Three AI products, three different AI architectures

Why

AI changes what one person can ship — and it changes what "good AI product judgment" means. Reading about that is one thing. To pressure-test it, I set out to ship three complete consumer products, each built on a deliberately different AI architecture: cloud LLM, a cloud model with an on-device fallback, and no model at all. Every one is live today with its own brand, website, and users' data to protect.

Cleared — an opinionated AI to-do app

clearedfirst.com · "Clear what matters. First." · live on the App Store

Most to-do apps are lists; Cleared has an opinion. Say a goal and AI plans the steps. Your tasks then compete — you judge them head-to-head in a bracket, or AI referees — producing a ranked tier list with one task spotlighted as clear next. Work in order until the app says "All clear."

• The AI bet — cloud LLM economics as a product constraint. The entire ranking runs in a single cost-optimized Gemini request, and app attestation ensures only the real app can spend model quota. Latency and unit cost were designed, not discovered.
• The agent bet — the app is also a tool surface. Cleared ships a remote MCP server, so Claude can read the list in priority order, file tasks into tiers, and run the same prioritization the app runs. It signs in with OAuth 2.1 against the app's own accounts, and writes go through the same rules the UI does — so anything Claude does is undoable in Cleared. Connecting is one URL and one approval: no API keys, nothing to install.
• React 19, TypeScript, Tailwind, Firebase (auth, sync, App Check), Gemini, and a remote MCP server on Cloud Functions — shipped as a PWA and, via Capacitor, an iOS app on the App Store.

IronProof — proof over motivation

ironproof.org · "The scale lies. Proof doesn't." · live on the App Store

A fitness progress tracker built on a simple insight: photos reveal what scale weight can't. IronProof turns progress photos into a timeline of transformation — weight and measurements overlaid on every shot, dates read automatically from photo metadata, synced across devices.

• The bet — breadth of shipping. One codebase runs on the web, iOS, Android, and desktop. It went through Apple's review and is live on the App Store.
• The hard parts were unglamorous product engineering: HEIC photo decoding, EXIF date extraction, offline-first storage with cross-device sync.

Grava — calorie counting, without the counting

gravacal.com · "Snap. Confirm. Eat." · live on the App Store

A native iPhone calorie tracker that removes the logging that kills every diet app: photograph a meal, a menu, a nutrition label, or a delivery screenshot, and AI estimates calories and macros. Estimates come back as honest ranges, and the app biases toward the top, middle, or bottom of the range depending on whether you're cutting, maintaining, or bulking — protecting the user's goal instead of faking precision.

• The AI bet — two models behind one estimator. Built in SwiftUI. Estimates run on Gemini by default, because accuracy is what makes a calorie estimate worth trusting; when the request fails, the quota is spent, or the phone is offline, the app falls back to Apple Intelligence on device — the Vision framework for food recognition and OCR, plus the on-device foundation model, calibrated with serving-size anchors the smaller model needs. The UI names which model answered, so a degraded estimate is never passed off as the best one.

What building all three taught me

• Architecture is a product decision. Cloud models buy capability and cost you unit economics; on-device models buy privacy and offline reach and cost you accuracy. Grava started fully on-device and moved to cloud-first once testing showed the small model's calorie estimates weren't good enough to trust — keeping it as the fallback rather than shipping "works offline" as a promise the accuracy couldn't cover. Choosing per product — and revising the choice when the evidence changes — is the actual skill.
• AI compresses build time, not judgment time. The models wrote a lot of code. Deciding what each product refuses to do — Cleared's single-request budget, Grava's ranges instead of false precision — was still the work.
• Shipping is the credential. Brands, websites, App Store review, auth, sync, data protection — the last 20% is where product managers earn the title.