5 Ways I Make Money With Hermes Agent

Business-focused summary: “5 Ways I Make Money With Hermes Agent”

The presenter argues that Hermes AI should not be treated as a “magic money printer” or fully automated money-making system. Instead, it should act like a junior operator that accelerates real business workflows (research, monitoring, drafting, summarizing, follow-ups), while the user provides the strategy, review, and decision-making.

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5 monetizable workflows / playbooks

1) Lead generation & outreach (prospecting system)

Core idea: Use Hermes for research + personalization + follow-up support, not bulk spam.

Recommended process

• Use ICP (ideal customer profile) to find targets
• Research each company (what they sell, why they might need your offer)
• Draft custom first message(s) for review
• Log everything into a table/Notion (for tracking + iteration)
• Do not auto-send; user reviews and sends

Example prompt output structure

• For each company: company name, website, what they sell, why they might need it, one personalized outreach angle
• Then generate 3 message variants for review

How to productize the offer (selling outcome, not method)

• Sell a “done-for-you weekly pipeline of qualified leads with custom opening angles”
• Position the value as pipeline creation, not “AI outreach”

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2) Content research (angle-finding & competitor intelligence)

Core idea: Hermes runs a recurring research loop to find what’s working and generate better content angles.

Research loop (repeats daily)

• Check competitors
• Check YouTube
• Check X
• Check keywords
• Check comments
• Convert signals into a brief
• Repeat

Example prompt

• “Act as my YouTube research operator”
• Inputs: AI agent niche
• Output: top videos outperforming channel average with:
  • title, channel, views, upload date
  • hook pattern
  • why it likely worked
  • how to adapt without copying
• Final output: top 3 ideas to film this week

Monetization example (service product)

• Built “Founder Funnel” to surface niche trends so founders/creators/agencies can create targeted content
• Hermes supports recurring jobs → turns research into a weekly content intelligence productized service

Strategic takeaway: “Speed + angle quality” beats raw content volume.

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3) Trend scout (real-time opportunity monitoring)

Core idea: Detect opportunities early (within hours) and respond quickly, because content cycles saturate fast.

Monitoring sources (customizable)

• X, YouTube, Hacker News, AI news sources

Example timing

• “Every morning at 8:00 a.m. check…”

Example prompt logic

• Look for AI-agent related topics (example keywords given in subtitle) and industry-specific keywords
• Filter for fresh, relevant, actionable items
• Output per opportunity:
  • source
  • why it matters
  • recommended response format (reaction short, thread, quick video)
  • draft hook

Value proposition

• “Speed is money” in content and client acquisition
• Hermes expands “surface area” of ideas while the user maintains judgment

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4) Market/trading alerts (high-level alerts, not autopilot trading)

Core idea: The dangerous version is letting Hermes trade real money automatically. The useful version is research + alerts (optionally paper/prediction-market monitoring).

Operational guardrails

• Do not place trades
• Trigger alerts only on abnormal movement/volume

Example alert rule (configurable thresholds)

• Monitor Polymarket categories (example list includes: crypto, elections, AI, sports, macro)
• Notify when:
  • market moves > 8% in 24 hours OR volume spikes unusually
• Alert contents include:
  • market link, current price
  • possible reason for the move
  • related news
  • what to verify before any action

Sensitivity customization

• Up thresholds to reduce noise (example: 25% in 24 hours)
• Use shorter windows for higher urgency (example: 25% in 1 hour)

Productization possibilities

• Paid alert system / newsletter / Discord bot / Telegram bot
• Consulting workflow for existing traders

Risk / positioning note

• Money comes from faster, cleaner, more consistent research—not prediction accuracy.
• Building real trading automation requires heavy risk controls (wallets, approvals, order signals, safety checks, etc.).

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5) Client ops (post-call follow-up & admin execution)

Core idea: Hermes improves founder/agency operations by handling the “invisible” work: summaries, reminders, follow-up drafts, recurring preferences, and turning conversations into next actions.

Example workflow

• After a client call:
  • convert notes → clean follow-up message
  • actions list with owners and deadlines
  • reminders
• Store durable client preferences in memory
• Do not store temporary task progress as memory
• “Ask me before sending anything externally”

Monetization

• “Businesses pay a lot” for workflows that prevent dropped follow-ups and reduce admin time
• Positioned as an operator-style system, not an attention-grabbing “AI money” pitch

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Key themes / business frameworks implied

• Outcome-based selling: sell the pipeline, intelligence, alerts, or operational follow-through—not “AI automation.”
• Agent as junior operator: Hermes handles repetitive research/drafting/monitoring; the human reviews and decides.
• Loop-based research: content/market research are recurring cycles, not one-off reports.
• Guardrails for risk: alerts are safe; autopilot trading is discouraged.

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Metrics / KPIs mentioned explicitly

• Trading / alert thresholds:
  • 8% movement in 24 hours
  • (adjustable) 25% change in 24 hours or 25% in 1 hour
• Content performance measurement (described, not quantified):
  • “videos outperforming their channel average”

(No CAC/LTV/revenue targets were stated in the subtitles.)

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Concrete recommendations (actionable)

• Start by deploying one workflow that saves time and creates opportunities—avoid building a “money machine” on day one.
• For outreach: draft + log + review; never auto-send bulk messages.
• For content: implement a daily research loop to generate weekly filming ideas.
• For trend scouting: run a morning monitoring job and respond with a specific format + draft hook.
• For market signals: use movement/volume thresholds with “no trading” guardrails and an explicit verification step.
• For client ops: after calls, generate follow-up messages with owners/deadlines/reminders, and store only durable preferences.

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Presenters / sources

• Presenter: The creator speaking throughout (name not provided in the subtitles).
• Referenced collaborator / source: Leon Abood (co-founder) — referenced for outreach deployment video.
• Referenced platforms / tools: Hermes agent, Notion, GitHub, Polymarket, X, YouTube, Hacker News.