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.

Introduced Loran lines for the Garmin OneHelm typescript application. Introduced various performance enhancements to reduce about 1kb of memory overhead to keep the application in-line with the OneHelm 150MB spec limit. Hyper-optimizations included reducing forced SVG layout counts from 2400 to 300, text alignment map pings from 600 to 300, and removed all 2100 event listeners entirely to reduce overhead further.

Event Listeners

Typically no one has to think about the overall number of web listeners their creating on their web page. Any modern device can support way more than even the most poorly optimized sites can truly throw at it; however, that's not the case for the Garmin device, which only allots 150MB of total RAM to any given OneHelm app. Removing the 2100 event listeners caused a noticeable drop in bootstrap times, and improved overall responsiveness (which unfortuntaely is still a work in progress overall).

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.

Integrated the ATProto authentication protocol to allow users to link their Bluesky profiles to xptracker. Used newly linked profiles to allow users to share their career journal entries directly to Bluesky. All this in preparation for more features to come.