scaling
5 free lessons tagged scaling across AI, Programming, Computer Science. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Training Diffusion Language Models at Scale
What it takes to train a diffusion language model to billions of parameters: the from-scratch recipe, adaptation from an autoregressive checkpoint, the supervision economics, and the capability that bidirectional training gets for free.
Scaling and Load Balancing
Master the mechanics of scaling distributed systems: when to scale up versus out, why statelessness is the prerequisite for horizontal scale, how L4 and L7 load balancers differ, which balancing algorithm fits which workload, and how to avoid turning your load balancer into the next single point of failure.
Microservices vs Monoliths
The honest case for each. When a monolith is correct, what microservices actually buy you (and what they cost), Conway's law, and how to spot a fake microservices architecture that's actually a distributed monolith.
Database Sharding
When one database isn't enough. How sharding splits data across nodes, the trade-offs of different sharding keys, and the operational headaches (hot spots, rebalancing, cross-shard queries) you sign up for.
Load Balancing
How load balancers spread traffic across servers, what L4 and L7 actually mean, the routing algorithms in real use, and the failure modes you need to design around.