Sending SMS, WhatsApp, and RCS messages should be simple — but in the real world, you end up dealing with registrations, approvals, carrier routing, country rules, retries, and webhooks just to answer one question: did the message land? This video shows how to avoid all of that complexity with Sent, a single API that can route messages across WhatsApp, SMS, and RCS automatically. Sent handles channel availability, formatting, fallbacks, compliance, delivery states, and webhook updates behind the scenes, so your app can send the right message through the best channel without maintaining separate integrations for each one. I’ll walk you through a full delivery tracking demo using the Sent's TypeScript SDK, Convex, real webhooks, approved templates, contacts, automatic routing, and AI agent setup with the Sent's MCP Server. We’ll cover: ✅ Why sending SMS gets complicated fast ✅ How Sent routes WhatsApp, SMS, and RCS from one API ✅ Setting up API keys, webhook secrets, and template IDs ✅ Sending real WhatsApp and SMS messages from a demo app ✅ Tracking queued, sent, delivered, and read states in real time ✅ Using templates, dynamic variables, buttons, and rich WhatsApp messages ✅ Handling Sent's webhooks with Convex or Next.js route handlers ✅ Using the Sent's MCP and docs to build faster with AI agents If you’re building apps that need reliable user messaging, this is one of the cleanest ways I’ve seen to ship multi-channel delivery without fighting every carrier and platform yourself.

AI coding agents are getting really good at writing code, but there’s still a big gap: a lot of real-world app setup happens outside the codebase. Things like authentication, environment variables, organizations, billing, plans, production config, permissions, and dashboard setup usually still require a human to jump between tools, copy keys, configure services, and connect everything manually. That’s where CLI tools become a game changer. In this video, I show how giving an AI coding agent access to the right CLI, skills, and project context lets it do way more than just generate code. Instead of only relying on MCP tools or local project files, the agent can start interacting with real services from the terminal and handle setup steps that previously fell back to us. For this demo, we use the Clerk CLI as the example. We start with a blank Next.js app, authenticate the CLI, run the setup flow, install Clerk’s agent skills, and then push the workflow further by asking an AI agent to build a full multi-tenant SaaS app with authentication, organizations, billing plans, feature gating, and an upgrade flow. The bigger idea is simple: as more platforms expose powerful CLIs, AI agents become much more useful. They can write code, configure services, set up products, connect infrastructure, verify the result, and help us ship full applications faster. We’ll cover: ✅ Why CLI tools unlock a new level of power for AI coding agents ✅ Where MCP tools and skills help, and where CLIs take things further ✅ How agents can now handle setup work we previously had to do manually ✅ Starting from a blank Next.js app ✅ Using the CLI to configure authentication from the terminal ✅ Installing agent skills so the AI has better product context ✅ Giving Claude Code access to CLI-powered workflows ✅ Creating a working sign-in and sign-up flow ✅ Setting up organizations for a B2B SaaS app ✅ Creating billing plans, pricing, and feature gates ✅ Testing subscriptions, organizations, and upgrades