strive.ai’s vision was founded on two key pillars: Enhance the Strava Experience and Enhance Athlete Performance through advanced analytics. Impactful data science requires enough data to be successful so what we did to start was offer some interesting features that created
As you may recall we added the real-time testing of AI predictors the last week of June. This was a very helpful first step in improving the quality of our predictors. This week we’ve added our first true ensemble learning
Warning this is a data nerd post! I met with a good friend yesterday to discuss various challenges we’ve been facing in strive.ai. He’s an uber experienced data rock star from my previous company. One of the things we discussed
Super excited to start laying down some advanced features to enhance your Strava experience. The key to any good AI is the data so over the next 6-8 weeks we’re enriching our data to unlock exciting new algorithms that we
Due to our steady growth we now need to be conscious of how much burden we’re putting on Strava’s servers as they rate limit apps to 600 hits / 15 minutes and 30K hits / day. Our original sync logic
I’m very excited to receive my Masimo Mighty Sat Pulse Oximeter tomorrow! I’ve been reading how a handful of professional triathletes have incorporated pulse oxygen data into their training. It’s intuitively obvious that the amount of oxygen in your blood will impact
As we head to the end of our 4rd month of beta usage I’m more than a bit disappointed at how accurate the bike prediction feature has been working. If you recall, we added a second algorithm in January in
One of the initial visions for strive.ai was to offer features that help athletes find training partners with similar characteristics. We tried one approach in the early beta that clustered athletes based on starred segment completion time. This approach had “mixed
We’ve been hard at work for the past 2 weeks refactoring our Factors and Trends features. Our mission is to continue to provide great visualizations and insights from your data!
Reader beware, this is a nerd post! One of the most common types of machine learning is used to detect outliers in a dataset. Today we introduced several “anomaly detection” algorithms into the beta that assess your activities, power thresholds and segment efforts. Why do we do