I built a free app for New Yorkers to save money on groceries
Details
- External ID
- 48854224
- Source
- HN
- Company
- —
- Product
- I built a free app for New Yorkers to save money on groceries
- Website domain
- sbnyc.app
- Launched
- July 10, 2026
- Cohort
- —
- Upvotes
- 13
- Upvotes percentile
- 0.6200716845878136
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
I built this because I see that grocery savings are achievable in NYC. People usually just go to the store they're used to going to, and it's rarely worth the effort of combing through card cashback, weekly coupons, CPG rebates.Most people leave real money on the table by not stacking them, and even more don't even know that these deals are out there.... so I built a way to automate it.You can use it for free, no login, currently NYC-only with ~690 stores.I built it so that you just search whatever you want (use commas if you want to search multiple items). Or - use the AI tool to help shop for you. If you're curious, it's powered by a trained LLama model.Honest limitations are coverage and freshness. Id love some feedback on where the data looks wrong or is stale.Question for the room - what to prioritize if you're working with messy, multi-source retail/pricing data? Is freshness or coverage the top priority if you cant get a uniform response from every source? curious on what to prioritize here.
Enrichment
- Theme
- personal finance and expense trackers
- Vertical
- Retail & commerce
- Function
- Vertical SaaS
- Audience
- B2C
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- grocery savings for new yorkers
- Manually corrected
- False
Could you build this?
Partial The web application interface and shopping basket optimizer are easy to vibe code, but scraping and normalizing weekly circulars and dynamic pricing across 690+ independent grocery stores requires continuous data engineering.
What it would actually take: The backend needs robust ETL scrapers (handling platforms like Flipp, Instacart, and fragmented independent bodega/supermarket flyers via OCR/vision models) running on daily/weekly schedules with schema normalization for SKU matching. Maintaining reliable scrapers against varying flyer layouts across hundreds of stores requires ongoing data maintenance and automated parsing pipelines.
Discussion
10 comments analyzed.
Competitors mentioned: Mr. Mango (fresh produce deals)
Concerns raised: Data includes closed stores, outdated information, Extraction cadence irregular, data normalization difficult, Search functionality doesn't understand location-based context, Most people need deals near them, not cheapest overall, Grocery stores don't publish pricing in accessible formats
Feature requests: Location-based deals near user (not just cheapest overall), Normalized, consistent data collection format, Better bot understanding of geographic queries
Competitors
Other products that read as similar to this one — 230 launches clear the similarity bar, closest 8 shown.
Attention rank: #94 of 231 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 254 days after the earliest competitor.
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Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a vertical saas tool for Insurance yet.