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> and a clever manually coded algorithm could do better and much more efficiently.

Sure, but how long would it take to implement this algorithm, and would that be worth it for one-off cases?

Just today I asked Claude to create a jq query that looks for objects with a certain value for one field, but which lack a certain other field. I could have spent a long time trying to make sense of jq's man page, but instead I spent 30 seconds writing a short description of what I'm looking for in natural language, and the AI returned the correct jq invocation within seconds.



I don’t think this is a bad use. A bad use would be to give Claude the dataset and ask it to tell you which elements have that value.


Claude answers a lot of its questions by first writing and then running code to generate the results. Its only limitation is the access to databases and size of context window, both of which will be radically improved over the next 5 years.


I would still rather be able to see the code it generates


Ha, I tried that before. However, the file was too large for its context window, so it only seemed to analyze the first part and gave a wrong result.


It was your own data, right ? Becouse you just donated half of it...


It's okay, I also uploaded an NDA in a previous prompt :-)


But how do you know it's given you the correct answer? Just because the code appears to work it doesn't mean it's correct.


But how do I know if my hand-written jq query is the correct solution? Just because the query appears to work it doesn't mean it's correct.


Because I understand the process that I have followed to get to the solution.


It can explain its solution. Point to relevant docs as well.


It can also very convincingly explain a non-solution pointing to either real or hallucinated docs.


You need to look at the docs.


Omg this is how llms used to trick me inventing out all these apis.


Look at the docs it links to.




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