dlevine

A few years back, I replaced all the bedroom light fixtures in my condo with fan/light combos. It has been getting hotter in SF, and I thought fans might help during the limited number of hot summer days. I didn’t have room for both a fan and a ceiling fixture, so I went with combo units.

I actually ended up with one fan that looks exactly like the enclosed one (I had limited clearance and needed something small). It works fine as both a light and a fan. If the bulb goes out, I can just replace the whole thing (it wasn’t that expensive).

For the other one, I bought something higher end bc I had enough clearance to go with larger blades (44” Minka Aire Light Wave). The light is a plug-in module, so if it goes out, I can find a replacement (assuming they still make the module).

Overall, these combined units aren’t perfect, but they let you add a fan without needing to add more wiring. The one thing to be careful of is to make sure your electrical boxes are fan rated.

Apparently 2 stones is a huge advantage. An estimate is that the computer is roughly 4-600 ELO stronger on an even match.

Also, the human played a strategy tailored to that huge initial advantage. He said that the AI did not handle this particularly well, and played high probability moves instead of trying to lure him into a mistake.

Also, even though this was the best Go engine, it was not running on a supercomputer, and had a relatively limited amount of time per move.

So, this was an important victory for a human, but not a sign that humans are now stronger than AIs at Go.

It’s not an either/or. It’s doing both. The video makes it easier to understand what the code does. We have used Looms on PRs since before AI, and agents are beginning to be able to do this on PRs they generate.

I think this is more a symptom of the problem than the actual problem.

The issue is that we can generate tons of code using AI, but then are blocked on having humans review all of it.

I don’t think we should auto-approve all of this code without human review - that clearly doesn’t work either.

What I do think we need is probably at least two-fold 1) better ways to explain these big PRs to human reviewers. 2) better ways to verify the functionality of a piece of code. Things like auto generating walkthrough videos

I’m not sure that even this is enough. I’m sure there will be agents that try to solve this problem.

I had a TI-81 (and then an 85 later on). I wrote a blackjack program. There was no link cable for the 81, so other kids had to type it in manually.

Subpixel rendering only gives you 3 effective pixels. M and N aren't really readable with less than 5.

It seems to have worked for Bill Gates as well. He definitely did some not so nice things when starting and running MS - I think it unfortunately goes with the territory of running a successful company at scale. But subsequently he has become more know for his philanthropy.

When I was in the college in the early 2000s, I had a friend who ran OpenBSD. He always sang its praises, mostly because it was the most secure operating system.

I tried a bunch of Linux Distributions and FreeBSD before mostly settling on MacOS, but never actually got around to running it.

Glad to see OpenBSD is still being actively developed.

I haven't done this that much, but have found it to be pretty useful.

When it just gives me the answer, I usually understand but then find that my long-term retention is relatively poor.

If someone uses AI to generate an output, that should be stated clearly.

That is not an excuse for it being poorly done or unvetted (which I think is the crux of the point), but it’s important to state any sources used.

If i don’t want to receive AI generated content, i can use the attribution to filter it out.

My theory is that, when done properly, it’s much closer to science than engineering.

And by “done properly,” i mean done in a regimented way with evals to verify that a wide range of inputs produce the desired outputs.

Prompting is much closer to discovering the properties of an already existing system than building something using engineering methods.

I had a licensed copy of Executor back in the mid-90s. It was the coolest thing ever. Thanks for being one of my inspirations to go into software development.

I think of LLMs as knowing a lot of things but as being relatively shallow in their knowledge.

I find them to be super useful for things that I don't already know how to do, e.g. a framework or library that I'm not familiar with. It can then give me approximate code that I will probably need to modify a fair bit, but that I can use as the basis for my work. Having an LLM code a preliminary solution is often more efficient than jumping to reading the docs immediately. I do usually need to read the docs, but by the time I look at them, I already know what I need to look up and have a feasible approach in my head.

If I know exactly how I would build something, an LLM isn't as useful, although I will admit that sometimes an LLM will come up with a clever algorithm that I wouldn't have thought up on my own.

I think that, for everyone who has been an engineer for some time, we already have a way that we write code, and LLMs are a departure. I find that I need to force myself to try them for a variety of different tasks. Over time, I understand them better and become better at integrating them into my workflows.

I’m job searching right now, and it’s definitely not the doom and gloom that I’m hearing about.

I think it’s a challenging environment for developers who are either inexperienced or who have skills that are out of date. I have found that companies are a bit more picky than i remember about knowing the exact tech stack they use, but they are still making offers and those offers are pretty good.

Note that I’m applying mostly to mid-sized non-public companies. I’m not sure what it’s like applying to MAANG-types right now.