It doesn't even need to do the timestamping/benchmarking that my singlethreaded does, but i couldn't make 4o nor any local LLM give code that gave correct output when run. This is by far the most trivial and easily checked thing i can personally verify.

Output:

    % python /tmp/collatz.py
    Number of vCPUs available: 12
    Checked Collatz conjecture for numbers 1 to 1000000
    Number with longest sequence: 837799
    Length of longest sequence: 525
    Time taken: 2.08 seconds
    python /tmp/collatz.py  19.47s user 0.41s system 728% cpu 2.731 total

Take small companies with eveyrone being business people besides the nerds in the IT department. If you don't have one security aware person, this IT department will write unsecure code without anyone in the company every questiong this every.

You know like 'hidden' api keys in an mobile app everyone can extract, still using plaintext for passwords or md5, having ports open, old operating systems etc. etc.

OK. Now consider that those same humans have no need to do it or even attempt it.

I'll have to check the actual results, of course, but that's much cleaner than what all the other LLMs output

N = 1000000 # Total numbers to check

chunk_size = N // num_vcpus

tasks = [(i * chunk_size + 1, (i + 1) * chunk_size) for i in range(num_vcpus)]

When num_vcpus = 12, chunksize = 83333, and 12*chunk_size = 999996, therefore we don't check the last four numbers.

I use ChatGPT around town and can’t always type easily. That and it is easier for me to speak stream-of-consciousness than slowly typing my thoughts. Typing is a significant bottleneck.

I’m often thinking of ideas while walking or riding the bus. For example, I make plans for an English discussion group I’m facilitating while walking to the class. Or I’ll iterate on software project ideas as they come to mind, like creating design documents, GitHub issues, etc.

Another use case is for live translation where I speak English to ChatGPT and it translates the output to various languages. Then, I show the translation to the other person and let them speak their response for ChatGPT to translate. The interesting thing is somehow Whisper translates the foreign language back to English when it returns the transcribed text, so I don’t need to submit that text to ChatGPT. This live translation of spoken conversations is a really important feature particularly since I interact with many people with varying degree of English proficiency speaking many languages (namely, Russian, Suomi, Czech, and Farsi.)

CSS frameworks come in very useful for this, ask it to generate Bulma or Bootstrap valid markup.