Everyone wants their own team of AI agents now — one checking your tasks, one answering in Slack, one watching your portfolio, one building dashboards, one shipping code. But setting that up yourself with open-source agents can get scary fast. You need to understand servers, security, integrations, permissions, agent memory, triggers, and what happens when something goes wrong. In this video, I show you how Hyperagent lets you build that same kind of AI team without needing a Mac mini, VPS setup, 3am debugging session, or deep technical knowledge. We build multiple agents live, including a personal assistant that checks Notion, Gmail, and Linear, plus an investment analyst that can research updates, respond through email and Slack, use skills, create dashboards, and improve itself over time. We'll cover: ✅ Why local AI agent setups can be risky if you don't know what you're doing ✅ What Hyperagent is and why it feels like the simple version of running an AI team ✅ How to create your first agent using chat ✅ Building a personal assistant agent for tasks, emails, calendar, Notion, and Linear ✅ Building an investment analyst agent with skills, Slack, email, and dashboards ✅ How Hyperagent handles integrations with granular permissions ✅ Using live mode, email, Slack, Telegram, webhooks, and schedules to trigger agents ✅ Shared memories, knowledge, teams, projects, and agent self-improvement ✅ How developers can connect GitHub and let agents collaborate on code This is how you go from "I wish I had an AI team" to actually running one — without needing to become a security expert first.

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

Most people are using AI agents for coding now — but their agents are still missing the upgrades that make them genuinely useful in real workflows. This video breaks down the five upgrades I personally use to make my AI agents smarter, more reliable, cheaper to run, and much easier to manage across my team. We’ll start by quickly clearing up the difference between plugins, skills, and MCP servers, then dive into the exact tools that level up your agents: fresh documentation with Context7, better engineering workflows with Superpowers, live web access through DataImpulse, structured task management with Linear, and long-term memory through Obsidian. I’ll show you how each one fits into an actual AI agent workflow, how to install or connect them inside tools like Cursor, Claude Code, Codex, OpenClaw, and Hermes, and why these upgrades matter if you’re trying to move beyond basic prompting into proper agent-powered productivity. We’ll cover: ✅ The difference between plugins, skills, and MCP servers ✅ Why plugins should usually be the first thing you look for ✅ How Context7 gives your agents fresh, version-specific documentation ✅ How Superpowers adds proper software development processes to your agents ✅ How DataImpulse helps agents access live public web data through residential proxies ✅ How to install and use the free DataImpulse MCP ✅ Why Linear becomes a control center for AI agent workflows ✅ How Obsidian can act as a shared second brain across all your agents ✅ How OpenClaw and Hermes can use Obsidian as a native memory layer If your agents still feel limited, forgetful, or inconsistent, these five upgrades will help you turn them into a serious workflow that can research, plan, build, track work, and remember what happened across sessions.

Episode 12 of our new Series 'Code with AI the Right Way' is here! — and this time, we're building a Zendesk / Intercom clone LIVE from scratch! (It even includes AI Agents and B2B Billing!) This is a LIVE build — mistakes, debugging, and all. That's the point. You learn more watching someone solve real problems in real-time than from a polished, pre-recorded tutorial.

Episode 11 of our new Series 'Code with AI the Right Way' is here! — and this time, we're building a Linear Clone LIVE from scratch! Set your reminder & hit the LIKE button here 👉 https://youtube.com/live/6To1ePo1rqc No pre-built code, no shortcuts. You'll watch the entire process unfold in real-time as we break down requirements, set up our AI tooling, and vibe code a full app from zero! Whether you're just starting out or leveling up, this episode shows you how to think through architecture first, then leverage tools like Cursor to move fast without breaking things. This is a LIVE build — mistakes, debugging, and all. That's the point. You learn more watching someone solve real problems in real-time than from a polished, pre-recorded tutorial. I'll see you soon. Peace PAPAFAM! ✌️