Similarly for me. I have a recent example too. I run a bunch of eps32 devices at home, using esphome. Pre-AI I wrote all the yaml myself and understood what’s happening and didn’t have any very bespoke hacks.

Recently I asked an agent for a feature where the screen will get dimmer after some time, and eventually after a day the screen will turn off and then turn back on if I tap, which should be pretty easy in esphome.

But: some past agent decided what it wanted wasn’t in esphome, so it made its own custom module in c++ for the display, spelunking in pdf datasheets; then the weeds were so deep the new agent decided to add on to that, and to read the power controller and display controller spec sheets and somehow implement my dimming feature by writing raw values to some nondescript registers.

The contraption worked until it didn’t and once every so often my esp32 would just completely hang with black screen and no connnectivity and needed a power cycle, presumably because whatever low level register writing the agent implemented eventually breaks something. I told the agent to get rid of all custom stuff and wherever possible just use default esphome yaml, now it’s been working fine. I don’t really have a point with the anecdote, just that I’m also getting tired of such scenarios like the OP.

> as an assistant that helps me make decisions, and ensures that I'm in the driver's seat

This... quite describes what the current AI feels like to me. So I really don't see a problem. If AI does too much for you, you should, uh, just prompt it to do less.

Everyone talks about how their AI escapes sandbox and virtualization or delete their entire repos or something. Perhaps they're using a skynet I'm not aware of. From my experience when I added "discuss; don't edit any file yet" at the end of the prompt I've never seen the LLM go against my will and edit the files anyway.

I've been thinking very similar thoughts. Without going into too much detail, a couple of observations:

When carpenters moved from mostly hand tools to mostly power tools, it didn't take away much of the need to understand the techniques of woodworking, it just made it all faster.

Reading a nonfiction book is very different than reading a book summary, even if (as is often the case), the ideas in the book could have been condensed way down. The point is that actually reading the whole book means you spend far more time engaging with the ideas, and so they become more deeply embedded in your neural network. In things like sports, this is just obvious, in that no one expects someone to become a good soccer player by just reading the rulebook, and obviously more practice is the main way you get better.

I’ve found AI works much better for constraint based problems than open ended “tasks”. I.e., if you specify all of the constraints of the problem, it works really well at achieving the objective. But the caveat is that you really do have to enumerate all of the constraints, because if you miss even one, it will use that as a loophole to shortcut the problem.

I think the solution is toward more formalization of the behavior we expect of various systems. E.g., produce a Lean proof that a program never behaves in a certain way, and if you can’t, iterate on the program until it satisfies such a proof. I’ve only tinkered around with this a little bit, but it seems promising on toy problems. Scaling up to tech-company level infra is going to take quite some time however I think.

Agree.

Creating products with AI is like creating a tightly packed ball of wool. Then, to try to understand what happened you have to somehow pick a strand and pull it out to have a look. Very difficult with tightly packed wool.

I've run some experiments in the past month that get me closer to being able to work with this opaque bundle.

It's the main thing I think about every day, how to solve this problem.

> Here's what I want: I want AI as an assistant that helps me make decisions, and ensures that I'm in the driver's seat.

You aren't going to get that. Why would they even offer you that? You're going to be the meatbag peripheral to an AI.

My experience over the past year has been the exact opposite. I work through a design with the agent, have it research any points I'm not confident on, drill down on details I feel fuzzy on, and then have it do an adversarial critique of it until it stops surfacing defects. By the time I'm setting it loose to do the actual implementation, there's very little left to decide and it's producing pretty tight code. Plus I now have some solid design documents, a decision history, an issues list, and a set of gotchas for the agent to keep in mind in future.

My LLM is an assistant that helps my designs and decision process, implements the final work expertly, and I'm always in the driver's seat. And the development speed is astronomical.

12 months ago I had ChatGPT successfully not to mention quickly, exctract elements of a PNG as SVG. About 6 or 7 times.

I jumped in to perform the same thing again, and it spat out a PNG. And then when I asked it generated a little python script to generate the SVG. The Result was garbage. It took me a while to get the result I had gotten consistently last time, and the results included it hallucinating a few svgs in the process.

I do wonder if we have already passed the best possible models and are over specialising them at this point.

> I want AI as an assistant that helps me make decisions, and ensures that I'm in the driver's seat.

Right now we’re still in the benchmaxxing phase, I feel like most AI models of combination of models and harness are not really a “product” yet in the sense of having a particular and opinionated UX engineered to be helpful at a specific task, there much more still in a race to be the smartest.

There’s a lot of room for diversification of the actual character of this interaction, for example in the direction you mention. I think once that starts we’ll get into the real “useful” phase, right now we’re really just showing off and exploring how much it can do, even if it’s incidentally really useful in some cases.

Personally I agree with the parent post, I hate it when AI tries to lead me “would you like me to ...” I’d much prefer something awaiting my instructions than wasting tokens tryitn or steer me to doing something inevitably dumb.

Absolutely mirrors my experience and wants

The places where I've seen unequivocal wins with AI are repetitive tech debt tasks that apply the same transformation across a large amount of code or refactor under a pre-existing test suite with good coverage.

I agree with this, but I'd add the caveat you have to do your own digging and researching (as the article says) to identify these tasks well. If you ask it for ideas, it gets painful.

YMMV (and I am very open to getting better at this).

What I've seen lately is that Claude got more and more up its own ass. It keeps assuming I know the terms it keeps making up, like "ensure the strategy holds through the gap". What the fuck does that mean, Claude? Speak like a normal person.

A few model iterations ago, I found its output when explaining what it wanted to do much easier to parse. Nowadays I just feel stupid because it keeps making up names for things and I have no idea what it means.

Earlier today it said something about "the two-terminal solution" and I was just asking it to make a simple Python script to test an HTTP endpoint. I just don't understand what it's talking about any more.

Has anyone else seen this?

I'm curious if it's possible in general for an AI to achieve this without some evolutionary forcing function that actually threatens their "biology".

Not talking about prompting Claude "please write me a philosophical dialogue". It has no skin in the game. If it writes 20 paragraphs of slop and is factually wrong and costs other people labor, is there any correction mechanism?

We can correct it explicitly via training I guess, but it's debatable to what extent accountability applies to AI.

We like to pretend that we have our nice epistemology, separated into a priori and a posteroi and all that, but at the end of the day, accountability is what underpins social relations and keeps things going (even if you're libertarian, you have to accept this - even if you don't agree some state should centralize it, it's still just true in terms of basic human social relations).

Of course, another possibility is utter domination - I am so much better than you (for some definition of better) that none of your puny responses matter. OK. Then don't claim both that, and that you're weird for disliking AI and that AI has no problems when integrating with society - too many people have these basic cognitive dissonances.

Boilerplate details mean we've failed as an industry. Why haven't we already got the computer to automate all the boring bits without AI?

You said what I was thinking. AI can do the things I always found mind numbing and tedious, but because I did it already early in my career, I know when to intervene because the agent is not doing it correctly. I think that is the OP's point.

So what? Jobs aren't supposed to be fun. Lots of people depend on those boring details.

And the team (AI) is really not that smart. Just very fast at pasting and gobling stack overflow and github repos together

> There is a (fairly) good book series where it turns out we are living in the matrix and someone finds a config file and effectively becomes a Wizard, and of course problems ensue with details (like flying by adjusting one’s position one feet up every second is just vibrating a lot)

This is very interesting. Which book series is this?

>We don't need to understand them to be able to verify they work correctly

I don't think that's the case and it's becoming more obvious every day. From people who cannot distinguish truth or fiction on online platforms, good code from bad, to insecure software that causes supply chain attacks, to investment vehicles or fake medicine peddled that does not work, the idiotic MBA manager who does not understand the workers they manage and so on. That is why we're living on BS metrics and crappy reviews, because to the people who manage organizations their own workplace is a Blackbox. The critique that "AI" systems are semantically blind token processors is correct, it's just not bothering anyone because it already applied to most modern companies.

When people become passive consumers rather than broadly competent users how something works cannot be evaluated so by definition all they have to go off is appearance, which is why we live in the age of influencers and marketing, because that is now the only factor that actually matters.

AI can do administrative tasks.

My opinion is that juniors need time in the saddle to develop good engineering judgement and taste.

Outsourcing critical thinking is seductive and a slippery slope. LLM lures juniors into feeling productive, and molds them into ineffectual middle managers to be readily replaced when there's a bad quarter.

Though not the other way around. You don't become good at something by handing it off. In that case you either are already good, or never become good.

I agree with your first part, that software is built to solve a problem. But I think you sort of sidestep what makes AI exciting to non-software people with your second part. The people who are excited about this aren't excited because they can ask for a todo app but because they can solve the problem the todo app was supposed to be soling for them without needing to find and learn a todo app.

AI is the new "excel spreadsheet with ridiculous formulas on top of VB script on top of pivot tables" for the non-programmers. Yes, the AI might not do it efficiently or even always give the right answers, but the spreadsheets didn't either. For non-programmers, the computer has always been a black box, data goes in, data comes out. Usually it's right, sometimes it's wrong and the users mostly had no idea why or how. But now they don't need the middleware app. They don't need to take their data and split it up into very specially structured files, and put just the right pieces of information on just the right lines and in the right spaces. They don't need to remember to do "(foo and (bar or baz))" to get the right answer, as opposed to "(foo and bar or baz)" and get the wrong one. AI for non-programmers is the closest we've ever come to "natural language programming" for people to use. It doesn't matter that it gets things wrong sometimes, or does it inefficiently or makes something unmaintainable unless you use the AI again. What matters to them is they can say "take all this data here and swizzle the frobs" in their native language instead of learning a specific programming syntax and system. And they can point it at some arbitrary data in whatever format they happen to have the data in, and they have a halfway decent chance of getting something that does what they want.

It's not going to be pretty, it's not going to be perfect and it's probably going to break in subtle and unexpected ways. But as long as most of the time its mostly good enough, people will still use it enthusiastically. We're effectively looking at "worse is better" for the modern age and for people that aren't and never were going to be programmers.

For one thing, it's not actually that useful to have such a person at your fingertips. You put up with the drain a junior places upon you because with training, he'll be able to come into his own and pay off the time invested. An LLM can't do that. Also, a lot of people incorrectly believe that an LLM is smarter than a new grad, and of course the companies making them have a vested financial interest in lying to you that their products are smarter than they are.

The pro argument is always quantity based, yet we never get to see the actual gain in quality. It's always "I'm doing SO MUCH more". Never "I'm doing better, with less effort."

It's also what we see in the wild. There are so many things going on, so many news about "AI". Are things getting better in any meaningful way? Why are we getting so much from "AI" yet things keep getting worse? The only real, objective and verifiable gain has been the stock price of a handful of companies, most of them heavily into infrastructure and manufacturing hardware.

Seems like a pretty clear pattern. There's so much output, but everything is worse. And the only argument we hear for why this is actually a good thing is just how much more output there is. Hmmm

I think the author means ecomonically valuable not valuable to you.

As long as these tools remain for your personal use there is nothing wrong with that. The moment other people rely on them however, it would become reckless oversight to operate in that way.

Tools? Yes. Reliable foundations? Eh, mostly not. Maybe I'm holding it wrong, but I'm not impressed. Not in the least because in order to get a good system you must have a good image of the system in your head. And if AI builds the system, how will you ever get that system in your head?

It’s always tools tho, and AI tools especially. Why not anything else?

Everyone feels like they are now the ceo of their own empire of ai flunkies who do the actual work guided by their above-all-that vision, and they luuuuuuv that.

But useless ceos generate bad output just like useless direct coders.

It's like everyone is adopting the once only-for-the-rich mindset where artisan is actually a derogatory term. Where Micheal Angelo is the same as the cow stall poop shoveler, because they actually do something directly themselves.

It doesn't handle anything, he reproduce statistical patterns. If it seems it cares about the details it's because he's been trained to regurgitate stuff. Another thing is being expert in something and being able to prompt the clanker, and those who are, usually underestimate how much their knowledge is steering the machine in the right direction

You're confusing translation and interpretation. A compiler is merely translating your code into a lower-level form. (There is a degree of heuristic-based interpretation that goes into generating good code, but the code's observable behavior is not allowed to change.)

But an LLM has to interpret your words and make judgment calls about the logical details, which is a very different thing. An LLM sufficiently intelligent to make acceptable judgment calls in all situations (acceptable enough to ship to production without human review of those details) would likely be indistinguishable from AGI.

I didn't interpret it as an argument about layers of abstraction. You need to be in the detail at the level you're writing (and it's certainly advantageous to know some of the detail of the abstractions below) because you're building abstractions on top of that, because the details subtly change at the base level you're building at, impacting the level that you're building to.

Your vouching for centralization of knowledge, and even understanding, which I think is a very bad idea. It’s not like the rest of us are becoming specialized at something else - knowledge, in the broad sense, is the foundation of all skills. Without it we have nothing to contribute