Uber allocates money across different regions and programs to incentivize riders and drivers to use Uber products. This incentive structure ultimately influences the market. This leads to the natural ...

Nowadays, AI is primarily associated with LLMs, where the goal is to develop autonomous agents with human-level intelligence (whatever that means). I have always found the goal of creating something w...

Computational efficiency is a significant challenge when scaling solutions to a marketplace as large and as complex as Uber. The running and tuning of the Uber rides business relies on substantial numbers of machine learning models and optimization algorithms. Luckily, many pieces of the system can be treated in parallel. Ray® is a general compute engine for Python® that’s designed for ML, AI, and other algorithmic workloads. We describe how Uber has adopted Ray to enable mission-critical systems. Using Uber’s mobility marketplace allocation tuning system as an example, we found performance improvements of up to 40 times that unlocked new capabilities. It also improved developer productivity by increasing iteration speed, reducing incident mitigation time, and lowering code complexity.

A couple of days ago, Cursor went down during the ChatGPT outage. I stared at my terminal facing those red error messages that I hate to see. An AWS error glared back at me. I didn’t want to figure it...

When interfacing with libraries written in C/C++ from Rust, it may require writing native callbacks to provide functionality or logic to the library. A C Callback is a function pointer that is passed ...