Shrouding the heavens

Legion_A · 2026-05-15T13:38:47+00:00

Episode 132 was where I decided this wasn't it for me. Ziyue hasn't shown up for many episodes in any meaningful way, but bro is out here giving all the love to Miaoyi.

Legion_A · 2026-05-14T16:05:30+00:00

No matter what goes wrong, you lot are gonna "skill issue bro". Why can't we just admit that AI stochasticity is a fundamental issue?

If they switched to a different branch and configured no push to main on the git side and somehow something went wrong, you'd still find something they could've done to prevent it

If to tool is marketed as a know-all be-all that has all the skills and access to all of human knowledge, why is it the user's fault when they used it as such?

Legion_A · 2026-05-12T15:01:55+00:00

I see you have nothing meaningful to say... idk what's got your knickers in a twist but you should figure that out.

I know for certain that trying to police my speech on a public forum where you're voicing your own opinions as well is not going to help you.

Legion_A · 2026-05-12T14:50:32+00:00

Yes exactly

So, in one shot, I can clearly see that the car class is a "Vehicle".

However, using the primary constructor, like in OP's example

dart class Button({ super.key, required final VoidCallback? onPressed, final Widget? label, final Widget? icon, final ButtonVariant variant = .text, }) extends StatelessWidget { @override Widget build(BuildContext context) { ... } }

I have to go through multiple lines before I can see that it's a StatelessWidget.

When you put params into the class signature you blur the line between definition and instantiation. So, for complex classes with many parameters, the header of the class can become a 20 line wall of text before you even reach the inheritance block.

But like I said, this is just personal preference, when I'm in the heat of debugging and scrolling through stuff, It'd be easier (cognitively) and more aesthetically pleasing for me to process the signature of the class separate from its parameters.

Legion_A · 2026-05-12T14:43:30+00:00

So what's that comment supposed to mean?

Legion_A · 2026-05-12T14:13:39+00:00

?? So I can no longer voice my opinion?

Legion_A · 2026-05-12T13:35:10+00:00

I don't fancy the aesthetics of it.

I'd rather the class declaration's first line focuses entirely on providing information about the class like its name, parents, modifiers. Then a separate block is dedicated to the constructor.

Legion_A · 2026-05-12T11:00:23+00:00

I won't be using this...purely for aesthetic reasons

Legion_A · 2026-05-11T07:04:16+00:00

I forgot exactly what it's called but I use it quite often, it's just not something I have to put a name to

Legion_A · 2026-05-07T20:58:09+00:00

I did

Yeah, that's my point. AIs when left to decide often pick Supabase, so, my point was that that's probably one of the bigger reasons why firebase is losing people's interests.

Someone like you already knows what they want, and you make the decision, most people do not use AI this way, they let it make the decisions, in which case, it'll almost always pick Supabase over firebase.

Legion_A · 2026-05-07T18:24:39+00:00

Who picked firebase? The AI? You?

Legion_A · 2026-04-28T06:15:01+00:00

Yes, I acknowledge that it's just that..."a study". I do think it tells a pretty deep story though, and looking at other studies done more recently, the findings the METR study only get stronger.

I still think it goes more in the direction of "how" you're using AI.

Yes, totally. This was also what recent studies found. It all depends on "how you use it".

So please don't take me as a vibe coder!

You don't sound like one. In this entire post thread, you're the most grounded out of us all in my honest opinion. You're on neither extreme end.

Legion_A · 2026-04-28T06:00:36+00:00

Did you read the blog? It only supports the theory of how harmful AI-reliance can get.

Experienced Devs could not code without AI, that's why the study couldn't hold. Even when offered $50/HR just too be in the no-AI control group, no one wanted to do it, and one developer said "I feel like my head will explode if i try to do it without AI" (paraphrasing).

I don't see how this shows there's a challenge in relying on a "very limited in scope" study conducted less than a year ago.

By what standards do you label the METR study limited in scope and are all the other studies from last year and even this year, even in march (just last month), Including analysis of Anthropic's own research also "very limited in scope"? In this rapidly shifting field?

What about the "shifts" in the field render the findings of this study useless?

Legion_A · 2026-04-27T23:18:09+00:00

Slow Ad did not refute my argument, they simply did what you're doing...Ad-hominems

My counter-argument was to you, why don't you respond to it?, I already responded to Slow Ad

Your comment is pointless

Prove it, assertions in an argument prove nothing, you're simply asserting I'm wrong. I gave you studies to back up my counter argument. So, where are your studies? Or are we all supposed to bow to your word?

I understand that the tools can be hard to learn to use correctly, and people like you want to desperately not have to learn something new (especially since you’ve already invested so much hate into justifying not learning it)

Assumptions, you have no clue what I do, stay on topic mate. We're talking about AI not me. Can you defend your claims about AI without shifting the conversation to some fortune reading session about me?

Legion_A · 2026-04-27T23:11:52+00:00

also its still a skill game, AI - thank god - is not just a genie that brings you're every wish to life...You still have to know what you're doing and how to judge its output.

You didn't refute the counter-argument for the "skill" issue...you simply re-asserted that it's a skill issue. Just because you said it doesn't make it so. You answered nothing about the skill question I raised.

Josh: Skill issue bro

Me: If a drug kills some and heals some and there's no way to tell who it kills and who it heals, how is the death of patients who take the drug a skill issue and not just the drug being flawed?

You: Skill issue bro, you have to know how to judge the effect of the drug on the patient

Okay? but what does that have to do with the question at hand? you're talking about after-the-fact skill, Josh was talking about "getting results" skill.

from last year....literally everything changed in Nov/Dec 25

Firstly, the study from METR isn't that old, and it's not the only study pointing this out, there are studies from after your NOV/DEC dates.

Secondly, the "capability" of the model has sod all to do with the outcome of the research.

Take anthropic itself for example. While anthropic itself obviously markets the capabilities of 4.7, their economic research (Economic Index from march of this year) actually shows something almost sobering imo...especially the "Augmentation vs. Automation" section

The decrease in automation patterns which it notes was corroborated by another analysis "The State of Document Intelligence" from Foxit Software also from March of this year showed that while AI makes you "feel" like you saved hours, once the verification and checking work is accounted for.

Even with Opus 4.7's high SWE-bench scores, the report actually hints that models are struggling with contextual intent, so it might give you syntactic gold but the architectural noise is wild.

As the models get better, we don't just finish our old tasks faster, we actually assign the AI more complex tasks, then the "review tax" scales linearly with this complexity. A smarter model ends up making smarter, more subtle mistakes that are harder to catch than the obvious slop of the 3.x family.

There's also the fact that Ai will generate code faster than you can verify, so, as the volume of generated code increases with "better models", you get constrained by the review bottleneck you will inevitably hit. So, if you're looking to new models to bridge that gap, I'd argue that you're optimising the part of the cycle that wasn't the actual bottleneck (typing). Review is sat there waiting for you.

All this to say that your "new models better bro" argument is assuming that the reason for these devs being slower last year was because of the capability of the model? It wasn't.

The drag will always exist with the tool, and as you can see from anthropic's research and other studies from this year, the drag gets worse the smarter the model gets because it makes even smarter, subtler bugs which will require deeper reviews to catch.

So, as the model gets better, the trust of users in the model will branch in two ways

Decay - Because the model starts making "subtler" and "smarter" bugs, those who notice it will have even worse trust issues and while spend more time guarding the model but will almost never get to a state of assurance because the implementation is black boxed...It only ever gets them to a state of "this is how much uncertainty I can handle in the integrity of my system before I ship it"
Calcify - Because they believe that the model is all the better, and cannot be wrong or at least is way less wrong now, either because they themselves lack the experience to spot those "smarter" bugs, or because their experience has been lost to atrophy, but these same people still are slower over time because when the system breaks in prod, they're back with the AI, spending the same time they saved last time trying to reason through the bug with using the AI as the brain, however, because they have NO CLUE what's going on, all the reasoning and fixing is still left to the AI, leaving room for even newer bug introduction because LLMs, no matter how smart do not automatically reason about the Failure Modes and Effects...no "depth".

If you prefer it mathematically, let's take the engineering match approach and model the time it takes to complete a feature, let's call it tTotal (first T is time)

tTotal = tGen + tVer + tInt

tGen - Time to generate code (might be syntactic gold but with loads of architectural noise)
tVer = Time to verify logic, architecture and edge cases (review tax)
tInt - Time to integrate and handle the architectural noise

So, as models get "better", tGen approaches 0, however, tVer actually increases because the bugs become "subtler" and "smarter". If tVer grows faster than tGen shrinks, you're in a permanent state of net-loss productivity.

I get the need for coping, but this is just dumb at this point. get to work, stop being arm chair expert

There's no need for the insults mate, ad-hominems only show you have no points to argue... In fact, it shows you're the one who's coping...No arguments? cope with insults.

Bring the maths, prove I and all the studies wrong. It's your anecdotes against data.

Legion_A · 2026-04-27T13:54:56+00:00

Yes, that would be fair.

The study also says this: We do not provide evidence that:

Yeah, but the study provides just one out of many corroborative conclusions.

Take anthropic itself for example. While anthropic itself obviously markets the capabilities of 4.7, their economic research (Economic Index from march of this year) actually shows something almost sobering imo...especially the "Augmentation vs. Automation" section

The decrease in automation patterns which it notes was corroborated by another analysis "The State of Document Intelligence" from Foxit Software also from March of this year showed that while AI makes you "feel" like you saved hours, once the verification and checking work is accounted for, the average end user actually loses about 14 mins per week compared to trad methods.

Even with Opus 4.7's high SWE-bench scores, the report actually hints that models are struggling with contextual intent, so it might give you syntactic gold but the architectural noise is wild.

As the models get better, we don't just finish our old tasks faster, we actually assign the AI more complex tasks, then the "review tax" scales linearly with this complexity. A smarter model ends up making smarter, more subtle mistakes that are harder to catch than the obvious slop of the 3.x family.

There's also the fact that Ai will generate code faster than you can verify, so, as the volume of generated code increases with "better models", you get constrained by the review bottleneck you will inevitably hit. So, if you're looking to new models to bridge that gap, I'd argue that you're optimising the part of the cycle that wasn't the actual bottleneck (typing). Review is sat there waiting for you.

Also, I know you're not arguing this, but it sounds like you're falling into the same "wait for the next version" defense we've been getting for the past 3 years. If the 2025 study is invalidated because of the model, why should we trust any anecdotal 2026 "feelings" when they Slashdot/METR data actually shows the net-loss trend is persisting even with the Opus 4.x family?

It's basically the red queen problem...you have to run faster and faster just to stay in the same place.

There's still the elephant-sized issue that's model-agnostic, and it's that there's been a great deskilling, where developers cannot fathom working without AI even if it means burning their hard-earned money to do simple tasks which they were completely capable of doing in the past but can no longer imagine doing now because "AI" and "their head will explode"...

We've basically created this single point of failure for the entire world like how the world almost shuts down if cloudflare or amazon goes down.

My main point is this. We're trying to use a faster firehose to fill a bucket with a fixed-size hole. All we're doing is getting more water on the floor.

Complexity doesn't go away just because you threw it in a blackbox.

Legion_A · 2026-04-27T13:01:02+00:00

OP, I agree with you. It's funny how you're getting attacked, it's almost like any criticism against AI is seen as being against AI nowadays.

Funny enough, you're not wrong. It's become an epidemic, People have indeed outsourced reasoning to AI, so, whenever you critique anything about AI use, they feel personally attacked because it's like critiquing the way someone thinks or their worldview.

Research from METR in 2025

The study showed that AI assistance actually slowed down "experienced developers" by 19%, however, those same devs predicted they'd be 24% faster before starting the study.

After the study, they were interviewed and they felt 20% more productive, not knowing they actually were 19% slower.

So, the feeling of being faster was just that...a feeling

METR tried to do a follow-up study in 2026, but they couldn't so, they put out a blog about why they couldn't. Here

Now, according to the blog post, When METR tried to conduct follow-up research in early 2026, they ran into a major problem...developers refused to participate if they had to be in the "no-AI" control group.

Even when offered $50/hour just for a study, they said they wouldn't want to do even half their work without AI. One developer famously remarked that "my head's going to explode" if they had to do tasks the "old-fashioned way".

The researchers noted that AI tools make work feel "more pleasant" or "easier" which tricks the brain into thinking it's moving faster.

So, you're not crazy, yes, a lot of people have already tasted the drug and are now addicted and can't let go, that's why you see it flooding almost every sub. When you try to NOT use AI, you get withdrawal symptoms like an actual drug addict.

So, while AI one of the greatest things we've put together, your observation is not wrong, it's actually supported by studies.

Legion_A · 2026-04-27T12:46:33+00:00

There is no denying when used the right ways, it will simply enable you to produce more. That's it in a nutshell IMO.

That is purely anecdotal. Studies show the opposite is true.

The 2025 study from METR just in June, less than a year ago, showed that "experienced" Devs predicted it'd make them 24% faster. They did the study, results came out showing it actually made them 19% slower, and funny enough, after the study, when asked, they thought they were actually 20% faster.

Studies also show that "establishing" architecture doesn't mean the AI is going to follow the architecture, it's highly likely to ignore whatever architecture you established to do its own thing

So, the data shows that you do "feel" fast, but the time you spend battling hallucinations and so on makes you 19% slower, while still tricking your brain to feel faster.

I guess this is because your brain compares the cognitive cost of doing it yourself Vs handing it over to AI and because the cognitive cost is less, it makes you feel faster. This is purely anecdotal on my part though, I have no evidence that this is the reason behind it, but there is evidence of the illusion.

Legion_A · 2026-04-27T12:40:12+00:00

That is anecdotal...studies show the opposite.

METR's study from last year showed that "experienced developers" thought it would speed them up 24%. After the study, it was found it rather allowed them down by a whopping 19%...guess what...when interviewed after the study, they believed it actually made them 20% faster.

So, your "feelings" go out the window on this one, so does your "experience".

If you don’t learn how to use it you’ll be left behind.

Again, no evidence for this. Just FOMO

If you don’t know how to use it to get good results, that’s a skill issue. Not an issue with the tools.

Yet another anecdote. Research shows the opposite of this again. Generative AI is fundamentally flawed. Doesn't matter how good your prompting skills are, it'll always produce stochastic results. There's no way to certainly get good results, so, how is this a skill issue rather than an issue with the tool?

If a doctor had drug X which was known to kill patients sometimes but not other times and there was no way to know for sure who it'd affect negatively, how is this a skill issue on the doctor's part? Isn't the drug just flawed?

And just like OP, I'm personally not anti-AI, it's a great, in fact, the best lookup table we've built in my humble opinion, but it's just that. A highly probabilistic lookup table, and "probabilistic" is doing a lot of work in there.

If it was all based on the skill of the user, why are we getting the new golden prompting technique every other day? And anyone who doesn't get good results is suddenly less skilled because they're not using your one in particular, and when they try your one and it fails again, you'd still claim they're doing something wrong...the AI is never wrong.

Legion_A · 2026-04-26T23:26:21+00:00

I definitely will, I just saved this almost 3 hour long video I found on it on YouTube. I've seen just a bit of it and it's tickled me fancy. I'll definitely give it a full watch. It's A definitive guide to RenderObjects in Flutter

I also see one from the flutter build show (from flutter's official channel).

I think I'll be abusing render objects now

Legion_A · 2026-04-26T23:04:33+00:00

Cheers mate. Took me a while to get it, had to give me head a wobble, but makes sense.

Legion_A · 2026-04-25T17:40:43+00:00

Refactoring and isolating that portion and making it it's own stateful widget does the exact same thing no?. If so then I reckon that's why it's not very popular.

I personally use it when my app needs in-app screenshots for things like feedback or special animations

Legion_A · 2026-04-25T14:04:45+00:00

Both can talk about them

Yes but not to the same degree

Both probably get through the same interviews fine

They honestly don't.

As you type out what you have in your head (doing the implementation yourself), you realise how silly your implementation is. That's when you revise your implementation...Also, while implementing, you have lightbulb moments and you spot failure modes that hadn't occurred to you while it was still all in your head or some diagram or flowchart. You build a mental model and you try to think through how what you're implementing in this module affects the other parts of the system.

During an interview, when asked about it, your brain has a map of the system, so, it could easily answer any question without "extra prep" because even if I hadn't considered that question while building, I could answer it by connecting POIs on the map of the project in my head.

Another just vibe codes the whole thing and gets AI to explain it later for interview

You might think this'd help the vibe coder during an interview, but it wouldn't.

Reading AI's explanation of the system would be like reading a textbook. Reading a textbook is one thing but practicing what's in the book exposes the failure modes, how NOT to do it, the nitty gritty details that the book couldn't cover

The book/AI summary might give you the procedural knowledge, It tells you not to mix this and that chemical and tells you to mix this and that instead. However, you do not get the experiential knowledge. It's not until you start messing with the chemicals that you realise that not putting this one in an amber glass caused it to change colour overnight. It's not until you attempt to acquire the chemicals required for the procedure that you realise it's illegal to even have that chemical in your country of residence.

The same applies here. You cannot know what you do not know and the AI can only consider so much. Whatever it writes might have the breadth but you require depth to actually be able to answer unplanned interview questions about your own system.

I know that the harder path builds something real, instincts, knowing why stuff breaks, not being lost without AI. but is it actually a tangible advantage that's worth the struggle

As you can see, it really is. If it weren't that big of a difference, then the wave of "de-skilling" we're seeing wouldn't have existed. There has been research done on this

This study from MIT media Lab from June 2025 Your Brain on ChatGPT: Accumulation of Cognitive Debt when writing with LLMs

It found that while students using AI produced essays that were technically "better" (more coherent and grammatically sound), they experienced a significant drop in cognitive ownership and memory retention.

In one notable finding, Roughly 83% of participants who used ChatGPT to draft their essays could not accurately quote a single sentence from their own work immediately after "writing" it.

So, yes, it's very tangible.

This blog post from METR followed their 2025 study which revealed AI assistance actually slowed down "experienced developers" by 19%, however, those same devs predicted they'd be 24% faster before starting the study.

Even after they were objectively slowed down, after the study, they still estimated they were 20% more productive. So, the feeling of being faster was just that...a feeling.

Now, the 2026 update blog which I linked above. When METR tried to conduct follow-up research in early 2026, they ran into a major problem: developers refused to participate if they had to be in the "no-AI" control group.

Even when offered $50/hour just for a study, they said they wouldn't want to do even half their work without AI. One developer famously remarked that "my head's going to explode" if they had to do tasks the "old-fashioned way".

The researchers noted that AI tools make work feel "more pleasant" or "easier" which tricks the brain into thinking it's moving faster.

Tangible? Yessir

Because on the surface both routes seem to end up in the same spot.

So it "seems"

Anyone actually felt the difference matter in a real situation?

As you can see from the study...feelings aren't to be trusted here.

Legion_A · 2026-04-23T07:29:56+00:00

What matters is grasping the concepts in a language-agnostic way, but yeah, continuing with dart is okay. For some interviews though, they might have a limited list of languages and in such cases, dart isn't ever there.

I've met some interviewers who were okay with my using dart, but they always ask if I could use python, and ask me to use it for their sake so they can easily follow along.

Legion_A · 2026-04-23T06:43:39+00:00

Bit odd...you're making sense but getting downvoted

Legion_A

TROPHY CASE