What I Built in 30 Days With My Local LLM Stack
In 30 days, I built over 20 production projects using my local AI model stack, achieving what would have taken months with traditional methods.
One engineer + local AI models. 20+ production projects. 30 days. Zero API costs.
For the last month, I ran an experiment.
Two Macs on my desk. A 48GB Mac Studio and a 16GB Mac Mini running as a local LLM cluster. Ollama. Open-source models. Zero data leaving my network.
My goal: build everything I’d been postponing for months — across networking, security, market intelligence, and infrastructure tooling.
The results surprised me.
30 days. 960 commits. 20+ projects shipped to production.
Not prototypes. Not demos. Production systems handling real traffic:
- An event management platform with full auth and admin
- A market intelligence engine scraping 60+ data sources
- A BGP/RPKI network intelligence tool used by the routing community
- An 8-stage LLM pipeline with self-learning capabilities
- Three interconnected security platforms with MITRE ATT&CK mapping
- Multi-agent orchestration frameworks
- And a dozen smaller tools — from travel expense calculators to social media automation
The Cost Comparison
I tracked every hour. Here’s what this would cost with human developers:
| Approach | Duration | Cost | Headcount |
|---|---|---|---|
| Local LLM (what I did) | 30 days | €150 electricity | 1 engineer |
| Senior Freelancer (€100/h) | 7–9 months | €140,000 – 170,000 | 1 person |
| Small agency | 5–6 months | €180,000 – 250,000 | 2–3 devs |
| In-house team | 4–5 months | €120,000 – 180,000 | 3 devs |
| Offshore team | 6–8 months | €60,000 – 90,000 | 3–4 devs |
Realistically, no single developer covers all these domains. You’d need a full-stack engineer, a data/backend specialist, a network security expert, and a DevOps engineer. That’s a 3-person team for 5–6 months. Budget: €180,000–250,000.
I spent €150 on electricity.
This Isn’t a “Developers Are Dead” Post
It’s the opposite. The role of the engineer is more important than ever — it’s just changing.
What I did in those 30 days wasn’t “prompting.” It was architecture. It was knowing which database to pick for time-series data vs. vector search. It was understanding BGP well enough to validate what the model generated. It was making deployment decisions that no model can make on its own.
The AI handled implementation velocity.
I handled judgment.
Three Things I’ve Learned
1. The Bottleneck Has Shifted
It’s no longer “how fast can I write code.” It’s “how fast can I decide what to build and why.” Product thinking is now the rate limiter — not engineering capacity.
2. Local Beats Cloud for Serious Work
No rate limits. No data privacy concerns. No API bills that scale with usage. A one-time hardware investment that pays for itself in a single project. And complete control over your stack.
3. Breadth Is the New Superpower
AI eliminates the ramp-up penalty for unfamiliar domains. I moved between BGP routing, LLM pipelines, and TypeScript frontends in the same afternoon. That kind of cross-domain velocity was impossible before — not because the knowledge didn’t exist, but because context-switching was too expensive.
What This Means for Teams
The 10x engineer was always a myth. But the 5x-leveraged engineer — someone who deeply understands systems and uses AI as an execution layer — is very real. And they’re going to reshape how we think about team size, project timelines, and what’s possible for small companies.
We’re entering an era where a solo founder with domain expertise and the right local setup can build what previously required a funded startup.
The question isn’t whether AI changes software engineering.
It’s whether you’ll be the one using it — or the one being outpaced by someone who does.
Built on a Mac Studio + Mac Mini cluster. No cloud APIs. No subscriptions. Just local models and a lot of coffee.