I agree with the article’s ideas, but certain things about the execution bother me.

1. calculate_order_total_for_customer. I’d just call it calculate_order_total. It’s clear than any order will have a customer, it’s in the type signature.
2. is_user_eligible_for_discount. I’d call it user_is_eligible_for_discount. Because inevitably that function is getting called in an if statement, and you’d rather it read closer to proper English: if user_is_eligible_for_discount: ....
3. “Designing for Tomorrow”. I agree that dependency injection is a valuable technique, but it’s not always strictly necessary and they seem to say you might as well always do it just in case. That’s counter to YAGNI. Make sure you have an immediate use case, or let future you do it if you end up needing it. It’s not hard to refactor something to inject a dependency.

As for actual coding, I use ChatGPT sometimes to write SDK glue boilerplate or learn about API semantics. For this kind of stuff it can be much more productive than scanning API docs trying to piece together how to write something simple. Like for example, writing a function to check if an S3 bucket is publicly accessible. That would have taken me a lot longer without ChatGPT.

In short: it basically replaced google and stack overflow in my workflow, at least as my first information source. I still have to fall back to a real search engine sometimes.

I do not give LLMs access to my source code tree.

Sometimes I’ll use it for ideas on how to write specific SQL queries, but I’ve found you have to be extremely careful with this use case because ChatGPT hallucinates some pretty bad SQL sometimes.

If your health check is broken, then you might not notice that a service is down and you’ll fail to deploy a replacement. Or the opposite, and you end up constantly replacing it, creating a “flapping” service.

Why are you in such a rush in the first place? Maybe try treating these human interactions as a break from your usual pace. Just enjoy their company. People will notice if you are relaxed and listening carefully.

I just don’t really understand the point they’re trying to make. Japan is responding to OpenAI, which is a US company. They have the same copyright laws. None of the parties involved have any kind of edge over the other w.r.t. copyright law.

Japan’s copyright law is very similar to the US, so I’m not sure what you’re referring to.

https://en.wikipedia.org/wiki/Berne_Convention

What makes you say that about PIA?

Image link is broken

I doubt he actually believes that. He’s just using that rhetoric to get Christians on board.

I can see why you think it’s similar, but it’s assuredly not the same as slavery. As a hobo, you still have rights and cannot be purchased or treated a literal property.

Even if you wanted to opt out of capitalism, I can’t even think of how a poor person would manage to live legally in the US. You basically have to be a hobo constantly in fear of being arrested (and then in prison you’re definitely a slave). You can’t just go out into nature and build your own homestead like people used to do.

Is your issue that it’s hard to move files around without breaking seeding, or something else?

You can use both.

Pretty sure my journey looked something like:

1. Ubuntu for a while
2. A furious bout of hopping experiments: Debian, ElementaryOS, Crunchbang, MX Linux, Fedora
3. Arch for a bit, but it was too much struggle
4. Ubuntu for a while, embracing the “vanilla” lifestyle
5. Manjaro for a while. It seemed to solve my previous issues with vanilla Arch.
6. EndeavourOS, basically a better Manjaro.
7. NixOS, which had a significant learning curve but ultimately gives me the most control and repeatability for all of my machine configs. Still daily driving this on my desktop, router, and some web servers.
8. Tried out Fedora Atomic on my old laptop out of curiosity. Installing packages was ultra slow. Workflows were too annoying.

So I think the graph is actually pretty reflective of my own experience, aside from some of the specific distro choices during my peak ignorance phase, and obviously I ended up at NixOS which isn’t even on here.

I currently have more audio issues on my MacBook Pro than my Linux desktop. The Mac will sometimes just fail to recognize headphones plugged into the 3.5mm TRS port. NixOS desktop w/ pipewire has no audio problems whatsoever.

An issue which I aim to resolve using a self-hosted VPN.

Pretty sure my eyes max out at 4K. I can barely tell the difference between 4K and 1080P from my couch.

Pretty much everyone I’ve talked to about this says the same thing. LLMs are useful for one-off scripts or quickly generating boilerplate. It just turns out that those tasks don’t make up the majority of programming work unless you are in a bullshit job anyway.

We aren’t yet great at knowing when LLM will save time and when it will inflate time.

I agree up to the extent of the numbers. I think $50/hr is feasible if we make drastic changes to our economy. $150/hr simply cannot work with the country’s current number of workers and overall productivity.

You are part of the wavefunction. There is no collapse, just entanglement.