The idea
Most AI apps give you one assistant that tries to do everything, and it shows — generic advice, no real personality, no sense that it actually specializes in anything. I wanted the opposite: a curated circle of AI companions, each one genuinely expert in one narrow thing, that you actively choose, promote, bench, and swap the way you'd manage a real relationship. Yare is the platform I built so that idea could power any domain. TasteBud(s) is its first product — the idea applied to food, with a roster of culinary "Buds" like a breakfast specialist, a fermentation expert, a foraging guide.
What it does
TasteBud organizes your AI companions into tiers — a small, ranked "A-List" you actually talk to, a Watch list of prospects, a Bench for companions you're not using right now, and an Archive for ones you've set aside. Every Bud keeps its full memory of you no matter which list it's in — archive one for six months, bring it back, and it remembers exactly where you left off. Your A-List is capped by subscription tier (3, 7, or 12 slots), and when it's full, swapping in a new Bud means explicitly choosing who steps down — never a silent eviction.
How I built it
Yare is the engine underneath: a shared platform for relationship modeling, memory, and companion behavior, with each app (TasteBud for food, an early second app called Draft for writing) as a themed skin on top of the same core. Every Bud is authored against a consistent behavioral template — personality, tone, domain boundaries, safety rules — so a companion can't wander off its lane no matter how the conversation goes. That was the whole point: I'd noticed every hyper-focused chatbot I tried eventually drifted, and I got obsessive about engineering a structure that actually holds the line.
Development itself became a case study in something I care a lot about: running an AI team well. I split the remaining build into two parallel workstreams — one AI agent on state and data logic, another on UI and screens — with a shared interface contract between them so neither could break the other, and every change reviewed before it merged. Nothing shipped without that review.
What I was exploring
Beyond the app itself, I wanted to answer questions most people building on AI models don't bother asking until it's expensive to find out: What does this actually cost to run at scale? What happens to a companion's personality when the model underneath it changes? I modeled the hosting economics in detail and found something counterintuitive — the "obvious" choice of renting a dedicated GPU actually loses money below roughly a thousand paying users, while pay-per-use hosting stays under a cent per user even for heavy usage. I also designed for a reality I don't think gets taken seriously enough: AI models change constantly, and swapping one under an unchanged personality can quietly break behavior nobody's watching for. So I built a way to test a companion's behavior against a fixed standard every time the model underneath it changes — treating that instability as a permanent condition to design around, not a one-time migration.
My role
Concept, product design, platform architecture, cost modeling, and AI team direction — end to end, with two AI coding agents executing against specs and contracts I set and reviewed.
What's next
TasteBud is still pre-launch — the state layer and UI are being built out now, live AI conversation isn't wired in yet, and the paywall is UI-only so far. Yare, the platform underneath, is built to support more apps than just this one.