511–520 of 589 posts
I believe this is true for literally anything that replaces practice. We're meant to build muscle memory for things through repetition, but if we sidestep the repetition by farming it out to another process, we never build muscle memory.
Data doesn’t support it
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As the other comment said, LLMs are not an abstraction. An abstraction is a deterministic, pure function, than when given A always returns B. This allows the consumer to rely on the abstraction. This reliance frees up the consumer from having to implement the A->B, thus allowing it to move up the ladder. LLMs, by their very nature are probabilistic. Probabilistic is NOT deterministic. Which means the consumer is neve…
So it's a noisy abstraction. Programmers deal with that all the time. Whenever you bring in an outside library or dependency there's an implicit contract that you don't have to look underneath the abstraction. But it's noisy so sometimes you do. Colleagues are the same thing. You may abstract business domains and say that something is the job of your colleague, but sometimes that abstraction breaks. Still good enough…
This article was probably written by AI, because anyone with half a brain could not read the study and come to the same conclusions. Basically, participants spent less than half an hour, 4 times, over 4 months, writing some bullcrap SAT type essay. Some participants used AI. So to accept the premise of the article, using an AI tool once a month for 20 minutes caused noticeable brain rot. It is silly on its face. What…
> What the study actually showed, people don't have an investment or strong memory to output they didn't produce. Problem with LLMs is, when you pass hours feeding prompts to solve a problem, you actually did help (a lot!) to produce the output.
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The vast majority of programmers don't know assembly, so can in fact not work without all the abstractions they rely on. Do you therefore argue programming languages aren't abstractions?
> The vast majority of programmers don't know assembly, so can in fact not work without all the abstractions they rely on. The problem with this analogy is obvious when you imagine an assembler generating machine code that doesn't work half of the time and a human trying to correct that.
A few things to note. 1. This is arxiv - before publication or peer review. Grain of salt.[0] 2. 18 participants per cohort 3. 54 participants total Given the low N and the likelihood that this is drawn from 18-22 year olds attending MIT, one should expect an uphill battle for replication and for generalizability. Further, they are brain scanning during the experiment, which is an uncomfortable/out-of-the-norm experi…
Earlier quoted context omitted.
So it's a noisy abstraction. Programmers deal with that all the time. Whenever you bring in an outside library or dependency there's an implicit contract that you don't have to look underneath the abstraction. But it's noisy so sometimes you do. Colleagues are the same thing. You may abstract business domains and say that something is the job of your colleague, but sometimes that abstraction breaks. Still good enough…
Noisy is an understatement, it's buggy, it's error filled, it's time consuming and inefficient. It's exact opposite of automation but great for job security.
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This comment reminds me of the so called Dunning Kruger Effect. That paper had pretty close to the same participation size, and participants were pulled from a single school (Cornell). It also has major methodology problems, and has had an uphill battle for replication and generalizability, actually losing the battle in some cases. And yet, we have a famous term for it that people love to use, often and incorrectly,…
I mean there are clear problems with heavy exposure to TV, video games and I have no doubt that there are similar problems with heavy AI use. Any adult with children can clearly see the addiction qualities and behavioral fall out.
One confounding problem with the argument that TV and video games made kids dumber is the Flynn Effect. https://en.wikipedia.org/wiki/Flynn_effect
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As the other comment said, LLMs are not an abstraction. An abstraction is a deterministic, pure function, than when given A always returns B. This allows the consumer to rely on the abstraction. This reliance frees up the consumer from having to implement the A->B, thus allowing it to move up the ladder. LLMs, by their very nature are probabilistic. Probabilistic is NOT deterministic. Which means the consumer is neve…
Ok, let's call it a stochastic transformation over abstraction spaces. It's basically sampling from the set of deterministic transformations given the priors established by the prompt.
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We're just moving up the abstraction ladder, like we did with compilers. I don't care about the individual lines of code, I care about architecture, code structure, rigorous automated e2e tests, contracts with comprehensive validation, etc. Rather than waste a bunch of time pouring over agent PRs I just make them jump over extremely high static/analytic hurdles that guarantee functionality, then my only job is to ide…
As the other comment said, LLMs are not an abstraction. An abstraction is a deterministic, pure function, than when given A always returns B. This allows the consumer to rely on the abstraction. This reliance frees up the consumer from having to implement the A->B, thus allowing it to move up the ladder. LLMs, by their very nature are probabilistic. Probabilistic is NOT deterministic. Which means the consumer is neve…