Navigating AI-Generated Code Execution: From WebAssembly to AWS Lambda
In our latest exploration of AI chart generation, we tackled the complexities of running LLM-generated code safely and effectively. With our tool, Quesma Charts, we pivoted from the innovative but impractical WebAssembly approach to a more robust solution using AWS Lambda. Here’s what we learned:
-
Performance & Security: Running code in the browser was promising but led to resource and security issues. AWS Lambda allows for sandboxed execution, reducing risks from untrusted code.
-
Enhanced User Experience: By shifting to a cloud-based approach, we simplified our user interface. The heavy lifting now occurs server-side, streamlining the data flow and improving chart generation efficiency.
-
Dependency Management: With Docker on Lambda, we created consistent execution environments, avoiding dependency conflicts and ensuring reliability.
Despite the transition to a less flashy solution, the stability and scalability gained are invaluable.
💡 What challenges have you faced running AI-generated code? Share your thoughts below!
