
Maze
用 Maze 整理和分享你的购物物品,打造视觉化精选集合。
@michaelaplesner · X
hey :) building @trymaze_app <3 it's like Pinterest for the things you actually bought.
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用 Maze 整理和分享你的购物物品,打造视觉化精选集合。
@michaelaplesner · X
hey :) building @trymaze_app <3 it's like Pinterest for the things you actually bought.

@CricTalk29 https://t.co/wWE5v5hcRl
@arush_shar17296 · X

在线订购餐厅美食,支持配送、预约和多种支付方式。
@RaoulKone197812 · X
Check out what I just built with Lovable!

AgList是查找和比较农业生物制品的独立平台。
@timnuss · X
Trust layer for agriculture

meraSaaS:26 个文档、照片、发票和计划工具集,文件保留本地。
@latent_node · X

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



每日写下三件感恩的事来养成平静习惯并加入温暖社区。
@TuloSaswat · X
It's not a startup to say. But a small initiative from my end.

通过文本或照片跟踪餐饮和锻炼,获得即时AI营养分析。
@_FrenchSalmon · X
Building Aumaï, nutrition coach Spoiler alert: you can connect via MCP

按SKU分析Shopify销售数据,发现隐藏的利润泄漏。
@okiela_io · X
Sales up but profit unclear? I help ecommerce sellers find hidden profit leaks by SKU. Send me 1 order export → 3 leaks:

浏览购买来自Nike、adidas、Puma等品牌的优质运动鞋。
@PaballoMot94275 · X
Check out what I just built with Lovable!