100 hours of Hermes Agent lessons in 46 minutes
A 47-minute walkthrough of all seven levels of Hermes Agent — from bare VPS to full MCP back end.
May 6thA complete build of an AI-powered agency operating system: meeting intelligence, self-improving outreach, and a custom mission control dashboard — all orchestrated by the Hermes agent.
A self-improving AI agent that records its own decisions and packages them into reusable skills can replace the repetitive front-end of an agency — outreach, meeting analysis, and follow-up — without a team.
Hermes is an open-source AI agent built by NousResearch that writes its own SOPs as 'skills' after completing complex tasks, compounding its usefulness over time instead of resetting on every session. This video is a full walkthrough of building a three-component agency stack on top of it: a meeting intelligence engine that ingests Fathom calls and flags hot leads, a self-improving SDR that learns which outreach messages close, and a React/Supabase mission control dashboard. The build uses Claude Code as the programmer agent while Hermes operates the system post-handoff, creating a clean orchestrator-builder-executor separation that reduces both cost and cognitive overhead.
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Revenue screenshot hook, introduces Hermes as the agent behind the results, promises a full from-scratch build.

Explains Hermes vs OpenClaw, the trajectory-to-skill loop, and NousResearch's self-improvement thesis.

Free model options (OpenRouter, local), Anthropic API costs, and why $0–20/month is realistic for starting out.

Level 0 (terminal/Telegram), Level 1 (Hermes Workspace dashboard), Level 2 (custom mission control) — most tutorials stop at Level 0.

One-command install, API key configuration, Telegram bot setup, and testing the base Hermes install.

Full walkthrough of the open-source dashboard: chat, files, terminal, cron jobs, memory files, skills browser. Includes troubleshooting the pnpm/gateway setup.

Documentation, security, technical, strategy, and historical research layers — how to build deep project context for any AI agent.

How to separate agent roles by capability tier to maximize efficiency and control cost at scale.

No clickbait: they solve different problems. OpenClaw as Swiss Army knife, Hermes as memory/skill specialist. Migration guide overview.

System design for the custom dashboard: what components are needed, how Hermes and Claude Code will divide the work.

Using a screenshot of the existing Agents in a Box app to drive Claude Code's design decisions. Building the React/Supabase scaffold.

Fathom webhook integration that pushes new call recordings into Supabase automatically, triggering the meeting intelligence pipeline.

Design principle: all dashboard features must be callable by agents, not just humans. Building the integration guide and endpoint registry.

The moment Claude Code finishes scaffolding and Hermes begins operating the system via natural language. The handoff pattern explained.

Auto-analysis of Fathom calls: key takeaways, next steps, hot lead scoring, and linking meeting data to outreach campaigns.

Building scheduled tasks entirely via natural language: nightly meeting processing, hot lead alerts, and a morning Telegram digest.

The agent analyzes reply rates, updates its own messaging patterns, and runs the next campaign with refined copy — no manual A/B testing.

Wiring Resend for transactional and sequence emails: domain verification, API integration, and tracking opens and clicks in the dashboard.

Automated pre-call sequence with no-show detection: webhook from Cal.com triggers a multi-touch Resend sequence to cut no-show rates.

Apify scrapes leads, CSV uploads into the dashboard, Hermes personalizes each message using meeting intelligence context.

Full system recap, what was built vs. planned, Agents in a Box community pitch, and preview of the Cloud Code Masterclass.
The gap between an AI assistant and an AI operating system is persistent memory and self-written SOPs — and closing that gap is a system design decision, not a tool purchase.
“I didn't write a single cold email. I didn't review a single discovery call. I didn't even draft the proposals. An AI agent did it.”
“An AI agent should get better the more you use it. Every other agent out there resets — Hermes doesn't work that way.”
“They are not the same two. They don't even compete in the same category in my opinion.”
“What you have now is a single brain that sees everything. Most agencies have scattered tools — CRM here, emails there, meeting notes somewhere else, founder's brain holding it all together.”
See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.
Fifty-four thousand dollars in thirty days, twelve payouts, six clients — and not one cold email written by hand. That's the opening claim, and the next four hours are the receipts: a complete rebuild of the AI agent system that runs the front end of his agency, from lead sourcing to meeting intelligence to self-improving outreach, all orchestrated by an open-source agent that gets smarter every time it completes a task.
Three-tier agent role separation that controls cost and prevents capability mismatch — use the right agent type for each layer.
Five-layer context architecture stored in a Claude Project that gives any AI agent deep, project-specific knowledge on every session.
Progressive capability tiers for Hermes deployment — each level adds visibility and control over what the agent is doing.
After each complex task, Hermes logs every decision and API call into a trajectory, then evaluates whether it can be packaged into a reusable skill file — the core self-improvement mechanism.
Every feature in a human-facing dashboard must also be callable via API so AI agents can take the same actions without opening a browser — baked in from design, not retrofitted.
“If you want to skip the entire thing, get all of this and also the application that I was showing you — Agents in a Box. First link in the description.”
Delivered at end after showing a more powerful version of what was built; creates aspiration gap. Complemented by a free Gumroad playbook (second link) for those not ready to pay.
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242:58A 47-minute walkthrough of all seven levels of Hermes Agent — from bare VPS to full MCP back end.
May 6thJack Roberts complete Hermes Agent mastery guide from memory systems through deployment in under 25 minutes.
May 24thA 27-minute free course showing how to build a Telegram-connected Claude Code agent with persistent memory, Siri voice triggering, and a multi-agent board of directors using Bun, Supabase, and an open-source GitHub repo.
February 13thSix composable agent patterns from Anthropic's own internal masterclass, with live prompts and honest advice on when to skip workflows entirely.
June 3rdAn 8-minute walkthrough of the Hermes Agent desktop app — installation, skills, Telegram setup, cron limits, and a candid verdict against Claude Code and Codex.
June 3rdA 12-minute setup guide for running Claude Code multi-agent Dynamic Workflows on free local models with no Anthropic account required.
May 31st