
TruckSpot — Find the Flavor Near You
TruckSpot - 实时美食车查找器,支持地图、菜单、特价和活动。
@LisaRoseFL · X
Check out what I just built with Lovable!
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TruckSpot - 实时美食车查找器,支持地图、菜单、特价和活动。
@LisaRoseFL · X
Check out what I just built with Lovable!

为餐饮企业生成精美在线菜单和AI营销内容。
@Lsdavo · X
built in 2 days with claude code. the unlock: gave it our design system first - colors, type, components. after that every iteration stayed on-brand automatically. no mockups. no handoffs. describe → review → refine.

用AI扫描收据或手动输入,按项目分割餐厅账单。
u/getawaykid · Reddit
I got tired of doing receipt math at restaurants, so I built a free bill splitter with optional AI scanning TL;DR: Receipt Split splits restaurant bills by item. Free for manual entry + real-time sharing. AI photo scanning is optional and costs money to run, so I only charge for that part. I built Receipt Split after one too many dinners where someone said “let’s just split it evenly” while my friend was sitting there with a salad and everyone else had entrees + drinks. It’s a web app —

eatsTO 是一款 AI 美食发现工具,在 10 秒内为你推荐多伦多的餐厅和夜生活。
gurunoor_singh · Product Hunt
eatsTO — What's the move tonight? AI-powered food discovery that actually picks for you.

比较纽约五区690多家超市的食品价格和本周特价。
eli_yumnik · HN
I built a free app for New Yorkers to save money on groceries


搜索餐厅,发现当地人真正吃饭的地方,避免旅游陷阱。
kingchesco · HN
I know $90 for a dining app sounds absurd. And it’s not even an app; it's a wrapper for an LLM. But it is how it is built that makes it so expensive. I had to make a whole API just to call it (which because its own SAAS). All just to bypass dumb google reviews. Google Maps and reviews send people to places optimized for tourists and good copywriters. To find actual local hole-in-the-walls algorithmically, I had to first build that api (called BWENDI), a "spatial gravity" engine using 100GB+ of tweaked OSM, GeoNames, and other proprietary data. Instead of aggregating reviews, it mathematically calculates foot-traffic, throughput, transaction stats, and economic criticality among other factors. Bwendi is A Python/Node ETL pipeline feeding an LMDB-backed context API. It uses a proprietary 1MB binary grid served via Cloudflare Workers for millisecond edge reads with near-zero overhead, hosted in Switzerland. This was done of course to get the purest location context around every street
