That would be a huge mistake if we expect many more hardware and OS running them efficiently than just iOS and Android.
Best is to have an IR.
Now maybe that IR can be turnt into native code. But we shouldn't be constrained.
As an incoming framework author, SwiftUI is problematic for instance because it has a programming model which is dated.
And I don't particularly enjoy the language either. Looked fine at first and then got more complex than I feel is needed.
oh yeah, disclaimer: I write UI frameworks and dabble in PLs.
It is virtual dom like.
We don't have access to a stable underlying element like we would with plain UIKit.
You can build declarative models without this virtualdomness. But SwiftUI was created during the react boom so they went with the Zeitgeist, understandably.
Now this is somewhat problematic if someone wants to implement better fine grained reactive systems. And I posit that the next paradigm is going to be in that direction given what I've been working on (furthering current reactive systems which only go halfway).
Just like any engineering and business decision, this is a tradeoff.
I probably don't see things from the same vantage point.
Can't help but notice a trend however. What if this accelerates, especially with AI being an enabler?
I think it'll become a trend at larger, well-capitalized companies and startups, but not universally. The article mentions hundreds of engineers have worked on Shopify's RN apps, so they're well positioned to maintain two native codebases with agents and practically infinite token spend. Migration will be a harder sell for resource-constrained businesses.
It also depends on features. Many apps don't rely on platform features (widgets, watch apps) and are essentially webviews, and those will continue to do just fine on RN.
Can React Native become a sort of Compiler which compiles to natives (Android/Apple) now that AI can help in that direction as well? I don't know much about mobile ecosystem though.
LOL, flutter/dart are pretty awesome and we have yet to encounter something that needed improvement.
We have small shims for IOS or Android BLE. But otherwise the discussion of should you go native is really a discussion of how many employees do you have to throw with this?
do you even needs thumbs up? I've been long suspecting that code models get better because they use our data and our results from feedback, status codes, green tests for reinforcement learning
Pretty great day for HN. You can’t just shut off people anymore with „machine did it, not you” as we live in new reality. Comments below make even more sound argument, for data viz we probably don’t need novel problem solving, just people interested to see viz
The steam engine replaced a lot of jobs. Tractors replaced a lot of jobs. Calculators replaced a lot of jobs. Computer used to be a job description before it was a personal device. There will be new jobs.
And for those who don't get to find a job, there'll be a convenient WWIII to utilize themselves, just like there was WWI after the steam engine replaced a lot of jobs and WWII after tractors replaced even more.
It won't happen overnight. Social change is constrained by integration speed. The average enterprise has many tasks that could be automated by models for several years already, yet they remain stubbornly unautomated. The reasons are primarily social:
• Lots of people aren't aware of what the models can do.
• They also aren't paying attention, and getting their attention is hard.
• Their impression of AI was formed by cheap low quality AI like free ChatGPT as of two years ago, Google AI overviews or Microsoft Copilot. So they think it sucks.
• A LOT of executives got burned by early pilots that overpromised then failed. Enterprise IT is a wasteland of dead AI pilots.
• Their IT systems are difficult to connect to models because they lack proper APIs or auth systems. Note the huge emphasis on fast computer use in the Astra announcement. A lot of work can only be done by clicking things.
• Executives don't want to let employees go, but aren't sure they can find new work for them either.
The last point is worth dwelling on. HN is full of socialists who imagine corporate executives as cigar smoking top hat wearers who chuckle all the way to the bank. The reality is more mundane: most executives don't want to lay people off and will fight hard to avoid doing so.
The average person in a position to make an AI project work either isn't incentivized with stock - this is often the case outside the US even for quite high ranking execs - or they are but don't believe laying people off will raise the share price enough to be worth the pain.
Also, executives often measure their success and self-worth by how many people report to them. Laying off half their department, even if it gets them a monetary bonus, would lead directly to a loss of social status as they can no longer say they manage 1,000 people but only 200, which matters if their social circle are all managers too (and for their wives, girlfriends, etc).
Layoffs suck and people HATE doing them, which is why companies often resort to forced percentage based layoffs to get managers to do it. If the incentives aren't there, the average executive will just sit on obvious AI deployment opportunities and/or deliberately sabotage them because getting rid of people is just all downside for them with no upside.
So institutions have enormous inertia. Model capabilities will run far ahead of what is actually used in reality, and this will continue for decades. It does mean that startups have a better chance than ever of outcompeting much larger incumbents though, as not hiring is far easier than firing.
Also: let's be honest. "I wrote a little app in a few weekends" -- "I built this in a few weeks", "this took me a month instead of a year"
These are productivity increases, sure, but still are $2000, $8000, $14000 of labor.
And doesn't count continued increase build time, more features, maintenance. The $20/mo SaaS isn't going away, and work can't pay you to endlessly tinker on fun internal projects and save $1200 of subscriptions for your $120k/yr salary.
It's fun, but SaaS is going to shift to more feature rich and we're going to see a ton of smaller projects that were pains to get started that can now work in a short amount of time.
Companies are going to get a huge surge of random internal projects and have to debate whether all the upkeep is valid for the pet projects, or if it's people fucking around at an untold scale.
Great point and writing. We have real world data point now which is waymo and how many drivers are still driving taxi in USA. All others are 10-100x time more complex than this, it takes literally decades
I had a 600MHz/64MB/9GB laptop that came with Windows mistake edition. I managed to survive first year of uni on it by switching to Vector Linux, which was really fast compared to Windows. (Of course, it had issues playing sound from more than one source, this was oss days).
Then one day the hard drive appeared to die. I eventually realised the issue was located around the 1.5gb mark, so I recreated my Linux partitions after 2gb and it worked fine for the rest of the year.
This seems to extend even beyond the arts. I would say that the race to add more features to web and mobile apps will face a similar obstacle.
How long can we continue making 10x more features just because we can, until somebody realizes that users do not have the attention to spend on them? There might be a shift, but currently, we are too focused on pleasing shareholders.
I think as a developer this is one of the biggest takeaways I've had from this whole thing. I used to view features sitting endlessly in a JIRA backlog as a bad sign; a signal that we weren't prioritizing the right thing. But in reality this serves as a natural selection mechanic; if devs don't care enough about it to get it done and PM doesn't see the value in it then it slowly fades away.
With agents you can just throw them at every JIRA ticket and get them (badly) implemented. There's no rhyme or reason if it's needed or even wanted, and spending time to think about it is 'wasted'. So the numbers on the burn down chart go up, but actual productivity is stagnant or becomes negative as unnecessary bloat sneaks into your app or game.
I'm a big fan of forgetting. I throw out the phrase "forgetting is the point" a lot, loosely paraphrased from some musician (I forget who!) that said if you couldn't remember the lyrics they must not have been very good. Somewhere in the original spirit of Agile there was no Jira board, because the next thing to do was obvious to the team, and if it wasn't obvious it was easily discoverable, just go ask some users what they need. The idea of writing down every idea and letting AI crap out the features is entirely antithetical to creating good software.
In his memoir Stephen King says not to write your ideas down since the good ones stick around anyway, and that a notebook is a way to immortalize bad ideas.
Damn, enshittification was already one of the toughest unsolved problems that successful software platforms face. It had not yet occurred to me that AI was going to increase the pace.
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