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Comment by pickettd | Hacker News Reader

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).

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

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I have no way of verifying any of those. Something I can easily verify, new games launched on steam.

January numbers are out and there were fewer games launched this January than last.

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.

[0] https://steamdb.info/stats/releases/

[1] https://www.a16z.news/p/charts-of-the-week-the-almighty-cons...

I got the numbers swapped. Turns out there was an increase of about 40 games between last January and this. Which is exactly what you wouldn’t expect if the 5-10x claims are true.

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.

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).

Or just don't publish them, because they don't want to deal with uses.

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)

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

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