Slapping an explicit "I did not put effort into this" label on slop content is not censorship, its quite opposite, it lets people filter out the noise. No human can hope to be able to do this on their own at the scale GPUs can pump out tokens.
Authentic voices are today being drowned in waves upon waves of AI slop spam. This is not quite censorship but the end effect is similar - human voice cannot be heard. Just this time not because someone stopped the human from talking, but because they as shouting 1000x louder instead, at pennies on the dollar it costs to create something truly valuable.
Probably not critical, just helpful to be able to understand where the source is willing to cut corners. Helps me understand if the given content source values are aligned with mine without risking wasting time to ingest slop just to come to the same conclusion.
The same way I look at food labels to support local produce and those who don't try to trick me into thinking I am consuming something it's really not.
I personally wouldn't care for AI usage in icons, I don't put a lot of attention to them. Someone else might.
Buying a license for a stock photo vs using AI to create what you need is cutting corners? Don't thing there is much difference in effort (might even be more on the latter)?
Buying a license for a stock photo is paying someone who has put the effort.
Well, okay, I am not sure how much effort goes into creating your typical stock photo, probably not a lot, just still more than 0 effort it takes for the AI to generate it.
And yes, I see difference between an automated system flooding the web with AI generated "news" in disguise vs a niche blogger using AI to iterate on an image with hand crafted prompts. Labels are not perfect, just better than nothing.
The lullaby for the Gen AI era exists, but its for CEOs and it is being played in management meetings, it's main theme is about maximizing EBITDA.
The defensive mechanisms kick in because even though more code is generated than ever, we are not observing an equivalent rise in software quality or usefulness, some would perhaps argue it's even opposite.
If gen ai for code was really what it is being sold as it would all be obvious to everyone, we would be seeing better software all around us everywhere and posts such as the one here would just be laughed off, delusional, but they are not.
The code explosion did happen, the value of software this code makes - not yet. Not to say it won't, its just not here right now, and it never happening is still a possible outcome.
I don't think so. GPT CODEX and Claude Code are already saying they use AI in their own codebases, and they have huge numbers of active users. Can that really be called a failure?
In my view, it's just that the existing infrastructure layer is so thick that it's not immediately visible—but AI adoption is already quite widespread across many companies.
People say program quality has declined, but I don't think so. The average quality of programs has improved significantly. You can see this by looking at open-source architecture books from 10 to 15 years ago.
That's why I think we need to first define what we actually mean by 'code quality' before discussing this issue.
Realistically, this discussion could easily drift into a debate about code quality. But if you look at older books on open-source architecture, there were many issues—runtime null references, confusing callback references, diamond dependencies, and so on.
These days, many of those problems are caught by linters and other tools. At the micro level, code quality has definitely risen compared to the past.
The real problem is that programs are getting larger. The minimum requirements for a program to be viable have gone up, while the available workforce hasn't kept pace. But in terms of quality, I think we can confidently say that overall code quality has improved compared to the past.
The issue is the gap between micro-level code hygiene and macro-level semantic coherence. And the key question is how we can maintain that macro-level semantic coherence while using AI-generated code. I think these are fundamentally different problems.
Adoption absolutely did happen, I am not questioning that. What I am questioning is whether it really is the revolution the people selling it want us to believe it is.
Just to disclose: I use those every day, I have two Claude Max 20 subscription myself. I am still in doubt how much more productive professionally it made me. I am having a ton of fun in exploring stuff I never would have otherwise though.
I define software quality in my daily life by this: how often I am delighted by the piece of software I use. Those moment are rare and far between, and it's not getting any better.
Senior, I respect your opinion. I think your skills are so advanced that you don't feel the impact as much. That's a different perspective.
>I am delighted by the piece of software I use. Those moments are rare and far between, and it's not getting any better.
First, regarding how much satisfaction we get from the software we use—I see that part a bit differently.
Because that's more about UX and product design than code quality. I think it's because in the business domain, people only attempt safe things. In the early days of the internet and the web, people didn't have a clear direction, so there were many bold attempts. Those original attempts felt fresh. But now we have a set of 'norms.' Most people just implement those norms. I think the reason you don't feel that freshness anymore is precisely because you're already so skilled.
What I'm focusing on is this: I'm not sure about my own productivity, but I'm finding joy in exploring areas I couldn't explore before. The problem is that most of this code just ends up implementing solutions that already exist. That seems to be the core issue.
In other words, both you and I have our threshold for 'average' set so high that the threshold for calling something 'good' has become too high as well.
> Because that's more about UX and product design than code quality. I think it's because in the business domain, people only attempt safe things.
Not really. It’s mainly about the lack of frustration. People will learn the most obtuse way of using a software if it’s important. But they want to spend that effort only once, and then have valuable results as the ROI. Meaning no random crashes, no slow process,…
When you can have something that runs without babysitting, or just be able to use the tool and do your tasks, it removes a lot of mental load.
What ethics do you see being superior in the company known for exposing underages to gambling? The anyone elses must be really bad guys. Yes I like my deck and I respect their contributions to gaming on linux, but Valve is no saint.
The gaming market is full of gambling. Steam has problems in that area but they're miles better than what Apple is doing. My impression of Steam is that it's a game delivery platform where some games sadly have gambling elements. My impression of the App Store is that it's a "gambling for kids" delivery platform.
I always think now it’s funny that we Hated valve when steam was first introduced. Their pioneering of always online DRM and no physical media, soon to be followed by Bethesdas horse Armour micropayment dlc. The base line has shifted soo far from the days of freely auto p2p downloading of fan made skins on map load in q3arena.
I was on steam beta, I thought it was great. No more having to dig around for patches, all my games in one place. It has its ups and downs, but I can't think of any other platform I'd rather use (though merit wise gog wins).
Yes, they basically pioneered lootboxes, they shaped and scaled microtransaction economy around randomized outcome purchases under disguise of gaming and then slapped a marketplace on top of it(to take even further cut %% nonetheless).
> I can't even picture gaben with a wispy moustache to twist evilly.
Add a 500m gigayacht to the picture, maybe it will make it easier. Or a fleet.
Why not restrict the addictive design of the algorithms instead? No need to blanket ban social media access if the social media cannot optimize to steal every last inch of their attention. Anything on the feed becomes opt-in only and no engagement data coming from the user or from the outside can be used to optimize it, thats it. Control is back in the hands of the content consumer, parental restrictions become trivial, no black box algorithm deciding what bubbles up and what gets buried deep down.
Any self improving loop where the user is not in control, thats it.
If I subscribe to A and get more of A - thats fine.
If the algorithm detects that I spend 0.5 second longer on average looking at content with feature A and so decides to show me more of A - thats bad. At most it might be allowed to ask me if I want to subscribe to more of things with explicitly defined feature A, but even that assumes we are fine with collection of behavioural data like this in the first place and so it is a stretch already.
Transparency and control is what makes the difference here in my opinion.
From what I've seen trying to play around with Claude Tag, it uses shared credentials. You set it up with Github org access, add it to public channel, and all random people in that channel can now ask it to do stuff with code. It's opposite of what Cursor does - each Slack member needing to connect their own Github account. It's a security nightmare basically.
I built a workflow execution engine that dispatches to my tool service. I had previously looked into using Temporal but at the time decided to focus on what differentiated the idea. Maybe in the future execution durability will be important enough to make using Temporal the right solution however as of now I am focused on explore the problem space using chat -> convert the good parts to a repeatable process -> trigger appropriately.
Because I am getting the call to fix it when it breaks. I don't have to fix assembly by hand because compilers are deterministic and I have maybe encountered a single real compiler bug in my whole career. Compilers have earned my trust. LLMs are eroding that trust more and more every day I work with them. I encounter LLM-created problems in basically every single diff they surface for me, just over the months the diffs are getting bigger and harder to review and uncover the problems.
LLMs are not an abstraction(not even a bad one) because by design what they are doing is disambiguation. Compilers are not doing that, what you put IN the compiler has to be unambiguous in the first place.
Disambiguation is not a functionality of an abstraction layer. A good abstraction layer is the one I don't have to understand and can trust, if I have to understand its inner workings to use it it ceases to be an abstraction. Except with LLMs you can't even do that, they are a black box you can have no hope of understanding.
And it is not to say LLMs and agentic coding tools are not useful, they are absolutely very useful. They are just not an abstraction layer.
If someone is trying to bend the rules of my passively managed index fund to their will, are they trying to actively manage my passively managed index ETF ?
Authentic voices are today being drowned in waves upon waves of AI slop spam. This is not quite censorship but the end effect is similar - human voice cannot be heard. Just this time not because someone stopped the human from talking, but because they as shouting 1000x louder instead, at pennies on the dollar it costs to create something truly valuable.