strogonoff

The ecosystem of open-source reusable components will remain a good idea as long as architectural principles like low coupling and high cohesion still apply—otherwise it’s wasting resources on reinventing wheels.

This can be said about many conflicts between the individual and society, though. In many ways we are prevented from just taking what we want and “keep us in shape” because if everyone did the same it would be a problem.

Historically, as far as I am aware, there was never a situation when deflation coincided with good things happening.

A healthy amount of inflation keeps the economy going.

To clarify, it is not if killing people aligns with your principles.

That, and the very idea of direct access to the territory (needed for any sort of “lossless compression”, among other things) is moot since we only perceive it indirectly through the lens of our consciousness, which is anything but objective.

Consequentialism is not the only option. It invaded the mainstream thanks to effective altruism and friends, but deontological ethics is alive and well.

Quitting your job instead of blowing up the office is well aligned with the latter.

The moment there are no more unconfirmed hypotheses you can assume something is wrong with sciences. All provable models (theories, explanations) that we have, or could have, are by definition wrong or incomplete.

Homegrown LLMs seem unlikely to be used by a significant fraction of LLM users, but yeah, there’s a blurred line.

> Surely an LLM teaching someone chemistry falls into the same general category?

I think the scale and the effort might break the analogy. Teaching yourself means a degree of patience and certain personality traits that would be rarer in a malicious person (along the lines of “a sufficiently smart person wouldn’t need to be a criminal to succeed”), with exceptions of course. Being taught by a teacher implies a degree of KYC and care about who you are. Being served on a plate the specific information on how to manufacture something dangerous bypasses those barriers, which I think changes the equation.

Because an individual has free will and agency, and scale matters.

If you teach someone chemistry, that someone probably has sense to not use it for criminal purposes. If you have a track record of teaching future terrorists specifically, you will be shut down. Crucially, before you educate them en masse.

If you’re comparing that to a tool that essentially educates thousands, millions of people, with no KYC, you bet budding terrorists are going to be disproportionately represented within that group.

So, application process, inspections, background checks. Sounds like a good start?

I don’t think anyone is talking about homegrown LLMs, this is commercial products for sale.

Well, manufacturing guns is probably heavily regulated…

There’s a difference between enabling another individual with free will and agency, and enabling an automated tool (as a bonus, then giving it to the masses & profiting from its use).

"Raw” is not an acronym.

Finding consistency goes hand in hand with modeling reality, and every model by definition 1) is wrong (incomplete/incorrect/unprovable) and 2) exists in your head only.

All we can say is that the universe has been consistent so far, during the brief time we observed a small part of it given the instruments we have.

We update our model(s) so that it keeps being consistent and predictable to us (that’s human nature), but no model can be probably correct and complete.

Django allowing queries to be anywhere is more or less in line with Python’s overarching “we’re all consenting adults here” ethos. There’s probably one correct way to do it, but if you want to shoot yourself in the foot then here’s your gun.

It definitely takes a bit of discipline. The key layers are somewhat easy to manage—middleware, context processors, views, template tags—but I’ve seen some hairy lasagne further obscuring where the queries happen on top of that. A well-documented abstraction can be useful, but if it is possible to keep it simple and obvious then that’s the way to go.

(Third-party dependencies can further complicate things, but at least you can expect a library using ORM to be in the installed apps list.)

When we give everyone a computer and want to restrict a group of people from some of its capabilities (for their own good), the consequence is a restricted computer. Not giving everyone that computer in the first place could help ensure it stays a true general purpose computer.

If tech is disrupting X, that’s a problem with X” tends to be said by people who have X comfortably sorted themselves (e.g., already have a PhD that counts as it’s from the pre-LLM days).

In the end it depends on whether one considers technological progress or human flourishing to be the end goal. Surprise—they are not always aligned, and sometimes are at odds with each other.

Reducing your worth to mere productivity is myopic. Your worth is what you bring others, and while for some people it can include literal production for others it doesn’t have to. There’s wisdom, emotional support, and many other things.

An exclusive choice between maintaining free will and agency vs. achieving some definition of “success” makes for a good story. You’d think the choice would be blindingly obvious, and yet turns out it isn’t (or to some it is blindingly obvious in the opposite direction).

The argument that those who refuse to use an LLM will fall behind because they “won’t be able to produce as much” hinges on more LoC being an objective good and typing speed being a bottleneck, which always struck me as a suspicious line of reasoning. In a well-thought-through architecture, you don’t need to type that much; add to that the observation that more LoC usually means more opportunity for error and the net effect suddenly becomes less obviously positive (and this ignoring other negative effects LLM-driven development might have on OSS ecosystem, such as GPL-washing).

Furthermore, when it does come to typing speed, good old non-LLM-powered autocomplete does still exist, and offers less opportunity for subtle errors within otherwise plausibly looking blocks of code.

Measuring information entropy seems to have parallels with the incompleteness theorem and with the hard problem. As we approach the seams (so to speak) in our ability to model the system in its own terms, our otherwise precise maps become distorted, things get self-referential and paradoxical.

I do remember that we are promised to work less thanks to this tech, and yet somehow the opposite happens. We have to work more, and we are paid less (if we are able to get a job).

> People who are chronically unemployed always sort of become broken

The causal link may be unclear but I recall there was research showing that at different ages people decline faster once they retire. Sense of purpose is a real thing.

What’s this new trend with allegations how people have jobs for the sake of having jobs?

People have jobs because this is an organised way for us to provide value to other humans and give our lives meaning; a way for us to express ourselves; to earn money that puts food on the table and helps raise our children.

Personally I thought opening doors was about the only thing they did—never seen them engage in any other activity.

I am not sure you provide a counter-argument, IMO offering more people a way to earn money and do something useful (as opposed to delegating these duties to [generally foreign-made] equipment) as a way of maintaining a healthy society still holds.

To assume that the universe is disorderly presumes knowing some esoteric objective truth about how the universe works. Since the field of natural sciences does not provide that truth (by design), and practices that do aim to provide that truth (metaphysics, religion, etc.) seem to have different versions of it, I would say that there’s a fair chance that the universe is not that disorderly and we just lack better models for describing it.

If comparing past industrialisation and automation events with hypothetical LLM dominance in the workforce, some of my questions would be: (0) how limited is the change to a specific industry (e.g., weaving vs. most of intellectual and creative work by everyone starting from children as soon as they can read and type to the elderly)? (1) how many people are affected—in absolute terms not just percentages that ignore population growth? (2) how quickly are they affected? (3) what can people do—where can people move to where they can keep working (e.g., operating/maintaining/manufacturing tractors and looms, intellectual/white collar work, etc.)? (4) what are the organised reskilling processes in place that facilitate said migration? (5) how competitive and diverse, vs. monopolistic and regulated, are the new industries that power the change? how concentrated is created wealth and how many new jobs do they create? (6) what laws (in letter or in spirit) are violated as the change is happening? (7) if it is shown that the current change is about similar to the past in above aspects, does that imply it’s OK that it happens all over again and we have not learned a lesson to go ahead with these things more carefully so that the majority doesn’t suffer as much?

When I was in Bangkok a good while back, I was surprised how there’re doormen at shopping mall—humans to handle simple things which in many parts of the world would certainly not involve a person.

A bit later I thought that it makes sense: it improves customer experience and slightly reduces unemployment in local population—a doorman job is better than no job.

Looking just at ambient air temperature is an easy mistake to make. I used to be like that, always surprised why people whine so much near the coast in winter with their comfortable numbers and thinking I’m superhuman compared to them. I stopped when I learned first-hand that their negative 17 degrees feel as bad as our negative 30.

If you live in a town in New South Wales where the average humidity is less than 50% in the wettest (or, should I say, least dry) season, you might not understand what it feels like in London where the average doesn’t dip below 65% any time of year.

Today London will feel at least 4 degrees Celsius hotter than Hong Kong. The latter is already an extremely unpleasant place to be in these conditions (and had in fact its own very hot weather warning issued), and unlike London it has a very strong culture of air conditioning.

4 degrees might seem like not a lot, but heat extremes are a tricky beast. Once your body cannot evaporate heat fast enough, you’re literally toast.