Hello World
The first post on Ben Ebsworth's blog: a living, personal-wiki space for exploring technologies, capturing learnings, and refining ideas over time.
This is my first post on my new fake blog! How exciting!
This site was originally built on the Gatsby base blog template here, with styling, tuning and components taken from the incredibly impressive blog done by Dan Abramov called overreacted.io. The site has since been rebuilt in Next.js (App Router).
The goals of this medium is to explore ideas in an open format to hopefully act as a mechanism for clean note taking but also encouraging more structured exploration of technologies. I would like to play with different technology and write posts here capturing the learnings for my own future reference. I'd like to continually update/clean blog posts overtime, further refining ideas, almost like a personal wiki.
Try it in the lab
All effects →Band Structure
physicsNearly-free electron E-k diagram with Brillouin zone gaps.
condensed mattersolid stateTransmission Line Pulse
engineeringTDR — a voltage pulse travels, reflects, and inverts on a mismatched line.
rftdrimpedanceWave Superposition
physicsInterference of two plane waves — beats, standing waves, and nodes.
wavesinterference
More from the blog
Two Number Sum
Three solutions to Two Number Sum, from O(n^2) brute force to an O(n) hash set to an O(1)-space two-pointer scan — and the memory-vs-compute trade-off behind each.
Backprop is just the chain rule
Training a neural network sounds mystical, but the engine underneath is one idea from first-year calculus: the chain rule, applied backwards through a computation graph and reusing its work. We trace a forward and backward pass through a tiny graph, see why we run it in reverse, and connect it to the downhill step that actually does the learning.
How to paint with noise
Image generators start from pure TV static and end with a photo. The trick that makes it possible is wonderfully sneaky: don't learn to paint, learn to remove a little noise, then run that backwards from static. We build the forward noising process step by step, see the signal-versus-noise schedule, and work out why predicting noise is such a clever thing to train.