human conceptual thinking is very much a multi-dimensional graph, which relies on light "approximate" concepts that are "good enough". LLM AR token generation is extremely one dimensional and doesnt care about the "weight" of the concept behind a token.

LLMs hold billions of parameters in "mind" at once. humans hold like four "concepts".

This is the essential mismatch and the primary reason LLM conversation can be so painful and exhausting.

Explaining this and limiting "concepts" to four at a time tops is one of the very few AGENTS.md / system prompts I always use, and it has proven invaluable time and again.

Thinking traces show how effective this is at forcing the LLM to simplify its thinking.

[edit] Also, myself and nearly all of my peers are struggling to choke down the flaws of LLM tooling along with the benefits. the speed at which LLM adoption is being forced, without truly crafting them into quality tools first, is not ok, and not normal.

LLMs have stirred an inhumane hunger and fear. the tech is fine, but the way tech companies (creators and consumers) are behaving should be deeply questioned.

it's NOT normal. it's not ok.

Personally, I find that its generated prose tends to have an undue weight to it, almost as if every topic I ask about somehow bears a heavy burden, or is otherwise load-bearing, to use its parlance.

Quite puzzling, really.

I view LLMs in education similarly to office hours. Some people abuse it to get homework answers without grappling with the material, but the optimal amount is not zero.

LLM certainly not a replacement for a book, where you get someone’s extended personal approach to a topic, thoughtfully organized, reviewed and edited, often times actual courses taught based on it, with answers checked and errata available online.

> I get exhausted reading LLM prose

So much this! If I see one more sentence with the words "genuinely" juxtaposed with "load bearing" my head is going to explode!

btw, I am building the tutorial here for anybody interested in this topic: https://github.com/avilay/learn-probml

So I have the LLM offer a very short explanation of something, and from there's it's just me asking questions. Anything that feels fuzzy or not fully internalized is something I poke at until I'm satisfied.

It really has helped me develop a sensitivity to what I understand vs what I don't, and the ability to drill into any part of it is amazing.

I will say, opus 5 is an egregiously bad case of this, but other LLMs have this too, just less bad.

Agreed.

I find Opus 5, and even Fable, to be overly wordy in eg PR descriptions and code comments.

However, I suspect that's more to do with what they are trained to do by default than LLMs in general. I have a little setup where I tell Claude to work together with Codex to tighten up prose and comments, and for me that produces much more palatable text that needs less human editing afterwards.

While my advice is specific to learning about codebases, the way I do it is to have it generate mock data and put it in the local development environment, and give me some exploratory commands, and then ask away. It's a machine after all, so I don't have to read its preceding prose to understand whether it did tell me something, it can just repeat it however many times I ask it, and the hands on commands etc. give me something to actually try and implement.

This is my biggest gripe with reading AI-generated text as well (ignoring the meta issue of whether it's worth taking the time to read something that an author didn't think was worth the time to write). It's gotten to the point that weird AI-style analogies just take me completely out of the text and kill my interest.

And I can usually tolerate a lot of purple prose.

I'm not sure I'm better off with humans though -- I'm not qualified to judge whether a source is a proper authority, not an I qualified to judge whether someone knows enough to point me to a reliable source.

It seems this is a fundamental epistemological problem to which there may never be an answer.

It's a loop that uses adversarial review to check several dimensions of the writing:

https://github.com/Vibecodelicious/llm-conductor/blob/main/w...

I’ve found the tone of Kimi K3 to be less obnoxious. Unfortunately it doesn’t wholly solve the issue, I don’t think any LLMs out there have a truly pleasant writing style, but at least not every assumption is “load bearing”.

Like I said, I'm essentially continually prompting to refine the material. LLMs certainly continue to append, and never cut back. It just keeps spitting out additional content at me. So that's a bit annoying too. But I can basically get figure out what's going on with a few extra promps.

If youre curious what i've got so far... just be warned it is quite literally AI slop plus me continually prompting for clarification/cleanup etc. : https://github.com/cmoscardi/ai-for-ai

It generates tutorials for you, and serves a webpage that lets you complete them. It does a remarkable job.

It still has a bit of the LLM prose problem, but it does help you fine tune the ‘voice’ it uses.

I've stopped using CC because of it. I find it insufferable.

This problem doesn't get talked about enough and is second only to the hallucination problem IMO.

AI produces so much noise to wade through in order to find signal, and the more expertise you have in a field the more that costs. That noise directly subtracts signifcantly from productivity gains.

And, I think the problem is directly related to the hallucination problem. It feels very much like an effort to kitchen sink the response in order to provide some value among possible hallucinations.

It also seems to be a byproduct of Gen AI operation. It just fundamentally doesn't understand what it's outputting, so doesn't know how to narrow down to the most salient bits.

It's just long. It just doesn't shut up. It's overly verbose. And you can't tell it to be concise or you degrade its quality.

If I ask what an integral is, the correct answer is that it is the continuos analog of a sum, generally used to calculate areas and volumes.

It should really be a single sentence, and then let me ask more about the terms I don't understand, and here's the beauty, in the previous one there can be only 5 terms I cannot know.

An LLM will vomit an entire page or more of explanation which isn't bad per se, but is an answer to something different: "give me a short introductory explanation to integrals". And that's not what I asked.

I'm actually going to make a prediction here as well. I think you will soon realize that using LLMs to clarify certain questions or ideas you have will turn out to have frustrations as well. And that you will soon direct those questions to either peers you know in real life or internet forums which are very likely to have a non-AI policy.

Often "be concise, to the point." is enough, but you can also paste it some stuff you like as an example text and ask to do style transfer.

Literally saying "one sentence response" solves most of this problem.

Opus 5 first built me a detailed plan, but a couple important details were either obviously wrong or felt unnecessary. I went back and forth asking for sources and more information probably like 4 times and every time it did the "in looking at things in more detail it appears my previous advice was incorrect" spiel. It just became exhausting at some point because it feels like it really lays bare how LLMs are just minimizing that loss function but don't actually "understand" anything. It was really useful as a search engine (it correlated some highly relevant source docs), but I just couldn't trust it to believe it was actually done at any step.

i can't even get agents to remember core instructions like "use jq instead of writing a python script to parse some json"..

I'm researching causal inference right now, and my main goal was to make sure I understand how to test estimation on synthetic data.

Basically, it's the same way it works with people. If you delegate a task that you don't understand, and you can't have a credibility proof (i.e. doctors, lawyers), then you research a topic well enough to be able to (1) define the task and (2) verify the end result.

Trust me when I say that in the hands of someone who doesn't have your experience, the LLMs would not be getting the results you get.

You might think what you're doing is trivial, it may be sessions that flow roughly, "Instrument this, okay this part is slow, profile this part, OK read the profile output and suggest a better approach".

But your experience will be steering it in the right direction, and you're probably unaware of just how much your experience is doing that guiding, as the LLM shoots off at 100mph, you feel like it's taking you with it, but you will be guiding it a lot more than you realise, and that's where learning and experience comes in, even if you're no longer operating at the lowest depth, your knowledge of that layer will be helping.

If nothing else, the experience to know when something is actually slow is a skill in itself. If a function takes 200ms, sometimes that's as quick as it can realistically go, and sometimes that's literally a million times slower than it could be, and there's actual skill and experience wrapped up in knowing what "slow" looks like.

What is fascinating is how you can witness it at so many levels of organization. One example: Employer executive get enamored with moving from labor to capital. They believe that by using LLMs, they can replace a lot of workers. At my place of employment, we have people that are surprised they can't file a Jira ticket describing a product ask, and have it kick off an implementation. You can build the skill to attempt that, but invariably you'll get back questions like "what do you mean by <x>" and "what do you want to do in this case, a, b, or c?"; questions that a product person or an exec are not well suited to answer.

In the past, programmers did that kind of interpretation and judgment call. So then you're in a quandary; who should do that work? Work that previously, you never imagined was an inherent part of what the replaceable code monkeys do at your beck and call?

And then, how do you hire for that? How do you find the training for the people that are experienced enough with... something... to know what a cohesive error response is, or what kind of telemetry strategy is best for that particular product and organization, what collection of product asks are incredibly complicated for what they're asking and can deliver 95% of the benefits at 5% of the work if we just do this instead, and whether you want to aim more towards thick or thin clients?

Who are those people? Wait, those are programmers? Wait, there's this whole collection of inherently human skills that we devalued, by not appreciating they were always quietly doing that for us in the past?

That's just one example. There's a repeating pattern of discovering where the work truly is, work that was embedded in manual patterns we might not have to involve ourselves with anymore, but is yet still essential. So the nature of our jobs changes massively, but the overall level of employment does not.

At least, not in the medium to long term. There is a lot of painful churn we have to suffer through first.

They tell you have "hit the nail on the head" when you really haven't.

They tell you have had a "great insight" when you are really haven't.

They give you the illusion of learning and progress but essentially give you faulty preconceptions will trip you up further down the road.

You can ask the LLM to be more critical and less sycophantic but that only gets you so far:

They want you to continue using, being dependent on and feeding data into the LLM--your independence isn't a priority.

I wasn’t even really concerned with optimizing low level code before LLMs and that wasn’t why I was hired either.

However following that low level thread: We can look at the reasonable options and immediately know if they’re reasonable or nonsense. Why? We know the code. Now zoom a level out, where I think our expertise really lies.

Building a complex system isn’t easy. There are customers with requirements, there are budgets, SLAs etc. Sometimes one customer needs X and one needs Y. Our expertise is taking all of this in, and producing something that balances all the different variables. It’s knowing that we’ll expect X events a second so we’ll need Y to ensure we can tolerate failure.

Is it possible LLMs will be able to do all of that too? Maybe. But then why would our customers need the enterprises they pay for?

I have stuff to do now, the value of the knowledge in a year or two isn't important if it solves the issues I have today.

I'm not 'wasting time' but I'm also not really learning.

The more things you understand, the higher the chance you'll spot a situation to use them in the future.

I think the best innovations come from times when someone is uniquely able to combine two of their previous experiences together. The more experiences you have in your back pocket the more combinations you have access to and the more likely you'll have a unique combination when the right problem comes along.

In particular it might be valuable to be in the habit of learning things that one is bad at doing.

Or not.

This is silly. This would be like arguing that encyclopedias made knowing things pointless. I learn new stuff for me.

Professionally, it's important to know enough to know if you're going in the correct direction. Practically, tokens are going to continue to cost money and knowledge can save you tokens.

1. It satisfies you curiosity (and curiosity is always valuable)

2. You can better utilize the LLM to expedite something you now have knowledge about

3. You still improve as an engineer/programmer/prompter/whatever

I still think it's very important not to outsource everything to AI because there is a lot of value in learning and doing things yourself which is an important part of life.

I can tell you that there is an enormous gap in ability between them despite them both using LLMs for daily IR work.

The reasons aren’t complicated. The senior responders have tacit knowledge of how breaches evolve and what to look for which gives them a much better framework for where to employ the LLM.

The juniors will normally start from “here are some logs, look for weird” which is fine but leads to tunnel vision and a lack of confidence in their reporting.

I don’t mandate that anyone do work with or without an LLM. I hire seniors based on experience and juniors based on interest. But my experience has so far been that our best up and comers focusing more on learning the technologies instead of leaving those details to the LLM are developing their intuition and understanding faster and in a more robust manner.

The main idea is you can do any style you want or like to learn.

In my experience it's infinitely easier and faster to learn deep, "boring" things when you understand how they relate to your shallow and wide understanding of all of the related components.

The LLM is merely a tool. And you can use it for domain discovery that enables efficient deep learning at an unprecedented rate or you can develop a cursory understanding of a topic and think yourself an expert.

It’s true of most things. Running, dieting, weightlifting being uncomfortable is a sign of progress.

This is the only path to mastery, or understanding if one prefers. There are no shortcuts to a person achieving deep understanding (a.k.a. "Aha!" moments).

Can a tool such as GenAI be beneficial to someone who already has done the work to understand? Absolutely. But it cannot infuse mastery into a person simply by its use.

Only the time and effort a person devotes can do that.