stephengrider/gophernotes — explained in plain English
Analysis updated 2026-08-04 · repo last pushed 2017-07-23
Prototype pattern recognition or neural network code in Go and share results with colleagues.
Build an interactive notebook that walks students through Go concepts like worker pools and channels.
Experiment with Go syntax and immediately see the output without setting up a full project.
Export notebook code into a standalone Go file or a ready-to-run container image.
| stephengrider/gophernotes | beppetemp/cartographer | d4l3k/turtle | |
|---|---|---|---|
| Stars | 8 | 8 | 8 |
| Language | Go | Go | Go |
| Last pushed | 2017-07-23 | — | 2018-01-27 |
| Maintenance | Dormant | — | Dormant |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 3/5 | 4/5 | 2/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires Go and the ZeroMQ messaging library to be installed, or downloading a large Docker image that bundles a full data science toolkit alongside Go.
gophernotes lets you write and run Go code inside Jupyter notebooks. If you've ever seen someone use a notebook to mix live code with explanatory text, equations, and charts all in one shareable document, this brings that same experience to the Go programming language. You can create these notebooks, share them through email or GitHub, and let others see both your code and its output together. Jupyter is a popular web-based tool normally associated with Python and data science. It works by connecting to a "kernel" behind the scenes, which is the engine that actually runs your code. This project is simply a Go kernel for Jupyter. Once installed, you open a notebook in your browser, pick Go from the language menu, and start typing. When you run a cell, the kernel executes the Go code and displays the results right below it. The project includes some built-in shortcuts, like a command to export your notebook code into a standalone file or even package it into a ready-to-run container image. This is mainly for people who want to explore Go interactively rather than writing full programs from scratch. A data scientist could use it to prototype pattern recognition or neural network code in Go, sharing the step-by-step results with colleagues. A teacher could build a notebook that walks students through Go concepts like worker pools or channels, showing the output of each snippet as they go. It is also handy for anyone who just wants to experiment with Go syntax and immediately see what happens, without setting up a full project. The installation process is fairly involved, requiring you to have Go and a messaging library called ZeroMQ already on your computer. The easiest path by far is using their pre-packaged Docker image, which comes with everything ready to go, though the tradeoff is that it is a very large download since it bundles a full data science toolkit alongside Go. The project originated at a 2016 hackathon and builds on earlier efforts to give Go an interactive coding environment.
A Go kernel for Jupyter notebooks that lets you write and run Go code in your browser alongside text and charts, then share the results with others.
Mainly Go. The stack also includes Go, Jupyter, ZeroMQ.
Dormant — no commits in 2+ years (last push 2017-07-23).
The explanation does not mention a license.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
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