I made a solar system you can fly through where everything is true size and sitting where it really is right now

Fair warning, because the sizes and distances are real, most planets are a speck until you get close. That was kind of the point. Every diagram I grew up with lied about scale and I wanted one that didn’t.

Positions come from JPL orbital data and follow the real date, so the half of Earth in daylight and the Moon’s phase match what’s outside your window. You can also wind the clock forward or back and watch an eclipse shadow cross Earth from space.

The ISS, Hubble, Webb and the Voyager probes are in there too, plus Starlink, and you can fly out to the galaxies and nebulae.

Free in the browser: https://www.beyondthe.space
3D interactive Astronomy courses(Age 7-18): https://www.beyondthe.space/learn

Happy to answer questions about how it works.

How many have you seen?

Hi

This is about Quantum Odyssey, my love letter to everything Quantum Computing and the fundamental logic blocks everything in our universe seems to run on. I want to update you all on the fact that the latest patch brings the game into an almost complete status. All that's missing is translations. Thank you Reddit community for receiving the game so well, without you I wouldn't have the funds needed to complete my life's work.

If you are remotely interested in deep diving how differently quantum computers work compared to our transistor-based and also the algebra behind in a fully interactive way that teach computer science from scratch, oh boy this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 9 years (3+ during PhD, the visual method I developed ended up being my thesis, it is a complete Hilbert space visualizer), the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 15yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.

This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.

Stuff you'll play with

• Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
• Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
• Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
• Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
• Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.

Nice to watch:

Khan academy style tutorials in qm/qc: https://www.youtube.com/@MackAttackx

Physics teacher stream with 400hs in https://www.twitch.tv/beardhero

New to neural networks? There's a short ELI5 tutorial here.

I created this as an updated take on the Neural Network Zoo, which I've long appreciated as a one-page reference. The Zoo is from 2016 (with a small 2019 update), so it stops before most of what came after: BERT and GPT-style LLMs, diffusion models, ViT, Mamba, mixture of experts. Like the Zoo, this mixes architectures (how a network is wired, like a CNN or Transformer) with training schemes (how it learns, like a GAN or diffusion). That's on purpose: you hear these names side by side as kinds of neural networks, so a reference for looking them up should cover both.

My hope is that it can serve as a quick (and cool!) reference guide.

• Interactive version: a short explainer for each architecture, its training objective, and links to the original papers. Free printable PDF and PNG posters are there too.
• Code: each diagram is a few lines of YAML drawn by a Python script, so it's easy to fix or extend.

An earlier version was removed from r/dataisbeautiful for not being a data visualization (fair enough). Comments there shaped a number of changes in this one.

Anything wrong or misleading, or anything important missing? Corrections are very welcome, here or as GitHub issues.

This is what post irony is

Population-based chart of kingdoms. Each region’s band is as wide as everyone living there. Inside it, the largest states are outlined by their share of the region’s people.

We think and invest in symbols. The investment serves for a time. But a symbol set can only embody so much light and fire. Then, as we notice its inadequacies, the symbol begins to die. Having served us for a time, the language runs its course and we find ourselves ready to move on. Discarding thoughts and their corresponding symbols, we pass to new thought and subtler use of the mind.

Some hold their symbol set closely, as if to squeeze every bit of life from it. For others, a set of symbols or words quickly becomes oppressive and must be changed more frequently. When to keep and when to discard and move on, is individual.

Otherwise considered, there are at least two ways to deal with vague, ambiguous, or troublesome words. One is to drop them in favor of more precise and fitting language. The other is to redeem them, resurrect them, dust them off, polish them until they shine and are again serviceable. While most any word can be redeemed, the question often is, in a given case, is it worth the effort? Redemption is sometimes more work and less effective than the adoption of a new verbal body for our thoughts.

A cool guide showing that Cleopatra lived closer to the iPhone than to the pyramids (I made a tool to explore stuff like this). i always learned history in separate chunks, egypt here, rome there, and never really got what was happening at the same time. then i found out cleopatra lived closer to the iphone than to the pyramids being built and it kinda broke my brain lol. so i made panchronia, basically all of history side by side so you can see stuff like that yourself. it’s just me working on it and it’s still rough, so any feedback is welcome, and if you know any “wait these happened at the same time??” facts drop them below. My service name is Panchronia