questionableans

Choosing the right problem to solve was always very important. The code wasn’t always the hard part, but arguably it really was the hard part more often earlier on.

For example, a personal computer operating system that runs all your existing programs at full speed but doesn’t crash all the time and the ability to run a virtual machine on an x86 PC both took as long as they did to get to market primarily due to the technical difficulty, not because it was hard to find people ready to pay for them. Those kinds of hard coding problems are less plentiful, or at least less obvious today.

My guess is in the end we’ll find the approach that works depends more on the personalities of the people involved as opposed to one approach being strictly better.

IMO, a large amount of work, including a large amount of technical work is wasted effort. At startups you see it when they go out of business, at a big companies you see it as bloat. So unless AI is directed at finding the right problems to solve, past some point engineering efficiency via AI at best saves some money and at worst means more ultimately low, zero or negative value work gets done.

Maybe this is why you’re seeing more effort shifted away from technical work. Maybe I’m a nihilist. Maybe both?

Actually, there is one other important thing that has changed: as developing a certain amount of functionality has gotten easier with modern languages, tools, libraries and coding agents, it’s also gotten less interesting for technology-driven people and more accessible to career-driven people.

Implementing the first text edit field in your program is kind of interesting when all you have is asm and 4k of RAM. Nobody wants to bother doing it manually when you have a web browser that can eat gigs of RAM.

Thanks for clarifying. There have always been technical leads and hands-on engineering managers, but two things have changed: 1. The balance of responsibilities attributed to different titles has varied over time and across different companies. 2. Early personal computers were so resource limited that there were only so many programs you could write that actually made effective use of large teams. Today we can write much larger programs, which requires more coordination (across people and with AI—which can reduce the number of people required).

I would also strongly push back on the assertions that being very technical means spending most of your time on the code, and that you have to be using AI all day for it to be useful. Let’s say I’m a technical lead or a manager reviewing a design doc for a large project. I have an idea based on my years of technical involvement for how to significantly improve the quality or delivery timeline for the project. I used to have to ask the doc author to look into this approach, not knowing for sure if it would directly translate. Now I can take my crazy idea, quickly dispatch a coding agent to try it out against the codebase, go to a meeting, and then check on what it came up with to give a much more informed and technically accurate suggestion on the doc. I still have to have the technical expertise to iterate a bit with the agent and not over or under state the possible impact of my idea. But this lets me take say an hour rather than a week to come up with a vetted recommendation that saves months on the schedule.

I’ve worked most of my career without dedicated product management support (very early startups and internal infrastructure teams at large companies), having to do that myself (both as an individual contributor engineer and as a very technical manager). So I have to wonder how much has the industry changed, and how much has your location within the industry drifted?

I’m curious, what are some examples of XYZ? What does “mostly technical” mean to you, and why do you think it is the industry doesn’t want that? Just trying to understand, because I don’t think I’ve experienced this myself.

That’s not at all what I’ve seen. If someone’s properly leveled at Staff, it tends to be a bit easier to sustain than Senior in the same company, but you do have to be selective about new roles at new companies. Age shouldn’t be an issue for someone with Staff+ skills except in the most dysfunctional, unprofitable companies.

You probably mean a level or three above Staff at FAANG.

I am going to judge, because people need to hear it. Anyone with $5M in invested assets who still thinks of making money as their top career priority needs to learn to think bigger. That’s your safety net, not your score in life. Now you can focus on what you find it rewarding to be doing, and how you can provide the most value to others.

This is about budget caps (“I don’t want to spend more than $100, cut me off once I spend that much”) not price caps (“no one is allowed to charge more than this price per token”).

If you just need to receive an SMS but don’t actually need a cellular network, Google Voice is free.

If you can stomach $3/mo + tax (totaling a little over $40/year), UltraMobile PayGo is the cheapest consumer option I’ve seen. It’s a T-Mobile owned MVNO. It comes with 100MB, 100 minutes and 100 text messages per month, and is pay as you go after that.

Past that, I’d look into IoT cellular options.

What kind of software are you talking about? Test harnesses were commonly used in 2006. JUnit was created in 1997.

Or it does make economic sense, but the people drawn to power are too deluded to understand.

I don’t mean for one data center, I mean globally. There are resource limits to this planet, and we’ve been stretching past them in the short term, and are starting to see the long term consequences. The problem is people think “oh, it’s ok if I pollute a little more because the world is large, and the god we’ll create in our image with these machines will figure it all out for us.” That’s an unsustainable business model based on impressive technology managed by people stuck in childlike fantasies.

More likely all they’ll pull ahead in is making an even bigger mess.

Winners don’t decide what is sustainable, the reality of the planet does.

It’s not just a feel good thing. There are real resource limits, even if someone chooses to foolishly not believe that. If you can get more done with a given level of power and other resources, you get more out of your data centers.

There is a huge business opportunity to become the world leader in sustainable data center design. Whoever wins could one day have immense influence over every single data center in the world (or even out of this world, where efficient resource utilization would likely be essential). It’s disappointing to see the United States miss out: US companies and research institutions would actually be extremely competitive in this field with the right government incentives.

There once was a time when tech companies mostly stayed out of SF. People who worked in the valley but hated it lived in SF amongst mostly people who had nothing to do with the tech industry. Then someone had the bright idea to put branch offices or even whole companies in SF. Convenient for the commute (and you could move to the East or North Bay), but then SF got overrun by the tech monoculture + NIMBYs trying to “keep it the same” by preventing construction, effectively kicking out so many of the people who were the reason why valleyphobic tech workers sought out the City of Saffron in the first place.

Or is it a transition to truly declarative programming?

Yes, and your second paragraph above is the interesting part that I would want the reader to focus on, starting from the title.

But a language being “typed” doesn’t tell us anything useful. Untyped languages are typed too: they’re uni-typed (every expression is an expression).

I think you do your analysis a disservice by focusing on “is assembly language typed?” as the top line question. The more interesting question you examine is what do the type constraints in inline asm offer, and how do they interact with the host language’s type system?

The only shaming I’ve seen are for people using AI badly or not at all.

I also think we should differentiate between programming to get a program you can do something else with, programming to understand the machine, and programming to give the programmer a logical challenge to keep their skills sharp.

And their team, if they treat them well.

Yeah, the car is significantly older than that, and I last tried the FSD trial a couple years ago.

I’m glad to hear it’s gotten better. This was a couple of years ago, when they were pushing the free trials. It’s an earlier Model 3, and I barely drive it.

I’m clearly not a typical driver. I love keeping the camera views all on screen for situational awareness. I don’t use cruise control and rarely use GPS navigation, even back when I used to drive cross country in previous cars. I guess I just got used to memorizing entire routes in my long distance biking days. Google Maps Street View really helps me understand exactly where I’ll be going.

Maybe just a motherboard if it would be compatible with standard drives, power supply, etc?

Tried FSD once. It was very confused about a traffic circle, cruised up to it & slammed on the brakes. Got rear ended (at low speed).

The incomplete, miscategorizing and glitching display of other vehicles and obstacles does not give me confidence that the car is able to correctly identify what’s around it, let alone drive accordingly.

Even the lane departure warnings are 90% false positives (but I’d rather be warned than not).

Just driving normally is much less stressful and more fun than supervising the car’s attempt at driving like a teenager.

Were Roland MIDI keyboards used by the developers to capture game audio tracks as played by musicians? Maybe they already had the driver code from that.