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Copy file name to clipboardExpand all lines: _posts/2025-04-11-Data-Annotation-Course-Lab.MD
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Welcome to the practical lab session for our Data Annotation course. In this lab, we will explore and compare modern development environments, focusing on **Firebase Studio** within the Google Cloud Platform (GCP) ecosystem, and contrasting it with alternatives like **Visual Studio Code (VS Code) paired with GitHub Copilot**, and the AI-native editor **Cursor**.
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TL;DR. VScode easily beats a remote servicelike Firebase Studio for a user, but this lab is really a meta-thing, ie it's really about understanding how Firebase Studio works under the hood, and how we would add in GitButler's approach and build [a gameifiedcollaborative development environment](https://github.com/FARTSlive).
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**TL;DR. We're probably going to use GitButler or our own IDE built on Tauri.** After all, VScode easily beats a remote service, especially a janky, half-baked product like Firebase Studio for any use case, but this lab is really actally a meta-thing. It's really about understanding how Firebase Studio works under the hood[it's an app running containers on GCP managed by k8s], and how we would add in GitButler's approach and build [a gameified, ie FUN with a scorekeeper or commitgraph, **collaborative** integrated development environment](https://github.com/FARTSlive).
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Our goal is to evaluate these tools for a specific **knowledge engineering project**: building a **collaborative** data visualization tools that help users maps connections, ie annotations that represent comments, questions, suggestions, various hot takes, and other forms of connection/relationship etc, across diverse domains like agriculture (soil/yield data), music (ambient tracks in film), and medical imaging (body/brain scans).
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Our goal is to evaluate different tools and IDE's **knowledge engineering projects** for **SHARED KNOWLEDGE** ... that means building **collaborative** data visualization tools that help users enjoy the process of social, multi-branch coding to map things like causation dependencies, connections ... that might be accomplished through annotations to the IDE's AI-assisted code generation ... it's HUMAN-in-the-loop stuff ... the AI can do the tedious stuff, but the humans are the ones who make the observations, commentary, sidenotes, inane commentary, questions/answers, suggestions, whatifs, various hot takes, and other forms of making connections/building relationships into the data ... we should expect that no-code development will happen across diverse domains like agriculture (soil/yield data), music (ambient tracks in film), and medical imaging (body/brain scans).
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