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I miss thinking hard

jernesto.com

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Re: I miss thinking hard

#281

Post by charcircuit »

aleph_minus_one wrote: post #221
boredtofears wrote: post #122

Earlier quoted context omitted.

In my experience AI is pretty good at performance optimizations as long as you know what to ask for. Can't speak to firmware code or complex cryptography but my hunch is if it's in it's training dataset and you know enough to guide it, it's generally pretty useful.

> In my experience AI is pretty good at performance optimizations as long as you know what to ask for. This rather tells that the kind of performance optimizations that you ask for are very "standard".

Most optimizations are making sure you do not do work that is unnecessary or that you use the hardware effectively. The standard techniques are all you need 99% of the time you are doing performance work. The hard part about performance is dedicating the time towards it and not letting it regress as you scale the team. With AI you can have agents constantly profiling the codebase identifying and optimizing hotspots as they get introduced.

Re: I miss thinking hard

#282

Post by Cthulhu_ »

vaylian wrote: post #252
oceanplexian wrote: post #186

Earlier quoted context omitted.

Coding is not at all like working a lump of clay unless you’re still writing assembly. You’re taking a bunch of pre-built abstractions written by other people on top of what the computer is actually doing and plugging them together like LEGOs. The artificial syntax that you use to move the bricks around is the thing you call coding. The human element of discovery is still there if a robot stacks the bricks based on a…

> You’re taking a bunch of pre-built abstractions written by other people on top of what the computer is actually doing and plugging them together like LEGOs. Correct. However, you will probably notice that your solution to the problem doesn't feel right, when the bricks that are available to you, don't compose well. The AI will just happily smash together bricks and at first glance it might seem that the task is don…

Not yet, anyway; I do trust LLMs for writing snippets or features at this point, but I don't trust them for setting up new applications, technology choices, architectures, etc.

The other day people were talking about metrics, the amount of lines of code people vs LLMs could output in any given time, or the lines of code in an LLM assisted application - using LOC as a metric for productivity.

But would an LLM ever suggest using a utility or library, or re-architecture an application, over writing their own code?

I've got a fairly simple application, renders a table (and in future some charts) with metrics. At the moment all that is done "by hand", last features were stuff like filtering and sorting the data. But that kind of thing can also be done by a "data table" library. Or the whole application can be thrown out in favor of a workbook (one of those data analysis tools, I'm not at home in that are at all). That'd save hundreds of lines of code + maintenance burden.

Re: I miss thinking hard

#284

Post by jack_pp »

dwrolvink wrote: post #175
CamperBob2 wrote: post #79

Earlier quoted context omitted.

Eloquent, moving, and more-or-less exactly what people said when cameras first hit the scene.

Interesting comparison. I remember watching a video on that. Landscape paintings, portraits, etc, was an art that has taken an enormous nosedive. We, as humans, have missed out on a lot of art because of the invention of the camera. On the other hand, the benefits of the camera need no elaboration. Currently AI had a lot of foot guns though, which I don't believe the camera had. I hope AI gets to that point too.

The footgun cameras had was exposure time.

1826 - The Heliograph - 8+ hours

1839 - The Daguerreotype - 15–30 Mins

1841 - The Calotype - 1–2 Mins

1851 - Wet Plate Collodion - 2–20 Secs

1871 - The Dry Plate - So it took 45 years to perfect the process so you could take an instant image. Yet we complain after 4 years of LLMs that they're not good enough.

Re: I miss thinking hard

#285

Post by repelsteeltje »

BoostandEthanol wrote: post #231

I’d been feeling this until quite literally yesterday, where I sort of just forced myself to not touch an AI and grappled with the problem for hours. Got myself all mixed up with trig and angles until I got a headache and decided to back off a lot of the complexity. I doubt I got everything right, I’m sure I could’ve had a solution with near identical outputs using an AI in a fraction of the time. But I feel better f…

I think the heart of the matter is this section in the blog:

> Yes, I blame AI for this.

> I am currently writing much more, and more complicated software than ever, yet I feel I am not growing as an engineer at all. [...] (emphasis added by me)

AI is a force multiplier for accidental complexity in the Brooks sense. (https://en.wikipedia.org/wiki/No_Silver_Bullet)

Re: I miss thinking hard

#286

Post by dkdbejwi383 »

helloplanets wrote: post #64
gyomu wrote: post #32

This March 2025 post from Aral Balkan stuck with me: https://mastodon.ar.al/@aral/114160190826192080 "Coding is like taking a lump of clay and slowly working it into the thing you want it to become. It is this process, and your intimacy with the medium and the materials you’re shaping, that teaches you about what you’re making – its qualities, tolerances, and limits – even as you make it. You know the least about wha…

And when programming with agentic tools, you need to actively push for the idea to not regress to the most obvious/average version. The amount of effort you need to expend on pushing the idea that deviates from the 'norm' (because it's novel), is actually comparable to the effort it takes to type something out by hand. Just two completely different types of effort. There's an upside to this sort of effort too, though…

Fair enough but I am a programmer because I like programming. If I wanted to be a product manager I could have made that transition with or without LLMs.

Re: I miss thinking hard

#288

Post by Terr_ »

darkwater wrote: post #185
fallous wrote: post #106

Earlier quoted context omitted.

You just described the burden of outsourcing programming.

With the basic and enormous difference that the feedback loop is 100 or even 1000x faster. Which changes the type of game completely, although other issues will probably arise as we try this new path.

That embeds an assumption that the outsourced human workers are incapable of thought, and experience/create zero feedback loops of their own.

Frustrated rants about deliverables aside, I don't think that's the case.

Re: I miss thinking hard

#289

Post by Terr_ »

hnlmorg wrote: post #228
Der_Einzige wrote: post #124

Earlier quoted context omitted.

Yet another example of "comments that are only sort of true because high temperature sampling isn't allowed". If you use LLMs at very high temperature with samplers which correctly keep your writing coherent (i.e. Min_p, or better like top-h, P-less decoding, etc), than "regression to the mean" literally DOES NOT HAPPEN!!!!

Have you actually tried high temperature values for coding? Because I don’t think it’s going to do what you claim it will. LLMs don’t “reason” the same way humans do. They follow text predictions based on statistical relevance. So raising the temperature will more likely increase the likelihood of unexecutable pseudocode than it would create a valid but more esoteric implementation of a problem.

To put it another way, a high-temperature mad-libs machine will write a very unusual story, but that isn't necessarily the same as a clever story.
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