I wear a WHOOP. I've coached people on movement and sleep for many years and I still can't answer that question for myself. The algorithm is locked. You get a number, you trust it, you stop there.

When we built Open Wearables, we decided the scoring layer should work differently. Sleep Score and Resilience Score shipped in v0.5 - every coefficient, every threshold, every weighting is in the repo and you can fork them, tune for endurance athletes or elder care or clinical populations. Moreover, you run them on your own infrastructure and the same algorithms feed the MCP layer so AI coaching can cite the actual data behind a recommendation instead of approximating.

We ran a benchmark to see how well @Claude Code actually refactors legacy code alone and then redid the same test, but this time with code-health guidance via MCP server.

• To limit any vendor bias, we used a public data set of 25,000 source code files from competitive programming, including carefully crafted unit tests. 

• We assessed agent correctness by running those tests. 

• We measured the Code Health impact using CodeScene.

• (See our research Code for Machines, Not just Humans for more details on the methodology and data)

Claude Code that was MCP-guided achieved 2 5x more more improvements in Code Health compared to unguided refactoring.

AI is incredibly good, I d even say almost perfect.

And for many people, that uniformity of perfect templates is starting to feel annoying.

People s motivations for wanting something of their own vary quite a lot.

So far, though, I ve most often heard these three answers:

• To make a lot of money.

• To work really hard until 30 so I can relax later in life.

• Time and location freedom.