@luisdepombo · X

@localmama4u · X

@atbigthumb · X

@ericbuildsio · X

Fix your habits with public accountability @publichabits

@CiprianiRanieri · X

Builders Map: community of builders from all over the world.

@waris_oloyede · X

A logistic solution to drivers and users

@klorityx · X

One workspace for project management, documentation, test management & CRM. Enterprise features without enterprise pricing.

@frankgitonga20 · X

Vibe coding your ideas 💡

@LedgerMart613 · X

Tovo - AI 会议/面试助手 — 在会议中，Tovo 是一款注重隐私的 AI 助手，完全在您的设备上运行，为会议和对话提供实时转录和智能协助，无需向外部服务器发送任何数据。

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

@GadgetsCars · X

Built a mountain-bike service-tracker with no accounts and no server-side data nothing to breach, I don't have your data. Garage stays on-device, cloud backup is encrypted on your phone before it leaves. Built with Claude behind a strict test+CI gate.