Local-only LLM inference
Higher hardware requirements than cloud AI APIs, but sensitive data never leaves the network. No vendor lock-in on model choice.
Projects
Things I am building in public
This is where I keep the longer-running projects: what I am trying to solve, what keeps breaking, and what I am learning as the architecture evolves.
Active project
Vinsium
Private AI infrastructure that runs entirely on your network. Vinsium combines zero-trust mesh networking, a local AI workflow engine, and enterprise identity into a single platform with no cloud dependency.
Status: in active development. If you want to follow progress or test early versions, reach out through the contact page.
Why I am building this
Organizations that care about data sovereignty need to run AI workloads without sending sensitive data to cloud APIs. Vinsium gives them local LLM inference, composable processing pipelines, and serious security controls without the enterprise overhead.
What I am learning
The hard problems are not the AI models — they are identity federation at the edge, audit chain integrity across distributed nodes, and building operational visibility that operators actually trust.
Current challenges
- Keeping audit chain integrity across distributed mesh nodes while maintaining tamper-evident logging.
- Balancing module sandboxing (trusted vs. untrusted security modes) with workflow flexibility.
- Making operational dashboards actionable rather than decorative — live health checks that map to real security controls.
- Identity provider federation edge cases across Okta, Entra ID, Auth0, and LDAP attribute mapping.
Architecture decisions and trade-offs
Composable pipeline modules
29 input/transform/AI/output modules instead of monolithic workflows. More wiring, but each module is independently testable and replaceable.
Tamper-evident audit chains
HMAC-SHA256 chain signing adds write overhead, but gives operators a verifiable, tamper-proof audit trail.
Open source project
Mistborn
Mistborn started as a personal experiment in making private networking and self-hosted services easier to run. It grew into a real platform used by builders who care about privacy without enterprise overhead.
How it started
A home-lab project that kept growing because each solved problem exposed the next frustrating one.
What went wrong
- Early installers assumed too much and failed on edge cases I did not know existed.
- I made deployment paths too clever in a few places, which made debugging painful for new users.
- I waited too long to simplify docs. Good defaults matter more than long option lists.
What I would do differently
I would optimize for simpler operational paths earlier, and treat docs as a product surface from day one.
Community contributions
- Bug reports that caught real-world DNS and networking edge cases before they became bigger problems.
- Pull requests and issue threads that improved install reliability and service ergonomics.
- Tutorial videos and independent reviews that brought the project to builders I never would have reached alone.
Fork it, break it, and make it yours.
Independent Coverage
Linux Pro Magazine, Awesome Open Source, and DB Tech covered Mistborn. Those early reviews helped shape where the project went next.
- Linux Pro Magazine (Nov 2020)
- Awesome Open Source (Jul 2020)
- DB Tech (May 2021)