XingfuY

XingfuY / microgpt-cpp

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Modern C++ incarnation of Karpathy's microgpt — pure autograd GPT in ~400 lines, zero dependencies

xyang.meprojectsmicrogpt-cpp
53
10
100% credibility
Found Feb 23, 2026 at 53 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
C++
AI Summary

A simple tool that learns patterns from a list of example names and generates new invented names.

How It Works

1
🔍 Discover the name inventor

You hear about this fun project that learns from a list of names and dreams up new ones just like them.

2
📥 Grab the simple files

You download the ready-made files to your computer to get started right away.

3
📝 Create your name list

You type a bunch of names, like Alice or Bob, into a plain text file to teach it what you like.

4
🚀 Start the learning magic

You launch the tool with your name list, and it begins training to understand patterns in seconds.

5
⏳ Watch it get smarter

You see the progress numbers drop as it learns more with each quick round.

6
✨ Generate fresh names

You hit go on creating, and it spits out brand new names that feel perfect.

🎉 Endless name ideas unlocked

Now you have your own personal generator for baby names, stories, pets, or anything fun!

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AI-Generated Review

What is microgpt-cpp?

microgpt-cpp ports Karpathy's minimal GPT from Python to modern C++, letting you train and run a tiny transformer on custom text datasets like names in under 400 lines. Dump text into a file, build with modern CMake, and train via CLI like `./build/microgpt input.txt 500`—it handles tokenization, optimization, and autoregressive generation out of the box. Zero dependencies beyond C++20 means it runs anywhere, perfect for dissecting autograd and GPT basics without external libs.

Why is it gaining traction?

It nails the "minimal viable GPT" vibe in modern C++ features, delivering full training loops and inference with KV caching, all testable via built-in suite. Devs dig the pure stdlib approach—no PyTorch or TensorFlow baggage—making it a fresh take on github modern c++ for ML experiments. The hook? Instant builds, passing tests on forward/backward passes, and generating coherent samples fast on CPU.

Who should use this?

C++ devs diving into autograd or transformers via modern C++ tutorial projects. Embedded engineers needing lightweight text gen without runtime deps. Instructors assigning it alongside modern C++ book chapters for hands-on neural net coding.

Verdict

Grab it for educational kicks—27 tests pass reliably, docs cover quick starts—but with 53 stars and 1.0% credibility score, it's an early experiment, not production-ready. Solid if you're prototyping modern C++ design for tiny ML toys.

(198 words)

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