Which measure? Like when folk say something is more "efficient" it's more time-efficient to fly but one trades other efficiency. Efficiency, like productivity needs a second word with it to properly communicate.
Whtys more productive? Lines of code (a weak measure). Features shipped? Bugs fixed? Time by company saved? Time for client? Shareholders value (lame).
I don't know the answer but this year (2026) I'm gonna see if LLM is better at tax prep than my 10yr CPA. So that test is my time vs $6k USD.
Time could be very expensive as mistakes on taxes can be fraud resulting in prison time. Mostly they understand people make mistakes - but they need to look like honest mistakes and llm may not. remember you sign your taxes as correct to the best of your knowledge - your CPA is admitting you outsourced understanding to an expert, something they accept. However if you sign alone you are saying you understand it all even if you don't.
These days productivity at a macroeconomic scale is usually cited in something like GDP per hour worked.
Most recent BLS for the last quarter ‘25 was an annualized rate of 5.4%.
The historic annual average is around 2%.
It’s a bit early to draw a conclusion from this. Also it’s not an absolute measure. GDP per hour worked. So, to cut through any proxy factors or intermediating signals you’d really need to know how many hours were worked, which I don’t have to hand.
That said, in general macro sense, assuming hours worked does not decrease, productivity +% and gdp +% are two of the fundamental factors required for real world wage gains.
If you’re looking for signals in either direction on AI’s influence on the economy, these are #s to watch, among others. The Federal Reserve, the the Chair reports after each meeting, is (IMO) one of the most convenient places to get very fresh hard #s combined with cogent analysis and usually some q&a from the business press asking questions that are at least some of the ones I’d want to ask.
If you follow these fairly accessible speeches after meetings, you’ll occasionally see how lots of the things in them end up being thematic in lots of the stories that pop up here weeks or months later.
Economy-wide productivity can be measured reasonably well, although there are a few different measures [1]. The big question I guess is whether AI will make a measurable impact there. Historically tech has had less impact than people thought it would, as noted in Robert Solow's classic quip that "You can see the computer age everywhere but in the productivity statistics". [2]
Number of features shipped. Traction metrics. Revenue per product. Ultimately business metrics. For example, tax prep effectiveness would be a proper experiment tied to specific metrics.
I hear this every day, and I'm sure its true sometimes, but where is the tsunami of amazing software LLM users are producing?
Where are the games that make the old games look like things from a bygone era?
Where are the updates to the software that I currently use that greatly increase it capabilities? I have seen none of this.
I get that it takes a long time to make software, but people were making big promises a year ago and I think its time to start expecting some results.
Reddit and GitHub are littered with people launching new projects and appear to be way more feature-rich than new tool/app launches from previous years. I think it is a lot harder to get noticed with a new tool/app new because of this increase in volume of launches.
Also weekend hackathon events have completely/drastically changed as an experience in the last 2-3 years (expectations and also feature-set/polish of working code by the end of the weekend).
I’d be interested where you’re getting your data. SteamDB shows an accelerating trend of game releases over time, though comparing January 2026 to January 2025 directly shows a marginal gain [0].
This chart from a16z (scroll down to “App Store, Engage”) plots monthly iOS App Store releases each month and shows significant growth [1].
> After basically zero growth for the past three years, new app releases surged 60% yoy in December (and 24% on a trailing twelve month basis).
It’s completely anecdotal evidence but my own personal experience shows various sub-Reddit’s just flooded with AI assisted projects now, so much so that various pages have started to implement bans or limits of AI related posts (r/selfhosted just did this).
As far as _amazing software_ goes, that’s all a bit subjective. But there is definitely an increase happening.
Specifically about custom CUDA kernels, I’ve implemented them with AI that significantly sped up the code in this project I worked on. Didn’t know how to code these kernels at all, but I implemented and tested a couple of variations and got it running fast in just two days. Basically impossible for me before AI coding (well not impossible but it would have taken me many weeks, so I wouldn’t have tried it).
The one thing AI is consistently better at than humans is shipping quickly. It will give you as much slop as you want right away, and if you push on it for a short period of time it will compile and if you run it a program will appear that has a button for each of the requested features.
Then you start asking questions like, does the button for each of the features actually do the thing? Are there any race conditions? Are there inputs that cause it to segfault or deadlock? Are the libraries it uses being maintained by anyone or are they full of security vulnerabilities? Is the code itself full of security vulnerabilities? What happens if you have more than 100 users at once? If the user sets some preferences, does it actually save them somewhere, and then load them back properly on the next run? If the preferences are sensitive, where is it saving them and who has access to it?
It's way easier to get code that runs than code that works.
Or to put it another way, AI is pretty good at writing the first 90% of the code:
"The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time." — Tom Cargill, Bell Labs
Look somewhere outside of the AI hype space. You’re seeing more AI competitors because it’s easy to build on top of someone’s existing model or API and everyone is trying to cash in. You saw the same thing with new crypto currency.
That’s an incredibly niche area. From their website it looks like there are 4k modules available. Is there a way to see historical data. Also is number of users available, so that you can rule out popularity growth?
Sounds exactly what a junior-dev would do without proper guidance. Could better direction in the prompts help? I find I frequently have to tell it where to put what fixes. IME they make a lot of spaghetti (LLMs and juniors)
I could try but then propably I have to open a thread like: "Tell HN: I crashed my 15 year old machine setup this evening by trying to switch to Linux" :-D
But this time would be really a great option to finally switch on this machine, you are right - I thought about it for years now and I found out in the meantime that most of the apps are running without any problems in virtualization (if required)
The thing is - by now, I'm too lazy :-)
(Esp. I should add a backup machine for this procedure, since this old Windows setup is in productive use)
Every company I’ve worked in the last 10 years used GH for the internal codebase hosting , PRs and sometimes CI. Discoverability doesn’t really come into picture for those users and you can still fork things from GitHub even if you don’t host your core code infra on it
Yep. As someone in a similar position to influence this, I’ll also be pushing for at least significant discounts in our contract. The challenge with migrating off for enterprise is going to be integrations & compliance. There are dozens of options that replicate the core PR workflow that people use, and it’s probably fairly easy to migrate that. The hard part is the hundreds of things that hook into GitHub that don’t have a simple migration, even internal tooling. So it just turns into a pretty big project.
I assumed it was because people here are constantly telling Arathorn that Element (not ElementX) is slow and buggy, and that when they last tried the default server (circa 2019 or so) is was buggy and full of rough edges
He's (in my mind) always positive, open, and willing to admit the shortcomings of the platform he shephards... but damn does he deal with a lot of undeserved criticism (and deserved criticism, where applicable)
I get similar ones from Zoom and other collaboration providers. Like folk make a meeting in Zoom and then can invite any email they know. Is that just me? Eventbrite, Meetup and Luma do similar.
Which measure? Like when folk say something is more "efficient" it's more time-efficient to fly but one trades other efficiency. Efficiency, like productivity needs a second word with it to properly communicate.
Whtys more productive? Lines of code (a weak measure). Features shipped? Bugs fixed? Time by company saved? Time for client? Shareholders value (lame).
I don't know the answer but this year (2026) I'm gonna see if LLM is better at tax prep than my 10yr CPA. So that test is my time vs $6k USD.
reply