
MotoDataHub
聚合欧洲二手车市场数据,轻松找到心仪二手车
@andrejbuday · X
I hope I am not late into the party. This is mine
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聚合欧洲二手车市场数据,轻松找到心仪二手车
@andrejbuday · X
I hope I am not late into the party. This is mine

一站式预订酒店、航班、汽车租赁和自行车租赁。
@kuldeepjoshi_10 · X
Check out what I just built with Lovable!

Reels, recipes, movies and places, saved from anywhere and remembered in one place.
@JoinFavorit · X

使用 AI 优化跨多个市场的产品列表和图像
u/Dfmarulanda · Reddit
roast my landing page - AI that rewrites your amazon listings (dondo.com) site: dondo.com what it is: you connect your store and it rewrites your product listings (titles/descriptions/keywords), makes studio + lifestyle images, and cross-posts them to other marketplaces. cheapest plan is $8/mo. been staring at this page for weeks so i honestly cant judge it anymore. curious what fresh eyes think: is it clear what the product does right away or confusing? does the headline pull you in

保存并浏览你想尝试的餐厅,帮助你决定去哪里用餐。
@danielendara · X
Sure! let's connect! My latest creation is

@zdogmode Places for the particular, right here https://t.co/iqfT9MTdnf
@discoverdiscoo · X
Places for the particular, right here

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

发现在文章、播客和目录中提及初创公司的地方
@jessomzy · X
hey, i'm building sightivo in the marketing space! it's for startups to boost their search rankings and visibility in AI answers. it looks at a ton of factors and finds the best place for YOUR company at your stage to get mentioned on next

See what DoorDash, Uber Eats, Grubhub, and catering marketplaces actually cost. Reconcile payouts, expose deductions, and track true delivery margin by location.
@myke86d · X
operator first

一个私密的地图,用于保存和分享地点、照片、视频和记忆,与信任的人分享。
@pinlytech · X

每日谜题,从表情符号猜测电影、歌曲、地点或短语。
u/Big_Cano · Reddit
I built and shipped a daily puzzle game in one day using AI — here's what actually turned out to be hard The code was the easy part. What took the real work was the puzzle design, and I didn't expect that. Some things I learned building an emoji-clue game: Emoji rendering is a minefield. I used 🫘 (beans) in an idiom puzzle. On Windows it's nearly unreadable — the puzzle failed even though the clue was literally the right object. Rule I ended up with: only pre-2020 emoji, and only ones t

Sunshine Atlas:转动地球发现全球最晴朗、最温暖或降雨最多的地点。
@Flightmussy · X