Nope, not the Transformers movie that you know. Read more to know what transformers are.
HER RUN👇👇👇
When you train for a marathon, running isn’t just about how fast you can sprint 5K. It’s about awareness: knowing your pace, reading the field, and conserving energy across the long haul. Imagine running in a pack: every runner checks not only who’s right ahead but the entire group. That’s how you find rhythm and endurance.
Transformers, the neural networks behind today’s most advanced machine learning models work in a similar way. Unlike older RNNs that process sequences step by step, transformers use attention: each token (like a runner) looks at all others, weighing who matters most in the race. And just like kilometer markers guide your pacing, transformers use positional encoding so the network knows whether a word appears at the start, middle, or end.
The most beautiful thing we can experience is the mysterious. It is the source of all true art and science.
Albert Einstein
Here’s my minimal PyTorch walk-through showing a single forward pass of scaled dot-product attention, the core of the transformer engine:
Python Code👇
import torch
import torch.nn as nn
import torch.nn.functional as F
n_tokens, d_model, d_k = 5, 16, 8
X = torch.randn(n_tokens, d_model) # embeddings
W_Q, W_K, W_V = nn.Linear(d_model, d_k, bias=False), nn.Linear(d_model, d_k, bias=False), nn.Linear(d_model, d_k, bias=False)
Q, K, V = W_Q(X), W_K(X), W_V(X)
scores = Q @ K.T / (d_k ** 0.5) # similarity scores
weights = F.softmax(scores, dim=-1) # attention weights
out = weights @ V # context-rich output
print(out.shape) # [n_tokens, d_k]
This little snippet shows the mechanics: each input creates queries, keys, and values. Scores measure how much each token should “pay attention” to the others. The result is a weighted sum, a refined representation where context is built-in. In training, layers upon layers of this process stack to form the deep understanding behind modern language models.
Interestingly, this reminds me of a recent article in New Scientist about quantum energy teleportation. Physicists showed that you can extract and even move energy across entangled particles, pulling useful power from what seems like empty space.
Read the article here: https://www.sciencedaily.com/releases/2025/09/250912195122.htm
Transformers work the same way with language: they extract meaning and signal from what initially looks like a mess of raw text. Both show us that hidden reserves are everywhere, you just need the right structure to tap into them.
And as a runner, I see the parallel in endurance training. Zone 2 running: long, slow miles, builds mitochondrial power, the invisible energy source that fuels long-distance races. It’s not flashy, it’s not about instant speed, but it creates the capacity to endure. The same way transformers gain strength by attending to every piece of information, endurance athletes grow strong by patiently building capacity.
True power comes from connections, context, and the hidden energy we learn to harness.
Whether it’s a marathon pack, a transformer layer, or a quantum system, the lesson is the same: true power comes from connections, context, and the hidden energy we learn to harness.
OGRap 