@YashSha06255909 · X

@ChetanJ344077 · X

@localmama4u · X

@juancastillamar · X

Hi Esther, I’m a frontend developer working in seo for let’s connect and keep growing!

@WingBeerSpecial · X

- Discover daily specials at restaurants near you. Currently limited to Ontario Canada. Let us know if you like to concept and want to see coverage in your area next!

@bhojan_qr · X

Paper menus belong in a museum. 🏛️🚫 We just launched BhojanQR. Beautiful digital menus, zero wait time, and seamless ordering—all from a single QR scan. 🚀🍽️ Live Link : #BuildInPublic #SaaS #FoodTech #BhojanQR

@versuchai · X

@Hafid_raji_ · X

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

poople game — 字母拼字游戏，逐个添加字母直到拼出目标单词

@Prakhar_3010 · X

@Bizzzyapp · X