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Comment by gitgud | Hacker News Reader

It's impressive, but to me it seems like the saddest development experience...

    agent = Agent(
        task='Go to hackernews on show hn and give me top 10 post titels, their points and hours. Calculate for each the ratio of points per hour.',
        llm=ChatOpenAI(model='gpt-4o'),
    )
    
    await agent.run()

Passing prompts to a LLM agent... waiting for the black box to run and do something...

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For that we wanted to give you more control as well with a for loop and you take actions step by step. I think all of these crewai like agent swarms are also very much black boxes.

How would you imagine the perfect scenario? What would make LLM outputs less of a black box?

I mean, is that really much different than an API? I pass a query, and get data back, and rarely do I get to inspect the mechanisms behind what's returning that data.

Okay that’s a pretty good point actually.

I guess I wrongly assume regular API’s are more reliable, but you’re right they’re basically black boxes too…

Well, i guess that with an LLM you kind of hope it will understand you whereas with a regular program you don't have to hope for that, just that it will work without throwing errors (usually with a magnitudes lower error rate).

It may give more transparency if it output the code to do scrapping with playwright.

Then we can review the code before actually running it.

And the reviewed result can be saved for future running.

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