Outside of the “stars” and directors, people working in the film/tv industry already have a fairly low income (median salary for professional actors is ~$47000/yr).

I started using it as an alternative to Octave/Matlab and Perl. Python is better at general programming than Octave/Matlab, and better syntax than Perl (IMO) while being almost as easy to do the same stuff I was using Perl for. It’s very good for quickly writing small scripts. Issues can arise on large projects/teams because of stuff like type safety, and it also has issues with performance.

I think compose files are usually pinned to a version, or use a .env file that needs to be changed to update to a new version.

I personally don’t update very often; usually not until I’m forced to for some reason. I find that just checking the documentation for any upgrade/migration guides, and doing it manually is sufficient. I don’t expose this kind of stuff publicly; if I did, I’d probably update regularly.

I’ve had “success” with using them for small one-off projects where I don’t care too much about correctness, efficiency, or maintainability. I’ve tried using various AI tools (Copilot, Cursor agents, etc) for more serious projects where I do care about those things, and it was counter-productive (as studies have shown).

Hmm, I was curious if ChatGPT still gives inefficient code when asking it to write quicksort in Python, and it still does:

def quicksort(arr):
    if len(arr) <= 1:  # Base case
        return arr
    pivot = arr[len(arr) // 2]  # Choose middle element as pivot
    left = [x for x in arr if x < pivot]   # Elements less than pivot
    middle = [x for x in arr if x == pivot] # Elements equal to pivot
    right = [x for x in arr if x > pivot]  # Elements greater than pivot
    return quicksort(left) + middle + quicksort(right)

That’s not really quicksort. I believe that has a memory complexity of O(n log n) on the average case, and O(n^2) for the worst case. If AI does stuff like this on basic, well-known algorithms, it’s likely going to do inefficient or wrong stuff in other places. If it’s writing something someone is not familiar with, they may not catch the problems/errors. If it’s writing something someone is familiar with, it’s likely faster for them to write it themselves rather than carefully review the code it generates.

In the grand scheme of things, I think AI code generators make people less efficient. Some studies have come out that indicate this. I’ve tried to use various AI tools, as I do like fields of AI/ML in general, but they would end up hampering my work in various ways.

I had a Mox and it was the most stable router I’ve ever had. Never had to power cycle it, and I even have to power cycle my fancy Asus somewhat often.

People have different levels of “nerves” as others, and it kind of sounds like you may filtering out applicants on an arbitrary metric (how nervous a person may be in an interview). Don’t have enough information about your process to say for sure (obviously), but it may be something to think about. Interviews can be very high-stakes for some people (such as “I may become homeless”), and not for others (“my parents are rich”). After hired, it’s not necessarily as high-staked, and toy problems aren’t what SEs work on day-to-day.

If doing an overlay network (network on top of the Internet), you probably won’t be able to do much better than Tor or i2p.

We confirm the trilemma that an AC [anonymous communication] protocol can only achieve two out of the following three properties: strong anonymity (i.e., anonymity up to a negligible chance), low bandwidth overhead, and low latency overhead.

https://freedom.cs.purdue.edu/projects/trilemma.html

This applies to all types of anonymous networks as well (BT, Wifi, etc).