My parents love using ChatGPT, asking it all kinds of questions. My mom discovered Claude and helps her immensely with her job - where she would have to take it home and work a few hours to be able to finish the tasks on her computer, as her company that still uses Office 98, now Claude does it in 5 minutes.
They fixed so many random issues using it, it is insane. My dad had a bike issue which would otherwise be solved by either trying to find obscure manuals from 20 years ago on random forums with me translating it from english to our language, or by taking it to a mechanic which could take months. This way, he just snapped a few photos, said what the problem is, and in a few minutes he had the fix.
I've built software that uses LLM's for a specific usecase - besides general adoption, professionals in the field contacted me and thanked me for making their lives easier, as the tasks would often take a lot of manual work. These people are earning way more from using my software, than I am from their subscriptions, which is still about 20x more than my API costs are.
While most non-dev people are behind the curve, the impact it has on their lives is becoming bigger and bigger by the day.
Maybe I downplayed it too much but I really think this is still "in distribution" (we always have to remember that we are tech savy people and we influence the people that surround us). I see the value, but in my opinion it's not a generational opportunity, but a great acceleration. We are treating it like generational opportunity. That's why I say "everyone know there will be a crash, but noone knows how big that will be". The AI industry is not (in my opinion obviously) worth $ 391B [1] of added value.
It is still "in distribution", that is why when its "distributed" properly, it will surely add much, much more value to the economy.
But it is a generational opportunity - we can remove a lot of barriers that come with knowledge, lack of it, access to it and more. Someone can easily get pretty on point medical advice without access to doctors. Get specific engineering advice without engaging with those engineers. We can apply common sense or specific knowledge on scale - in a world where about 50% of people have IQ under 100 and access to knowledge is gated behind lines and payments, this has a huge chance ot improve their lives.
And there is the whole shadow inference economy - just for example, a few corporations I have worked with in insurance and telecommunications have been slowly introducing it inside their workflows and their data tooling, being able to clean data, tag it, analyse it in a way that before would probably cost them billons in human costs.
One of them has a database going back to the 80's, with data being formatted and reformatted in all shapes and sizes, coming back all the way from paper records for some of their oldest clients. Cleaning this up was unimaginable before as a "something we can do in a day" project, but was more of a "possible with insane costs". This lead to all further activity being shaped by decisions someone made 40+ years ago, details being lost, data being thrown away or saved in random notes.
And there's millions of companies like that all around the world, which can now do "impossible" and become much more efficient and productive for a much cheaper price and in way less time than ever.
Sorry, my bad, might be 97 running on windows 98 - but yes, this is a giant corporation serving hundreds of corporate customers and a few hundred thousand private ones, using nearly 30 years old software because the management does not see reasons to upgrade and spend the extra cost associated with it. New machines and Windows XP are only used by upper management.
Worst part?
Their whole software stack is running on some version of Visual Basic, written by a dude that did not trust "others code" so he wrote everything from scratch, and retired about 5 years ago.
Nobody knows how any of it works, or has any clue. The company will continue to run it and pay him for consultations as long as he is able to do it.
Back in the times of GPT3 text completion, right before the API came out, a contemporary art museum asked me to collaborate on a project. The project was supposed to include a chatbot, and I was like okay I can probably hook something up.
Then I remembered the "text completion LLM thingy" I saw on HN, and tried it out in the playground. Once I gave it an IRC style example of a conversation to complete, I was like hm, this could work. Then I figured out I could "sort" people into different groups based on personality using the same text completion engine and some answers they provided. Then I noticed I could have it provide me with JSON directly.
That's when I realized how big this could be for code and data analysis - even tried to convince an at the time cofounder to pivot into AI coding, but to no avail.
Once the API was released and the art project chatbot got launched (and the theater show associated with it, which even won some awards), people who used it loved the chatbot, got into heated arguments with it, tried to teach it things, talked about their lives and were sad when it didnt remember something.
That was when I understood the social impact this could have on people - they really behave like its a person on the other side. They show interest, think it displays emotion, try to entertain it, be polite, ask about its thoughts and hopes and dreams. And even when they knew they were talking to a machine, they were still trying to be friends and make it happy, which was quite beautiful to see.
Later on, I had a third oh shit moment - once the 3.5 API was out and about, I prototyped a Rust code generation harness for a client, akin to a primitive claude code. That was the "I'm getting a bit worried" oh shit moment, and it caused a lot of reflection and thinking about the future. And I happily welcome it.
I also remember doing this. Chats, first parts of books, title pages and all, just to give it a chance of saying something in the ballpark of what I was looking for. I remember very vividly that chats or books by Linus Torvalds would be more technically accurate that say Lincoln. It's obvious of course, but I found it really enlightening. It could code a bit actually, not great, but well enough to push me into an existential crisis. I started doing a master to re-educate myself because I could see "interesting" times coming.
I actually emailed OpenAI back then saying they should be careful because this is much greater than the public or even they themselves think. They actually replied! They thought it was cool, but very limited and I shouldn't be too impressed. Good times.
What is a larger scaler for you? What is "outside harness an LLM"?
What is _the proof_ if all the proofs are not _proofs_?
I don't babysit my LLM based services which are used by coaches and clients around the world. One of my LLM based solution get 30-4k daily hits and I have users coming back on the regular to use it. without babysitting, doing things that would take them hours of manual work and research.
I don't babysit the developers I work with and our clients, which both use LLM's themselves and at scale with their clients, serving all kinds of LLM powered services to millions of users worldwide.
You are not "seeing" the large adoption because:
- The technology is "a few years old" in its usable state
- The corporate adoption cycle is slow
- You have to understand the technology to use it in a good way, which most corporate devs and PM's do not
So it will take a bit for the "obvious" adaptation on large scale.
But you won't "know" when the large adoption happens.
Silent inference is growing every day, and that is what real adoption looks like - not an LLM being in your face chatbox, but running in the background, sorting, finding, fixing things, aligning data, figuring out analytics, tuning the ads, cleaning the datasets.
Perry definitely looks interesting, was just looking at getting one of these to include into my framework.
Would love to see more about it, or see more about the actual compiler docs.
While the UI framework part is neat, I prefer not to force everything into TS. Combining it means UI definitions and semantics get mixed into AST, making the unbundling of them a humongous task in itself.
Exactly the reason I built my own with pretty similar native UI semantics which supports Rust, Go, Kotlin and more (https://hypen.space) - would love to integrate Perry with it to compile TS apps directly into the runtime - but while the idea itself is great, looking at the documentation makes it hard to implement, and a lot of parts seem confusing.
Can I just use the compiler without the rest of the framework?
What is the architecture? What are the limits?
After digging through the documentation, I'm unfortunately just more confused honestly. There are dozens of packages and slop markdown files such as `BUG_STRING_COMPARISON.md` and or `PERRY_UI_IMPLEMENTATION.md` which is an instruction file left for the LLM that just makes me trust the project less.
So while the idea is cool and the performance seems cool, the AI slop presentation would definitely need improvement. Adding a human touch would make it much, much better, as one could actually understand what they are dealing with.
They were a great brand, cool phones, one of early Android players.
After being bought out by Google, Motorola had some of the best devices out there with stock android, especially in the budget segment (and loved among android devs).They had one of the best smartwatches in the game at the time - Moto 360 (2014!!).
Then, after dropping the Nexus 6, Google stripped the patents and sold them to Lenovo. For a while it was ok, even dropping the relatively innovative Moto Z which had all the cool "modular" addons, played with it for a bit and seemed cool.
And then, things seemed to start taking a turn for the worse as Lenovo kept enshitiffying it more and more, using the brand name as a wedge in the market in which they are basically forgotten. They have the Razr brand which is cool, but the segment that was their best (budget phones) is now ruined with adware so they can extract every bit of value from it.
Such a sad ending for a company that was so early in the space.
FWIW, the worst thing I can say about the Moto Edge 50 Neo (a midrange phone) from a year ago is that it had "sponsored" apps pre-installed. They could be uninstalled (not just deactivated) the usual way and never came back.
... I was so mad every time Motorola screwed the pooch in this era.
I was a first-gen Moto X user... on Verizon. I didn't get the Lolipop update forever and a day. I was a first-gen Moto Hint owner. We didn't get the wake word update, we got told to buy the Hint 2. And then finally, I was a first-gen Moto 360 owner. We didn't even get Wear OS updates at all. Not WearOS 2, not even WearOS 1.6. Every single first-gen product got immediately dropped for second-gen shit, and we got abandoned.
Interesting, agents seem to always fall in a spectrum between overengineering and underengineering, where they will either go wild and overengineer a simple solution, or do the minimal effective thing to pass with "//TODO fix for prod".
It's the same patterns we see with human devs, just applied on codebases at scale.
The conclusion has an interesting tibid tho - maybe the frameworks and their developers should start including more abstract focused primitives instead of just the low level ones, similar to what Encore did, as that way the behavior is encoded at the framework level.
I'm actually working on that - it's called Hypen - (hypen.space).
You can build your core in Go or any other supported language, and write the UI in the Hypen DSL.
While desktop is still in the works and should be out in the next week or two, currently the alpha supports Native iOS, Android, Web and Web Canvas, and just like mobile, the Desktop will be _real_ native.
I mostly agree with the article - I believe the differentiation should be between documents and applications.
While HTML serves its purpose, especially for documents, the modern web is a giant mess of that legacy, combined with unfriendly ergonomics and glue/hacks built on top just so we as developers can have better DX for creating complex software on top of it.
Building a browser means having to deal with all that legacy, wether we like it or not, so most of the browser market got captured by the big players who have enough manpower to cover all those edge cases. That also means we have to deal with whatever technical choices or bloat they make, causing an infinite stream of issues, from memory usage, to size, to limitations that don't make sense in 2026 but are still there because someone 20 years ago decided to write them like that. As I deal with mobile webviews a lot in my daily work, I unfortunately had to get familiar with quite many gotcha's and edge cases, and some are just... absurd in this day and age.
I believe we need a separation between an application layer and the document layer, and especially between the UI language and the actual application code - script tags serve their purpose, but again, they are a hacky solution with its own bag of tricks, and those tricks impact all of the software built upon it.
Now, a bit of a shameless plug I've been working on something to fill that gap, at least for myself and hopefully for others who encounter the same issue - it's called Hypen (https://hypen.space) and it's a DSL for building apps that work natively on all platforms, with strict separation of code/UI/state, and support for as many languages and platforms as I can maintain, not "just javascript". While currently it's focused on streaming UI, it's built with Rust and WASM at it's core and will soon allow fully "compileable" apps.
While it may not be the future of software, once you get into building something like that, it becomes obvious that the way we are building now is at least wrong, and at best kafkaesque.
Back in the heyday, I used to work in a startup devoted to the cinema world, where with one app you could buy tickets for all cinemas - even those that did not "officially" support it.
Among them were arthouse theaters in Hamburg, which I often used for testing, as most of the time reserving a few seats would not matter as they would be empty, at least during the day. Some of them had projections of old movies, and I was like "if I lived there, I'd go every day".
Ironically, now I live between 2 art cinemas in my city and rarely go to any of them :)
My parents love using ChatGPT, asking it all kinds of questions. My mom discovered Claude and helps her immensely with her job - where she would have to take it home and work a few hours to be able to finish the tasks on her computer, as her company that still uses Office 98, now Claude does it in 5 minutes.
They fixed so many random issues using it, it is insane. My dad had a bike issue which would otherwise be solved by either trying to find obscure manuals from 20 years ago on random forums with me translating it from english to our language, or by taking it to a mechanic which could take months. This way, he just snapped a few photos, said what the problem is, and in a few minutes he had the fix.
I've built software that uses LLM's for a specific usecase - besides general adoption, professionals in the field contacted me and thanked me for making their lives easier, as the tasks would often take a lot of manual work. These people are earning way more from using my software, than I am from their subscriptions, which is still about 20x more than my API costs are.
While most non-dev people are behind the curve, the impact it has on their lives is becoming bigger and bigger by the day.
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