Job Type: Part-time, ongoing Hours: 10–20 hours per week, flexible schedule Location: Remote, U.S.-based candidates Contract Type: Hourly through Upwork Project Overview I’m looking for a reliable, educated, U.S.-based assistant to support me with business operations, research, communication, and light project coordination. This is a part-time ongoing role for someone who wants simple, consistent work, extra income, and a long-term collaboration. The work is remote, flexible, and suitable for someone who is organized, professional, responsive, and comfortable communicating with U.S.-based people and businesses. I’m looking for someone in the U.S. who can help with U.S.-focused communication, research, coordination, and professional support when needed. This is not a heavy technical role and not a high-pressure full-time position. I’m looking for someone dependable, easy to work with, and interested in building a long-term working relationship. Responsibilities You may help with tasks such as: Online research and summarizing information clearly Drafting professional messages, emails, and outreach templates Supporting communication with U.S.-based contacts, companies, or candidates Organizing notes, documents, spreadsheets, and task lists Tracking daily or weekly progress on business-related tasks Reviewing information and preparing short reports Supporting business, hiring, recruiting, or software-related projects Helping organize simple workflows and project updates The exact tasks will depend on your strengths and availability. Requirements Must be based in the United States University graduate / bachelor’s degree preferred Excellent written English Professional and clear communication style Reliable availability for part-time work Organized, detail-oriented, and responsive Able to follow instructions and ask clear questions Honest, dependable, and easy to work with

Vercel Eve might be the new standard for building production-ready AI agents. Before this, building a real agent meant hand-rolling a mountain of plumbing: streaming, model routing, durable long-running workflows, safe sandbox execution, Slack/web channels, MCP connections, human-in-the-loop approvals, observability, scaling, and more. In this video, I break down Vercel’s full Agent Stack and show how Eve brings it all together with a filesystem-first framework for agents. We build and inspect a data analyst agent called Pulse, run it from the web, terminal, and Slack, look at sub-agents, schedules, Workflows, AI Gateway, Sandbox, Connect, and how it all deploys alongside a Next.js app. 🎯 What You'll Learn: ✅ Vercel Eve for building filesystem-first production AI agents! ✅ Vercel AI SDK + AI Gateway for streaming, model calls & model routing ✅ Vercel Workflows for durable long-running agent execution ✅ Vercel Sandbox for safely running agent code in isolated environments ✅ Skills, tools, sub-agents & schedules for more powerful agent behaviour ✅ Slack, terminal & web channels for talking to the same agent from anywhere ✅ Next.js integration with withEve so your app and agent deploy together + SO MUCH MORE!

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