@Flightmussy · X

@aneta_dv · X

MeetSpot 聚点 — 多人聚会地点智能推荐工具，输入 2-10 人地址，用球面几何算法计算公平中点，AI 评分推荐附近高评分场所，覆盖 350+ 城市，免费使用无需注册。

@pgardunoc · X

I vibe coded this for the World Cup, and other projects

@jozef_x1 · X

Vibe coded this in base44: brought in 25 listings from my WP site, with an export plugin then imported into Next level.

@aaronbatilo · X

Shout out I made it. Shout out to me. Pre-vibe coding

@jusDiggingholes · X

Not really a saas or startup. Retired military here. We get moved a lot. Making a site for military or retirees to get a good snapshot of their next or last base area: Crime. Schools. Costs etc.

@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

Midjourney 样式代码库 — SrefHunter 汇集各种社交媒体平台的 Midjourney 样式代码

@4MikeJoe · X

CARFAX for Airbnbs!

@umeridrisi · X

Lets make it together and fix the issues that other link in bio builders have. I am currently using this for my own bio pages too. Go try it here at Tell me what you want to add… #vibecoding #buildinpublic