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 weightlifting log for my own training
A personal training app shaped by use across different gyms. September’s update added machine-aware logging, backoff suggestions based on the actual top set, and a Traveling Push/Pull/Legs program builder. A searchable archive now brings 107 workout records into the app, with original notes preserved and recalled estimates labeled.
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 now runs in Docker on Akamai Cloud (Linode) in Dallas, with private Tailscale access and daily database backups. Its history import paired a useful feature with restore testing, duplicate prevention, rollback checks, and verification that existing workouts stayed unchanged. 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.
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