@hbute1 · X

@localmama4u · X

@mithvinkousthub · X

@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!

@saidzaripov · X

wen20 · V2EX

外贸人使用的工具：一个 3D 装箱工具分享 外贸集装箱 装货 3D 可视化工具 工具地址： https://waimao.himyou.com/container 工具特点： - 默认包含 20 尺标准柜、20 尺高柜、40 尺标准柜、40 尺高柜、45 尺标准柜、45 尺高柜，可自定义集装箱大小 - 盒体和圆柱体货物布局 - 多种自动装箱算法，自动计算装箱利用率 - 导入、导出 货物列表 - 支持底部托盘

@timnuss · X

Trust layer for agriculture

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