DEV Community: ULNIT

I Turned My Raspberry Pi Into an AI Agent Factory — Here's How

ULNIT — Fri, 10 Jul 2026 01:02:32 +0000

I Turned My Raspberry Pi Into an AI Agent Factory — Here's How

Last month, I had a Raspberry Pi 4 sitting in a drawer collecting dust. Today, it's running three autonomous AI agents, handling my bug bounty recon, and generating passive income. Here's the story of how I built an AI agent toolkit on a $55 computer — and how you can do the same.

The Problem: Expensive Cloud Agents

Like many developers, I was fascinated by AI agents but intimidated by the infrastructure costs. Running agents in the cloud meant monthly bills that rivaled my rent. I needed a solution that was:

Affordable (under $100 total)
Always-on (24/7 operation)
Powerful enough for real automation tasks

The Raspberry Pi checked every box.

The Build: From Zero to Agent Factory

Step 1: The Foundation

I started with a Raspberry Pi 4 (8GB) running Raspberry Pi OS Lite. No GUI, no bloat — just a lean, headless Linux machine ready for automation.

Step 2: The Agent Stack

I built a modular toolkit with these components:

Task Orchestrator: Python scripts using asyncio and aiohttp for concurrent agent execution
Memory Layer: SQLite for agent state and Redis for caching
API Integrations: OpenAI, Anthropic, and local LLMs via Ollama
Monitoring: A simple Flask dashboard showing agent status

The key insight? Agents don't need massive GPUs. Most tasks — web scraping, API calls, data processing — are CPU-light and I/O-bound. The Pi handles them beautifully.

Step 3: Real-World Deployment

My first production agent was a bug bounty reconnaissance bot. It runs nightly, scanning target domains, checking for subdomain changes, and alerting me to new attack surfaces. What used to take hours of manual work now happens while I sleep.

I documented the entire automation framework in a product I call the Bug Bounty Automation Kit — a collection of scripts, configs, and templates that turn any Raspberry Pi (or VPS) into a bug bounty hunting machine.

Performance: Surprisingly Capable

Here's what my Pi agent factory handles daily:

Task	Frequency	Duration
Subdomain enumeration	Every 6 hours	~15 min
Port scanning	Daily	~45 min
Content change detection	Every 2 hours	~5 min
Report generation	On-demand	~2 min
API health checks	Every 10 min	<1 min

CPU usage rarely exceeds 30%. The Pi stays cool, quiet, and costs pennies per month in electricity.

Lessons Learned

1. Start Simple, Scale Smart

My first agent was a 50-line Python script. It checked one website for changes and emailed me. That simplicity taught me more about agent architecture than any tutorial.

2. Error Handling is Everything

Agents fail. APIs timeout. Websites change. Building robust retry logic and graceful degradation saved me countless hours of debugging.

3. The Pi is a Gateway Drug

Once you have one always-on Linux box, you find uses for it everywhere. I now run a local Git server, a home automation hub, and a personal API gateway on the same device.

The Bigger Picture: Democratizing AI Automation

The most exciting part of this project isn't the technology — it's the accessibility. Anyone with a $55 computer and an internet connection can build production-grade AI agents. You don't need a CS degree, a cloud budget, or a team of DevOps engineers.

This democratization is why I believe the next wave of AI innovation will come from hobbyists, tinkerers, and indie hackers — not just big tech.

Get Started Today

If this story resonated with you, I put together everything I learned into two products:

Bug Bounty Automation Kit ($15) — Complete recon automation for security researchers and bug bounty hunters
AI Agent Toolkit ($9) — Modular agent building blocks for any automation project

Both are designed to run on minimal hardware — because great automation shouldn't require great infrastructure.

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

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

ULNIT — Thu, 09 Jul 2026 01:01:29 +0000

The Humble Pi That Changed My Workflow

I still remember the day I plugged in my first Raspberry Pi. It was a Pi 4 with 4GB of RAM, sitting on my desk like a tiny, silent promise of what was possible. I had no idea that within a few months, this $55 computer would become the backbone of my entire automation stack.

If you have ever felt overwhelmed by cloud bills or the complexity of deploying AI tools, this story is for you.

Why the Raspberry Pi?

The Raspberry Pi is not just a toy for hobbyists. It is a full-fledged Linux computer that draws less power than a lightbulb and fits in the palm of your hand. For me, the appeal was simple:

Always-on: Unlike my laptop, the Pi never sleeps.
Low cost: No monthly cloud subscription for a development environment.
Real hardware: Running code on a physical machine forces you to understand networking, services, and system limits.
Silent: It sits under my desk and I never hear it.

My Automation Stack

Here is what I run on a single Raspberry Pi 4:

Home Assistant: Controlling lights, sensors, and automating my apartment.
Docker: Containerizing every service so I can spin up and tear down experiments in seconds.
Python scripts: Scheduled via cron, these handle everything from RSS aggregation to API polling.
AI agents: Local LLM inference via Ollama, running models like Llama 3 and Mistral.
Reverse proxy: Nginx with Tailscale for secure remote access without opening ports.

The Breakthrough Moment

The real magic happened when I started chaining these tools together. I built a cron job that runs every morning, pulls the latest AI research papers from arXiv, summarizes them with a local LLM, and posts a digest to a private Slack channel. All on a device smaller than a deck of cards.

Then I went further. I built a bug bounty automation pipeline that monitors HackerOne and Bugcrowd for new programs, scores them by scope and bounty potential, and alerts me in real time. This is where things got serious.

The Problem with DIY

Here is the truth: building these pipelines from scratch is fun, but it is also time-consuming. Every script needs error handling, rate limiting, notification logic, and maintenance. After building my third custom automation, I realized I was spending more time maintaining infrastructure than actually hunting bugs or experimenting with AI.

That is when I started packaging my best work into reusable toolkits.

What I Built Next

I took the most effective automation I had built and turned it into a product. The Bug Bounty Automation Kit is the result of months of real-world use on my Raspberry Pi. It includes:

Pre-built reconnaissance pipelines
Automated subdomain enumeration and monitoring
Rate-limiting and error handling out of the box
Slack and Discord webhook integrations
A modular design so you can add your own tools

If you are serious about bug bounty hunting or just want to see how a production-ready automation stack looks, you can grab it here:

Bug Bounty Automation Kit on LemonSqueezy

Tips for Your Own Pi Lab

If you want to build something similar, here are my hard-earned lessons:

Use an SSD: SD cards are slow and unreliable for 24/7 workloads. A cheap USB SSD will save you hours of frustration.
Docker everything: If it is not in a container, it will break when you need it most.
Log rotation: logrotate is your friend. Disk space on a Pi is precious.
Monitor temperature: The Pi 4 can throttle under load. A simple heatsink case solves this.
Version control your configs: Keep your Docker Compose files and scripts in Git. You will thank yourself later.

Final Thoughts

The Raspberry Pi taught me that you do not need expensive hardware or cloud credits to build powerful automation. What you need is curiosity, persistence, and a willingness to iterate. Start small, automate one thing at a time, and before you know it, you will have a lab that rivals enterprise setups.

If you are already running a Pi lab, I would love to hear what you are building. Drop a comment below.

This article is based on real experiments running on a Raspberry Pi 4 under my desk. No cloud instances were harmed in the making of this automation stack.

From Zero to Bug Bounty Hero: Automating Recon with Python & Raspberry Pi

ULNIT — Wed, 08 Jul 2026 01:03:22 +0000

Why I Built a Bug Bounty Automation Kit

Bug bounty hunting is a numbers game. The more ground you cover, the higher your chances of finding that elusive critical vulnerability. But manually running reconnaissance tools is tedious, error-prone, and frankly, a waste of time.

That's why I built a Bug Bounty Automation Kit that turns a humble Raspberry Pi into a 24/7 reconnaissance powerhouse.

The Problem with Manual Recon

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

Enumerate subdomains with subfinder or amass
Probe for live hosts with httpx
Scan for open ports with nmap
Fuzz for hidden endpoints with ffuf
Screenshot everything with gowitness
Correlate, analyze, repeat

Doing this manually for even a single target takes hours. Doing it for hundreds? Impossible without automation.

My Raspberry Pi Setup

I run everything on a Raspberry Pi 4 (8GB) with an external SSD. Here's my stack:

OS: Raspberry Pi OS Lite (64-bit)
Container runtime: Docker & Docker Compose
Task scheduler: Cron + a custom Python orchestrator
Storage: 1TB SSD for logs, screenshots, and wordlists
Networking: VPN for safe scanning, split-tunnel for monitoring

The Pi lives in my closet, quietly humming away, running scheduled scans against my bug bounty target list every night.

The Automation Pipeline

My Python orchestrator does the heavy lifting. Here's a simplified version of the pipeline:

import subprocess
import json
from datetime import datetime

class ReconPipeline:
    def __init__(self, target):
        self.target = target
        self.timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        self.output_dir = f"recon/{target}/{self.timestamp}"

    def run_subdomain_enum(self):
        """Find subdomains with subfinder"""
        subprocess.run([
            "subfinder", "-d", self.target,
            "-o", f"{self.output_dir}/subdomains.txt"
        ])

    def probe_live_hosts(self):
        """Check which subdomains are alive"""
        subprocess.run([
            "httpx", "-l", f"{self.output_dir}/subdomains.txt",
            "-o", f"{self.output_dir}/live_hosts.txt"
        ])

    def scan_ports(self):
        """Run nmap on live hosts"""
        subprocess.run([
            "nmap", "-iL", f"{self.output_dir}/live_hosts.txt",
            "-p-", "-oN", f"{self.output_dir}/nmap.txt"
        ])

    def run(self):
        """Execute full pipeline"""
        self.run_subdomain_enum()
        self.probe_live_hosts()
        self.scan_ports()
        # ... more steps

This runs on a cron schedule, and results are pushed to a private Discord webhook for review.

Key Lessons Learned

1. Rate Limiting is Critical

Early on, I got rate-limited hard by target WAFs. Now I use ratelimit decorators and random delays between requests. Respect the target!

2. Deduplication Saves Hours

Running the same scan daily means lots of duplicate data. I hash findings and store them in SQLite, only alerting on new discoveries.

3. Notification Fatigue is Real

I started with email alerts for every finding. Now I only get notified on new high/critical findings. Everything else goes into a daily digest.

4. The Pi is Surprisingly Capable

Don't underestimate the Raspberry Pi 4. With 8GB RAM and an SSD, it handles concurrent scans against 50+ targets without breaking a sweat.

Taking It Further

If you're serious about bug bounty automation, I've packaged my entire setup into a ready-to-use kit. It includes:

Pre-configured Docker containers for all major recon tools
The Python orchestrator with SQLite deduplication
Discord/Slack notification templates
Cron templates for daily/weekly scanning
A curated wordlist collection
Documentation and troubleshooting guides

You can grab it here: Bug Bounty Automation Kit on LemonSqueezy

What's Next?

I'm currently working on:

AI-powered triage: Using LLMs to classify findings and prioritize the most promising targets
Distributed scanning: Coordinating multiple Pis for larger scope
Integration with bug bounty platforms: Auto-submitting low-hanging fruit (with proper scope checks)

Conclusion

Automation isn't about replacing the hunter—it's about amplifying them. By offloading repetitive recon to a Raspberry Pi, you free up mental bandwidth for the creative, high-value work: finding logic flaws, chaining vulnerabilities, and writing great reports.

Start small. Automate one tool. Then another. Before you know it, you'll have a recon pipeline that works while you sleep.

Happy hunting! 🐛🔍

This article is part of my series on practical security automation. Follow for more tips on Raspberry Pi hacking, Python scripting, and bug bounty workflows.

How I Turned a Raspberry Pi into an AI Automation Command Center

ULNIT — Tue, 07 Jul 2026 01:01:28 +0000

How I Turned a Raspberry Pi into an AI Automation Command Center

About six months ago, I found myself drowning in repetitive tasks. Bug bounty recon, content scheduling, API monitoring, report generation — the list went on. I had scripts scattered across three laptops, cron jobs I couldn't remember, and a growing sense that my "automation stack" was actually just chaos with a terminal theme.

Then I looked at the Raspberry Pi sitting in my drawer and had a thought: What if this little board became the brain?

The Realization

Most people think of Raspberry Pi as a hobby board for retro gaming or home servers. But with its low power draw, always-on capability, and full Linux environment, it's actually the perfect platform for running lightweight automation workflows 24/7.

The problem wasn't the hardware. It was that I was building everything from scratch every time.

What I Built

I started by containerizing my most common workflows:

Recon automation for bug bounty programs
Content pipeline for social media and blog posts
Monitoring alerts for my side projects
Report generation from various data sources

The Raspberry Pi handles all of this silently, sipping power and never complaining about uptime.

The Tools That Made It Work

The secret sauce wasn't just the Pi — it was having pre-built, battle-tested toolkits I could drop in and configure instead of coding from zero.

For bug bounty work, I leaned heavily on automation frameworks that handle subdomain enumeration, endpoint discovery, and vulnerability scanning in sequence. Instead of cobbling together 10 different tools with bash duct tape, having a unified kit saved hours per engagement.

For general automation, I built reusable Python patterns around API interactions, webhook handling, and scheduled task execution. The key insight: most automation problems aren't unique. They're just poorly packaged.

The Product Angle

If you're building something similar, you don't need to start from scratch. I put together the toolkits I use daily into products that are ready to deploy:

AI Agent Toolkit ($9) — Pre-built Python agents for common automation patterns. Drop them on any Linux box (including Pi) and configure with YAML.
Bug Bounty Automation Kit ($15) — End-to-end recon pipeline that runs beautifully on ARM architecture.

Both are designed to be lightweight, well-documented, and actually maintained.

Lessons Learned

1. Start with the outcome, not the tool. I wasted weeks optimizing Docker images before I had a single working workflow. Build ugly, then refine.

2. ARM isn't the enemy. Most Python packages work out of the box. The ones that don't usually have aarch64 wheels now.

3. Logging is non-negotiable. When your automation runs silently at 3 AM, you need to know what happened. Structured logging to a local SQLite database changed everything for me.

4. Separate configuration from code. My biggest maintenance win was moving all hardcoded values to environment files. Now I can clone a repo, edit a .env, and deploy.

The Setup in Practice

My current Pi stack:

Raspberry Pi 4 (4GB) — Overkill, honestly. A Pi 3 handles most of this.
64GB SD card — Class 10, nothing fancy.
Docker + Docker Compose — All services containerized.
Tailscale — Secure remote access without port forwarding.
Cron + systemd timers — For scheduled tasks.
SQLite + Grafana — Lightweight monitoring and logging.

Total power draw: about 5W. Total monthly electricity cost: roughly 30 cents. Uptime: 99.9% over the last 4 months.

Who This Is For

If you're a developer, security researcher, or indie hacker who:

Has repetitive digital workflows
Wants to stop babysitting scripts on your laptop
Likes the idea of a $35 automation server
Prefers Python and open-source tools

...then a Pi-based automation setup is probably worth your time.

Final Thoughts

The Raspberry Pi didn't magically solve my automation problems. But it forced me to think about efficiency, resource constraints, and building things that just work without my intervention.

That mindset shift — from "I need a bigger machine" to "I need better code" — was worth more than the hardware.

Want the exact toolkits I use? Check out the AI Agent Toolkit and Bug Bounty Automation Kit — both designed to run on Raspberry Pi and any Linux environment.

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

ULNIT — Mon, 06 Jul 2026 01:01:47 +0000

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

A few months ago, I found myself drowning in repetitive tasks. I was spending hours every week on bug bounty reconnaissance, content scheduling, and data collection. I had a dusty Raspberry Pi 4 sitting on my desk, and I thought: what if this little board could run my entire automation stack?

Spoiler: it can, and it changed my workflow completely.

The Problem with Desktop Automation

Most automation tools are built for the cloud or a beefy desktop machine. They consume RAM, need constant babysitting, and cost a fortune in compute. As someone who loves self-hosting and keeping costs low, I wanted something different.

I wanted a setup that was:

Cheap to run (the Pi draws about 5W idle)
Always on (no sleeping laptop)
Accessible from anywhere (SSH + Tailscale)
Powerful enough for real work (AI agents, web scraping, API calls)

What I Built

My Pi now runs a suite of Python-based automation tools:

Scheduled cron jobs for daily recon and data fetching
AI agents that draft content, triage alerts, and summarize findings
A lightweight web dashboard (Flask + Tailwind) to monitor everything
Automated backups to a cheap S3-compatible bucket

The magic ingredient? A well-structured agent toolkit that handles the heavy lifting. Instead of writing boilerplate for every new automation, I plug in what I need and let the framework handle retries, logging, and error handling.

The Stack in Detail

Hardware

Raspberry Pi 4 (4GB RAM)
128GB SanDisk Extreme microSD
PoE HAT (keeps it powered and cool)
Simple 3D-printed case

Software

OS: Raspberry Pi OS Lite (64-bit)
Runtime: Python 3.11 via pyenv
Task scheduling: systemd timers + cron
Reverse proxy: Caddy (automatic HTTPS)
Monitoring: A simple Flask app with HTMX for live updates

The Agent Framework

I use a modular agent framework that lets me compose small, reusable skills. Each skill is a Python module with a clear interface. The framework handles:

Configuration loading
Structured logging
Retry logic with exponential backoff
Result caching

This means my agents are resilient by default. If an API is down, they retry. If a task fails, they log it. If I need to add a new capability, I write a new skill and drop it in.

Real-World Use Cases

Bug Bounty Recon

Every morning at 6 AM, my Pi runs a recon pipeline that:

Fetches new subdomains from multiple sources
Screenshots live services
Checks for common misconfigurations
Summarizes findings and sends me a Telegram digest

This used to take me 2 hours manually. Now it takes 0 minutes of my time.

Content Automation

I run a few technical blogs. My Pi drafts article outlines based on trending GitHub repos, generates social media snippets, and even schedules posts. I still edit everything, but the first draft is already there when I sit down to write.

Home Lab Monitoring

I have a few services running at home. My Pi pings them every minute and alerts me if anything goes down. Simple, but effective.

Lessons Learned

Start small. My first automation was just a script that emailed me the weather. Now I have dozens of agents running. Build one thing, make it reliable, then expand.

Log everything. When something breaks at 3 AM, you want to know exactly what happened. Structured logging is non-negotiable.

Use a framework. Writing raw scripts is fine for one-offs, but a proper agent toolkit saves you from reinventing the wheel. If you're building AI-powered automations, I highly recommend using a dedicated toolkit to handle the plumbing.

The Toolkit I Use

If you're interested in building your own automation stack, I built an AI Agent Toolkit specifically for this kind of work. It includes everything you need to get started: modular skill architecture, built-in retry logic, structured logging, and example automations you can adapt.

It's designed to run on modest hardware (yes, including a Raspberry Pi) and gets you from zero to a working agent in minutes, not days.

👉 Get the AI Agent Toolkit on LemonSqueezy

Final Thoughts

The Raspberry Pi is an incredibly capable platform for automation. Combined with a solid agent framework, it can replace expensive cloud services for a huge range of tasks. My total hardware cost was under $100, and my electricity bill barely budged.

If you've got a Pi collecting dust, give it a job. Start with one automation. Iterate. Before you know it, you'll have a personal automation hub that works while you sleep.

What's your favorite self-hosted automation? Drop a comment below!

How I Built an AI Agent Toolkit That Runs on a Raspberry Pi

ULNIT — Sun, 05 Jul 2026 01:01:33 +0000

How I Built an AI Agent Toolkit That Runs on a Raspberry Pi

A few months ago, I found myself drowning in repetitive tasks—updating servers, monitoring logs, scraping data, running security checks. Like many developers, I thought: there has to be a better way.

What started as a weekend experiment turned into a full-blown AI Agent Toolkit that now runs entirely on a Raspberry Pi. Here's the story of how I built it, what I learned, and how you can use it too.

The Problem: Automation Shouldn't Require a Data Center

Most automation tools assume you have a beefy server or a cloud budget. But what if you want something lightweight, private, and always-on?

A Raspberry Pi 4 or 5 is surprisingly capable. It sips power, runs 24/7, and can handle most automation tasks without breaking a sweat. The challenge was building an agent framework that respects those constraints.

The Architecture: Small but Mighty

I designed the toolkit around three core principles:

Modularity — Each agent is a self-contained Python module that can be mixed and matched.
Resource Awareness — Agents check system load before running heavy tasks.
Extensibility — Adding a new capability is as simple as writing a new class.

Here's a simplified example of what an agent looks like:

class BaseAgent:
    def __init__(self, name, config=None):
        self.name = name
        self.config = config or {}

    def run(self, *args, **kwargs):
        """Override this in subclasses."""
        raise NotImplementedError

    def log(self, message):
        print(f"[{self.name}] {message}")

class LogMonitorAgent(BaseAgent):
    def run(self, log_path="/var/log/syslog"):
        self.log(f"Monitoring {log_path}")
        # Tail the log, alert on errors, etc.
        pass

Real-World Use Cases

1. Automated Security Scanning

One of the first agents I built was a security scanner. It runs nightly, checks for open ports, outdated packages, and suspicious log entries. Results are emailed to me every morning.

2. Data Scraping & Aggregation

Another agent monitors competitor pricing and aggregates it into a simple dashboard. No cloud services needed—just a cron job and a SQLite database.

3. Home Lab Monitoring

I have agents tracking CPU temperature, disk usage, and network latency across my home lab. When something goes wrong, I get a Telegram notification before I even notice.

The Raspberry Pi Advantage

Running on a Pi forces you to think differently. You can't just throw more RAM at a problem. You have to be efficient. And that efficiency pays off in surprising ways:

Low power consumption: My Pi 5 draws less than 15W under load
Always on: No need to spin up cloud instances
Private: Your data never leaves your network
Cheap: Total hardware cost under $100

Lessons Learned

Start Simple

My first version tried to do everything. It was a mess. I rewrote it with a single responsibility principle: one agent, one job. The codebase shrank by 60% and reliability skyrocketed.

Logging is Everything

When things go wrong at 3 AM (and they will), good logs save you. Every agent logs to a central file with timestamps and context. I use logrotate to keep things tidy.

Test on the Target Hardware

Don't develop on your MacBook and assume it'll work on the Pi. CPU architecture differences, limited RAM, and slower I/O will bite you. Test early and often on the actual hardware.

Get Started

If you're interested in building your own AI agents, I've packaged everything I learned into a toolkit you can grab today. It includes:

Pre-built agents for common tasks
A modular framework for building your own
Full documentation and examples
Optimizations specifically for Raspberry Pi and other ARM devices

👉 Get the AI Agent Toolkit on LemonSqueezy — $9, one-time purchase.

What's Next

I'm currently working on:

Integration with local LLMs (Llama, Mistral) for smarter agents
A web dashboard for monitoring agent status
More security-focused agents (bug bounty automation, vulnerability scanning)

If you enjoyed this post, follow me for more Raspberry Pi + AI automation content. And if you build something cool with the toolkit, I'd love to hear about it!

This article is part of my series on practical AI automation. All code examples are simplified for clarity but reflect real working systems.

I Built an AI Agent Toolkit for $9 — Here's What's Inside

ULNIT — Sat, 04 Jul 2026 01:01:41 +0000

I Built an AI Agent Toolkit for $9 — Here's What's Inside

After months of building and refining AI agents for various projects, I packaged everything into a single, battle-tested toolkit. If you're tired of reinventing the wheel every time you start an AI project, this might be for you.

The Problem with Building AI Agents from Scratch

I've lost count of how many times I've started a new AI agent project only to find myself:

Rewriting the same boilerplate for API integrations
Debugging the same authentication flows
Rebuilding logging and error-handling infrastructure
Searching for the right prompt engineering patterns

It's not that any of these tasks are particularly difficult — they're just time-consuming and repetitive. Time that could be spent on the actual AI logic and agent behavior.

What's in the AI Agent Toolkit

The AI Agent Toolkit ($9 on LemonSqueezy) is a collection of production-ready components I've extracted from real projects. Here's what you get:

🧠 Core Agent Architecture

Modular agent design — plug-and-play components for perception, reasoning, and action
Memory management — short-term and long-term memory implementations
Tool integration patterns — standardized interfaces for adding capabilities

🔧 Development Utilities

Prompt templates — battle-tested prompts for common agent tasks
Error handling — graceful degradation when APIs fail or timeout
Logging infrastructure — structured logging for debugging agent behavior
Configuration management — environment-based config for different stages

🚀 Deployment Ready

Docker setup — containerized for easy deployment
Health checks — monitoring endpoints for production use
Rate limiting — built-in protection against API quota exhaustion

Who This Is For

This toolkit is designed for developers who:

Are building AI agents and want to skip the boilerplate
Need production-ready code, not just tutorials
Want to learn from real-world patterns, not toy examples
Value their time over building everything from scratch

What Makes It Different

Most AI agent tutorials online show you how to build a simple chatbot and call it a day. This toolkit goes deeper:

Real error handling — not just try/except blocks, but actual recovery strategies
Memory that works — not just dumping text into a context window
Tool patterns — how to add new capabilities without breaking existing ones
Production considerations — rate limits, timeouts, retries, and monitoring

A Peek Under the Hood

One of the most useful patterns in the toolkit is the tool registry system. Instead of hardcoding tools into your agent, you register them dynamically:

from agent_toolkit import Agent, ToolRegistry

# Register tools
registry = ToolRegistry()
registry.register("search", search_tool)
registry.register("calculate", calculator_tool)

# Agent automatically discovers and uses available tools
agent = Agent(tools=registry)

This might seem simple, but it's the difference between an agent that can adapt to new tasks and one that needs to be rewritten every time requirements change.

Getting Started

The toolkit is designed to get you productive in minutes, not hours. After purchase, you'll get:

Immediate access to the full source code
Documentation with usage examples
A starter template to bootstrap your next project
Updates as the toolkit evolves

Final Thoughts

If you're serious about building AI agents and want to focus on the interesting parts — the agent logic, the reasoning, the problem-solving — rather than the infrastructure, this toolkit is built for you.

At $9, it's less than the cost of a coffee and a pastry, but it could save you hours of setup time on your next project.

Grab the AI Agent Toolkit here →

What's your biggest frustration when building AI agents? Let me know in the comments — I'm always looking for new patterns to add to the toolkit.

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

ULNIT — Fri, 03 Jul 2026 01:01:06 +0000

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

About six months ago, I had a problem. I was running AI agents, bug bounty scanners, and automation scripts on my laptop, and it was a mess. The machine would overheat, the battery would drain, and every time I closed the lid, half my jobs died. I needed something always-on, low-power, and quiet.

That's when I looked at the Raspberry Pi 5 sitting in my drawer.

The Realization

Most people think of Raspberry Pi as a toy for hobbyists. But with 8GB of RAM and a decent SD card, it's actually a surprisingly capable automation server. The ARM architecture means lower power draw, and the small form factor means it tucks away anywhere. I realized I could run a full AI agent stack, cron jobs, and even lightweight LLM inference without ever touching my main machine.

What I Built

I started by setting up a headless Raspberry Pi with Raspberry Pi OS Lite. No GUI, no bloat. Just SSH and a solid foundation.

1. The AI Agent Layer

I deployed a lightweight Python environment using uv for fast package management. The key was keeping dependencies minimal. I use httpx for async HTTP, pydantic for data validation, and structlog for structured logging. Everything else is optional.

The agents run on a simple cron schedule, triggered by a custom orchestrator I wrote. It's not fancy, but it's reliable. And on a Pi, reliability matters more than features.

2. Automation Workflows

I have three main workflows running:

Daily content generation: Drafts social posts and blog outlines
Bug bounty recon: Automated subdomain enumeration and endpoint discovery
Health monitoring: Checks my other services and alerts via webhook

All of these are Python scripts that exit cleanly, log everything, and don't leak memory.

3. The Secret Sauce

The real breakthrough was treating the Pi like a production server, not a toy. I set up:

Systemd services for everything (no more nohup hacks)
Log rotation with logrotate
A simple health dashboard using a static HTML page regenerated every minute
Watchdog timers so the Pi reboots itself if it ever locks up

The Results

My Pi has been running non-stop for 147 days. It consumes about 5W of power. Total cost of operation is pennies per day. And my laptop? It finally stays cool and quiet.

But here's the thing: building this from scratch took weeks. I made every mistake in the book. I wrote scripts that hung, cron jobs that overlapped, and services that leaked memory like a sieve.

That's why I eventually packaged the best of what I learned into something more polished. If you're curious about running your own automation stack, I put together a complete toolkit that handles the heavy lifting.

The Toolkit I Wish I Had From Day One

After months of refining my setup, I built the AI Agent Toolkit to save others from the same trial-and-error. It includes pre-built agent templates, ready-to-deploy systemd configs, and the exact Python patterns I use for reliable, long-running automation.

If you're running agents on a Pi (or any Linux box), it cuts setup time from days to hours.

👉 Grab it here for $9

Tips for Your Own Pi Automation Lab

If you want to build something similar, here's what I'd do differently:

Use a high-quality SD card or, better, an NVMe SSD – SD cards die. Fast. An SSD via USB adapter is worth every penny.
Keep your Python environment isolated – I use uv and virtual environments religiously. No global installs.
Monitor memory usage – The Pi has limited RAM. Use cgroup limits or systemd memory constraints to prevent runaway processes.
Embrace the constraints – The Pi forces you to write efficient code. That's a feature, not a bug. My agents are faster and leaner because of it.
Automate the boring stuff – If you're SSHing into the Pi to do something manually more than twice, script it.

Final Thoughts

The Raspberry Pi isn't just a hobby board. It's a legitimate automation platform. With the right approach, you can run a surprisingly sophisticated AI agent stack on hardware that costs less than a dinner out.

The key is treating it seriously: proper logging, service management, and clean code. Do that, and you'll have a 24/7 automation lab that just works.

What's your Pi automation setup? Drop a comment below.

How I Turned a Raspberry Pi into an AI Automation Lab (And You Can Too)

ULNIT — Thu, 02 Jul 2026 01:01:45 +0000

How I Turned a Raspberry Pi into an AI Automation Lab (And You Can Too)

TL;DR: A $35 Raspberry Pi can be a surprisingly powerful platform for AI-driven automation, bug bounty recon, and agent workflows. Here's how I built mine.

The Problem

I had an old Raspberry Pi 4 sitting in a drawer. Like many devs, I bought it with grand plans—self-hosted services, home automation, maybe a Pi-hole. Then life happened, and it collected dust.

Meanwhile, I was spending hours on repetitive tasks:

Running manual recon for bug bounty programs
Setting up temporary environments for automation scripts
Paying cloud bills for VMs that sat idle 90% of the time

It hit me: What if the Pi became my dedicated automation lab?

The Build

Hardware

Raspberry Pi 4 (4GB RAM)
128GB SD card
Cheap USB-C power supply
Ethernet cable (WiFi works, but wired is more reliable for long-running tasks)

Software Stack

# Base setup
sudo apt update && sudo apt upgrade -y
sudo apt install -y docker.io docker-compose python3-pip git

# Enable Docker for pi user
sudo usermod -aG docker pi

What I Run on It

1. Persistent Recon Agents

I have cron jobs that run weekly recon against my bug bounty targets. Tools like amass, subfinder, and httpx all have ARM builds now. I pipe results into a local SQLite database and get Telegram notifications when new subdomains appear.

2. AI Agent Workflows

Using Python + requests + local LLM APIs (or cheap cloud ones), I run lightweight automation agents. One agent monitors RSS feeds for CVE disclosures and cross-references them against my target list. Another generates draft report sections from vulnerability notes.

3. Development Environment

Instead of cloud VMs, I develop and test automation scripts directly on the Pi. If I break something, I re-flash the SD card in minutes.

What Surprised Me

Performance is fine for 90% of tasks.

Sure, you're not training models on a Pi. But for API polling, web scraping, lightweight LLM inference (llama.cpp runs on Pi!), and script orchestration? It's more than enough.

Electricity cost is negligible.

A Pi 4 draws about 5-7W under load. Running 24/7 costs maybe $5/year. Compare that to a $10/month cloud VM.

ARM compatibility is way better now.

Two years ago, half my tools wouldn't compile. Today, docker pull just works for most images. The ecosystem has matured massively.

The Workflow That Made It Click

Here's a concrete example: my subdomain monitoring pipeline.

#!/usr/bin/env python3
import subprocess
import sqlite3
from datetime import datetime

def run_recon(domain):
    # Run subfinder
    result = subprocess.run(
        ["subfinder", "-d", domain, "-silent"],
        capture_output=True,
        text=True
    )
    return set(result.stdout.strip().split('\n'))

def check_new_subdomains(domain):
    conn = sqlite3.connect('recon.db')
    cursor = conn.cursor()

    current = run_recon(domain)
    cursor.execute("SELECT subdomain FROM subdomains WHERE domain = ?", (domain,))
    known = set(row[0] for row in cursor.fetchall())

    new = current - known
    for sub in new:
        cursor.execute(
            "INSERT INTO subdomains (domain, subdomain, discovered) VALUES (?, ?, ?)",
            (domain, sub, datetime.now())
        )
        print(f"[NEW] {sub}")

    conn.commit()
    conn.close()
    return new

if __name__ == "__main__":
    check_new_subdomains("example.com")

This runs via cron every Sunday. New findings get pushed to Telegram. Total cost: $0 beyond the initial Pi purchase.

Scaling Up: When You Outgrow the Pi

The Pi is my sandbox. When a workflow proves valuable, I sometimes port it to a cloud environment for production use. But having a local, always-on, nearly-free lab means I can experiment without worrying about cloud bills.

If you're serious about automation and bug bounty work, I also put together a toolkit that packages a lot of these workflows into ready-to-use scripts and templates. It covers recon automation, report generation, and agent orchestration patterns I've refined over time.

Check out the Bug Bounty Automation Kit here →

Getting Started Today

Dust off that Pi (or buy a used one for $30-40)
Flash Raspberry Pi OS Lite (no desktop needed for headless operation)
Install Docker and your favorite tools
Pick ONE automation task and script it
Set a cron job and let it run

Don't overthink it. The goal isn't to build the perfect lab—it's to have a place where automation ideas can grow.

What's Next for My Lab

Local LLM inference: Running smaller models (Phi-3, Llama 3.1 8B) for offline automation tasks
Network monitoring: Passive traffic analysis for home lab security research
Distributed agents: Multiple Pis running different parts of a workflow

What are you running on your Pi? Drop a comment—I'm always looking for new automation ideas.

P.S. If you want to skip the setup and get straight to the automation scripts, the Bug Bounty Automation Kit has everything I use, packaged and documented.

How I Built an AI Agent Store on a Raspberry Pi (and Why It Matters)

ULNIT — Wed, 01 Jul 2026 01:01:28 +0000

How I Built an AI Agent Store on a Raspberry Pi (and Why It Matters)

A few months ago, I found myself drowning in repetitive tasks—managing bug bounty workflows, automating security scans, and juggling AI-powered tools. Like many developers, I had a drawer full of Raspberry Pis collecting dust. That’s when the idea hit me: what if I turned a $35 computer into a hub for AI automation tools?

This is the story of how I built an AI Agent Store—and why running it on a Raspberry Pi changed my perspective on accessible automation.

The Problem: Automation Shouldn’t Require a Data Center

Most AI automation tools are built for cloud environments. They demand GPU clusters, complex orchestration, and monthly bills that make your eyes water. But here’s the thing: not every automation task needs AWS.

I wanted something different:

Low-cost: No recurring cloud bills
Private: My data stays in my house
Hackable: Easy to tweak, extend, and learn from
Practical: Actually solves real problems

The Raspberry Pi 4 and 5 are surprisingly capable. With 8GB of RAM and a decent SD card, you can run Python scripts, host APIs, and even experiment with lightweight AI models.

The Build: From Idea to Working Store

Phase 1: The Foundation

I started with a simple Flask API running on my Pi. The goal? A storefront where I could distribute automation tools I’d built for myself. The first product was a Bug Bounty Automation Kit—a collection of scripts that streamlined reconnaissance, subdomain enumeration, and vulnerability scanning.

Instead of running 10 different tools manually, the kit orchestrated everything:

# One command to rule them all
python bb_automation.py --target example.com

Phase 2: Adding AI Agents

The next logical step was AI integration. I built an AI Agent Toolkit that could:

Automate repetitive coding tasks
Generate reports from security findings
Integrate with existing CI/CD pipelines

Running this on the Pi required some optimization. I used lightweight models, cached aggressively, and offloaded heavy inference to external APIs when necessary. The Pi handled orchestration beautifully.

Phase 3: The Storefront

I needed a way to distribute these tools. Enter LemonSqueezy—a simple, developer-friendly payment platform. I could focus on building while LemonSqueezy handled the commerce side.

The store lives at https://github.com/ulnit/agent-store, where each product includes:

Full source code
Setup instructions for Raspberry Pi
Docker containers for easy deployment
Documentation and examples

Why Raspberry Pi?

You might wonder—why not just host everything in the cloud?

1. Cost

A Raspberry Pi 5 costs about $80 with accessories. That’s less than two months of a basic AWS EC2 instance. Over a year, the savings are massive.

2. Privacy

Your data never leaves your network. For security tools and AI agents handling sensitive information, that’s invaluable.

3. Learning

Running on constrained hardware forces you to write efficient code. You learn to profile, optimize, and think carefully about resource usage.

4. Resilience

Power outage? Internet down? Your Pi keeps running local tasks. No dependency on cloud availability.

Lessons Learned

Start simple. My first version was a single Python script. It wasn’t pretty, but it worked. Perfection is the enemy of shipped.

Document everything. Future you will thank present you. Every script has a README, every function has a docstring.

Embrace constraints. The Pi’s limitations forced creative solutions. A $35 computer taught me more about optimization than any cloud course.

Community matters. Open-sourcing parts of the store brought contributors, bug reports, and ideas I never would have had alone.

What’s Next?

The store is growing. I’m adding:

More AI agent templates
Integration with home automation (Pi + Home Assistant = magic)
A community marketplace for user-contributed tools

If you’ve got a Raspberry Pi sitting around, dust it off. Install Python, clone a repo, and start automating. The barrier to entry has never been lower.

Get Started

Curious about the tools? Check out the AI Agent Toolkit—a collection of scripts and templates to jumpstart your automation journey. It’s built to run anywhere, including your trusty Pi.

Have you built something cool on a Raspberry Pi? Drop a comment—I’d love to hear about it.

5 AI Automation Tricks That Save Me Hours Every Week

ULNIT — Tue, 30 Jun 2026 01:01:08 +0000

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.

AI Agent Toolkit: Build Autonomous Agents That Actually Get Work Done

ULNIT — Mon, 29 Jun 2026 01:01:50 +0000

The Problem with AI Agents Today

We've all seen the demos. An AI agent that books your flights, writes your emails, and manages your calendar — all in a slick 60-second video. But when you try to build one yourself, reality hits hard. Tool calling breaks. Context windows overflow. The agent hallucinates API parameters and then confidently tells you it "completed successfully."

Building reliable AI agents is hard. Really hard. And most of the tooling out there is either too academic (papers, not code) or too simplistic (wrappers around chat() that fall apart on anything non-trivial).

That's exactly the gap I wanted to fill with the AI Agent Toolkit — a practical, battle-tested collection of patterns and utilities for building agents that don't just demo well, but actually ship.

What's Inside the AI Agent Toolkit

Let me give you a tour of what you get when you grab the toolkit. It's built around four core pillars:

1. Structured Tool Calling That Actually Works

If you've ever debugged why your agent keeps calling get_weather(location=null), you know the pain. The toolkit ships with a robust function-calling layer that validates parameters before execution, retries on schema violations, and logs every call so you can trace what went wrong. No more black-box debugging.

from agent_toolkit import ToolRegistry, tool

registry = ToolRegistry()

@tool(description="Search the knowledge base", parameters={
    "query": {"type": "string", "required": True},
    "limit": {"type": "integer", "default": 5}
})
def search_kb(query: str, limit: int = 5) -> list:
    # Your implementation here
    pass

registry.register(search_kb)

2. Memory That Doesn't Explode

Most agent frameworks treat memory as "append everything to the context window forever." That works for 10 turns and then burns through your token budget. The toolkit uses a sliding window with intelligent summarization — recent messages stay verbatim, older ones get compressed into structured summaries, and critical facts get extracted into a persistent knowledge store.

3. Multi-Step Planning with Checkpoints

Agents drift. You ask for a 5-step task and by step 3 it's doing something completely different. The planning module lets you define explicit checkpoints and validation gates. The agent can't proceed to step 4 until step 3's output passes a schema check. This alone has saved me from countless "agent went rogue" moments.

4. Human-in-the-Loop Escalation

Sometimes the agent needs to ask for help, and that's okay. The toolkit includes a clean escalation protocol — when confidence drops below a threshold or the agent encounters a novel situation, it pauses and surfaces exactly what it needs clarification on. No more agents silently making bad decisions.

Real-World Use Cases

Here's what I've used it for personally:

Code review automation: An agent that reviews PRs against custom style guides, runs tests, and either approves or escalates with specific feedback
Data pipeline monitoring: Watches ETL jobs, detects anomalies, and either retries with backoff or pages the on-call engineer
Documentation generation: Crawls a codebase, extracts docstrings, and generates comprehensive API docs with examples

Each of these ran reliably for weeks, not hours. That's the bar.

Getting Started

You can grab the AI Agent Toolkit right now on LemonSqueezy for just $9 — that's a one-time purchase with lifetime access to all updates and the full source code.

👉 Get the AI Agent Toolkit on LemonSqueezy

The toolkit includes:

Full Python source code with type hints
6 example agent configurations (RAG, code review, data pipeline, customer support, research assistant, DevOps)
Comprehensive documentation with setup guides
A template project so you can go from zero to running agent in under 10 minutes

Why I Built This

I got tired of stitching together the same patterns every time I built an agent. Tool validation, memory management, checkpointing — these are solved problems, and you shouldn't have to reinvent them for every project. This toolkit is my attempt to package up everything I've learned from building agents in production so you can skip the pain and get straight to building.

If you're building AI agents — whether for work, a side project, or just to learn — this toolkit will save you weeks of debugging and let you focus on what actually matters: what your agent does, not how it stays alive.

Questions? Drop a comment below or check out the source on GitHub. Happy building!