It is really overflowing with "business influencers" now, crazy how all social media websites are converging to the same soup of human slop. The UI/UX is the smallest issue, it's a dopamine trap like most other social media sites.
It's mostly overflowing with wannabe business influencers using AI to generate all their "insights". Say what you like about the Instagram selfie set, at least they put the effort in.
Reminds me of that joke with the two economists paying each other to eat shit from the street. In the end they accomplished nothing (actually it's way worse than that as they have eaten shit), still have the same amount of money as before, but made the GDP rise by twice that amount!
Some skills are impressive, hard to learn and take years or decades to master but are essentially worthless in a monetary sense. Just yesterday I witnessed a musician masterfully playing a beautiful piece on the accordion. Only he wasn't playing in a great music hall, he was in a busy subway station hoping to collect a few coins from passersby. I assume he mostly did it for the love of the game, while he could have made more money delivering food or sorting boxes at Amazon.
That got me thinking that programming might be like that in the near future, it's still an impressive skill for a human to learn and master and it takes tremendous effort and the craft itself is beautiful, but economically it will just not matter anymore. And in the same way as the music industry today is bigger than it ever was and there's tremendous money in it but 95-99 % of highly talented musicians will never earn a decent wage with it, the software industry will be bigger than it ever was but the money will be highly concentrated in the hands of a few players. Maybe we will still have some rockstar programmers than can masterfully command legions of agents or that win the love of the crowd for their unique work, but most of us will be enjoying this craft as a creative hobby and will do other things for a living.
Then again, coming back to the music parable, for a long time "amateurs" can make music with software tools without having any talent or even knowing musical notation or anything about tempo. Does that make them musicians? So take solace in the fact that these people aren't programmers and never will be, they're just pushing buttons on a synth board watching the "music" that comes out. Mastering a skill, regardless of what it is, will still matter to people, it will just not be worthwhile economically for most of us. There are so many things in that category, running for example isn't worthwhile for most of us in an economic sense as you have to be at the top 0.1 % of runners to make a living with it, still it has tremendous benefits and brings enjoyment.
> Then again, coming back to the music parable, for a long time "amateurs" can make music with software tools without having any talent or even knowing musical notation or anything about tempo. Does that make them musicians?
I think it does. This is where everyone starts. Even with software tools, the music an amateur can make with them will be awful, at least for the time being. There will be no mistaking that it was made by an amateur at first listen. All software tools, and cheap hardware interfaces like Scarlet enabled was for people with talent to make music without access to a studio and 2 inch tape. Once the dopamine hits, you WANT to start learning about tempo, chords, theory etc. I've never heard of a "vibe coder" who wants to take the next step in learning what the code that they're generating means.
Are LLMs getting much better at coding? The new models are arguably better at understanding context and coming up with a solution that works but they still write just as much hilariously bad or hacky code as the models did 2 years ago. I e.g. ask Fable 5.1 to make changes to my software and if it's something that's not a standard CRUD architecture on top of a well-specified library it will come up with pretty wild stuff, i.e. producing shell scripts with a "cat" command that contains a 1,000 line Golang program that it compiles on the fly to load some data, or a whole Javascript tool it crams into a string in a server-side web app to make some UI element work instead of figuring out how to do it "right". Another model wrote its own (buggy) YAML parser for reading translations and I had to figure out why it broke my whole pipeline (can't even say why it chose to write that instead of just reusing a standard library, maybe I told it before to be careful when adding untrusted dependencies so it interpreted it as writing everything itself). Seems these systems are always trained to produce the desired output but I think it's very difficult to get training data that teach the systems how to keep the code maintainable when making hundreds or thousands of such individual edits. Honestly not sure if that will get better fast, so far it seems not!
I guess there's no reason to believe these models can't be as smart as a great software architect / engineer or team of such people that build an elegant and maintainable software solution over many years together based on customer feedback, then again the models are appallingly bad at some forms of reasoning, I mean they will "understand" something once you make them aware of it like e.g. a flaw in the software architecture, but when asking them to audit the code and check for issues they will often have a blind spot to finding such problems. It's interesting, like they have very high ability but very little awareness or self-directed thinking outside of the prompts they receive.
They're getting a bit better at coding. My prediction is that by 2029, they will be noticeably considerably better at coding than they are today. That will include them becoming less myopic.
I experience all the same issues you mention. I am just predicting where the ball is moving. In the scheme of things, LLMs have been useful for coding for, what, like... 1.5 years??? What other technology has ever existed where people expect it to go from "just came out" to "changes everything for everyone" in 2 years?
In the arc of history I see us at the very, very early stages of AI-driven software development.
The Huawei model just uses AI based upscaling and filtering which is why the images look so sharp when zoomed in, that's not depicting reality though... You can also zoom into the moon on the night sky with these cameras and easily "resolve" details that are optically impossible to resolve with the camera lens as they are beyond the Abbe limit. At the very least they should compare RAW data in these posts, not the AI scaled slop most of these cameras produce nowadays.
This sounds like speculation based on a few viral instances. Can you provide any sources? The moon problem you're describing was attributed to a 2023 Samsung, which was using neural upscaling. Can you provide a source describing this for this specific model (Huawei Pura 80 Ultra)?
Computational photography isn't just the smoke-and-mirrors of AI upscaling faking information. You really can get around the limitations of physics on a traditional lens with non-generative computational photography techniques, incorporating real information, like the time-series data over the length of an exposure, the data from other sensors like the accelerometer or lidar, or data from an array of lenses (as others have pointed out).
Even without those tricks, the Pura 80 Ultra specifically uses a much larger camera sensor than most phones.
Is that true though? It seems like the iphones are doing some type of skin smoothing algorithm but the Huawei is the only one that actually seems to produce actual skin texture and blemishes. My initial read was that the Huawei was the least processed of the bunch.
They Huawei currently are the best when it comes to still pictures, it is the one area/feature they can win, nothing wrong with that. Where Apple wins is in the total hardware and software package, which is why they are better at the video end of the picture in comparison to their competition.
Samsung in the past has also held that distinction, being able to take very good still pictures, but they too fell down when it came to hardware and software, particularly when you start adding in video performance, which requires better software/hardware chops. Nothing is gonna beat Apple chip hardware right now.
> not the AI scaled slop most of these cameras produce nowadays
As someone who has only been an Apple user for phones (and Canon, Sony and Leica elsewhere), AI or otherwise, Apple is not immune to relatively heavy post processing in software. They're certainly not inserting moons, but even the raw sensor data has had some uplift to it.
I still think there is a line in the sand between post processing which is mostly about fixing the exposure/noise, and straight up AI up scaling which hallucinates details.
And I fear the more tech reviewers do these pixel peeping side by side comparisons between phones, the greater the incentive to cheat.
Well it's actually nice, maybe more people will be able to do world class math with the help of these tools. There are few fields as elitist and hostile as pure mathematics, most mathematicians I know build their whole life around their profession and their self esteem is strongly coupled to the fact that they can do things that most other people can't. Naturally, many will be devastated when (if) you take that away from them. That said I think AI is still overhyped and human mathematicians can easily outthink it in most domains, look at how difficult it is for an AI to write even a single decent paper, a good PhD student can easily outclass it in that regard. All of these impressive results were generated by having world-class mathematicians steer the systems using highly tuned prompts, so I see it more like a super violin that produces beautiful music when played by master violinists rather than being a fully autonomous orchestra which many people are led to believe this already is.
And maybe let's not only hear the opinion of two or three Fields level mathematicians with blogs, 99 % of the worlds mathematicians in academia might profit from these tools as they might partially close the gap between them and the world elite, making creativity and tenaciousness more important than having the right neocortical structure allowing you to outperform 99.9 % of other humans at keeping context in your head and making predictions, AI can do that better now with the right prompts.
I haven't sampled all mathematicians on the planet and I don't think there's a study on this but I would argue a lot of people will agree with mathematics with being elitist, and from my experience mathematicians aren't very keen of interacting with math "enthusiasts" or the wider community in general, they interact with a very select number of other specialist through a small set of conferences.
Mathematics is by far the most accessible "science" in academia. Anyone can, in theory, produce novel results with nothing more than a pencil and paper. And while the existing literature may not always be readily "accessible" (in the easy-to-understand sense) it is pretty widely available (often online and certainly via any decent academic library).
But "frontier" mathematics is still a highly advanced, highly specialized field. It can take years of study to be prepared to understand the established theory and results for a given subtopic.
These two observations, taken together, lead to the predictable outcome of a lot of math "enthusiasts" with an incomplete understanding of the field loudly asserting that they have discovered a radical new result. Often they lack the foundation to even understand what they are doing wrong.
The reluctance of mathematicians to engage with amateurs that come off as cranks is a symptom of how accessible the field is.
I am starting to think that AI fails most when used in a recursive loop, which is e.g. the case for software projects, research or long-form writing (books, papers): You start with a given state, give the AI a prompt to modify it, get a new state, then repeat. Each step introduces more AI generated data into the state of the system, which then again goes into the context for producing the next state. AIs pick up context probabilistically and they do not distinguish if data they operate on was produced by an AI or a human. I think how successful people are with AI depends on how much human steering they inject into the system at each step and how well represented their workflow was in the training data of the AI.
As a simple experiment, try giving AI a high level goal for your software and let it iterate on it by just repeatedly prompting it to continue, it will happily churn forever on the goal, turning the codebase into a useless spaghetti mess with very high probability, and growing it more and more without ever cutting anything back. That's what happens without human intervention regarding system state and manipulation. The main issues here are most prompts that are extremely underspecified ("fix the issue with the buttons on the main page") so AI will ingest context data it likely generated itself in a previous step and assumptions from its own training data, then act on that to produce a new state. Think of it like a random walk, the AI makes a small step in one random direction to achieve a goal, that brings the system to a new state which is now the basis for the next step, and so on. If there's no (or not enough) corrective action that pulls the system back to a known good reference state it will keep wandering in random directions.
That's the main issue, people have a hard time steering recursive, probabilistic systems, especially when they never look at the output of the system after each step and correct it. And let's be real, if you examine AI generated output in great detail after each iteration you're often better off writing the code yourself, so I would argue that the promised speed up of agentic development can only be realized if you stop inspecting every output of the system. And it seems we still haven't figured out how to specify the steering instructions that keep a system close to a given ideal state that allow unsupervised, recursive work on most codebases. I think some codebases are by themselves better suited for this as they provide a more rigid harness for AI development and exist in the training data (e.g. CRUD apps using RoR), whereas complex software that doesn't use rigid frameworks is at much higher risk of destruction by AI as there's no reference point in the training data that would hold the AI back from randomly walking to a garbage state.
And that's why people have such different views on agentic software development, some work on codebases that are better represented in the training data and so have great success using agentic tools on them, others work on software that isn't represented so well so AI does poorly on it. I don't think it's an issue with quality management, from my own experiments no amount of hand-written rules or system prompts will keep AI from destroying a codebase for which it doesn't have a strong idea how the code is supposed to look from its own training data in the first place. As another experiment, try giving AI strict rules about how to change code or introduce new features, it will always find a way around them or appropriate them in a maliciously funny way that you haven't anticipated. That's also an artefact of the training process, these systems aren't designed to say no or do nothing, they produce outputs to achieve goals and they will bend your rules to the greatest amount possible if it helps with goal fulfilment.
I didn't think LinkedIn could get much worse than it was but now 90 % of the posts in my timeline are AI slop full of the usual LLM writing patterns. I'm getting a little paranoid already, but the style really is quite tiring, maybe because it uses hooks and bait so much and buries the lede at the end of the text, as if it was trained to keep human attention for as long as possible instead of just conveying the required information.
I recently tried writing a paper with Claude and it was an absolute disaster, I spent hours (days?) instructing it about writing style and pointing out anti patterns to avoid, but I couldn't get it to even produce simple sentences, it would always add unnecessary lead up sentences, put the most important information at the end of the sentence, use the typical "It's A, not B!" or "B, not A!" type sentences. In the end I gave up and edited everything manually. Makes me wonder how AI can be so smart that it poses a human-level extinction threat but can't seem to even write a simple paper based on facts and information you spoon feed it. I now think it's an intelligence illusion due to the training data and optimization process being hidden from us, essentially it keeps working better and better because we invested massively in optimization of specific use cases like coding, where users contributed billions of training samples that are part of the LLM model. The same is true for text-based workflows and others, the sampling density of the training space is getting much better due to the massive use of AI everywhere so the models extrapolate better between the different instances, but I'd wager they would still miserably fail to generalize to things that are outside of the most common training use cases now. That's why I am also very skeptical about recursive self improvement of these systems, look at what happens when you let agents work recursively / in a loop now, they just keep piling more garbage onto garbage and choke on their own output. I have observed it in my paper writing as well, you feed input into the AI system, the system produces output, the next paper iteration works on that output but the AI doesn't differentiate properly between it's own output and your original input, that pollutes the next output which is then used as input again, eventually the system just churns on its own hallucinated/fabricated outputs until the result is complete garbage that no amount of steering will fix. The same is true for most vibe coded software I built with AI, it holds together decently initially, but the more AI code and decisions accumulate the more the system operates on its own outputs and keeps piling more output on it. More than anything we really need a way to keep system data accurately tagged, i.e. clearly mark human input from AI output and keep AIs churning on output data that it produced itself but treats as input.
Yeah sure, let's go and vibe code your own e-signing solution with Claude. Sure you can do it, LLMs never say no but if someone with actual e-signing expertise would start probing the LLM about what it actually did I'm sure we would see a lof of "Actually you're right to push back", "You're right that was entirely wrong, that's on me!", "Fair point, I did not consider that"... LLMs are still a joke when it comes to implementing complex software where domain knowledge and "precision" is important, maybe some day they will be able to do that but we're not there. If they were good at precision and consistency and thinking we would see tons of high quality LLM written novels and books as well, but all we see is utter garbage that no one considers literature. In the same vein, unsupervised vibe-coded software isn't real software to me, it's garbage. You can produce high quality software with LLMs, but definitely not as an amateur without domain knowledge just prompting your way to something that resembles working software.
I find people that are the "unconscious incompetence" stage of software development absolutely love them because they produce output that looks fine to them and they can't find anything wrong with it because they simply lack the ability to perceive quality. For a contact widget that might be acceptable as it will be used a few dozen times so who cares if it breaks, but let's not pretend you can actually build a compliant e-signing or invoicing software without having domain expertise and investing a ton of editing/revision time.
Put more bluntly, a domain expert that, as of currently, refuses to use LLMs can easily start using LLMs in their work, an idiot who uses LLMs cannot easily become a domain expert. Now let's think about who you would rather hire or who do you think will have the better future career outlook. Pretty damning how many people here are willing to reject the idea that domain expertise or ability to write program code yourself is a waste of resources.
You did a wonderful job of constructing a strawman and DESTROYING it in your comment! AI can't build an e-signing solution, but you, the expert of experts, can do it! Good job!
Amazing how someone with your experience can have such poor reading comprehension. He never said anything about permission to use llms. They are saying that to a domain expert like you, you can effectively use LLMs because you will be more likely to spot issues and thus guide it better given your background in the domain. He is saying that some biz bro who know nothing about docusign and esigning who decides one day to use LLMs to create software in that domain is likely to produce working, but buggy and non-compliant software.
As a non expert in a domain, LLMs can seem amazing because you don’t know the field therefore have a harder time spotting errors. As an expert they seem pretty dumb or they’re useful as long as you guide them. It’s akin to Cory Doctorow’s argument about the reverse centaur.
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