synthetic examples
Back to proof
ShotSort
An AI-assisted macOS workflow for renaming, tagging, searching, quarantining, and organizing screenshots and local files.
low-confidence review
human-correctable memory
Recruiter answer
What proof does this case study show?
This case study shows AI implementation ability through confidence-aware AI suggestions, quarantine, human correction, undo, search, and local workflow adoption.
Role fit
- AI productivity workflow implementation
- Local-first AI tooling
- Operator enablement and adoption
Validation signals
- Confidence-aware suggestions
- Quarantine for uncertain output
- Undo and correction loop
Problem definition, workflow design, evaluation criteria, source selection, validation, rollout, user feedback, adoption, and outcome framing.
Code, app structure, scripts, UI wiring, parsing, tests, and iteration support. I stayed accountable for whether the workflow was useful and honest.
Problem
Operators accumulate screenshots and local files faster than they can name, sort, retrieve, or trust.
What I Designed
Designed the intake, confidence, quarantine, undo, search, and sync workflows, then used AI-assisted coding to implement and iterate.
Validation
The key test was whether AI output stayed reviewable and easy to correct, especially when file names or tags were uncertain.
Public Handling
The public view uses fake screenshots and synthetic queues so real local artifacts never become portfolio material.
Artifacts and proof
What the public proof can show.
These artifacts make the workflow concrete while keeping private data, credentials, logs, internal strategy, and real customer/user records out of public view.
Fake file list with suggested names, tags, confidence, quarantine state, and undo path.
Example of human edits flowing back into search and future organization patterns.
Low-confidence output stays quarantined until a human accepts or edits it.
No real local screenshots, file paths, or private desktop artifacts are public.
Positioning note
This is operator-led AI implementation proof: workflow judgment, validation, rollout thinking, adoption, and AI-assisted build support where coding is involved.
Good fit for teams that want AI-assisted productivity workflows designed around trust, review, and repeat use.