A visual breakdown of how an AI agent thinks, calls tools, handles approvals, and turns context into useful work.
A live demo of an AI agent that generates UI on the fly — components, layouts, and interactions rendered as the model reasons.
A real $1 payment from your terminal to Dash via Stripe Link and the Machine Payment Protocol — agent-issued token, push approval, no card form.
An agent that scores new NYC listings against your preferences and only emails when something clears the bar — pure signal, zero noise.
The same question, two very different answers. Naive keyword retrieval next to a designed pipeline — chunks, scores, reranking, citations, and an uncertainty flag where the corpus has caveats.
Describe the task, answer eight questions, get the simplest system that actually works — from no AI at all to a multi-agent loop. The fastest way to disqualify the wrong architecture before code.
The shape of a tool decides what an agent can and can’t do. A one-line `sendEmail` next to a structured `queueEmailForApproval` — schema, evidence, confidence, approval gate, audit trail.
AI voice agents for GLP-1 support — safe, on-brand, available 24/7.
AI medical scribe that captures and structures clinical conversations.
Collaborative AI writing environment for teams.
Personal AI stylist that helps you discover and refine your look.
Growth toolkit for newsletter creators — analytics, experiments, and lift.
Stand out from the crowd.
AI-native go-to-market system.
Personal finance, simplified.
Agents that search, reason, call tools, access APIs, remember context, and execute multi-step work — not just respond to prompts.
Workflows that run across email, Slack, CRMs, calendars, databases, and internal tools — so the manual back-office work stops being yours.
Distinctive, well-built products where the design is part of the value — not a generic shell wrapped around a chat box.
The unsexy but important part: authentication, databases, APIs, deployment, observability, permissions, security, reliability.
AI is useful when it is attached to real work.
Not every problem needs an agent.
An agent without tools is a chatbot. An agent without boundaries is a liability.
Autonomy should increase only when the system earns trust.
Reliable AI systems need tests, traces, and feedback loops.
Sometimes the right interface is chat. Often it is a dashboard, approval queue, command palette, or recurring report.
Companies need to know what the agent saw, what it did, and why.
Tell us a little about what you’re working on. We’ll reply within a day.
| Agents |  Vercel AI SDK Claude Agent SDKMCP |
|---|---|
| Models | OpenAI AnthropicRealtime |
| Evals | PromptfooRaindrop |
| Observability | Langfuse  Sentry PostHog |
| Memory | Mem0  pgvector Supabase |
| App |  Next.js React TypeScript Tailwind |
| Infra | VercelSupabasePostgres |
| Misc |  Stripe ResendFirecrawl |
“The site itself is meant to be the first evidence.”