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Nicholas Synovic
Nicholas Synovic

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Use Your Tokens Before You Lose Your Tokens

If you have the privilege of a GitHub Copilot Education license or a workplace-wide Google AI Plus subscription, I have one primary piece of advice: burn through your credits. These institutional offerings provide a unique “sandbox” where you can fail for free. My recommendation for mastering these agents is to start small but think critically.

Begin with a project you already know inside and out. Take a well-documented method and ask the agent to document it from scratch. Does it capture the nuance? Does it understand the “why” behind the logic? Now, expand the scope: provide the agent with the entire class or module and repeat the task. Observe how the quality of the output shifts as you provide more project context. This exercise isn’t just about documentation; it’s about learning the “contextual threshold” of the model you are using.

Once you understand the agent’s baseline, move into active validation:

  • Tooling Audit: Ask the agent to identify all the explicit and implicit configuration options within your codebase. See if it can find the “ghosts” in your architecture.
  • Security & Memory Loops: Ask the agent to generate a memory-safe implementation of a function, then validate that code against a tool like valgrind. If it fails, pass the valgrind error log back into the agent. Watching an agent respond to a debugger’s output is the best way to understand its ability to “reason” through technical constraints.
  • Planning vs. Execution: Use the Plan Mode to have the agent tackle a specific GitHub Issue. Evaluate it not just on the code it writes, but on the logic of the steps it proposes.

We are at a unique juncture where LLMs trained on code are only going to become more pervasive and more capable. Use the opportunity your institution has provided to become a leader in understanding what these agents can—and cannot—do. Identify the patterns that lead to failure and the strategies that lead to success.

It is a tall order to stay ahead of this curve, but as students, scientists, and engineers, we are built for this challenge. Burn the tokens, make the mistakes, and break the models now. These agents are here to stay, and the best time to learn their limitations is while someone else is picking up the tab.

This is a section of a larger blog post I made on my website. Feel free to read the full post for free here. Thanks!

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