Mayank A. recently shared something that caught my attention: "This guy built GPT from scratch in pure C.

No PyTorch. No TensorFlow. No libraries.

Just raw C code." The punchline in his post is even sharper: "This is how you actually understand transformers. Not by importing torch.nn.Transformer. By writing every matrix multiplication yourself."

I agree with the spirit of that message. If you have ever trained a model and felt like the results were magic, building a tiny GPT-like model in a low-level language forces you to replace "magic" with mechanics. You stop thinking in terms of APIs and start thinking in tensors, gradients, stability tricks, and performance constraints.

In this post, I want to expand on what Mayank A. highlighted, why each component matters, and what you actually learn when you rebuild the transformer stack end to end.

Why "pure C" changes the learning game

Frameworks are incredible for shipping. But they are also incredible at hiding details.

When you write a transformer in pure C, there is no safety net:

You implement every matrix multiply and you feel the computational cost.
You handle memory layout and you learn why contiguous buffers matter.
You debug NaNs by tracing the exact operation that exploded.
You see that "backprop" is not a button, it is bookkeeping.

Mayank A. is pointing to a simple truth: the fastest way to understand a system is to remove abstractions until you can no longer avoid the fundamentals.

This does not mean everyone should train in C forever. It means that doing one serious from-scratch build can permanently level up how you use higher-level tools.

What was implemented (and why it is not "just coding")

Mayank A. called out a specific list of components. Each one is a lesson.

Custom random number generator (xorshift)

If you initialize weights, shuffle data, or sample tokens, you need randomness. A simple xorshift RNG is fast and easy to implement.

The bigger lesson: reproducibility and distribution quality matter. Poor randomness can subtly impact training, debugging, and your ability to compare experiments.

Character-level tokenizer

A character-level tokenizer is the simplest possible text encoding: each character maps to an ID.

Why it is educational:

You avoid complex BPE implementations.
You get a direct, visible mapping from text to tokens.
You learn the cost: longer sequences for the same text.

That tradeoff is real. Character tokenization keeps the pipeline simple, but a 32-token context window becomes very small in terms of words. Which brings us to the model size choices.

Multi-head self-attention

Attention is the heart of the transformer. Implementing it yourself forces you to confront:

Q, K, V projections (and their shapes)
scaled dot-products and why the scaling is needed
masking (especially for autoregressive GPT-style generation)
softmax stability

Once you implement attention manually, the API-level call stops looking like wizardry. It becomes a very specific sequence of linear algebra operations.

RMS normalization

RMSNorm is a normalization approach that avoids centering and normalizes by the root mean square. It is popular in modern transformer variants because it can be simpler and stable.

Implementing it from scratch teaches you:

normalization is not optional, it is a training stabilizer
small numerical choices (epsilon values, accumulation precision) can decide whether training works

Softmax from scratch

Softmax seems trivial until it is not.

If you compute softmax naively, you will overflow. The standard fix is the "subtract max" trick:

find max(logits)
compute exp(logits - max)
divide by sum

Writing this yourself makes numerical stability concrete, not theoretical.

Full backpropagation

This is the point where many "from scratch" projects quietly stop. Forward passes are straightforward. Backprop is where you prove you understand the computation graph.

You must compute gradients for:

embeddings
linear layers
attention (including the softmax)
normalization

And you must do it efficiently enough that training actually progresses.

If you can backprop through attention in raw C, you do not just "know transformers". You know how transformers are trained.

Adam optimizer

Adam is not just "SGD but better". It is a per-parameter adaptive learning rate method with momentum (first moment) and variance estimation (second moment).

Implementing Adam teaches:

why bias correction exists
how optimizer state doubles (or more) your memory footprint
why mixed precision and numeric ranges matter

The model configuration: small, but instructive

Mayank A. shared concrete specs:

64 embedding dimensions
4 attention heads
2 transformer layers
32 token context window

This is tiny by modern standards, and that is the point.

A small model:

trains faster on limited hardware
is easier to debug
makes it feasible to run experiments end-to-end

But it also exposes limitations quickly. With a 32-token context window and character tokens, coherence will be short-range. That teaches another important lesson: scaling laws are not just hype, they reflect real capacity and context needs.

What you actually learn by writing every matrix multiplication

Mayank A. wrote: "By writing every matrix multiplication yourself." That line matters because matrix multiplication is the dominant cost in transformers.

When you implement matmul in C, you immediately care about:

shape discipline (M x K times K x N)
cache friendliness and stride order
batching and reusing buffers
the difference between correctness and performance

Even if you later rely on optimized BLAS or GPU kernels, you will better understand why certain shapes are faster, why attention is expensive at long context lengths, and why model architecture choices affect latency.

A practical path if you want to learn this way

You do not need to start by cloning a full GPT training stack. A good progression looks like this:

Implement a minimal tensor struct and a few ops (matmul, add, layer norm or RMSNorm).
Write a forward-only transformer block and verify shapes at every step.
Add an autoregressive loss (cross-entropy) and confirm it decreases.
Implement backprop for one component at a time (start with linear layers).
Add attention backprop and validate gradients with finite differences on tiny inputs.
Add Adam, then train a toy dataset until you can overfit.

Key habits that make this work:

Print shapes and small slices of tensors.
Add assertions for NaN and Inf detection.
Keep everything small until it is correct.

Why this post went viral (and what it teaches about LinkedIn content)

Beyond the engineering, Mayank A.'s framing is a masterclass in LinkedIn content that travels.

It works because:

The hook is extreme but clear: "GPT from scratch in pure C."
The constraints are memorable: "No PyTorch. No TensorFlow. No libraries."
The bullet list signals substance fast.
The takeaway is identity-based: real understanding comes from fundamentals.

If you are studying content strategy, this is a pattern worth noting: a short, high-contrast claim plus concrete receipts (the implementation list) creates credibility quickly. That is why viral posts in technical niches often look like "here is the build, here is the spec, here is the lesson".

The real takeaway

Not everyone needs to write GPT in C. But Mayank A.'s point stands: if you want deep intuition, you cannot outsource all the thinking to a framework.

Building from scratch forces you to learn the transformer as a system:

data representation (tokenization)
numerics (softmax stability, normalization)
learning dynamics (backprop, Adam)
compute realities (matmul, memory, efficiency)

And once you have that, you can return to PyTorch or TensorFlow with a different mindset: you will know what the abstractions are doing, when they help, and when they hide a bug.

This blog post expands on a viral LinkedIn post by Mayank A., Follow for Your Daily Dose of AI, Software Development & System Design Tips | Exploring AI SaaS - Tinkering, Testing, Learning | Everything I write reflects my personal thoughts and has nothing to do with my employer. 👍. View the original LinkedIn post →