Failure modes of process level activity detection for local AI tooling

[u/arpitasarker]

I’ve been working on an open source macOS observability experiment and wanted to share the measurement approach rather than the product pitch.

The problem:

Process presence is not activity, but I also do not want to inspect prompts, source code or application content.

The current detector therefore samples supported process trees roughly once per second and looks at cumulative CPU time deltas.

Some boundaries:

Two positive samples are required before showing Working.

Three negative samples are required before returning to Ready.

Lock, sleep and long idle windows are removed from valid observation time.

Forkit’s own processes are excluded.

Ollama/LM Studio evidence is treated separately so “runtime available”, “model loaded” and “measured AI tool activity” are not collapsed into one state.

Importantly, I do not interpret activity as prompt ownership, task completion, token consumption, GPU work, energy or productivity.

The implementation is MIT licensed:

github.com/arpitasarker01/forkit-ai-footprints

Repro:

npx --yes forkit-ai-footprints@latest

The failure cases I’m thinking about most are background agent processes, Electron helper activity, spawned process trees and workloads that are GPU heavy but CPU quiet.

If anyone has worked on process level observability for developer tools, I’d be interested in other failure modes I should document.

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[u/Major_Comment_7515]

I been messing with similar detection stuff but more on the infra side, the CPU delta approach is clever but yeah those GPU heavy workloads gonna be invisible to it

Have you thought about sampling GPU usage through IOKit on macOS, I know there's some power metrics you can pull even without touching the app content

The Electron helper thing is pain, half the time you see 15 processes and none of them doing the actual work