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.
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).
And as another example, you see people producing CUDA kernels and MLX ports as an individual (with AI) way more these days (compared to 1-2 years ago), like this: https://huggingface.co/blog/custom-cuda-kernels-agent-skills
January numbers are out and there were fewer games launched this January than last.
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.
[0] https://steamdb.info/stats/releases/
[1] https://www.a16z.news/p/charts-of-the-week-the-almighty-cons...
Also the accelerating trend dates back to 2018 if you remove the early COVID dip. Which is exactly my point. You can look at the graph and there is no noticeable impact correlated to any major AI advancements.
The iOS data is interesting. But it’s an outlier because the Play Store and Steam show nothing similar. And the iOS App Store is weird because they’ve had numerous periods of negative growth follow by huge positive growth over the years. My guess is that it probably has more to do with all of the VC money flowing into AI startups and all the small teams following the hype building wrappers and post training existing models. If you look at a random sample of the iOS new apps that looks likely.
Seriously go to the App Store, search AI and scroll until you get bored. There are literally thousands of AI API wrappers.
I wrote a python DHCP server which connects with proxmox server to hand out stable IPs as long as the VM / container exists in proxmox.
Not via MAC but basically via VM ID ( or name)
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 LabsHave you ever looked for, say, WisprFlow alternatives? I had to compare like 10 extremely similar solutions. Apps have no moat nowadays.
That's happening all over the place.
But the numbers of lfg is basically the same, maybe a few percent more. But not dozens of modules more per day more...