Working on a new data pipeline for finding and ingesting public job listings that then get matched to xptracker users by relevance. The feature is currently in beta, but the above screenshot offers a little sneak peak!

General Process

The overall pipeline covers several steps:
- Download public listings via publicly accessible API's.
- Store their various meta fields, whatever is available.
- Enrich stored listings using a lightweight AI model (like Claude's Haiku).
- Compute job matches to users with access to this feature.

Cost Cutting

Anything that uses LLM models in this way is bound to introduce an upfront cost, that's why we introduced checks into the pipeline that only poll for and tailor results to users who have access to this feature. Anything with heavy AI usage on xptracker will require a subscription to access in the future.

Added (anonymized) metrics gathering as a “pro” feature to xptracker. Allows users to see number of visitors to their resume and career journal, as well as track which labels people are most interested in.

Developed the label generator and auto-labeler for xptracker. Keep it a secret though, it’s currently restricted to select users only while undergoing testing. ;)

Introduced the new more welcoming onboarding workflow to xptracker, including the option to automatically parse an uploaded resume.

Created a compositing script and pipeline to combine variables from multiple NetCDF4 files into a single raster layer stored in GeoTIFF files.