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AI = AN INTERN
Prompting an AI agent and then reviewing the code can be slower than just coding it yourself.
But when? Do we have simple heuristics for when to use an agent vs code it yourself?
Use an agent when it's frontend code (which you can instantly inspect for correctness), or interaction with some API (where you know what to do but not the specific incantations for executing the API calls), or a data analysis, or a prototype.
Do it yourself when it's a domain you know very well, or it's highly contextual backend code, or you want to iterate a lot on the result. In these circumstances, writing the prompt takes more time than writing and reviewing the code.
Roughly speaking, if you visualize your app as concentric circles, AI agents are good at the "shallow" layers on the outside (like the frontend, or graphs/charts read from your database) but dangerous in the "core" areas where high context is needed and low error rates are critical.
In bullet point form, AI is better for:
- frontend over backend
- reads over writes
- shallow over core
- prototypes over production
- starting vs maintaining
- error-tolerant domains over error-intolerant
- visual output over financial
- low-context over high-context
So, AI is an intern. These criteria could themselves be used by AI agents to signal greater uncertainty when tackling high-context problems. Like an intern saying that they'll try hard, but they might not have enough context.
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AI is perhaps the most competitive field in the most competitive sector in the world.
Somehow @elonmusk built and funded the team capable of getting to global #1 while simultaneously running SpaceX, Tesla, X, Boring, and Neuralink.
It is superhuman.

Sawyer Merritt10.7. klo 13.57
xAI: "We doubled our compute to 200,000 GPUs at an unprecedented rate, with a roadmap to 1 million GPUs. Progress in AI is driven by compute and no one has come close to building at this magnitude and speed."

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