The Process
1
Technical Evaluation
~1 weekI evaluate your external touchpoints as your pro user: portal, console, SDKs, ADKs, and developer docs.
Report + Video
2
Product Showcase
~2 weeksYour product, integrated into a production-grade application that turns developers into advocates.
GitHub + Video + Tutorial
3
AI Ecosystem Builder
Ongoing PartnershipSustained engagement through technical partnerships, community-led growth, and continuous ecosystem development.
Retainer Available
Featured Work
Systems Engineering
Argus
A TUI-based unified control plane for agent sandboxing, written in Rust. 8 modules, 9 dependencies, no async runtime. 3.7 MB binary.
Explore →Product
ArchByte
Real-time architecture maps for any codebase. Your model, your machine.
Explore →Showcase
Silicon Cortex
Event-driven AI agent orchestration on Kubernetes. Zero idle cost.
Explore →Showcase
Meridian
Uber for RoboTaxis. AI agents coordinating at scale.
Explore →Frequently Asked Questions
What AI systems do you build?
Agent orchestration layers, event-driven pipelines, multi-agent architectures, and developer tools. Rust for systems work, Python for AI services, TypeScript for full-stack. Every project ships with real error handling, observability, and infrastructure as code.
What is the Customer Zero approach?
I use your product the way a real engineer would. SDKs, APIs, docs, CLI, deployment. I find the friction, the missing error messages, the config that doesn't work on first try. You get a detailed technical report before your customers hit the same walls.
What does a product showcase look like?
A production-grade application that integrates your product and ships to GitHub with a video walkthrough. Not a hello-world. Real architecture decisions: event-driven patterns, Kubernetes, observability, CI/CD. The kind of code that makes engineers say "they actually used it for real."
What tech stack do you work with?
Rust for systems and CLI tools (Argus). Python for AI services and event-driven backends (Silicon Cortex, Meridian). TypeScript for full-stack and visualization (ArchByte). Kubernetes, Kafka, NeonDB, Redis, Docker, Cloudflare across projects. Claude, OpenAI, and Gemini for LLM integration.
How do you measure success?
Adoption. Revenue. Customers won.