Introduced several user experience refinements to the platform at large:
- Labeling insights module on the dashboard page.
- Journal entry lookup on new descriptor entries.
- Improved labeling interface for easier labeling.
- New weekly email summaries on public view metrics and job matches.

All done while testing out the free trial of Claude Fable 5.

Introduced an abstraction class to enable default filtering on contents only visible within the view port of the Garmin OneHelm Canyon Runner app.

Process

This was a fun problem to think through. After porting the functionality to the new Typescript codebase 1:1, I started testing various new overlays we introduced to the map. They loaded fine, but were laggy and made the Garmin go unresponsive for seconds at a time. I knew there had to be performance optimizations to be made by restricting the overall number of waypoints, but I didn't want to create these optimizations on a per-module basis. I created a basic proof of concept view port filter that takes any dataset containing coordinates, and returning it filtered for only items currently being viewed on the leaflet map.

Claude Information Gathering

To confirm the abstracting I created was going to be applicable across the code base, I utilized Claude to outline the functionality of the legacy code base, and compare it against our re-worked version. Seeing the repeated patterns, I worked through a series of updates to the PoC in order to make it more universal and extendable.

Performance Impact

By implementing the filter, we were able to stabilize load times and reduce the overall hangups the Garmin experienced. The above screenshot depicts our performance testing, with the largest peaks occurring before we introduced this filtering (around the 766 mark).

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.

Optimized processing of encoded tile data on the Garmin OneHelm Canyon Runner app by utilizing onboard proxy server caching capabilities. Reducing cursor and touch event listener counts, offloading data-storage off the device to continue working within the 150MB memory limit, and targeted-access of per-pixel data to return the SST/CHL reading from a WMS PNG tile on click. Utilized a new local performance tracking server to track performance optimization change effects on the device.

Garmin OneHelm <--> Proxy Server <--> HTTP Requests

What's in the encoded data?

Depends! We can store sea surface temperature readings, chlorophyll concentrations, salinity, and so on. So basically, just raw data. We process satellite files from known databanks to create mapping tiles with this data so we can decode them on the front-end. We have 3 channels of 8-bit data to work with, so if we're trying to store, say, the logarithmic-scaling dataset for Chlorophyll concentrations, we can normalize the single concentration value to 24-bits before we distribute those bits across each 8-bit channel.