Tag: GitHub

HeliOS-Studio: AI Startup Studio Ignites
HeliOS-Studio: When the Infrastructure Becomes the Product
Published: February 15, 2026 (retrospective)

Three years of AI infrastructure work—Control Tower, SentinelForge, LocalLLM-Router—converged into one idea: what if the automation stack itself became a startup studio? HeliOS-Studio is the answer. GitHub-orchestrated, Ollama-powered, CrewAI-driven. It ships products, not just prototypes.

The Studio Model
```
HeliOS-Studio
├── control-tower     (workflow orchestration)
├── sentinelforge     (secure agent execution)
├── llm-router        (zero-cost inference)
└── blog-agent        (content automation)
```
First product out of the studio: quickstart-smb-ai — an AI readiness toolkit for UK SMEs. From idea to GitHub repo with full business plan, revenue model, and MVP build plan in under 24 hours.

What the Studio Produces

Output Time to Ship Previous Time

Business plan 2 hours 2 weeks

MVP codebase 6 hours 2 months

Blog post 45 minutes 3 hours

GitHub repo + docs 30 minutes 4 hours

The Philosophy
1. Infrastructure first: build the factory before the product.
2. AI amplifies expertise; 25 years of cybersecurity knowledge makes the outputs trustworthy.
3. Open source where possible—the community improves what you start.
Interested in AI-accelerated product development? Let’s build something.

Next: 3-year journey retrospective (Mar 2026).
February 15, 2026
Control Tower Blueprint: Orchestrating Multi-AI Chaos
Control Tower Blueprint: From AI Chaos to Factory
Published: July 20, 2024 (retrospective)

By mid-2024 I was juggling Claude, Copilot, Perplexity, and local Ollama instances simultaneously. Great results—but token burn, context loss, and manual coordination killed efficiency. Control Tower v1.0 was my answer: a GitHub-orchestrated system that turned ideas into production code with minimal human input.

The Workflow
```
Idea → GitHub Issue → Claude researches
     → Copilot codes → I approve PR → deployed
```
Key design decisions:
– Priority + budget fields on every issue halt overspend automatically
– Human gate on every PR—AI proposes, I approve
– Nightly decision cycle—agents run overnight, I review at breakfast

The Numbers

Metric Before Control Tower After Gain

Code hours/week 20h 4h 80% reduction

Token cost/week £120 £12 90% reduction

Projects shipped/month 1 4 4x

GitHub commits/month 45 200+ 4.4x

What Made It Work
1. Local-first routing: Proxmox + Ollama handled 70% of queries free.
2. Scoped permissions: AI never had write access without explicit approval.
3. Repo as truth: Every decision documented in GitHub—zero tribal knowledge.
Control Tower didn’t just code; it scaled my fractional CISO practice and laid the foundation for SentinelForge.

Want to automate your AI workflows? Book a Secure AI QuickScan.

Next: CrewAI launch transforms agent security (Oct 2024).
July 20, 2024
GitHub AI Boom: 65k+ Projects Spark My Homelab
GitHub AI Boom: My Homelab Goes Local
Published: November 20, 2023 (retrospective)

November 2023’s generative AI explosion on GitHub—repos tripled to 65k+—pushed me from cloud experimentation to Proxmox homelab builds. ChatGPT Enterprise handled scripting, but token costs and privacy concerns demanded local LLMs. Enter Ollama’s early hype.

From Cloud to Containers

GitHub’s AI project surge validated my pivot: Ollama promised free, private inference on Proxmox VMs. First homelab setup:
– Llama2 on RTX 3090 GPU passthrough
– QNAP NFS for model storage
– Uptime Kuma monitoring from day one

Test Latency Cost Verdict

GPT-4 (cloud) 2.1s $0.03/1k tokens Reference

Ollama Llama2 1.8s $0 Winner

Local Router (planned) 1.2s $0 Future

First month: 87% cost savings routing simple queries locally. Cloud reserved for complex reasoning tasks only.

Early Router Sketches

The LocalLLM-Router concept was born here: route 70% of queries to Ollama, reserve cloud API for edge cases. This became the foundation of Control Tower’s cost engine a year later.

Lessons
1. Proxmox GPU passthrough is powerful but finicky—document everything.
2. Model size matters: 7B handles most IT tasks; 13B for nuanced cybersecurity analysis.
3. Privacy wins: Sensitive client data never leaves the homelab.
Want to build your own AI homelab? Let’s talk.

Next: M365 Copilot GA forces enterprise governance thinking (Jan 2024).
November 20, 2023

Output	Time to Ship	Previous Time
Business plan	2 hours	2 weeks
MVP codebase	6 hours	2 months
Blog post	45 minutes	3 hours
GitHub repo + docs	30 minutes	4 hours

Metric	Before Control Tower	After	Gain
Code hours/week	20h	4h	80% reduction
Token cost/week	£120	£12	90% reduction
Projects shipped/month	1	4	4x
GitHub commits/month	45	200+	4.4x

Test	Latency	Cost	Verdict
GPT-4 (cloud)	2.1s	$0.03/1k tokens	Reference
Ollama Llama2	1.8s	$0	Winner
Local Router (planned)	1.2s	$0	Future

Tag: GitHub

HeliOS-Studio: AI Startup Studio Ignites

The Studio Model

What the Studio Produces

The Philosophy

Control Tower Blueprint: Orchestrating Multi-AI Chaos

The Workflow

The Numbers

What Made It Work

GitHub AI Boom: 65k+ Projects Spark My Homelab

From Cloud to Containers

Early Router Sketches

Lessons