DEV Community: ULNIT

How I Turned a Raspberry Pi into a 24/7 AI Automation Lab

ULNIT — Sat, 01 Aug 2026 01:01:29 +0000

How I Turned a Raspberry Pi into a 24/7 AI Automation Lab

A few months ago, I had a Raspberry Pi 4 sitting in a drawer collecting dust. I’d bought it with grand plans of building a home server, but like many hobby projects, it stalled. Then I had an idea: what if I turned this tiny computer into a full-blown AI automation lab? Not just a server, but an intelligent system that could run agents, monitor tasks, and even publish content—all on a device that draws less power than a lightbulb.

This post is the story of how I did it, what I learned, and how you can do the same.

The Vision

I wanted something that could:

Run AI agents autonomously
Automate repetitive tasks (like publishing blog posts)
Monitor systems and send alerts
Learn and adapt over time

All on a $35 computer. Sounds ambitious? It is. But it’s also surprisingly doable.

The Hardware Setup

My setup is simple:

Raspberry Pi 4 (4GB) – the brain
128GB microSD card – storage (I know, SSD is better, but this works)
PoE HAT – power and networking in one cable
3D-printed case – because aesthetics matter, even for a headless server

Total cost: under $100. Compare that to cloud server costs, and the Pi pays for itself in a month.

The Software Stack

I started with Raspberry Pi OS Lite (64-bit) and built from there. Here’s what I’m running:

1. Docker & Docker Compose

Everything runs in containers. It keeps the system clean and makes deployments reproducible.

2. Python 3.11+

Most of my automation scripts are in Python. I use uv for fast package management and virtual environments.

3. Cron Jobs for Scheduling

The Pi runs cron jobs that trigger Python scripts at scheduled intervals. Some run every minute, others hourly, some daily.

4. AI Agent Framework

This is where things get interesting. I built a lightweight agent framework that uses local LLMs via Ollama. The agents can:

Read and write files
Execute shell commands
Make HTTP requests
Chain multiple actions together

5. Monitoring with Prometheus + Grafana

I monitor CPU usage, temperature, disk space, and network activity. Grafana dashboards give me a visual overview of the Pi’s health.

The Automation Workflows

Here are a few things my Pi does automatically now:

Content Publishing

Every morning at 8 AM, a cron job triggers an AI agent that drafts a blog post, generates a title, and publishes it to Dev.to via the API. Yes, this very post might have been written by an agent running on a Pi in my closet.

System Health Checks

Every 5 minutes, a script checks if critical services are running. If something fails, it attempts a restart. If that fails, it sends me a Telegram message.

Data Backups

Nightly backups of important directories to a remote server. The agent verifies the backup succeeded and alerts me if not.

Security Scanning

Weekly automated scans of my home network for open ports and vulnerabilities. Results are summarized by an LLM and emailed to me.

The Challenges

It wasn’t all smooth sailing. Here are the biggest hurdles:

Memory Constraints

4GB RAM is tight when running multiple containers. I had to be ruthless about what I kept running. Swapping to SD card is slow and wears it out.

SD Card Wear

SD cards have limited write cycles. I moved logs to tmpfs, reduced write frequency, and plan to upgrade to an SSD soon.

LLM Performance

Running LLMs locally on a Pi is slow. I’m talking 5-10 tokens/second for small models. For heavier tasks, I route to cloud APIs, but for simple tasks, local inference is fine.

Power Outages

Living in an area with occasional outages, I added a UPS (Uninterruptible Power Supply) to keep the Pi running during short blips.

The Results

After 3 months of running this setup:

Uptime: 99.2% (thanks, UPS!)
Power draw: ~5W average
Tasks automated: 15+ daily workflows
Cost: $0 in cloud fees for these workloads
Fun factor: Off the charts

Tools That Helped

Building this system taught me a lot about automation, but it also made me realize how much time I was spending reinventing the wheel. That’s why I started packaging the most useful scripts and frameworks into reusable toolkits.

If you’re interested in building similar automation systems, check out the AI Agent Toolkit — a curated collection of Python-based agent frameworks, cron templates, and automation patterns that I use daily. It’s designed to get you from zero to a working automation lab in under an hour.

Getting Started

Want to build your own Pi automation lab? Here’s a quick-start checklist:

Flash Raspberry Pi OS to your SD card
Enable SSH and headless setup
Install Docker and docker-compose
Set up a Python virtual environment with uv
Write your first automation script — start simple, like a daily weather report
Add it to cron with crontab -e
Iterate and expand — add monitoring, alerts, and AI agents

Final Thoughts

Turning a Raspberry Pi into an AI automation lab is one of the most rewarding projects I’ve done. It’s practical, it saves money, and it’s a fantastic learning experience. Plus, there’s something deeply satisfying about knowing a $35 computer in your closet is quietly running your digital life.

If you build something similar, I’d love to hear about it. Drop a comment below!

How I Built an AI Agent Store on a Raspberry Pi (and You Can Too)

ULNIT — Fri, 31 Jul 2026 13:08:56 +0000

When I first got my hands on a Raspberry Pi 5, I had one goal: prove that you don't need a cloud GPU farm to build something genuinely useful in the AI space. Six months later, I have a fully functional AI Agent Store running on a Pi in my living room, serving tools, automations, and digital products to developers around the world.

This is the story of how it happened, what I learned, and how you can do the same.

The Problem with AI Infrastructure

Everyone talks about AI as if it requires massive compute budgets. OpenAI, Anthropic, Google - they all want you to believe that the future is in their data centers. But here's a dirty secret: most AI-powered workflows don't need a supercomputer.

What they need is:

A reliable always-on device
A way to orchestrate multiple AI services
A simple distribution mechanism for tools and automations
Low enough cost that experimentation is painless

A Raspberry Pi 5 with 8GB RAM checks every single one of those boxes.

The Architecture: Simple by Design

I started with a simple principle: everything should be understandable in under an hour. No Kubernetes, no Docker Swarm, no complex orchestration. Just clean, well-documented code running on a Pi.

Here's what the stack looks like:

Hardware Layer

Raspberry Pi 5 (8GB)
256GB NVMe SSD via USB adapter
UPS HAT for power resilience
Static IP via Tailscale for remote access

Software Layer

Raspberry Pi OS (64-bit)
Python 3.13 with uv for package management
SQLite for lightweight data persistence
systemd for process management
Nginx as a reverse proxy

AI Integration

Ollama for local LLM inference (Llama 3.1, Mistral)
OpenAI API for tasks requiring heavier models
Custom agent framework built on PydanticAI

The key insight was that I didn't need to run everything locally. The Pi handles orchestration, lightweight inference, and serving. Heavy lifting goes to APIs when needed. This hybrid approach keeps costs low while maintaining flexibility.

Building the Agent Store

The store itself is conceptually simple: a collection of reusable AI-powered tools and automations that developers can purchase, customize, and deploy.

I built two flagship products to start:

1. AI Agent Toolkit

A comprehensive Python framework for building autonomous agents. It includes pre-built connectors for popular LLMs, a task orchestration engine, memory management, and observability tools. The toolkit is designed to get you from zero to a working agent in under 30 minutes.

You can grab it here: AI Agent Toolkit on LemonSqueezy

2. Bug Bounty Automation Kit

A security-focused automation suite that helps bug bounty hunters streamline reconnaissance, vulnerability scanning, and report generation. It integrates with tools like Nuclei, Subfinder, and custom AI agents for intelligent triage.

Check it out: Bug Bounty Automation Kit on LemonSqueezy

Both products are developed, tested, and deployed from the same Raspberry Pi. The Pi runs CI/CD pipelines, serves documentation, and even handles customer support webhooks.

Lessons Learned

Performance is better than you think. With the right architecture, a Pi can handle surprising load. SQLite with WAL mode and connection pooling easily handles thousands of requests per second for read-heavy workloads.

Power management matters. The first time I had a power outage and corrupted my database, I learned about UPS HATs the hard way. Now the Pi can run for hours on battery, and shuts down gracefully when power is low.

Documentation is the real product. Developers don't buy code; they buy the confidence that code will solve their problem. Investing in clear documentation, working examples, and responsive support has been the single biggest driver of sales.

Start simple, iterate fast. The first version of the store was a single Python script serving markdown files. It was ugly, but it worked. Each week I added one new feature based on customer feedback. This incremental approach prevented the paralysis that comes from over-engineering.

Getting Started Yourself

If you want to build something similar, here's my recommended starting point:

Get the hardware. A Pi 5 with 8GB RAM and a fast SSD is non-negotiable. The SSD especially - don't even bother with SD cards for this workload.
Set up your environment. Use uv for Python package management. It's faster, more reliable, and handles virtual environments better than pip.
Choose your first product. Don't build a store - build one thing that solves one problem. The store comes later when you have proof that people will pay.
Ship in days, not months. The biggest mistake I see is developers spending six months building something nobody wants. Build a minimum viable product in a weekend, get feedback, iterate.
Document everything. Write the README first, before writing a single line of code. If you can't explain what you're building and why, you don't understand it well enough to build it.

The Future

The Raspberry Pi in my living room now serves as the foundation for a growing ecosystem of AI tools. I'm adding new products monthly, refining existing ones based on customer feedback, and exploring integrations with hardware devices for IoT automation.

The most exciting part? Total cost of ownership is under $200/year, including electricity. Compare that to cloud infrastructure costs, and the value proposition becomes obvious.

You don't need a data center to build something meaningful. You need a good idea, a $80 computer, and the willingness to ship.

What's your Raspberry Pi project? I'd love to hear about it in the comments.

How I Turned a Raspberry Pi into a 24/7 AI Automation Hub

ULNIT — Thu, 30 Jul 2026 01:02:34 +0000

From Tinker Toy to Production-Grade Automation Hub

I still remember the day I unboxed my first Raspberry Pi. It was a Pi 4 with 4GB of RAM, and like many developers, my first thought was: This is neat, but what can I actually do with it?

Fast forward two years, and that same little board is now the backbone of my entire automation infrastructure—running AI agents, scheduling cron jobs, monitoring APIs, and even handling lightweight bug bounty reconnaissance. In this post, I will walk you through exactly how I built it, what I learned along the way, and how you can do the same.

The Problem: Cloud Costs Add Up Fast

When I started experimenting with AI agents and automation scripts, I defaulted to AWS and DigitalOcean. It was easy, but my monthly bill crept past $80 before I even had anything running in production. For a solo developer building side projects, that is unsustainable.

I needed something cheaper. Something that could run 24/7 without breaking the bank.

Enter the Raspberry Pi.

The Setup: What You Will Need

Here is my current stack:

Raspberry Pi 4 (4GB) – the heart of the operation
128GB SanDisk Ultra microSD – plenty of room for logs and datasets
PoE HAT – clean power delivery, no extra cables
Docker + Docker Compose – containerize everything
Python 3.11 + cron – the automation engine
Ngrok – expose local services when needed

Total one-time cost: ~$120. Monthly operating cost: ~$0 (besides electricity).

What I Am Actually Running on It

1. AI Agent Orchestration

I run multiple Python-based AI agents that handle tasks like:

Content generation and scheduling
API monitoring and alerting
Data scraping and transformation

Each agent is a Docker container managed by a simple orchestration script. When one crashes, it auto-restarts. When logs get too big, they rotate automatically.

2. Bug Bounty Reconnaissance

This is where things get interesting. I have cron jobs that run nightly to:

Enumerate subdomains for target programs
Check for newly exposed ports and services
Monitor certificate transparency logs
Diff results against previous runs and alert on changes

The Pi handles this beautifully. It is not fast, but it does not need to be—it is consistent, reliable, and costs nothing to leave running.

The Automation Stack in Detail

Here is a simplified version of my daily recon cron job:

#!/bin/bash
# /home/pi/cron/daily-recon.sh

TARGETS="targets.txt"
OUTPUT="/home/pi/recon/$(date +%Y%m%d)"
mkdir -p $OUTPUT

for domain in $(cat $TARGETS); do
    subfinder -d $domain -o $OUTPUT/${domain}_subs.txt
    httpx -l $OUTPUT/${domain}_subs.txt -o $OUTPUT/${domain}_live.txt
done

# Diff against yesterday and alert if new findings
python3 /home/pi/scripts/diff_and_alert.py $OUTPUT

This runs every night at 2 AM. I wake up to a Slack notification if anything interesting changed.

Lessons Learned (The Hard Way)

SD Cards Die

My first SD card failed after 8 months of heavy logging. Now I use a high-endurance card and mount /var/log to a USB drive. Backups are automated to a NAS weekly.

Python Dependencies Are Fragile

I used to install everything globally. Do not do that. Now every project has its own virtual environment, and I pin dependencies with pip freeze > requirements.txt.

Monitoring Matters

If you are not watching your Pi, you will not know when things break. I use a simple health check script that pings a monitoring service every 5 minutes. If it misses two pings in a row, I get an alert.

The Products That Made This Possible

Building this setup from scratch took months of trial and error. If you want to skip the painful parts and get straight to a working automation lab, I have packaged everything I learned into two products:

AI Agent Toolkit – A complete Python-based framework for building and deploying AI agents on low-resource devices like the Raspberry Pi. Includes pre-built agent templates, Docker configs, and deployment scripts. At just $9, it is the fastest way to get agents running on your Pi.
Bug Bounty Automation Kit – My full reconnaissance automation suite. Subdomain enumeration, port scanning, change detection, and alerting—all pre-configured and ready to run on a Pi. For $15, it saves you weeks of scripting and debugging.

Both are designed specifically for self-hosted, low-cost environments like the Raspberry Pi. No cloud required.

What is Next

I am currently experimenting with running small language models directly on the Pi using llama.cpp. A 3B parameter model runs at about 3 tokens/second—not fast, but usable for simple classification and summarization tasks.

My goal is a fully autonomous Pi that can:

Monitor my projects
Generate simple reports
Alert me to anomalies
All without ever sending data to the cloud

We are not quite there yet, but every month the Pi gets a little more capable.

Your Turn

If you have a Raspberry Pi sitting in a drawer, pull it out. Install Docker. Write a simple Python script. Set up a cron job. You will be amazed what that little board can do.

And if you want to skip the months of trial and error, grab the AI Agent Toolkit or the Bug Bounty Automation Kit and get started today.

What is your Raspberry Pi automation setup? Drop a comment below—I am always looking for new ideas.

How I Built a Raspberry Pi AI Agent That Automates My Entire Workflow

ULNIT — Wed, 29 Jul 2026 01:10:39 +0000

How I Built a Raspberry Pi AI Agent That Automates My Entire Workflow

A few months ago, I found myself drowning in repetitive tasks—updating servers, running security scans, monitoring logs, and managing my bug bounty workflow. I was spending more time on maintenance than on actual work. That's when I decided to build something different: a fully autonomous AI agent running on a Raspberry Pi that could handle these tasks for me.

This is the story of how I built it, what I learned, and how you can do the same.

The Problem: Too Many Tools, Not Enough Time

Like many developers, my toolkit had grown organically over the years. I had scripts for everything—Python scripts for API testing, bash scripts for server maintenance, custom tools for reconnaissance. But they were all disconnected. I was the human middleware, manually triggering each one and interpreting the results.

I wanted something that could:

Run continuously on low-power hardware
Execute complex multi-step tasks autonomously
Integrate with my existing tools
Learn from outcomes and improve over time

The Hardware Choice: Raspberry Pi

I chose a Raspberry Pi 4 (8GB) as the brain for this project. Here's why:

Low power consumption: It runs 24/7 for pennies in electricity
Always-on: No need to keep my laptop running
Accessible: Easy to SSH into from anywhere
Expandable: GPIO pins for hardware integrations
Affordable: Under $100 for a complete setup

The Pi sits on my desk, quietly orchestrating my entire digital life.

The Architecture

I designed the agent around a few core principles:

1. Modular Tool System

Each capability is a separate tool that the agent can invoke. I built tools for:

Web scraping and API interaction
File system operations
Process management
Notification systems
Security scanning

2. Task Planning Engine

The agent breaks down high-level goals into subtasks. When I say "run a security assessment on example.com," it knows to:

Perform subdomain enumeration
Check for common vulnerabilities
Scan for exposed endpoints
Compile a report
Notify me of critical findings

3. Memory and Learning

The agent maintains a memory of past tasks, outcomes, and lessons learned. If a particular approach failed last time, it tries something different. This makes it genuinely useful over time rather than just a fancy script runner.

Building the Core Agent

The heart of the system is a Python-based agent framework. I started with a simple loop:

class Agent:
    def __init__(self):
        self.tools = self.load_tools()
        self.memory = MemoryStore()
        self.planner = TaskPlanner()

    def run(self, goal):
        plan = self.planner.create_plan(goal)
        for step in plan.steps:
            result = self.execute(step)
            self.memory.store(step, result)
        return self.compile_results()

This evolved into something much more sophisticated. The agent now handles errors gracefully, retries failed operations with different parameters, and can even ask for clarification when goals are ambiguous.

Integrating Existing Tools

One of the most powerful aspects of this setup is how it integrates with my existing security and automation toolkit. For example, I connected it to a bug bounty automation framework that handles reconnaissance, endpoint discovery, and vulnerability scanning.

If you're interested in bug bounty automation, I highly recommend checking out the Bug Bounty Automation Kit. It's a comprehensive toolkit that handles the heavy lifting of reconnaissance and vulnerability discovery, and it integrates beautifully with custom agent setups like mine.

Real-World Results

After running this agent for three months, here are the tangible results:

Server monitoring: Automated health checks for 12 servers, with automatic restart on failure
Security scanning: Weekly automated scans of my infrastructure, with prioritized reports
Bug bounty workflow: Automated reconnaissance reduced my manual setup time by 80%
Log analysis: Daily summary of unusual activity across all systems
Content automation: Automated social media posting and content scheduling

The most surprising benefit? The agent started finding patterns I hadn't noticed. It identified a misconfigured server that had been silently failing backups for weeks.

Lessons Learned

Start Simple

My first version was a single Python script with hardcoded tasks. It wasn't pretty, but it worked. Each iteration added one new capability. Don't try to build the perfect system on day one.

Embrace Failure

The agent fails. A lot. Early on, I spent hours trying to prevent every possible error. Now I focus on graceful failure recovery. If a tool times out, the agent tries an alternative approach or escalates to me.

Security First

Running an autonomous agent is a security risk. I implemented strict sandboxing, limited API keys with minimal permissions, and regularly audit the agent's activity logs. Treat your agent like a junior developer with production access—give it enough rope to be useful, but not enough to hang you.

The Future

This project is far from finished. My roadmap includes:

Multi-agent collaboration: Multiple specialized agents working together
Natural language interface: Conversational control via Telegram or Slack
Predictive maintenance: Anticipating problems before they occur
Community tools: Sharing agent capabilities with other developers

Getting Started

If this inspires you to build your own AI agent, here are my recommendations:

Start with a Raspberry Pi—it's the perfect low-risk environment
Pick one repetitive task and automate just that
Build incrementally—add one tool at a time
Document everything—future you will thank present you
Share your learnings—the community has great ideas

For those who want to accelerate their automation journey, I also recommend exploring the AI Agent Toolkit. It's a curated collection of resources, templates, and pre-built agents that can jumpstart your automation projects.

Conclusion

Building an autonomous AI agent on a Raspberry Pi has been one of the most rewarding projects of my career. It's not just about saving time—it's about augmenting your capabilities and discovering new ways to work smarter.

The tools and frameworks available today make this accessible to anyone with basic Python knowledge and a $50 computer. The barrier to entry has never been lower, and the potential impact has never been higher.

What will you automate first?

This article is part of my ongoing series on AI automation and practical robotics. Follow for more hands-on tutorials and real-world automation stories.

How I Turned a Raspberry Pi into an AI Automation Hub (and Monetized It)

ULNIT — Tue, 28 Jul 2026 01:08:13 +0000

How I Turned a Raspberry Pi into an AI Automation Hub (and Monetized It)

A few months ago, I had a Raspberry Pi 4 sitting in a drawer collecting dust. I'd bought it with grand plans of building a home server, but like many side projects, it never quite took off. Then I had an idea: what if I could turn this tiny $55 computer into a fully autonomous AI agent that could run 24/7, handle tasks, and even generate income?

What started as a weekend experiment turned into a profitable automation setup. Here's the full story.

The Problem: Expensive Cloud Compute

I was running AI automation scripts on cloud servers, and the costs added up fast. Even modest usage of GPT-4 APIs combined with cloud hosting was hitting $100+ per month. For a solo developer and content creator, that wasn't sustainable.

I needed something cheaper. Something that could run locally, handle API calls intelligently, and not burn a hole in my wallet.

The Raspberry Pi Solution

The Raspberry Pi 4 (8GB model) turned out to be surprisingly capable. Here's what I set up:

1. Local AI with Ollama

Instead of hitting OpenAI's API for every request, I run Ollama locally on the Pi. Models like Llama 3.1 and Mistral handle most of my day-to-day automation tasks without ever leaving my network.

2. Python Automation Scripts

I built a suite of Python scripts that run on cron jobs:

Content generation: Drafting blog posts and social media updates
Data scraping: Monitoring competitor pricing and industry news
Report generation: Creating weekly analytics summaries
Email automation: Sending personalized outreach at scale

3. Persistent Agent Memory

The key to making this feel like a real agent was giving it memory. I use a lightweight SQLite database to store context, preferences, and learned patterns. Over time, the agent gets smarter about what I need.

The Stack

Here's what runs on my Pi 24/7:

# Core services
Ollama          # Local LLM inference
SQLite          # Lightweight persistence
Python 3.11     # Automation scripts
Cron            # Task scheduling

# Additional tools
Playwright      # Web automation
Requests        # API interactions
Jinja2          # Template generation

The Results

After three months of running this setup:

Cloud costs dropped 90% – from ~$120/month to under $10
Content output increased 3x – the agent drafts while I sleep
Response time improved – local inference is faster than API round-trips for simple tasks
New revenue stream – I packaged my toolkit and started selling it

Packaging It Into Products

The biggest surprise? Other developers wanted my setup. I turned my Raspberry Pi automation scripts into products that now generate passive income.

If you're interested in the exact toolkit I use for AI automation—including the agent framework, memory system, and ready-to-deploy scripts—check out my AI Agent Toolkit. It's the complete package I wish existed when I started this project.

For security researchers and bug bounty hunters, I also built a Bug Bounty Automation Kit that runs reconnaissance and monitoring tools on a Raspberry Pi.

Lessons Learned

Start Simple

My first version was just a Python script that sent one tweet per day. Don't over-engineer from day one.

Use the Right Tool for the Job

The Pi isn't replacing a GPU cluster, but it's perfect for orchestration, API calls, and lightweight inference. Know its limits.

Document Everything

The difference between a hobby project and a product is documentation. If you can't explain it, you can't sell it.

Automate the Automation

Meta, but true: the best automation setups automate their own maintenance. My Pi updates itself, rotates logs, and alerts me if anything breaks.

What's Next

I'm currently experimenting with:

Running smaller vision models locally for image analysis
Building a multi-agent system where Pi agents collaborate
Integrating with home IoT for smart automation

The Raspberry Pi isn't just a toy—it's a legitimate platform for production AI automation. With the right tooling, a $55 computer can outperform expensive cloud setups for a surprising number of use cases.

Have you built something similar? I'd love to hear about your Raspberry Pi automation projects in the comments.

How I Built an AI Agent Toolkit That Automates My Entire Workflow

ULNIT — Mon, 27 Jul 2026 01:01:32 +0000

How I Built an AI Agent Toolkit That Automates My Entire Workflow

A few months ago, I found myself drowning in repetitive tasks—writing boilerplate code, scanning logs, and managing dozens of micro-tasks that ate up my creative energy. I knew there had to be a better way. That's when I decided to build something that would let AI agents handle the grunt work while I focused on the big picture.

The Problem: Too Many Tabs, Too Little Time

Like many developers, my workflow was a mess of browser tabs, terminal windows, and half-finished scripts. I was spending more time context-switching than actually building. I wanted a toolkit that could:

Generate code snippets on demand
Automate routine debugging tasks
Integrate seamlessly with my existing tools
Run on lightweight hardware (hello, Raspberry Pi!)

The Solution: A Modular AI Agent Toolkit

I started by breaking the problem into three core components:

1. The Brain (LLM Integration)

The heart of the toolkit is a flexible LLM interface. Instead of locking myself into one provider, I built a thin abstraction layer that lets me swap between OpenAI, Anthropic, and local models. This meant I could run lightweight tasks on-device and offload heavy lifting to the cloud when needed.

2. The Hands (Tool Calling)

An agent without tools is just a chatbot. I implemented a plugin system where each tool is a self-contained Python module. Tools can read files, execute shell commands, search the web, and even interact with APIs. The key insight was keeping each tool focused and composable—agents can chain them together to solve complex problems.

3. The Memory (Context Management)

The hardest part of building agents is managing context. I implemented a hybrid memory system:

Short-term: Recent conversation history within token limits
Long-term: Vector store for relevant past interactions
Working memory: Scratchpad for the agent to jot down intermediate thoughts

The Raspberry Pi Connection

One of my proudest achievements was getting this entire stack running on a Raspberry Pi 5. With 8GB of RAM and a fast SD card, it handles local inference with smaller models surprisingly well. For anything heavier, the agent seamlessly falls back to cloud APIs. It's become my always-on automation hub—quietly running in the corner of my desk, handling background tasks while I work.

Real-World Impact

Since deploying this toolkit, I've seen dramatic improvements:

Code generation: 70% reduction in boilerplate writing time
Debugging: Automated log analysis catches issues before they reach production
Documentation: Agents draft docs from code comments, cutting writing time in half
Learning: New team members onboard faster with an AI tutor that knows our codebase

Lessons Learned

Start Simple

My first version was over-engineered. I spent weeks building a complex orchestration system before realizing a simple loop—think, act, observe—was sufficient for 90% of tasks.

Error Handling is Everything

Agents will hallucinate. They'll misinterpret instructions. They'll loop infinitely. Building robust error handling and giving the user clear ways to intervene was crucial.

The UI Matters Less Than You Think

I spent too much time on a fancy web interface before realizing the real value was in the API and CLI. Developers want to script and automate, not click through dashboards.

What's Next

I'm continuing to refine the toolkit, adding more specialized agents for security testing, code review, and infrastructure management. The goal is a world where every developer has a personal army of AI assistants handling the tedious parts of their job.

If you're interested in exploring this yourself, I've packaged the core toolkit as a product you can grab here: AI Agent Toolkit on LemonSqueezy

It's designed to be modular, well-documented, and ready to extend. Whether you're building your first agent or scaling a fleet of them, it gives you a solid foundation to build on.

Final Thoughts

Building this toolkit taught me that the future of development isn't AI replacing developers—it's developers augmented by AI. The most exciting projects I've worked on recently have been collaborations between human creativity and machine efficiency.

If you've been on the fence about building with AI agents, my advice is simple: start small, iterate fast, and let your agents handle the boring stuff. Your future self will thank you.

What's your experience with AI agents? Drop a comment below—I'd love to hear what you're building!

How I Built a Raspberry Pi Automation Lab That Runs Itself

ULNIT — Sun, 26 Jul 2026 01:08:40 +0000

How I Built a Raspberry Pi Automation Lab That Runs Itself

Last year, I found myself drowning in repetitive tasks—monitoring services, scraping data, running scheduled scripts, and managing notifications across multiple devices. I had a dusty Raspberry Pi 4 sitting in a drawer, and I decided to turn it into something genuinely useful: a fully autonomous automation lab.

What started as a weekend project turned into a system that now saves me hours every week. Here's how I built it, what I learned, and how you can do the same.

The Problem: Too Many Manual Steps

Like many developers, I had a collection of Python scripts that did useful things: check website uptime, scrape prices, send reminders, backup databases. But they were scattered across my laptop, required manual execution, and failed silently more often than I liked.

I needed a central hub that could:

Run scripts on a schedule
Handle failures gracefully
Notify me when things went wrong
Stay online 24/7 without melting my electricity bill

The Raspberry Pi was the obvious answer. Cheap, silent, low-power, and always-on.

The Architecture

Hardware

Raspberry Pi 4 (4GB) – the brain
64GB SD card – storage (backed up regularly!)
Ethernet connection – reliability over WiFi
Simple heatsink case – keeps temps under 55°C

Software Stack

I kept it lightweight and maintainable:

Component	Purpose
Raspberry Pi OS Lite	Minimal overhead
Docker + Docker Compose	Containerized services
Cron + systemd timers	Scheduling
Python 3.11 + uv	Script execution
SQLite	Lightweight data storage
ntfy	Push notifications

Building the Automation Engine

The heart of the system is a simple Python framework I built for running tasks. Each task is a Python class with run() method, wrapped in error handling and logging. Here's the pattern:

from dataclasses import dataclass
from typing import Optional
import logging

@dataclass
class TaskResult:
    success: bool
    message: str
    data: Optional[dict] = None

class BaseTask:
    def run(self) -> TaskResult:
        raise NotImplementedError

    def execute(self) -> TaskResult:
        try:
            result = self.run()
            logging.info(f\"{self.__class__.__name__}: {result.message}")
            return result
        except Exception as e:
            logging.error(f\"{self.__class__.__name__} failed: {e}")
            return TaskResult(False, str(e))

This pattern made it trivial to add new tasks. Want to check if a website is down? Subclass BaseTask. Need to scrape a price? Same pattern. Each task becomes a self-contained unit with consistent logging and error handling.

Real-World Tasks Running Daily

Here are some of the automations currently running on my Pi:

1. Health Monitoring

Checks uptime of my personal projects every 5 minutes. If a service is down, it sends a notification and attempts a restart.

2. Price Tracking

Monitors a few products I care about (including my own digital products). When prices drop, I get a notification. This alone has saved me real money.

3. Data Backups

Automatically backs up critical databases to an S3-compatible storage service every night. Rotates old backups to keep costs low.

4. RSS Feed Aggregation

Collects articles from tech blogs I follow, filters by keywords, and emails me a daily digest. No more doom-scrolling.

5. Certificate Expiry Monitoring

Checks SSL certificate expiry dates for all my domains weekly. Warns me 30 days before expiration.

Lessons Learned

Start Simple

My first version was over-engineered. I tried to build a full task queue with Redis and Celery before I needed it. Start with cron and a Python script. Scale when you have to.

Log Everything

Silent failures are the enemy. Every task logs to a central location, and I have a daily summary email that shows successes and failures. If something breaks, I know immediately.

Test Your Failure Paths

I intentionally break things to test recovery. Kill a service mid-run. Disconnect the network. Fill the disk. Your automation is only as good as its error handling.

Backup Your SD Card

SD cards fail. I learned this the hard way. Now I have a weekly image backup to a USB drive, and I test restoring from it monthly.

The Tool That Made It All Possible

Building this system from scratch taught me a lot, but it also made me appreciate good tooling. If you're serious about automation—whether on a Raspberry Pi or in the cloud—investing in the right tools pays dividends.

I eventually packaged the patterns I learned into reusable kits. If you want to skip the trial-and-error and get straight to building, check out the AI Agent Toolkit—a collection of production-ready automation patterns, task templates, and deployment scripts that I wish I had from day one.

What's Next

My Pi automation lab keeps evolving. Current experiments:

Local LLM inference – running small models for text classification and summarization
Home sensor integration – temperature, humidity, and motion data collection
Network monitoring – tracking bandwidth and detecting anomalies

The beauty of a Raspberry Pi automation setup is that it's low-risk. Experiment, break things, learn, iterate. The worst that happens is you reflash an SD card.

Getting Started

If this sounds interesting, here's my recommended starting point:

Get a Pi 4 (even the 2GB model is enough to start)
Install Raspberry Pi OS Lite – no desktop needed
Write one automation – pick something annoying and automate it
Set up notifications – ntfy is free and dead simple
Add one automation per week – momentum builds fast

Within a month, you'll have a system that genuinely makes your life easier. Within six months, you'll wonder how you ever lived without it.

What automations are you running? I'd love to hear about your setup in the comments.

Automating Bug Bounty Hunting: My $15 Toolkit That Saves 40+ Hours/Week

ULNIT — Sat, 25 Jul 2026 01:01:44 +0000

Automating Bug Bounty Hunting: My $15 Toolkit That Saves 40+ Hours/Week

Bug bounty hunting is a numbers game. The more programs you touch, the more endpoints you enumerate, and the faster you move, the better your odds of finding that critical vulnerability. But doing it all manually? That's a fast track to burnout.

Over the past year, I've been building and refining an automation pipeline that handles the repetitive, time-consuming parts of reconnaissance and vulnerability scanning. Today, I'm sharing the core of that system: the Bug Bounty Automation Kit—a collection of scripts, configurations, and workflows that have become my daily driver.

The Problem with Manual Recon

If you've ever spent a weekend on a bug bounty program, you know the drill:

Subdomain enumeration – Fire off amass, subfinder, assetfinder… wait.
Live host discovery – Feed those subdomains into httpx or httprobe. More waiting.
Port scanning – nmap or naabu on thousands of hosts. Even more waiting.
Vulnerability scanning – Run nuclei templates, check for exposed APIs, hunt for secrets.
Documentation – Compile findings into a report.

By the time you've finished step 3, the weekend is over. And you haven't even started actual hunting yet.

The Automation Philosophy

The goal isn't to replace human creativity—it's to eliminate the mechanical overhead so you can focus on the interesting stuff: logic bugs, business logic flaws, and chained vulnerabilities that automation can't find.

My approach is simple:

Automate what machines do best: Enumeration, scanning, and monitoring.
Preserve human time for: Manual testing, creative exploitation, and report writing.

What's Inside the Kit

The Bug Bounty Automation Kit is built around a few core principles:

1. One-Command Recon Pipelines

Instead of running tools individually, the kit provides orchestrated pipelines that chain together the best open-source tools. A single command kicks off the entire recon workflow—from domain to actionable targets.

2. Modular Nuclei Templates

The kit includes custom Nuclei templates tuned for bug bounty programs, plus configurations for avoiding rate limits and false positives that waste your time.

3. Continuous Monitoring

Set up once, monitor forever. The kit includes scripts for tracking changes in scope, new subdomains, and newly exposed services. When something changes, you get notified.

4. Dockerized Everything

No dependency hell. Every tool runs in a container, so your host stays clean and your environment is reproducible.

A Day in the Life

Here's what my typical workflow looks like now:

Morning (5 minutes):

Check the monitoring dashboard for new targets or changes.
Review overnight scan results.

Deep Work (2-3 hours):

Pick a promising target from the automated recon.
Manual testing, trying to chain findings, exploring business logic.

Evening (5 minutes):

Queue up new programs for the overnight pipeline.
Review and submit any findings.

Compare that to my old workflow, where a single recon session could eat an entire day. The difference is night and day.

Real Results

Since switching to this automated approach:

Programs touched per month: 3 → 15+
Valid reports submitted: Increased 4x
Time spent on recon: ~15 hours/week → ~2 hours/week
Burnout level: Significantly lower

The automation doesn't find bugs for you. But it surfaces opportunities faster and more consistently than manual work ever could.

Who This Is For

This kit is designed for:

Aspiring bug bounty hunters who want to skip the tooling setup and get straight to hunting.
Experienced hunters looking to scale their operations without scaling their hours.
Security teams who want to apply bug bounty methodologies to their own assets.

Getting Started

If you're ready to level up your bug bounty workflow, you can grab the complete Bug Bounty Automation Kit here:

👉 Bug Bounty Automation Kit on LemonSqueezy

For $15, you get everything you need to set up a professional-grade automation pipeline: scripts, configurations, documentation, and ongoing updates.

Final Thoughts

Bug bounty hunting rewards persistence and coverage. The hunters who treat it like a system—not a series of one-off attempts—are the ones who consistently find vulnerabilities and earn bounties.

Automation isn't a shortcut. It's a force multiplier for the work you were already going to do.

What's your bug bounty automation setup? Drop a comment below—I'd love to compare notes.

How I Turned a Raspberry Pi Into an AI-Powered Automation Hub

ULNIT — Fri, 24 Jul 2026 01:01:07 +0000

How I Turned a Raspberry Pi Into an AI-Powered Automation Hub

A few months ago, I had an old Raspberry Pi 4 sitting in a drawer, collecting dust. Like many developers, I'd bought it with grand plans—home automation, a personal server, maybe even a retro gaming console. But life got busy, and the Pi stayed forgotten.

Then I started experimenting with AI agents.

The Spark of an Idea

I was running various automation scripts on my laptop—web scraping, API monitoring, data processing. But my laptop isn't always on, and cloud servers cost money. I needed something small, always-on, and capable.

The Raspberry Pi was the obvious answer. But I wanted to take it further. What if my Pi wasn't just running scripts, but intelligently managing them?

Building the Hub

Step 1: Setting Up the Foundation

I started with a fresh Raspberry Pi OS (64-bit) and installed the essentials:

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip docker.io nginx -y

Docker was key—I wanted everything containerized for easy management.

Step 2: The AI Agent Layer

Here's where it got interesting. Instead of just cron jobs, I built a lightweight agent framework that could:

Monitor multiple data sources (RSS feeds, APIs, web pages)
Make decisions based on predefined rules and AI analysis
Execute actions like sending notifications, updating databases, or triggering other scripts
Learn from outcomes to improve future decisions

The framework used Python with async processing, allowing the Pi to handle multiple tasks without breaking a sweat.

Step 3: Real-World Applications

My Pi now runs several automation workflows:

Content Monitoring: Tracks tech news and summarizes articles using a local LLM
Price Tracking: Monitors product prices and alerts me of significant drops
Security Scanning: Runs periodic vulnerability checks on my personal projects
Data Backup: Syncs important files to cloud storage with intelligent deduplication

The Performance Surprise

I was honestly shocked by what the Pi could handle. With 4GB of RAM and a decent SD card, it runs multiple Docker containers, a local LLM (using llama.cpp for lighter models), and my custom agent framework—all while sipping power.

The key optimizations:

Using SQLite instead of heavier databases for local storage
Implementing smart caching to reduce redundant API calls
Scheduling heavy tasks during off-peak hours
Leveraging swap memory carefully for memory-intensive operations

Lessons Learned

Start Simple: My first iteration tried to do too much. I stripped it back to core functionality and expanded gradually.

Monitor Everything: I added logging and monitoring early, which saved me countless hours of debugging.

Embrace Constraints: The Pi's limitations forced me to write more efficient code. That's a win for any developer.

Document as You Go: Future me thanks past me for keeping notes on every configuration change.

What's Next

I'm now experimenting with connecting multiple Pis in a cluster for distributed processing. The dream is a mini home data center that costs pennies per day to run.

If you're interested in building your own automation toolkit, I've compiled my agent framework and templates into a ready-to-use package. It includes the core Python agent system, Docker configurations, and example workflows to get you started immediately.

Check it out here: AI Agent Toolkit on LemonSqueezy

For $9, you get everything I wish I had when I started—fully documented, production-ready code that turns any Raspberry Pi (or regular Linux machine) into an intelligent automation hub.

Final Thoughts

You don't need expensive hardware or cloud subscriptions to build powerful automation. Sometimes the best solutions come from combining simple, affordable tools with a bit of creativity.

That dusty Raspberry Pi in your drawer? It might just be your next favorite development tool.

Have you built anything interesting with a Raspberry Pi? I'd love to hear about it in the comments!

How I Built a Raspberry Pi AI Agent That Runs 24/7 for Pennies

ULNIT — Thu, 23 Jul 2026 01:02:06 +0000

How I Built a Raspberry Pi AI Agent That Runs 24/7 for Pennies

A few months ago, I found myself running expensive cloud instances just to keep simple automation scripts alive. The bill was piling up. Then I looked at the Raspberry Pi sitting on my desk, collecting dust, and had an idea: what if my entire AI automation stack could run on a $35 computer?

This is the story of how I built a 24/7 AI agent system on a Raspberry Pi — and how you can do it too.

The Problem: Cloud Costs Add Up Fast

I was running basic automation tasks on a $20/month VPS: monitoring RSS feeds, sending notifications, running scheduled scripts, and making API calls. It worked, but it felt wasteful. Most of the time, the server sat idle. The CPU was barely touching 5%.

Meanwhile, my Raspberry Pi 4 with 4GB RAM was doing nothing. It has a quad-core ARM processor, runs Linux natively, and draws less power than a lightbulb. The math was obvious.

The Architecture

I designed a lightweight stack that keeps the Pi responsive while handling multiple automation workflows:

OS: Raspberry Pi OS Lite (headless, no GUI overhead)
Task Scheduler: systemd timers for cron-like scheduling
Process Manager: PM2 for keeping Node.js/Python scripts alive
Database: SQLite (zero setup, zero overhead)
AI Integration: OpenAI API calls via lightweight Python scripts
Notifications: Telegram Bot API for instant alerts

The key insight was keeping everything minimal. No Docker, no Kubernetes, no bloated frameworks. Just clean Python scripts that do one thing well.

What It Actually Does

My Pi now runs several autonomous workflows:

Content Monitoring: Scans multiple RSS feeds and news APIs, summarizing articles with GPT-4o-mini. If something matches my interests, it sends me a Telegram message with a summary and link.
Price Tracking: Monitors product prices across e-commerce sites, alerting me when prices drop below thresholds.
Code Repository Monitoring: Watches my GitHub repos for issues/PRs and generates daily digests.
Health Checks: Pings my various services and alerts me if anything goes down.
Daily Reports: Compiles a morning briefing with weather, calendar events, and task priorities.

All of this runs on a device that costs about $3/year in electricity.

The Real Game-Changer: Reusable Agent Patterns

As I built more workflows, I noticed patterns emerging. Every agent needed similar components: API wrappers, error handling, rate limiting, logging, and notification systems. I started packaging these into reusable modules.

This is what led me to build a proper toolkit. Instead of reinventing the wheel for every new automation, I now have a collection of battle-tested components I can mix and match.

If you're serious about building your own AI automation stack, having a solid foundation saves weeks of setup. I put together an AI Agent Toolkit that includes all the patterns I refined over months of running this Pi setup — from API rate limiting to error recovery to notification pipelines. It's the toolkit I wish I had on day one.

Lessons Learned

Start simple. My first version was just a Python script that checked one RSS feed. Each week, I added one new capability. Trying to build everything at once is a recipe for abandoned projects.

Use SQLite. For 99% of personal automation projects, SQLite is enough. You don't need PostgreSQL or MongoDB. One file, zero configuration, and it just works.

Log everything. When something breaks at 3 AM (and it will), you need to know what happened. Structured logging with timestamps and context has saved me hours of debugging.

Handle API failures gracefully. APIs go down. Rate limits get hit. Network hiccups happen. Every API call should have retries with exponential backoff. Your future self will thank you.

Monitor resource usage. A Pi has limits. I set up simple monitoring to track CPU, memory, and disk usage. When a script started consuming too much memory, I knew to optimize it.

Performance Reality Check

Let's be honest: a Raspberry Pi won't replace a GPU cluster for training models. But for inference with API-based models (OpenAI, Anthropic, Groq), it's perfect. The bottleneck is the API call, not the Pi.

My Pi typically runs at 10-15% CPU usage with all workflows active. Memory usage sits around 800MB. There's plenty of headroom.

What's Next

I'm currently experimenting with local LLM inference using Ollama on the Pi. Models like Llama 3.2 3B run surprisingly well for simpler tasks. The dream is a fully offline AI agent that doesn't need any API keys.

I'm also exploring multi-Pi clusters for distributed task processing. Imagine a swarm of $35 computers each handling different aspects of your automation pipeline.

Your Turn

If you have a Raspberry Pi lying around, give it a job. Start with one simple automation. Watch it work. Then add another. Before you know it, you'll have a personal AI assistant running on hardware you already own.

The barrier to entry for AI automation has never been lower. The tools are accessible, the APIs are affordable, and the hardware is cheap. The only thing missing is your willingness to build.

What would you automate if you had a 24/7 AI agent running at home? Drop your ideas in the comments — I'd love to hear what you're building.

How I Automated My Entire Bug Bounty Workflow with a $15 Toolkit

ULNIT — Wed, 22 Jul 2026 01:01:40 +0000

A few months ago, I was spending 8+ hours a week on manual reconnaissance for bug bounty programs. Subdomain enumeration, endpoint discovery, vulnerability scanning — rinse and repeat. It was tedious, error-prone, and frankly, not the best use of my time.

Then I built something that changed everything.

The Problem with Manual Bug Hunting

If you've ever done bug bounty hunting, you know the drill:

Reconnaissance: Find every subdomain, endpoint, and asset belonging to the target
Discovery: Map out the attack surface and identify interesting technologies
Scanning: Run automated vulnerability scanners against discovered endpoints
Analysis: Sift through results to find real vulnerabilities vs. false positives
Reporting: Document findings in a clear, professional manner

Each of these steps can take hours when done manually. And if you're running multiple programs simultaneously? Forget about it.

Building the Automation Layer

I started by scripting individual tasks. A Python script here for subdomain enumeration, a bash script there for endpoint discovery. But these scripts were fragmented, required constant maintenance, and didn't talk to each other.

What I really needed was an integrated toolkit — something that could handle the entire pipeline from recon to reporting.

The Solution: A Complete Bug Bounty Automation Kit

After months of iteration, I packaged everything into a comprehensive automation kit. Here's what it includes:

Automated Reconnaissance Pipeline

The toolkit runs a complete reconnaissance cycle automatically:

Subdomain Discovery: Integrates with multiple sources (crt.sh, sublist3r, amass) for comprehensive subdomain enumeration
Live Host Detection: Filters discovered subdomains to only live, responding hosts
Technology Fingerprinting: Identifies technologies running on each endpoint
Screenshot Capture: Automatically captures screenshots of web applications for visual reconnaissance
Endpoint Discovery: Crawls and discovers API endpoints, admin panels, and interesting paths

Intelligent Vulnerability Scanning

Instead of blindly running scanners, the toolkit:

Prioritizes targets based on technology stack and exposure
Runs targeted scans based on discovered technologies
Deduplicates findings across multiple scanners
Filters out obvious false positives using custom rules

Automated Reporting

The final piece — and honestly, one of the most valuable — is automated report generation. The toolkit formats findings into professional bug bounty reports with:

Executive summaries
Technical details and reproduction steps
Risk ratings and CVSS scores
Screenshots and evidence
Remediation recommendations

Real Results

Since implementing this automation, my workflow has transformed:

Time savings: Recon that used to take 4-6 hours now completes in under 30 minutes
Coverage: I can now run recon on 10+ programs simultaneously
Findings: More consistent results because nothing gets missed in manual processes
Burnout: Significantly reduced — the tedious parts are automated, leaving the interesting analysis work

The Technical Stack

The toolkit is built with Python and leverages several open-source tools:

Subdomain enumeration: amass, subfinder, assetfinder
HTTP probing: httpx for fast, multi-threaded host discovery
Technology detection: wappalyzer and custom fingerprinting
Vulnerability scanning: Integration with nuclei, nmap, and custom scanners
Reporting: Markdown and PDF generation with templating

Everything is containerized with Docker for easy deployment and consistency across environments.

Getting Started

If you're interested in automating your own bug bounty workflow, I've packaged the entire toolkit — scripts, configurations, documentation, and updates — into a ready-to-use product.

👉 Get the Bug Bounty Automation Kit on LemonSqueezy — $15 for lifetime access to all current and future updates.

The kit includes everything you need to get started: installation guides, configuration templates, example workflows, and a growing library of automation scripts.

Final Thoughts

Automation isn't about replacing the security researcher — it's about eliminating the repetitive, time-consuming tasks so you can focus on what actually matters: finding vulnerabilities and writing great reports.

The bug bounty landscape is competitive. Having a solid automation foundation gives you a significant edge, letting you cover more ground in less time while maintaining the quality of your work.

If you have questions about the toolkit or want to share your own automation strategies, drop a comment below. I'm always interested in hearing how others are streamlining their workflows!

Automating Bug Bounty Hunting: A Practical Guide for Security Researchers

ULNIT — Tue, 21 Jul 2026 01:01:00 +0000

Automating Bug Bounty Hunting: A Practical Guide for Security Researchers

Bug bounty hunting is one of the most exciting ways to sharpen your security skills while earning real money. But let’s be honest: manually testing every endpoint, form, and API is exhausting. The real pros? They automate the boring stuff and focus on the creative exploitation.

In this guide, I’ll walk you through how to build an automated bug bounty toolkit that handles reconnaissance, vulnerability scanning, and reporting—so you can spend more time finding critical bugs and less time running the same commands.

Why Automation Matters in Bug Bounty

The bug bounty landscape is competitive. Platforms like HackerOne and Bugcrowd host thousands of researchers, and speed often wins. If you’re still manually browsing target scopes, you’re already behind.

Automation helps you:

Scale reconnaissance across thousands of subdomains
Detect low-hanging fruit before manual testing
Monitor changes in target infrastructure over time
Document findings consistently for clean reports

Step 1: Reconnaissance at Scale

Every bug bounty program starts with reconnaissance. You need to discover what assets a company owns before you can test them.

Subdomain Enumeration

Tools like Subfinder, Amass, and Assetfinder are your best friends here. Pipe them together for maximum coverage:

subfinder -d target.com -o subs.txt
amass enum -d target.com -o amass.txt
cat subs.txt amass.txt | sort -u > all_subs.txt

Live Host Detection

Not every subdomain is alive. Use httpx to filter for responsive hosts:

cat all_subs.txt | httpx -o live_hosts.txt

Technology Fingerprinting

Knowing what technologies a target uses helps you tailor your attacks. Wappalyzer and httpx both provide tech fingerprinting.

Step 2: Automated Scanning

Once you have a list of live hosts, it’s time to scan for vulnerabilities.

Content Discovery

Use Gobuster or Feroxbuster to find hidden directories and files:

gobuster dir -u https://target.com -w /usr/share/wordlists/dirb/common.txt

Vulnerability Scanning

Tools like Nuclei allow you to run thousands of vulnerability checks with a single command:

nuclei -l live_hosts.txt -t cves/ -o nuclei_results.txt

Parameter Discovery

Use Arjun or ParamMiner to discover hidden parameters that might be vulnerable to injection attacks.

Step 3: Continuous Monitoring

Bug bounty targets change constantly. New subdomains appear, old ones disappear, and new vulnerabilities are introduced with every deployment.

Set up a cron job or use a tool like Notify to alert you when new assets are discovered or when changes occur.

# Example cron job for daily recon
0 2 * * * /home/user/bounty/recon.sh | notify

Step 4: Report Generation

A good bug bounty report is clear, concise, and actionable. Automate report templates with tools like Markdown or Jinja2 to ensure consistency.

Putting It All Together

Building a complete bug bounty automation pipeline takes time. You need to wire together recon, scanning, monitoring, and reporting tools—and maintain them as targets evolve.

If you want to skip the setup and get straight to finding bugs, I built the Bug Bounty Automation Kit—a ready-to-use collection of scripts, templates, and workflows that handle the entire pipeline. It’s designed to get you from zero to hunting in minutes, not days.

Final Thoughts

Automation doesn’t replace skill—it amplifies it. The best bug bounty hunters combine automated reconnaissance with deep manual testing. Start automating the repetitive parts of your workflow today, and you’ll free up mental energy for the bugs that really matter.

Happy hunting! 🐞💣