This past week I've been working with our data store at Canyon Runner to collect and process boat depth sensor readings from our fleet of boats.

Purpose

This work is preliminary research to determine if we have enough data to provide more real-time depth readouts and charts for our customers. The aim here is to provide more real time charting for inlets that often have rapidly changing underwater conditions after storm surges.

Approach

The approach here requires mapping our depth sensor readouts (which don't come with geo-coordinates) to the boats coordinate readouts aligned by time, with a sliding window of four seconds (an arbitrary time chosen to prevent data loss while still maintaining accuracy). These points then get mapped to a grid for consistency (see screenshots), and the median value is displayed and interpolated to create a relief chart.

This work is still ongoing, and is predominantly just research to see how feasible using this data will be to provide customers with more up-to-date depth readouts.

By the Numbers

A single inlet across a 6-month time span might have anywhere from 4-12 million data points depending on how much traffic is passing through that inlet. The goal here is to make it render on a map, while offering various data filters, and still keeping it performant.

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.

Integreated the ATProto OAuth protocol to xptracker, authorizing only the scopes that are required for functionality.

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.