Weekend Reading #64

Weekend Reading: A weekly roundup of interesting Software Architecture and Programming articles from tech companies. Find fresh ideas and insights every weekend.

This week: CAP theorem basics, Uber’s probabilistic heatmaps, Dropbox’s context-aware AI, and Lyft’s modern ML platform architecture.

CAP Theorem

👉 Helpful if you're designing systems that need predictable trade-offs.

A clear walkthrough of the CAP theorem with practical examples. Explains why distributed systems must choose between consistency and availability when partitions happen.

Uber: Enhancing Driver Heatmaps with Deep Probabilistic Models

👉 Great read if you’re into ML forecasting or geospatial modeling.

Uber engineers describe how they improved driver heatmaps using deep probabilistic models. The system predicts demand more accurately and helps drivers position themselves.

How Dash uses context engineering for smarter AI

👉 Useful if you're exploring ways to ground LLMs in a real-world context.

Dropbox explains how its AI assistant, Dash, uses context engineering to understand user intent. They combine embeddings, metadata, and search signals to improve responses.

Lyft: Rethinking ML Platform Architecture

👉 Recommended for anyone building or modernizing an internal ML platform

Lyft shares how they rebuilt their ML platform to improve the efficiency of model training, deployment, and iteration. The new architecture focuses on reliability and team velocity.