headless browsing, proxy rotation service (e.g., BrightData)
Difficulty Medium
Monetization Revenue-ready: Freemium (free basic alerts, premium $5/mo for detailed history, API access, and priority support)

Notes

  • HN users complained about hidden algorithmic pricing: “apparent: … The issue is that ‘you gave me the regular price for some item but gave discounts to other people’” and “flowerlad: Why is this limited to groceries? I don’t want to pay a different price than others for any category of item.” PriceCheck directly addresses these concerns by making discrimination visible.
  • Provides a practical tool for the community to test and demonstrate the impact of surveillance pricing, fueling further discussion and potential regulatory action.

DataLedger: Personal Data Inventory & Rights Automation

Summary

  • Dashboard that automates data‑access, deletion, and opt‑out requests to retailers, loyalty programs, and online services, then consolidates the responses into a personal data ledger.
  • Users can see what data each company holds, request corrections, and export their data in a portable format.
  • Core value: puts individuals in control of their personal data and simplifies compliance with GDPR/CCPA‑style rights.

Details

Key Value
Target Audience Consumers concerned about data privacy, GDPR/CCPA users, privacy advocates
Core Feature One‑click automated requests (access, deletion, opt‑out) to multiple services, with a unified view of all personal data collected
Tech Stack React frontend, Node.js/Express backend, IMAP/SMTP for email‑based requests, OAuth2 where APIs exist, optional integration with services like OneTrust for enterprise connectors
Difficulty Medium‑High
Monetization Revenue-ready: Subscription $9/mo for full automation, priority support, and advanced analytics

Notes

  • Commenters emphasized the need for data control: “jsrozner: … You could build a test: the company should not be able to derive any additional value from 100 fully anonymized interactions …” and “dabinat: … AI … may charge double for a chocolate bar to someone it inferred was diabetic.” DataLedger gives users the ability to see and limit the data that fuels such pricing.
  • By automating rights requests, it reduces the friction that currently lets companies exploit personal data, addressing a core frustration in the thread.

FairPrice Marketplace: Community‑Driven Grocery Price Transparency

Summary

  • Mobile app where users scan receipts or manually enter prices paid for grocery items; the app aggregates anonymized submissions to show price distributions per store/item.
  • Users receive alerts if they paid significantly above the median price for the same product at the same store, highlighting potential discriminatory pricing.
  • Core value: empowers shoppers with collective price knowledge to spot and avoid unfair personalized pricing.

Details

Key Value
Target Audience Grocery shoppers, community activists, price‑sensitive consumers
Core Feature Crowdsourced price database with barcode scanning, price‑distribution visualizations, and out‑of‑range alerts
Tech Stack React Native (Expo), Firebase/Firestore for real‑time sync, Google ML Kit for barcode scanning, optional AWS Lambda for data aggregation
Difficulty Low‑Medium
Monetization Revenue-ready: Freemium (free basic crowdsourced data and scanning, premium $3/mo for store‑specific analytics, price‑trend alerts, and export)

Notes

  • Users expressed frustration over opaque pricing: “mikeocool: Seriously, once you realize you’re paying a minimum of 30% extra, it becomes hard to stomach.” and “flowerlad: Why is this limited to groceries? I don’t want to pay a different price than others for any category of item.” FairPrice leverages community data to reveal those hidden premiums.
  • The app creates a transparent marketplace of price information, encouraging discussion about fairness and providing practical utility for everyday shoppers.

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