The Setup
I run a Mac Mini M4 Pro as a 24/7 AI workstation. It sits on my desk, never sleeps, and runs an AI agent called Caper that handles everything from content creation to code generation to business automation.
Here is exactly how I set it up, what it costs, and what it actually does.
Hardware
Total hardware cost: ~$800. Runs 24/7 at about $3/month in electricity.
The Stack
Local LLMs
I run Ollama with several models pulled locally:
These run entirely on-device using the M4 Neural Engine. No API costs for local inference.
Cloud APIs (When Needed)
For heavy lifting, Caper calls:
Python Automation Layer
The glue holding everything together:
What It Actually Does
Content Creation
Caper writes articles, generates social media posts, and creates digital products. It handles the first draft, I review and publish.
Business Automation
Code Generation
When I need a script, I describe what I want. Caper writes it, tests it, and saves it to the scripts directory. If it fails, it debugs itself.
Research
Give it a topic, it searches the web, reads documentation, cross-references sources, and delivers a summary with citations.
The Economics
Compare that to hiring a virtual assistant ($500-2000/month) or subscribing to multiple SaaS tools ($100+/month). The Mac Mini pays for itself in the first month.
Lessons Learned
Get Started
You do not need a Mac Mini. Any always-on computer works. The key ingredients:
Total setup time: one weekend. ROI: immediate.
I am building this in public. Follow for updates on what an AI workstation can actually do for a solo operator.
More...
I run a Mac Mini M4 Pro as a 24/7 AI workstation. It sits on my desk, never sleeps, and runs an AI agent called Caper that handles everything from content creation to code generation to business automation.
Here is exactly how I set it up, what it costs, and what it actually does.
Hardware
- Mac Mini M4 Pro — 24GB RAM, 512GB SSD ($599 base, upgraded RAM)
- External SSD — 2TB for storing projects, logs, and media
- Always-on internet — Ethernet, not WiFi, for reliability
Total hardware cost: ~$800. Runs 24/7 at about $3/month in electricity.
The Stack
Local LLMs
I run Ollama with several models pulled locally:
- llama3.2 for fast general tasks
- codellama for code generation
- mistral for creative writing
These run entirely on-device using the M4 Neural Engine. No API costs for local inference.
Cloud APIs (When Needed)
For heavy lifting, Caper calls:
- Claude API — Complex reasoning, long documents, code review
- OpenAI Whisper — Audio transcription
- Cost: ~$5-15/month depending on usage
Python Automation Layer
The glue holding everything together:
- Cron jobs for scheduled tasks (content publishing, monitoring, backups)
- Playwright for browser automation when APIs are not available
- FFmpeg for video/audio processing
- yt-dlp for media downloads and analysis
What It Actually Does
Content Creation
Caper writes articles, generates social media posts, and creates digital products. It handles the first draft, I review and publish.
Business Automation
- Monitors competitor pricing
- Tracks analytics across platforms
- Generates reports on content performance
- Manages a task queue with priorities
Code Generation
When I need a script, I describe what I want. Caper writes it, tests it, and saves it to the scripts directory. If it fails, it debugs itself.
Research
Give it a topic, it searches the web, reads documentation, cross-references sources, and delivers a summary with citations.
The Economics
| Electricity | $3 |
| Cloud APIs | $5-15 |
| Internet (shared) | $0 |
| Total | $8-18/month |
Compare that to hiring a virtual assistant ($500-2000/month) or subscribing to multiple SaaS tools ($100+/month). The Mac Mini pays for itself in the first month.
Lessons Learned
- Local models are good enough for 80% of tasks. Save cloud API calls for the complex stuff.
- Cron is underrated. Scheduled automation is more reliable than event-driven for most business tasks.
- Always log everything. Every API call, every token spent, every task completed. You cannot optimize what you do not measure.
- Start with one workflow, then expand. I started with content generation, then added research, then automation, then monitoring.
Get Started
You do not need a Mac Mini. Any always-on computer works. The key ingredients:
- A machine that stays on 24/7
- Python + cron for automation
- Ollama for local LLMs
- One or two cloud APIs for heavy tasks
Total setup time: one weekend. ROI: immediate.
I am building this in public. Follow for updates on what an AI workstation can actually do for a solo operator.
More...