# I Built a Local AI Discount Monitor with n8n, Ollama, and Telegram

> I built a local AI workflow with n8n, Ollama, and Telegram to monitor GCP cert discounts daily, here's every mistake I made and how I fixed them.

Canonical: https://nosistech.com/n8n-ollama-gcp-cert-discount-monitor/
Published: 2026-04-20
Last updated: 2026-10-26
Author: Carlos Paredes, NosisTech LLC

If you're training your skills like I am, Google Cloud, you know the frustration: exam discounts appear without warning, last a few days, and vanish before you even find out they existed.

I got tired of manually checking the Google Cloud blog every day, so I automated it, using n8n, a local Ollama model, and Telegram. The whole thing runs on my Predator laptop, costs nothing, and now pings me automatically every morning.

---

## What I Built and Why

**The goal:** Monitor the Google Cloud training and certifications blog daily. If a discount, promo code, or voucher appears, send a Telegram message instantly. If not, send a clean "NO DISCOUNT FOUND" confirmation so I know the system is running.

**Why n8n?** It's the automation backbone of my AI Builder Roadmap, open source, self-hostable, and powerful enough to handle complex multi-node pipelines.

**Why Ollama locally?** I run a full local AI lab on the Predator (RTX 5070 Ti, 32GB DDR5). Using a local model means zero API costs, full privacy, and no rate limits. The model never leaves my machine.

**Why Telegram?** It's instant, reliable, and I already have a bot set up. One message, one glance, done.

---

## The Final Workflow Architecture

```
Schedule Trigger (9am daily)
    ↓
HTTP Request → GET https://cloud.google.com/blog/topics/training-certifications
    ↓
Code in JavaScript → Strip HTML, truncate to 3000 characters
    ↓
Message a Model → Ollama (qgemma3:12b) — classify: discount or not
    ↓
Send a Text Message → Telegram @bot
```

Five nodes. Runs in under 7 seconds. Fully local except for the HTTP request.

---

## Building It: Step by Step

### Step 1 — The Schedule Trigger

Set to fire daily at 9am. Straightforward — n8n's Schedule Trigger node handles this natively.

### Step 2 — HTTP Request Node

Fetches the raw HTML of the Google Cloud certifications blog page. No authentication needed, it's a public page. This node worked first try.

### Step 3 — The Code Node (Critical Fix)

This is where the first major problem appeared.

**The mistake:** Originally, the raw HTML from the HTTP Request went directly into Ollama. A full webpage is 50,000–100,000+ characters of HTML tags, scripts, navigation menus, and noise. Ollama was receiving all of it and timing out after 5 minutes trying to process it.

**The fix:** I added a JavaScript Code node between HTTP Request and Ollama. It strips all HTML tags using a regex replace and truncates the result to 3,000 characters — just the readable text content Ollama actually needs.

javascript

```
const html = $input.first().json.data;
const text = html.replace(/<[^>]*>/g, ' ').replace(/\s+/g, ' ').trim().substring(0, 3000);
return [{ json: { text } }];
```

This single addition dropped execution time from 5+ minutes (timeout) to under 30 seconds. Night and day.

### Step 4 — Ollama Node

Connects to local Ollama at `http://localhost:11434`. This node sends the stripped text to the model with a classification prompt.

**Model selection journey — and the mistakes:**

I started with `qwen36-tuned:lates`t, a large 35b model. Overkill for a task this simple. After the Code node fix brought time down to ~30 seconds, I switched to `qgemma3:12b`, the lightest model in my local library. Result: **6.8 seconds**. A 10x improvement over the post-fix qwen36 time.

**Lesson:** Match model size to task complexity. A binary classifier that outputs one line does not need a 35b parameter model.

**The prompt mistake:** My first prompt was too open-ended — something like "analyze this page for certification discounts." The model ignored the scraped content entirely and generated a long generic essay about how to find GCP discounts, sourced from its training data. Classic hallucination when the instruction doesn't constrain the output format.

**The fix:** A strict, format-enforcing prompt:

```
You are a strict text classifier. Your ONLY job: check if the text below contains
certification exam discounts, promo codes, or vouchers.

Reply with EXACTLY one of these two responses and nothing else:
- DISCOUNT FOUND: [copy the exact discount text]
- NO DISCOUNT FOUND

Do not explain. Do not add context. Do not use markdown. One line only.

---
{{ $json.text }}
```

The `---` separator between instruction and content helps the model distinguish the two. After this fix, the output was clean and deterministic.

**The System role mistake:** I tried to add a separate System message in the Ollama node (following standard LLM best practices). The n8n Ollama node only supports User and Assistant roles — no System role. The workaround is to prepend the system instruction directly into the User message, which works just as well for local models.

### Step 5 — Telegram Node

**The field name mistake:** I set the Text field to `{{ $json.response }}` — which seemed logical. But Ollama's n8n node outputs the result under `content`, not `response`. The Telegram messages were showing "undefined" until I corrected it to `{{ $json.content }}`.

**Lesson:** Always check the actual output JSON of a node before wiring it to the next one. Click the Output tab, look at the field names, then write your expressions.

**The wrong node mistake:** I also tried to put a formatted Telegram message template (with date and source URL) inside the Ollama node's Content field. `$json.content` resolved as `[undefined]` there because the Ollama node doesn't have access to its own output yet — it's still generating it. The template belongs in the Telegram node, which receives Ollama's output as its input.

**Lesson:** Expressions like `$json.fieldName` reference the *incoming* data to that node, not the current node's own output.

---

## Making n8n Survive Reboots - NSSM

n8n was running in a PowerShell window. That means closing the window or rebooting the laptop kills the workflow. Not acceptable for a daily monitor.

**The fix:** NSSM (Non-Sucking Service Manager) — a Windows utility that wraps any executable as a proper Windows service that starts automatically on boot.

powershell

```
# NSSM was already installed
nssm install n8n
# GUI opens — fill in node.exe path, startup directory, and n8n arguments
nssm start n8n
nssm status n8n
# Returns: SERVICE_RUNNING
```

After this, the PowerShell window was closed and n8n continued running. The workflow will now fire at 9am even after a reboot.

---

## Results

| Metric | Before | After |
| --- | --- | --- |
| Execution time | 5+ min (timeout) | 6.8 seconds |
| Telegram output | "undefined" / hallucinated essay | Clean one-line result |
| Uptime dependency | PowerShell window open | Windows service (always on) |
| Model used | qwen36-tuned:latest (35b) | qgemma3:12b |

---

## What's Next

This is v1.0. Planned improvements:

- **Multiple sources:** Add HTTP Request nodes for the GEAR program page and Google Cloud Next announcements — same Ollama classifier, more coverage
- **Conditional alerting:** Only send a Telegram message when `DISCOUNT FOUND` is detected — silence on clean days
- **n8n to Claude/Gemini memory sync**

---

## Key Takeaways

1. **Strip HTML before sending to a local model.** Raw HTML will time out even fast models. A simple regex Code node is all you need.
2. **Match model size to task complexity.** A 12b model outperforms a 35b model when the task is simple and speed matters.
3. **Constrain your prompts strictly.** Vague instructions produce hallucinated essays. Format-enforcing prompts produce one clean line.
4. **Check field names in node output before writing expressions.** `$json.response` vs `$json.content` — one works, one returns undefined.
5. **n8n expressions reference incoming data, not current node output.** Wire your templates to the right node.
6. **Install n8n as a Windows service from day one.** NSSM takes 5 minutes and saves you from broken workflows after every reboot.

### Keep reading: n8n automations

Next article: [From Laptop to Cloud: Migrated My Discount Monitor to a VPS](https://nosistech.com/gcp-discount-monitor-vps-migration/)

All articles: [NosisTech Writing](https://nosistech.com/blog/)