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This is a great writeup! Could you share more about the sandbox <-> client communication architecture? e.g., is the agent emitting events to a queue/topic, writing artifacts to object storage, and the client subscribes; or is it more direct (websocket/gRPC) from the sandbox? I’ve mostly leaned on sandbox.exec() patterns in Modal, and I’m curious what you found works best at scale.


After reading the article, I built a tool like that with sprites.dev. There's a websocket to communicate stdout and stderr to the client.

Web app submits the prompt, a sandbox starts on sprites.dev and any Claude output in the sandbox gets piped to the web app for display.

Not sure I can open source it as it's something I built for a client, but ask if you have any questions.


Can you describe a little about how you go from raw text to a summary? Is this supervised learning? If so, what is the training data?


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