sampling
5 free lessons tagged sampling across Math, Programming, AI. 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.
Sampling and Aliasing: The Rule You Cannot Break
Turning a continuous signal into numbers is safe only above a specific rate, and below it the damage is silent and permanent. This lesson derives the Nyquist limit, shows exactly where a too-high frequency reappears, and explains why the fix has to be analogue and has to happen before the converter.
How Profilers Work, and How to Read a Flame Graph
A profiler is not a neutral observer: sampling and instrumentation see different things, distort the program in different ways, and answer different questions. This lesson covers how each works, why CPU time and wall-clock time give opposite answers, and how to read a flame graph correctly, including the axis that means nothing and is misread constantly.
Distributed Tracing and the Art of Throwing Data Away
Tracing every request through every service produces the most useful telemetry you have and more of it than anyone can afford. This lesson covers how context propagation actually stitches a trace together, the head versus tail sampling decision and why it determines which incidents you can debug, and the collector pipeline where all of it is enforced.
Speculative decoding: making LLM inference faster without changing the output
How draft-then-verify decoding gets multiple tokens per forward pass of a large model, why rejection sampling makes it provably lossless, and where the draft comes from (small models, Medusa heads, self-speculation, EAGLE trees).
LLM Inference Internals: KV Cache, Sampling, and Serving at Scale
A deep dive into how large language models actually run in production — why prefill is fast and decode is slow, how the KV cache works, sampling strategies like temperature and top-p, speculative decoding, and continuous batching with vLLM.