They got occupied by donating millions to slop AI linux and leeching off of open source with no contributions.
Theres a single solo developer who donated 40k this year to postgres, their name is next to AWS on contributors. Digital ocean sells scam postgres services for newbs and has never contributed a dime (afaik).
Complete slop overpriced cloud platform now too thats leaned way to hard into AI nobody goes to them for.
Llms are not good at web design or writing css, this art form will live on for a long time to come. It is art, I dont care what people say, and as we all know machines mostly suck at creating art. Anthropic claims to have started training in "taste", but im sure they've realized that cannot be distilled into vector embeddings. Taste is somewhat empheral and is dictated by society and culture that machines can't experience.
In fact, I think it will have more staying power than most, if not all of web dev domains of expertise.
Theres simply no "soul" behind llm crafted websites. There are companies out there, like lovable et al who sell the idea that web design is solved but are still hiring six figure retainer design firms to do their frontend.
I believe the best web platforms will heavily invest in human crafted UI/UX because that is what will make you stand out. The rest will get thrown on the slop pile and forgotten.
I see your point and I agree that ultimately web design will be incredibly hard to automate, probably harder than almost any other programming domain, where people will likely stay for accountability reasons.
But the thing is that for the vast majority of web pages/applications, it barely matters what does the design look like, especially for B2B/internal apps. All the dashboards, CRMs, tracking tools, etc -- these exist mostly for functional requirements, so the design just needs to be inoffensive.
B2C market might hold longer, but ultimately there might emerge some sort of higher-level design specification language which would sidestep the original problem or being too recognizable.
I shared your sentiment maybe a year+ ago when it comes to css, and I do think it's one of the areas AI in general struggles with (where art and code meet), but it's a lot better today with the latest OpenAI and Anthropic models as someone that works on design systems for a living (for now). It follows stylistic patterns, it can inspect the issue in the browser and have a better understanding of CSS upstream effects than it used to (it used to suck at this and paint itself into a corner over and over like some game of whack-a-mole), and can even tackle things using latest interop features.
It does make really silly mistakes now and then, like using fallback css variables in the wrong order, but they are fewer and farther in between.
"I believe the best web platforms will heavily invest in human crafted UI/UX because that is what will make you stand out. The rest will get thrown on the slop pile and forgotten."
AI model taste is not good, but taste is easy and many cheap human have soul, only hard part is memorize CSS trivia, I predict cost of UI/UX go down and CSS education business also go down
Im going to continue to use fast open source models like Deepseek and GLM, I like most others, have zero need for the worlds smartest person to be my assistant.
I dont understand this business model, it seems clearly unsustainable (at their prices). When people start to realize they dont need to spend all this money on turbo PhD intelligence so they can build react apps, Anthropic will be in a tough spot (if they already aren't).
You will say, "but enterprise!!" Nope, plenty of companies are waking up to this reality as well. It turns out that if you made money pre-AI without spending 5k a seat a month for your engineers dev tools, you probably don't need to now to continue too.
Training costs are not in free fall, and not every "post training" process is equal, Everyone does this differently. The costs certainly haven't gone down, to train a new 10T param model from scratch, like at all.
You cannot point at Xiaomis tiny model and compare it to training a 10T param model. The levels of dunning krueger are getting out of hand.
You also arent calculating the salaries of the people involved, of which there's only a few thousand worldwide in your hiring pool.
> You also arent calculating the salaries of the people involved, of which there's only a few thousand worldwide in your hiring pool.
There were probably a few dozen 20 years ago, and the existing pool grew from somewhere. AI (nee machine learning) is not rocket science - but it is a science, so it rewards those who are literate, curious, can hypothesize, experiment and learn from results to gain an intuition. I bet most statistics-adjacent professionals (millions, worldwide) can move laterally into AI research and excel at it. Nevermind AI has been among the most-in-demand undergrad programs since ChatGPT dropped. The only limited pool is for celebrity AI talent.
you haven't cited any examples, nor acknowledged this is how everything in computing has gone, DK is a two way street I suppose
stable diffusion is an excellent example, the price to train today's or yesterday's frontier is the analogy, many more people will be able to train up what we have today, especially as more of the pipeline is open sourced and the knowhow distills
>stable diffusion is an excellent example, the price to train today's or yesterday's frontier is the analogy,
Dont use llms to draft your responses to people, extermely cringe. That explains the llm induced Dunning Krueger.
What examples... There are no public numbers regarding how much it costs to train a 10T model, atleast nothing concrete.
You should just use common sense and think about how you have to fit tensors in memory to train, and the larger the model is the more GPUs you need. Then you could also use common sense to think about how GPUs have gone up in price, not down.
> Dont use llms to draft your responses to people, extermely cringe
You are throwing around unfounded and incorrect accusations. Why? It's a weak ass rebuttal mate
common sense is using things like history to guide expectations instead just saying things without evidence that are contrary to the patterns in computing history
With the utmost respect, if you've only been here 20 days you might not be fully cognizant of the site norms and culture. It's not about being pro or anti AI, it's low effort comments.
"X is the Y of Z" might not be insightful enough to prevent being downvoted. It's basically a saying, and the author likely spent like 20 seconds coming up with it. Probably I spent more time just writing this comment, than parent spent thinking+writing theirs.
Insightful critique of AI companies get upvoted here, more so than insightful cheering, I'd say.
More like lighting a fire in your codebase at the speed of light.
Nobody needs this, and the quality of models they're deploying, or all llms for that matter, require heavy human review (if you care at all about maintainability).
One developer can barely keep up with the daily output of multiple llm sessions writing code at 50 tps.
Im convinced anyone confidently thinking 2k tps is a no brainer is probably not concerned about the quality of their work (many such cases).
Occasionally it takes me at most 10 minutes to figure out that a solution the LLM took an hour to churn out is not up to par and I need to throw it away entirely.
There are diminishing returns, but half of the rate you mentioned would already help me keep focus.
You’re thinking about this the wrong way. Yes, any individual developer doesn’t need this much power all the time. But any individual developer would like to get an answer quickly, which this does. You share the speed and capacity of this over a whole organization. Any one developer only uses a fraction of its capabilities at any given time. Getting an “instantaneous” answer is quite helpful. Think of it like a shared compiler; a developer is not constantly compiling, but having an “instantaneous” compiler is quite useful.
most of the time developers arn't looking for answers. They want to implement some feature. And I'd hazard most developers arn't using these things as a crafter implementer but a frankenstein of architect, implementer, tester, builder and deployers.
And the dev, if they're sane, wants to actually evaluate what it's doing in those arenas. Speed might be a psychology killer then. Actually being able to see the development process as it does it's good think and it's wrong think is a benefit for local development.
I don't think speed of tokens is all that magical if there's a huge imbalance of ability to "human in the loop" it's processs.
Code is an answer to a coding question or direction. Getting an answer quickly is always better than getting an answer slowly. You can evaluate the answer at your leisure. The human can take as long as the human wants. Meanwhile, the infrastructure is generating answers quickly for other people. Faster is always better, even with a human in the loop.
1. You don’t know anything about me, so don’t presume.
2. Which nursery rhyme? The tortoise and the hare is a fable, not a nursery rhyme. Even if you think the lesson was “faster is not always better,” which it wasn’t, faster is sometimes better. And in particular, when a machine is involved, all things being equal, faster is better. Would you rather use a fast compiler or a slow compiler, all things being equal except cost to keep the analogy with the original? Compiler A produces the same binary with the same optimization levels a compiler B, but in 1/10th the time. Assuming you can afford it or someone above you in your organization is willing to buy it for you, you take compiler A. If you can’t afford it, you lust after it. Why, because a slow compiler results in a slow iteration loop no matter what. With an instantaneous compiler, the loop is as slow or fast as the human part of it. You want to think some more, rearchitect this or that? Fine. Your code is always ready to run.
I get the sense that you’re anti-AI. That’s fine. I’m not sold myself, though I’m increasingly trending that way. For those that choose to use AI, I submit that a fast AI, all things equal (same output), is better than a slow AI for the same reason that a fast compiler is more preferable. Note that I’m not saying that faster overall development at all costs is preferable, or that we should just throw everything at the AI and stop thinking.
speed gets you to have flow though, rather than breaking your concentration with various waits that aren't actually times where you can stop and think because its 7s surrounded by tiring reading.
a milisecond response, that you can then spend 30s focused on is better.
its the same as a build tool. just slow enough build tools really suck, compared to either an instant build or a 30min build
evaluating the areas isnt the same as watching the llm harnes say "thinking" between submitting a request and getting a response
I thiml of it as driving. If youre in kansas and on q highway with no features, go ahead, speed away. But the reality is, a human in the loop system needs a variable speed car. Like all driving, sure, some will go faster.
But the opinion is about psychology and expectations.
If you expect the singularity, then of course this sounds crazy. To christians, ghosts also sound crazy. It dont mean a think if yoire biased to expect AGIM
At those token rates you could treat the LLM as a compiler and rebuild your software from scratch every week. That would mean the end for AI slop because people who spend the effort to build a process that creates high quality software from scratch can take full advantage of the one shot performance of an LLM without having to release the process itself.
Youre completely missing my argument. Speed is not the bottleneck, its the review burden.
You need human review when working on anything not table stakes. You cannot write shitty code for an avionics package on an airplane under the assumption that if we encounter bugs, its no big deal because we can rewrite it super fast..
It seems like all these bullish takes on AI are coming from people who think all software runs in a browser, and simply exists to take stripe payments or serve ads. Most software is actually very high stakes and lives depend on it and have high compliance burdens.
Just because it can "one shot" (which actually means 90 tools calls and a whole lot of brute forcing against tests/linter/compiler, and possibly reward hacking) to something that runs, does not mean it will continue to run well.
People treat llm made software like disposable code. You're the only one using it, typically on a single machine with no real world multi-user testing, but for some reason we derive from the fact it worked well the 3 times we used it, on a single machine with a single user that it is evidence of llm solving programming. Its bogus.
You can lessen the human review burden by pitting multiple agents against each other. Ask 3 agents to implement the same solution, then set them up against each other in differential tests. This can find bugs in them or clarify the spec. When tokens get cheap it is worth having multiple replicas, taking advantage of different programming languages, libraries and agents. You don't keep one version, you have a population, and it tests itself. It's easier to implement again than to imagine testing scenarios anyway.
Ive been pretty skeptical of your coverage as well, and you're in the OpenAI daybreak program and huffing and puffing about how great OpenAI is while having 900k subs, seemingly only talking up OpenAI products...
OpenAI and others are infesting the influencer space with what is basically dark money, not saying you're one of the guys on the take but I just don't trust any large influencer atm who only talks positively about OpenAI (or any lab).
I guess you can prove us AI cyber doubters all wrong with this CtF tho.
Not gonna defend the OpenAI shilling, but the guy recently had a pretty brutal tragedy in his personal life and I assume the AI content is just a low effort move to keep the channel and marketing for his business afloat. The lack of elaborate educational content may be rather a symptom of personal resource exhaustion and struggle, than popular enshittification. Time will tell, but maybe cut him some slack for the moment.
Theres a single solo developer who donated 40k this year to postgres, their name is next to AWS on contributors. Digital ocean sells scam postgres services for newbs and has never contributed a dime (afaik).
Complete slop overpriced cloud platform now too thats leaned way to hard into AI nobody goes to them for.
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