I build web, mobile, and AI-enabled systems with an emphasis on clarity, maintainability, and practical architecture.
My current work centers on Modelomics: allocating AI intelligence efficiently, using the minimum effective intelligence, measuring return on intelligence, and avoiding intelligence debt.
- Modelomics research, writing, and applied frameworks
- AI product design and workflow automation
- Web and mobile application development
- Systems integration and orchestration
- Building software with clear tradeoffs and long-term maintainability
- Languages: TypeScript, JavaScript, Dart, Python, SQL
- Frontend: React, Next.js, Flutter
- Backend: Node.js, NestJS, REST APIs
- State Management: Zustand, Redux
- Styling: Tailwind CSS, Radix UI
- Data: PostgreSQL, MongoDB
- AI / Automation: LLM workflows, RPA, web scraping, workflow orchestration
- Testing: Jest, Cypress
- DevOps: Docker, GitHub Actions, CI/CD, AWS, GCP, DigitalOcean
Modelomics is my framework for allocating AI intelligence efficiently.
- Minimum Effective Intelligence: use the smallest effective capability for the task
- Intelligence Debt: account for the long-term cost of over-allocation
- Return on Intelligence: measure value relative to compute, latency, and maintenance cost
- Progressive Intelligence Escalation: start simple and escalate only when needed
I write about the framework here:
- Realtime AI experiences in the browser
- Healthcare interoperability and credentialing systems
- Open-source product and platform contributions
- Automation tooling for operational workflows
- Modelomics writing and framework development
- AI governance and allocation strategy
- Product-minded engineering
- Reliable workflows and maintainable systems
- Practical uses of automation in real organizations
- Email: [email protected]
- LinkedIn: linkedin.com/in/adenleabiodun
- Website: adenleabiodun.com
- Twitter/X: @abbeyseto
Thanks for visiting. I am always open to thoughtful conversations about software, AI systems, and better ways to allocate intelligence.