Hermes Agent gets smarter every time you use it. Here's how to turn that into $3,000 a month.

Most AI agents forget everything the moment you close the tab.

Next session, back to zero. You explain context again. It starts over. Every time.

Hermes works differently. It saves what it learns. Every task you give it, it writes the procedure to a file on your disk. Next time you run something similar, it finds that file and uses it. A month in, your Hermes has 30-50 of these skills sitting in a folder. It gets faster. It gets more accurate. It compounds.

I set it up on a regular laptop. No special hardware. Took about 30 minutes. First week I closed three clients at $300-400 each doing competitor research reports. Actual work per report: 15 minutes.

Here's the full setup.

What Hermes is

Open-source agent framework from Nous Research. 140,000 GitHub stars in three months. Most-used agent on OpenRouter right now. NVIDIA featured it in a May blog post running on their new DGX Spark workstation.

You don't need that hardware. A MacBook with 16GB RAM works. So does any Windows machine with a mid-range GPU.

Three folders on your disk do all the work:

~/.hermes/memory/     your preferences, projects, patterns
~/.hermes/sessions/   indexed history of everything
~/.hermes/skills/     learned workflows saved as .md files

That skills folder is the whole point. Agents with 20+ self-created skills complete similar tasks 40% faster than a fresh instance. Not better output. Less time to get the same result.

The service

Competitor research reports for early-stage startups and small SaaS companies.

A founder wants to know what their three main competitors are doing. Pricing, positioning, what customers hate about them, where the gaps are. Normally that's 3-4 hours of work for someone. I charged $300 and delivered same day.

Hermes does the actual research in 15 minutes.

What most people pay:

Service                          Cost
─────────────────────────────────────
Freelance analyst                $150-300
Research firm (minimum)          $500-2000
DIY                              3-4 hours of your time

What this costs:

Tool                             Cost
─────────────────────────────────────
Hermes Agent                     $0
Ollama                           $0
Qwen 3.6 27B model               $0
Your laptop                      $0
Electricity                      ~$2/month
─────────────────────────────────────
Total                            $0-2/month

Setup (30 minutes)

Step 1. Local model server

Go to [lmstudio.ai](//lmstudio.ai). Download and install it.

Open LM Studio, go to the Discover tab, search Qwen 3.6 27B. Pick Q4 quantization. Download takes 10-15 minutes.

After that: Developer tab, load the model, enable "Serve on Network" in settings, hit Start Server. Runs on:

http://localhost:1234

Open that URL in your browser. If you see JSON, it's working.

If you prefer terminal, use Ollama:

ollama pull qwen3.6
export OLLAMA_HOST=0.0.0.0
ollama run qwen3.6 -c 65536

That -c 65536 flag is not optional. Ollama defaults to 4K context. Hermes needs 64K. Skip it and nothing runs.

Step 2. Install Hermes

bash scripts/install.sh

source ~/.bashrc

hermes --version

Get the install script from: [github.com/NousResearch/hermes-agent](//github.com/NousResearch/hermes-agent)

Windows users run this inside WSL2.

Step 3. Connect to your model

hermes model

Pick "Custom endpoint" from the menu.

URL:        http://localhost:1234/v1      (LM Studio)
            http://localhost:11434/v1    (Ollama)
API Key:    leave blank, press Enter
Model name: exact filename from LM Studio, or "qwen3.6" for Ollama

If you get "Model context too small" at startup, go back to your model server and set context to 65536. This is the most common problem. Fix is always on the model server side.

Step 4. First session

hermes

Paste this as your first task:

Research three competitors for a project management tool targeting
freelancers. For each: positioning, pricing, top customer complaints
from reviews, one gap in their offering. Save this as a skill so we
can reuse the process next time.

Hermes breaks it into subtasks, searches, writes the report, saves the procedure to ~/.hermes/skills/. Next research task runs faster because the skill is already there.

Type /exit when done.

Step 5. Check it worked

ls ~/.hermes/skills/

You should see .md files. Open one. It's a structured workflow with steps and notes. That's Hermes learning.

Empty folder means the install didn't finish. Re-run the script.

Telegram gateway

hermes gateway

Pick Telegram. Go to @BotFather, create a new bot, paste the token.

Now you can text your agent from your phone while the laptop runs at home. Changes how it feels completely.

Finding clients

Three places that worked week one:

Upwork. Search "competitor analysis" or "market research." Filter by last 7 days. Send 10-15 short messages per day. Offer to send a sample report. Build the sample with Hermes before you have any clients.

X/Twitter. Search "anyone know" + "competitor research." Founders post this constantly. Reply, offer a sample, don't pitch.

Cold email. Go to Product Hunt, filter launches from last 30 days. Email the founder directly. One sentence, link to sample. Subject: "quick competitor research for \[product name\]."

First client usually comes in 3-5 days if you're sending enough messages.

The math

Week 1
─────────────────────────────────────
Setup                        2 hours
Outreach per day             1 hour
Reports delivered            3
Revenue                      $900-1,200
Work per report              15-20 min

Month 1
─────────────────────────────────────
Reports sold                 10-15
Revenue                      $3,000-4,500
Retainers started            2-3
Monthly recurring added      $600-900

Month 3
─────────────────────────────────────
Skills in ~/.hermes/skills/  30+
Time per report              10 min
Retainer clients             6-8
Monthly recurring            $1,800-2,400
One-off reports              $1,500-2,000
Total                        $3,300-4,400/month

Common problems

"Model context too small" at startup. Set context to 65536 on your model server. This is 80% of all setup issues.

Hermes is slow. Drop from 35B to 27B model, or Q6 to Q4 quantization. CPU-only means 2-3 minutes per response. Get a GPU or use the cloud API.

Hermes forgets between sessions. Check ~/.hermes/ has files. If empty, re-run the install.

WSL2 can't reach the model server. Enable mirrored networking in WSL settings on Windows 11 22H2+. Or run the model server inside WSL2 instead.

Full tool stack

Tool              Purpose                Cost
────────────────────────────────────────────
Hermes Agent      agent framework        free
                  github.com/NousResearch/hermes-agent

LM Studio         local model server     free
                  lmstudio.ai

Qwen 3.6 27B      the model              free
                  via LM Studio or ollama.com

Stripe            payments               2.9% + 30c

Startup cost: $0. Time to first client: one week.

After every delivered report, ask two things. First, a review. Second, one founder they know who might need this.

Founders know founders. By month two referrals replace most of the cold outreach.

The skills folder fills up. Work gets faster. Margin gets better.

Build one report before you have a client. Send it as a sample to 10 people tomorrow.

more setups like this every week. [t.me/GipArcAI](//t.me/GipArcAI)