Why AI Automation Matters Now
A year ago, I was spending 15+ hours a week on repetitive tasks — code reviews, email triage, data entry, bug reports. Fast forward to today, and I've cut that down to maybe 3 hours. The secret? A handful of AI automation workflows that run on autopilot.
These aren't enterprise SaaS tools with five-figure price tags. Everything here runs on commodity hardware (I use a Raspberry Pi 5) and open-source Python libraries. Here's what's actually working for me right now.
1. Automated Code Review First Pass
Before any PR lands on my desk, an AI agent runs through it and flags the obvious stuff — missing type hints, untested edge cases, SQL injection vectors, and hardcoded secrets. It posts a summary comment directly on the PR so I can skip straight to the architectural concerns.
How it works: A GitHub webhook triggers a Python script that feeds the diff into an LLM with a structured prompt. The output is a markdown checklist. Total cost: ~$0.02 per PR in API credits.
# Simplified webhook handler
import json, subprocess
def handle_pr_webhook(payload):
diff = payload["pull_request"]["diff_url"]
review = subprocess.run(
["ai-review", "--diff", diff, "--output", "markdown"],
capture_output=True, text=True
)
post_pr_comment(payload["pull_request"]["id"], review.stdout)
If you want the full agent toolkit — pre-built webhook handlers, review templates, and a local CLI — I packaged everything I use into the AI Agent Toolkit ($9 on LemonSqueezy). It includes the code review agent, a data pipeline builder, and a cron job manager.
2. Smart Email Triage with LLM Classification
My inbox used to be a black hole. Now a cron job runs every 15 minutes, classifies every new email into one of four buckets (urgent, reply-later, newsletter, spam), and moves them into corresponding labels. Urgent emails trigger a push notification to my phone.
Key trick: Use few-shot prompting with 3–5 examples of each category. It's dramatically more accurate than zero-shot, and the examples are small enough to fit in the system prompt.
3. Automated Bug Bounty Recon
Bug bounty hunting involves a lot of repetitive scanning — subdomain enumeration, port scanning, screenshotting, and technology fingerprinting. I wired all of these into a single pipeline that runs daily against my target list and produces a digest of new findings.
If you're interested in the full setup, I documented the entire workflow — tools, configs, and automation scripts — in the Bug Bounty Automation Kit ($15 on LemonSqueezy). It covers the recon pipeline, Nuclei template automation, and a Slack/Discord alerting system.
4. Raspberry Pi as a 24/7 AI Server
Most people think you need a GPU rig to run AI workloads. You don't. A Raspberry Pi 5 with 8GB RAM handles text-based LLM inference just fine for automation tasks. I run smaller quantized models locally via Ollama and only fall back to cloud APIs for heavy lifting.
What runs on the Pi:
- Email classifier (every 15 minutes)
- GitHub webhook receiver
- Daily bug bounty recon pipeline
- System health monitoring with AI-powered anomaly detection
Power consumption: ~7 watts. That's roughly $0.50/month in electricity for a 24/7 AI assistant.
5. One-Click Data Extraction with Structured Output
Scraping websites and extracting structured data used to mean writing XPath selectors and praying the site doesn't change. Now I paste raw HTML into an LLM with a JSON schema and get clean, typed data back instantly.
The trick: Use function calling / tool use mode with a strict JSON schema. This forces the model to output valid, parseable data every time — no regex cleanup needed.
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": f"Extract: {html}"}],
tools=[{
"type": "function",
"function": {
"name": "extract_products",
"parameters": {
"type": "object",
"properties": {
"products": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string"},
"price": {"type": "number"},
"url": {"type": "string"}
}
}
}
}
}
}
}]
)
The Bottom Line
AI automation isn't magic — it's just stitching together the right tools with a bit of Python. The five tricks above save me roughly 12 hours a week, and they all run on a $60 Raspberry Pi. If you want to skip the wiring and grab pre-built toolkits, check out the resources linked above. Otherwise, the building blocks are all open-source and waiting for you to connect them.
All the code and configs I use live at github.com/ulnit/agent-store — pull requests welcome.
Top comments (0)