lordnacho

With anything that requires patience, you have a problem competing with immediate rewards.

Why should I learn Stoke's Theorem? Why should I learn Nietzsche? Questions you can't answer until you learn them, and questions that compete with "how will I find a job" and "how will I find a girlfriend".

The problem is, if you don't learn how to answer such questions, you will not know that such questions exist. You will find a job and a partner, and think you never needed to contemplate any of the things you didn't think about.

The heist itself only took 20 minutes, but the preparation is a business that has gone on for years. Much like a heist movie, the exciting bit is just the conclusion after a long build-up where Danny gets the gang together, plans it, and executes it.

A related issue is when you set up automation, and it turns out the automation script runs as a different user with different authorizations from the user who tested the script. It's just one of those little annoyances that appears when you try to go from "works on my machine" to "works".

I always build a massive testing harness. I care about correctness, of course, and that is normally dealt with by the AI without having to ask. If you ask for a function, it will write tests.

But I also care about the complexity classes of the solution. I prod it about the time and space complexity, and about cache locality.

I do a lot of black box testing: take the solution, see if it does what I expected, then bake in those expectations in a document.

I find that if I do those things, it produces better code than I could do in the same time.

I also think it's more or less the same as how I used to work, just more thorough and with fewer time-consuming, trivial bugs.

You just get an agent to look for traps.

I actually found code exactly like that would break a test on a certain date, using an agent.

It's really good at flagging things like this, which tend to be leftovers from a human process that nobody has had the time to clean.

There's already hedge funds that scrape the insider selling data for the executives.

You can then build a statistical arbitrage alpha based on the difference between an ordinary amount of selling (conditioned on a bunch of stuff) and a lack of selling/accelerated selling.

It's not as simple as that.

If you're building a home lab, you might just give up because you are tired of searching for Cisco router syntax.

LLM can be the little bit of lube you need to understand things at a higher level, because the tedious things are not as bad as they used to be.

You then end up actually getting a few things in your lab to work correctly, get a confidence boost, and follow up with some searches about how routing actually works and why the syntax is the way it is.

That doesn't address what I'm saying.

If the parrot randomly yaps out a solution to an open math problem, it's a useful parrot.

The fact that it sometimes says things that aren't useful, well, old intelligence does that all the time.

If you've used AI recently, you can't avoid having found it useful. Just general day-to-day intelligence like finding things in your email, writing code for you, that kind of stuff, it does well. People have some weird hangups about it being stochastic, when it doesn't really matter.

The stochastic parrots thing is already old. It might have made sense at one point, but now that the parrots are solving millennium problems, I think people have to admit things have changed.

He is right that the AI giants would like a bit of regulatory capture.

It's not clear how the re-writes were done.

If it were me, I would start by getting a summary of what problems the existing solution solves. I would also make sure that summary had the non-functional requirement. Maybe write a test suite that opens the page and does all the stuff.

Then ask the LLM to implement in another language, with those solutions in mind, but first coming up with reasons why the target language might not be as good, and where it might have useful features that the original language didn't. Use the test suite to check if things are working.

I honestly don't think you would land far away. You would have to answer a few questions along the way, but it would mostly be plain sailing.

I would expect to be able to do this with very little human attention, whereas a language rewrite two years ago might take a whole month, not including learning the new language.

It's unique, but it encodes your birthday and sex.

There's only 500 numbers it could be, assuming someone knows those other things about you.

In any case, there are alternative systems for authorisation.

Completely real.

I had a chat today with a friend about this. I think my working thesis is that AI is bad news for outsourcing/offshoring.

What was it good for? Well, often it was prompting a person in a low-cost location what code to write. He would pick it up in his timezone and implement it overnight, and present findings/questions as a response.

The problem is, coding has made implementation super cheap. It is now so cheap you have to compare it to the cost of dealing with someone in another timezone, who does not have the same cultural context on the problem. They also can't ask you clarifying questions while you're asleep. AI wins against the offshore guy, on both money and context.

Who does it help? Amazingly, the experienced onshore dev who thought he was going to be unemployed past 40. This person is now able to implement several things concurrently, has experience in making technical decisions, and has local cultural context.

> First, if you crack open a history book (or have an agent do it for you), you'll notice that the world goes backwards quite often.

The only thing I can think of is the reduction of truffles as an ingredient, which apparently happened around WWI. We eat a lot fewer of them than before.

> Second, I'm really curious what's your "adapt" path, because I suspect you're in denial too. Accept that, before long, an LLM will not need you to write good prompts, tell it to "keep going", or say "no, that's wrong". What's your value then? Does your employer keep you on the payroll as charity? For old times' sake? If your value is just supervising AI, then your value is declining rapidly.

Your employer cannot talk to all the LLM sessions at the same time. He also can't write a prompt that will do it for him.

> People in unskilled professions don't have choice when automation shows up and takes their jobs. Software engineering may go down in history as one of the few highly skilled professions that didn't just keel over and die, but enthusiastically embraced its own demise.

It's changed several times over the decades. How many people are working on a mainframe nowadays? How many people are writing assembly? Look at how many languages have come and gone too. LLM is a big change, but that doesn't mean software people will be gone.