I built GPT from scratch to understand how it works

Show HN: KVBoost – chunk-level KV cache reuse for HuggingFace, 5–48x faster TTFT

I built GPT from scratch to understand how it works is active in the show hn launch space.

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If you’ve ever been curious about how GPT actually works under the hood, I built a small project you might find interesting. I implemented a GPT-style transformer from scratch in a single notebook—covering tokenization, embeddings, causal self-attention, training, and autoregressive text generation without relying on high-level abstractions. The focus was on mechanistic clarity rather than scale or performance, and the notebook is structured to read more like a technical walkthrough than an experiment log. Feedback from people who’ve built or studied transformers would be very welcome.

Within the show hn launch space, the company focuses on its core offering rather than a broad product line.

The company is listed at the Show HN launch stage of development.

Website: pythongiant.github.io/KVBoost