amatuer_sodapop

> The bill updates the legal definition of "bot" to encompass accounts operated by generative artificial intelligence - including systems that create synthetic images, videos, audio, and text.

I'm not sure how enforceable that is tbh.

> In your app code, control flow also changes based on data using `if` statements. And you don't need to know the data to write your code, right?

Yes. Which is why it's in my app code, committed to source code, and with a guarantee of determinism. If you don't consult the runtime information (i.e. data), there's no reason why it has to be generated at runtime.

> We are working on making the execution more dynamic, possibly allowing code to be generated and executed progressively when data arrives and new prompts are given.

I think you just described how a react agent works.

> you cannot pause it to wait for human interaction for a day, then resume it

Sure you can! Have you seen langgraph, trigger, temporal?

Also, how do you pause based on an AST that might get invalidated if you recompute it on new contextual information?

Does the human keep approving until your AST works?

Sorry to be terse, but surely I'm missing something here.

Since you're computing the AST upfront, you'd get it wrong more times than a react agent that does it as and when needed. So, do I pay OpenAI everytime you make a mistake on the AST?

Or is your claim that your AST is infallible?

Oh no! Please don't do this. This is one of those tarpit ideas in the "AI" space.

> do not even need to know all the data to perform operations on it or make decisions

If I know code-generation is going to be possible without any contextual information, I might as well generate the code using copilot or Curor and commit my code. Why do I need a runtime agent to do it?

What if the control flow has to change based on a result it receives?

What if the plan up front is wrong and needs to change halfway? Do I run the entire thing again with a new plan? What if my tools are not idempotent?

What if it generates a recursive loop?

Also, if I really want to do this, and if my tools are safe, why don't I just do a raw Open AI / Claude call and get deno subhosting [1] or E2B [2] to run it?

[1] https://deno.com/subhosting

[2] https://e2b.dev/

> > Observability Remains Unchanged

> Actually it doesn't, many standard interesting metrics will break because long-polling is not a standard request either.

As a person who works in a large company handling millions of websockets, I fundamentally disagree with discounting the observability challenges. WebSockets completely transform your observability stack - they require different logging patterns, new debugging approaches, different connection tracking, and change how you monitor system health at scale. Observability is far more than metrics, and handwaving away these architectural differences doesn't make the implementation easier.

Nice! How do you figure out which repos to list? Is there a submission/approval process?

I think AI-powered is fast approaching the "webscale" status.

Your point being?

I'm not commenting about the white boarding.

> Your options are to either suck it up and brush up on basic data structures and algorithms or interview at places that don't test for it.

The article is about the fact that if you did just this, you're at a disadvantage because people use Chat GPT to cheat on these and get ahead.