24/7 Intelligence: Automating Your Second Brain with Hermes AI Agents

You open your note-taking app and stare at a graveyard of clipped articles, half-finished thoughts, and scattered bookmarks. "Read later" really means "never read." You're spending two hours a week just organizing folders, tagging, and trying to remember where you put that one brilliant framework you saved last month—and when you actually need it for a project, it's nowhere to be found.

The average knowledge worker spends 20% of their week just looking for internal information or tracking down past insights. That's one full day every week lost to poor organization. If you value your time at $100/hr, you're burning over $40,000 a year just playing librarian to your own thoughts.

The fastest way to fix this isn't to build a more complex tagging system or switch to yet another note app. It's to stop organizing manually altogether. By deploying Hermes AI agents as your personal research assistants, you can automate your second brain to do the heavy lifting: ingesting, synthesizing, and connecting information while you sleep. The result? A knowledge base that actually thinks with you, rather than just storing dead text.

What Automating Your Second Brain Actually Does

Here's the full loop in plain language:

1. Information Capture: You drop a link, voice memo, or text snippet into a designated inbox (like a Telegram bot or an email address).
2. Data Processing: A Hermes AI agent picks up the raw input, extracts the core text, and summarizes the main points using hermes-3-llama-3.1-8b.
3. Categorization & Tagging: The agent analyzes the context and automatically assigns relevant tags, categories, and priority levels based on your predefined schema.
4. Synthesis & Linking: The agent queries your existing database for related concepts and generates bidirectional links, connecting the new idea to your existing knowledge graph.
5. Final Storage: The fully processed, tagged, and linked markdown file is pushed directly to your Obsidian or Notion vault.

Total time from capture to organized note: under 30 seconds. Your involvement: 5 seconds to send the initial link or thought.

Who This Is Built For

This workflow is for:

• Researchers and Writers who need to synthesize large volumes of articles, papers, and books without losing track of core arguments and quotes.
• Product Managers and Founders who consume endless industry reports and need a system that automatically surfaces market trends and competitor insights.
• Content Creators who capture fleeting ideas on the go and need them structured and ready for drafting when they sit down at their desks.

This is not for teams looking for a shared enterprise wiki—if you need collaborative knowledge management across departments, you're better served by a centralized platform like Confluence or Slab. This system is designed for individual, high-leverage personal knowledge management.

What This Keeps Costing You

Without this workflow, here's what next week looks like:

• Spending 3 hours manually reading, summarizing, and tagging the 15 articles you bookmarked over the weekend.
• Losing $300 worth of your time (at $100/hr) just doing digital janitor work instead of deep, focused creation.
• The hidden cost of context switching: every time you stop drafting to search for a lost reference, it takes 23 minutes to regain your flow state.
• The mounting anxiety of a bloated "Inbox" folder that never reaches zero, making you avoid your note-taking app altogether.
• The opportunity cost of forgotten ideas: the brilliant marketing angle you thought of in the shower, quickly jotted down, and never found again when launch day arrived.

The real issue isn't the note-taking app itself—it's that you are acting as the manual processor for every piece of data that enters your system. Here's how to fix it by putting Hermes AI to work.

How to Build It: Step by Step

This tutorial will walk you through setting up an automated pipeline using n8n, a Telegram bot for capture, and Hermes AI for processing, feeding directly into a Markdown-based vault like Obsidian.

Step 1: Set Up the Capture Interface

We need a frictionless way to get information into the system. A Telegram bot is perfect because it's available on every device and supports text, links, and audio.

First, open Telegram and search for BotFather. Create a new bot and grab the API token.

In your n8n canvas, add a Telegram Trigger node. Connect it to the bot using your token, and set it to listen for any new message.

{
  "nodes": [
    {
      "parameters": {
        "updates": [
          "message"
        ]
      },
      "name": "Telegram Trigger",
      "type": "n8n-nodes-base.telegramTrigger"
    }
  ]
}

Watch out for: Ensure you set the Telegram node to only accept messages from your specific Chat ID, otherwise anyone who finds the bot can inject notes into your vault.

Step 2: Route the Incoming Data Type

Your input could be a raw thought, a web link, or an audio transcript. We need to route it so Hermes AI knows how to process it.

Add a Switch node in n8n. Create two rules based on the incoming message text:

1. Is URL (using a regex check for http).
2. Is Text (default fallback).

If it's a URL, route it to an HTTP Request node or a scraping service like Browserless to extract the page content. If it's plain text, route it directly to the next step.

<!-- Image: n8n canvas showing the Telegram trigger connected to a Switch node, splitting logic between URLs and plain text snippets -->

Step 3: Configure the Hermes AI Agent for Synthesis

This is the core of the automation. We will use the Hermes 3 model via an API provider (like OpenRouter or Together AI) to process the raw text.

Add an HTTP Request node to call the AI API. We'll use the hermes-3-llama-3.1-8b model because it's fast, highly capable of reasoning, and cost-effective for large contexts.

Here is the exact prompt structure to use in the system message:

{
  "model": "nousresearch/hermes-3-llama-3.1-8b",
  "messages": [
    {
      "role": "system",
      "content": "You are an expert knowledge management assistant. Analyze the user's input and output a strict JSON object with the following keys: 'title' (a short, descriptive title), 'summary' (a 3-bullet point summary), 'tags' (an array of 3-5 relevant lowercase tags), and 'connections' (an array of 2 related broad concepts)."
    },
    {
      "role": "user",
      "content": "{{ $json.extractedText }}"
    }
  ],
  "response_format": { "type": "json_object" }
}

Watch out for: You must enforce JSON output both in the prompt and in the API parameters. If you don't, the model might include conversational filler like "Here is your summary:" which will break your parsing logic.

Step 4: Format the Output into Markdown

Now that Hermes AI has structured the data, we need to format it into a clean Markdown file that your second brain can read.

Add a Code node in n8n to take the JSON response and map it into a Markdown template.

const data = JSON.parse($input.item.json.choices[0].message.content);
const date = new Date().toISOString().split('T')[0];

const markdown = `---
title: ${data.title}
date: ${date}
tags: [${data.tags.join(', ')}]
---

# ${data.title}

## Summary
${data.summary.map(item => `- ${item}`).join('\n')}

## Concepts
${data.connections.map(concept => `[[${concept}]]`).join(' ')}

---
## Raw Input
${$node["Telegram Trigger"].json.message.text}
`;

return { markdown: markdown, filename: `${data.title.replace(/[^a-z0-9]/gi, '-').toLowerCase()}.md` };

This step ensures every note has consistent YAML frontmatter, clean headings, and bidirectional link syntax ([[Concept]]) ready for Obsidian.

Step 5: Push to Your Vault

The final step is saving the file. If you use Obsidian and sync via a cloud drive (like Dropbox, Google Drive, or GitHub), use the corresponding n8n node to write the file directly to your Inbox folder.

If you use Notion, you'll use the Notion node to create a new database item, mapping the title, tags, and content fields directly.

{
  "parameters": {
    "operation": "upload",
    "fileId": "YOUR_DROPBOX_FOLDER_ID/Inbox/{{ $json.filename }}",
    "binaryData": true
  },
  "name": "Dropbox Upload",
  "type": "n8n-nodes-base.dropbox"
}

Watch out for: Always push new files to a designated "Inbox" or "To Review" folder in your vault, rather than dumping them straight into your main archive. This gives you a chance to review the AI's connections before they become permanent.

Tools Used (And Why Each One)

n8n — The orchestration engine that ties the whole workflow together. Chosen over Zapier or Make because it allows for complex branching, custom code execution, and has no artificial limits on steps per execution. Pricing: Free if self-hosted, cloud plans start at $24/month. Free alternative: You can run it entirely locally for zero cost.

Telegram — The capture interface used to send text and links on the go. Chosen over custom web apps or email because it is instant, works offline (queues messages), and has a robust bot API. Pricing: Free. Free alternative: None needed, it's completely free.

Hermes 3 (via OpenRouter) — The AI model used to process, summarize, and categorize the text. Chosen over GPT-4o because the hermes-3-llama-3.1-8b model is incredibly fast, highly capable of structured JSON output, and costs a fraction of a cent per request. Pricing: ~$0.10 per 1M tokens. Free alternative: You can run llama-3.1-8b locally using LM Studio or Ollama if you have the hardware.

Obsidian — The destination for the processed knowledge. Chosen over Notion because it uses local Markdown files, ensuring you truly own your data, and its graph view is unparalleled for visualizing the connections the AI generates. Pricing: Free for personal use. Free alternative: Logseq (open source).

Real-World Example: Alex's Story

Alex runs a boutique market research agency and was spending 10 hours a week just organizing clippings, reading industry newsletters, and trying to connect trends for client reports.

Before automating her second brain, her workflow was chaotic: she'd email links to herself, drop PDFs onto her desktop, and occasionally paste notes into a disorganized Notion page. When it came time to write a quarterly report, she would waste hours just trying to find the source of a statistic she vaguely remembered.

She set up this Hermes AI workflow on a Sunday afternoon. She connected a Telegram bot to n8n, configured Hermes to extract market trends, and routed the output directly to her Obsidian vault's "To Review" folder.

The first week, she forwarded 45 articles and reports to her bot while commuting. By Friday, instead of a massive reading backlog, she found 45 perfectly formatted notes. Each one had a 3-bullet summary, was tagged by industry, and automatically linked to broader concepts like "[[Consumer Behavior Shifts]]" or "[[Supply Chain Disruptions]]".

By month two, after tweaking the system prompt to explicitly look for "contradictory opinions," she was discovering connections she wouldn't have made manually.

Result: 10 hours of manual reading and organizing reduced to 1 hour of high-level review. She used the recovered 9 hours to take on an additional client, adding $4,000 to her monthly revenue.

Gotchas, Edge Cases, and Hard-Won Tips

Gotcha: If you don't restrict your Telegram bot, it will process messages from anyone who finds it. Always add an IF node right after the trigger to check that the sender's Chat ID matches your own. Ignoring this means paying API costs for spam and cluttering your vault with garbage.

Watch out: Web scraping can fail on paywalled or heavily JavaScript-rendered sites. If your HTTP Request node fails to pull the text, configure n8n to send an error message back to your Telegram bot so you know the link needs manual attention, rather than failing silently.

Tip: Don't let the AI create unlimited new tags. It will create a messy taxonomy (e.g., tagging one post "AI", another "artificial intelligence", and another "machine learning"). In your system prompt, provide a hardcoded list of 10-15 approved broad tags, and instruct the model to strictly choose from that list.

Tip: Keep the original raw text at the bottom of the Markdown file. LLMs occasionally hallucinate or miss a subtle nuance in their summaries. Having the original raw input appended at the bottom ensures you never lose the source material if you need to double-check the context.

What It Costs and What You Get Back

| Item | Before | After | |------|--------|-------| | Time on organizing notes | 10 hrs/week | 1 hr/week | | Infrastructure cost (n8n Cloud) | $0 | $24/month | | API cost (at ~100 articles/mo) | $0 | $1/month | | Net weekly time recovered | — | 9 hrs |

Valuing your time at $100/hr:

• Weekly value recovered: 9 hrs × $100 = $900/week
• Monthly infrastructure cost: $25
• Net monthly ROI: $3,575

Break-even: The first hour of the first day you use the system. If you self-host n8n and run a local model, your monthly cost drops to $0.

Start Building Today

Stop acting as the manual librarian for your own thoughts and let Hermes AI turn your scattered bookmarks into a connected knowledge graph.

Here's how to start in the next 60 minutes:

1. Sign up for a free OpenRouter account at openrouter.ai to access the Hermes 3 API.
2. Create a new Telegram bot via BotFather and save the token.
3. Open an n8n workspace (cloud or local) and drag in a Telegram trigger node.
4. Build the core loop: Telegram Trigger → Switch (URL/Text) → HTTP Request (Hermes AI) → Code (Markdown formatter).
5. Send your first test link to the bot and watch it appear in your note-taking app fully summarized.

You don't need a perfect tagging system to start—just get the data flowing and let the AI do the heavy lifting.

[related workflow: Setting Up Local LLMs for Private Note Processing]