@BMFDigital · X

@sriram3720 · X

u/Nox21125 · Reddit

I made Slick, the World's Most Customizable Search Engine For the past year, I've been building Slick , an independent search engine focused on privacy, speed, and giving users much more control over how search works. One thing that has always bothered me about modern search engines is that they expect everyone to search the same way. Whether it's AI overviews, ranking decisions, or the layout of the results page, you're usually stuck with whatever the search engine decides is best. I wa

u/AlarmingPepper9193 · Reddit

I pulled the raw HTML of a bunch of "SEO-ready" startup sites. Google can see them fine — but AI search can't. I went down a rabbit hole reading SEO threads where founders were panicking that their React/Lovable/Vite sites weren't ranking. The usual advice is "JavaScript sites are invisible to Google, you need SSR." So I started pulling the raw HTML (what a crawler sees before any JavaScript runs) for a bunch of real sites people posted. Here's what actually surprised me: almost none of t

johnbuildss · HN

Bloomiro lets you connect your site and ask an AI agent what to fix for better SEO and AI search visibility, from missing pages and weak structure to schema, content gaps, and competitor analysis.

@EcoSEOone · X

@chandlerwilcox · X

(not finished)

@AjayGB4 · X

@chandlerwilcox · X

grenzfrei · HN

CiteReady – Can AI search (ChatGPT, Perplexity) cite your site?

@cairndev · 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