
GitHub Codespaces
replit
StackBlitz
CloudShell
vscode.dev
CodeTasty
AWS Cloud9
StackHive
Scale Nucleus
ML Image Classifier
Aquarium
Prodigy
mlblocks
PerceptiLabs
Machine Learning Playground
Roboflow Universe
GitHub CodespacesBased on our record, GitHub Codespaces seems to be a lot more popular than Scale Nucleus. While we know about 152 links to GitHub Codespaces, we've tracked only 2 mentions of Scale Nucleus. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
First, remote dev environments became table stakes. GitHub Codespaces, Gitpod, and self-hosted dev containers became how serious teams worked. Every engineer I know who ships to production now SSHs into a box they didn't provision, edits files with whatever editor is installed, and commits from a terminal. An IDE-bound agent requires you to also forward your IDE to the remote box, which most people don't bother... - Source: dev.to / 4 months ago
This package provides support for managing GitHub Codespaces in Emacs and connecting to them via TRAMP. It provides a handy completing-read UI that lets you choose from all your created codespaces. - Source: dev.to / 5 months ago
GitHub Codespaces provides 60 hours of free compute time every month, which is more than enough for scoped home assignments or interviews. Itโs a full VSCode in the browser at github.dev or vscode.dev. - Source: dev.to / 8 months ago
GitHub Codespaces - Cloud development. - Source: dev.to / about 1 year ago
https://github.com/features/codespaces All you need is a well-defined .devcontainer file. Debugging, extensions, collaborative coding, dependant services, OS libraries, as much RAM as you need (as opposed to what you have), specific NodeJS Versions โ all with a single click. - Source: Hacker News / over 1 year ago
At Scale we built a tool for model debugging in computer vision called Nucleus (scale.com/nucleus) designed exactly for this, which is free try out if you're curious to see where your model predictions are most at odds with your ground truth. Source: over 4 years ago
To address your point about gathering edge cases, which can also be defined as cases of low model fidelity for our use cases, there is active learning and tools such as Aquarium Learning and Scale Nucleus which make it easy to implement into workflows. Source: about 5 years ago
replit - Code, create, andlearn together. Use our free, collaborative, in-browser IDE to code in 50+ languages โ without spending a second on setup.
ML Image Classifier - Quickly train custom machine learning models in your browser
StackBlitz - Online VS Code Editor for Angular and React
Aquarium - Improve ML models by improving datasets theyโre trained on
CloudShell - Cloud Shell is a free admin machine with browser-based command-line access for managing your infrastructure and applications on Google Cloud Platform.
Prodigy - Radically efficient machine teaching