24:I[50237,["/_next/static/chunks/0db4v-xi1q-jq.js","/_next/static/chunks/02-tqo9.1..63.js","/_next/static/chunks/0d3shmwh5_nmn.js","/_next/static/chunks/12wcqrolhhiqu.js"],"Posts"] 25:T488,In January I woke up in the middle of the night really mad at my iPhone camera. I couldn't stop thinking how complicated it is to do basic tasks while shooting photos: framing, adjusting the brightness and color etc.

All night I kept getting up and writing down ideas about how the camera app could be easier to use.

Here's the result — a proposal for a smartphone camera interface that uses exclusively easy and obvious gestures, and an app that embodies the principles.

My hope is that the app will succeed, but also that other camera app programmers will pay attention and adopt some of these principles.

In the past few months I've taken many pictures that I never could have gotten with the regular camera app:

• nicely framed images of friends rock climbing

• a super zoomed-in and contrast-boosted photo of a fish that I wanted to identify

• hundreds of artistic black-and-white photos

• some weird photos of brutalist architecture with neon colors overlaid

If users like the interface, the next step will be to add RAW support and really concentrate on the quality of the images.

Let me know what you think!26:T4d4,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 or neighborhood, so that the LLM stack could breathe and think properly.

So far the results are good.

Rip apart my edge architecture, the gravity math, or the aggressive pricing. I’m here for the roast.

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