A podcast and journalist directory with honest data labels
Public data with its source and date, and old data clearly marked as old.
A working sample we built to show how we would solve a problem like this. It is not a delivered project.
In plain terms
A searchable directory of podcasts and journalists built from public sources, with every value tagged by where it came from and how recent it is. Old imported data is marked as historical and never presented as fresh, and permission to contact anyone is never assumed.
- Every fact shows its source and date.
- Old data is clearly labelled and never passed off as current.
- Search and export work from a single screen.
What this does not claim: The public data is real: 969 podcasts, 652 journalist bylines and 150 podcast feeds. The older legacy records and the answer key used to score accuracy are simulated, so accuracy figures compare the matcher with our own key and not with the real world.
What it looks like
The directory's search screen. The list on the left shows podcasts matching a search. The panel on the right shows one podcast's details, with every value labelled as observed and the place and date it came from. The access-key box at the top left has been hidden on purpose.
Technical caption
Search UI with filters (kind, status, contact route), 25 rows per page, CSV export, and a detail panel exposing per-field provenance (source, field, observation date) and status flags. Demo token field redacted.
Technical details
Pulls 969 podcasts from Apple's public search API, 652 journalist bylines from outlet RSS feeds and checks 150 podcast feeds. A matcher is scored against a hidden ground-truth key on simulated legacy rows (747 podcast, 508 journalist). Provenance and status are stored per field, and deliverability and contact permission are never inferred.
- Python
- PostgreSQL 16 (Docker)
- Apple public podcast search API
- RSS feeds
- CSV export
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