Under the hood.
The technical side: personal projects, how I use AI coding tools, and what I learn by building and using the results.
Personal project
DocLifts — a live workout app with AI assistance
A live multi-user Alpha built around real training: photo-assisted machine identification, machine-specific history, editable programs and set notes. Recent work added account isolation, image screening before storage and public access through Cloudflare Tunnel. Read-only MCP connectivity is live, with account consent and revocation. Muse has read app workouts and imported notebooks; bulk history reads keep the request count down.
Read the process and see the app →Tools
- Stack →
What I'm building with right now: hosting, app stacks, models, agent runtime, workflow infrastructure.
How I work
A recent personal build went from planning to production using a coordinated multi-LLM workflow: separate tools for design review, implementation, code review, and test authoring, all kept aligned through version-controlled docs instead of shared memory. The human stayed in the loop for decisions and real-world verification. Claims were checked, not trusted.
That discipline is the point. The interesting part is not which model you use; it is the operating system around the tools.
Currently building
I’m building personal tools and experimenting with coding agents and automation. DocLifts is now live at doclifts.runthe.ai, running on a VPS with separate user accounts and AI machine identification. Next comes consent-based access for agents, followed by more approachable onboarding and data portability. I’m also exploring private, access-controlled tools in the cloud for a particular person or team.
Elsewhere
- GitHub
- github.com/cjenoch
- linkedin.com/in/chris-enoch
Talk through a technical task
Have a workflow or integration you want to work through? Bring the constraints and what you've tried.