# A lake of GitHub activity

> Build a lake of GitHub activity on magmalake: see it running, then build it by handing a coding agent the skills and a prompt, or step by step from pixi and mojoshelf to Orca pipelines, a flare console, a local run and a deploy to AWS.

The example is [mojo-census](https://github.com/magmalake/mojo-census): it imports every day of the [GH Archive](https://www.gharchive.org/) into an Iceberg table, finds the Mojo repositories in it, reads each one's git history, and serves a web console that shows how Mojo libraries have grown. Everything in it is Mojo, in one pixi project. An agent built it from a prompt, with only the skills as documentation.

## See it running

The example runs on AWS, deployed with `orca aws` as one image of 71.1 MB compressed, which holds the scheduler, the three pipelines and the console. Both sites are public to read.

[Orca's UI orca.mlake.dev  The three pipelines, their runs and logs, the Dista timeline of a run's tasks, the lineage of each table, and links to the code that made them.](https://orca.mlake.dev) [Mojo census census.mlake.dev  How Mojo libraries grew: their size and age, the Mojo versions and tooling they moved to over time, and a page per repository.](https://census.mlake.dev)

## How it fits together

Three Orca pipelines write five Iceberg tables. gharchive runs on a schedule; each day it commits starts mojo\_repos, and each batch of new repositories starts mojo\_history. The console reads the last three tables.

## Build it yourself

Two ways, one project. Each page stands on its own.

[Let an agent build it  Install the skills for Mojo, magmalake, mojoshelf and AWS, then give a coding agent the prompt that built mojo-census.](https://magmalake.dev/getting-started/agent) [Step by step  Install pixi and mojoshelf, create the project, read the code the agent wrote, run it locally, and deploy it to AWS. Or clone the example and run it as it is.](https://magmalake.dev/getting-started/step-by-step)