ZetsApp Suite

General

Markov Chains: The Strange Math Behind Google, ChatGPT & Nuclear Bombs | Zetsapp

Independence means knowing the result of event A tells you nothing about event B. In formal probability: P(B|A) = P(B). This makes calculations tractable because you can just multiply probabilities. P(two heads) = P(head) × P(head) = 0.5 × 0.5 = 0.25. Once you have dependence, you need conditional probabilities, and the math gets much harder — which is exactly why Bernoulli and others assumed independence, and exactly why Markov's willingness to tackle the dependent case was so significant.

👤 admin📅 Apr 15, 2026⏱ 50 min

General

How GPUs Run 36 Trillion Calculations/sec | Zetsapp

Think of a CPU as a jumbo jet: fast, flexible, capable of landing at 40,000 different airports (meaning it can run any operating system, application, or hardware interface you throw at it). Its 24 cores are each highly sophisticated — they can handle complex branching logic,

👤 admin📅 Apr 14, 2026⏱ 51 min

Universe

Antimatter Explained: The Mystery That Shouldn't Let Us Exist | Zetsapp

Most physicists would have thrown away the negative solution as unphysical. Dirac didn't. He proposed that the negative-energy solution corresponded to an entirely new particle — the same mass as an electron, but opposite charge. Four years before anyone had seen one, he predicted the positron.

👤 admin📅 Apr 13, 2026⏱ 52 min

How Diffusion Models Work: The Physics Behind AI Image & Video Generation | Zetsapp

Diffusion models were named after the physical diffusion process for a precise mathematical reason. The forward process (adding noise to images step by step) follows a stochastic differential equation identical to the Langevin equation describing Brownian motion.

👤 admin📅 Apr 10, 2026⏱ 39 min

General

SSL Certificates Explained: How HTTPS Stops Man-in-the-Middle Attacks Cold | Zetsapp

HTTP (HyperText Transfer Protocol) was invented in 1991 by Tim Berners-Lee for sharing documents between academics. Security wasn't the priority. Packets travel through dozens of routers and switches between your laptop and a web server, and any one of those hops could be controlled by an attacker

👤 admin📅 Apr 9, 2026⏱ 48 min

Multi-Layer Perceptrons: Where LLMs Hide Their Knowledge | Zetsapp

In GPT-3, the MLP blocks account for approximately 116 billion of the 175 billion total parameters — around 66%. Attention blocks account for ~58 billion, with the rest in embeddings and normalization. Despite attention getting all the theoretical glory, the majority of the model's "brain" by raw parameter count is in these relatively simple matrix-multiplication blocks. Understanding MLPs is understanding where the model actually learned its knowledge.

👤 admin📅 Apr 8, 2026⏱ 50 min

The Attention Mechanism: Heart of Every Transformer

Attention doesn't create new information from thin air. It routes existing information from one position in the sequence to another. Think of each token as a node in a graph, with attention weights determining how much each node "receives" from every other node.

👤 admin📅 Apr 7, 2026⏱ 49 min

Inside the Transformer: How GPT Actually Processes Language

A 2020 paper by Kaplan et al. at OpenAI established that language model performance scales predictably with three factors: model size (parameters), dataset size (tokens), and compute (FLOPs). Crucially, the scaling is smooth and power-law — double the parameters and performance improves

👤 admin📅 Apr 6, 2026⏱ 49 min

Backpropagation Explained: The Algorithm That Teaches Neural Networks

Backpropagation doesn't "teach" anything. It computes. The gradient it produces tells you which direction to nudge weights to decrease cost on the current training example. The actual adjustment is made by gradient descent. Think of backprop as a very smart accountant who calculates where money was lost — and gradient descent as the manager who makes the financial decisions based on that report.

👤 admin📅 Apr 3, 2026⏱ 54 min