So where are all the AI apps?

well, many apps i made are really good but i would never bother to share it, takes unnecessary effort and i don't really know what works best for me will work like that for others

Theres tons of ai apps. They're all general use chatbots or coding agents. Manus, Cursor, ChatGPT. Almost every app that has a robust search uses a reranker llm. AI is everywhere.

As far as totally new products - I built one (Habit.am - wordless journaling for mental health) and new products require new habits, people trying new things, its not that easy to change people's behavior. It would be much easier for me to sell my little app if it was a literal plain old journal.

I'd take this info with a grain of salt. You have to understand how new some of these developments are. It's only been a couple of months since we hit the opus 4.5+ threshold. I created 4 react packages for kicks in a weekend: https://www.hackyexperiments.com/blog/shipping-react-librari...

One problem with a lot of the skepticism around AI produced software is that it focuses on existing ways of packaging and delivering software. PyPi packages are one example, shipping “apps” another.

While it’s interesting to see that in open source software the increase is not dramatic, this ignores however many people are now gen-coding software they will never publish just for them, or which winds up on hosting platforms like Replit.

By "apps" this author apparently means "PyPi packages". This is a bafflingly myopic perspective in a world of myopic perspectives. Do we really expect people vibecoding "apps" to put anything on PyPi as a result? They're consumers of packagers, not creators.

I don't blame people for responding to the title instead of the article, because the article itself doesn't bother to answer its own question.

maybe some developers are more productive while the rest of em is laid off.. keeping the same release cadence but with fewer devs?

i know maybe this is not to your analysis as its about open source stuff, but this is the sentiment i see with some companies. rather than have 10x output which their clients dont need, they produce things cheaper and earn more money from what they produce. (and later lose that revenue to a breach :p)

The bottleneck shifted but didn't disappear. Getting to a working prototype in a weekend is real, but error handling, edge cases, and ops work hasn't gotten much faster. Distribution is completely unchanged too. A lot of these 'where are the AI apps' questions are really asking why there aren't more successful AI businesses, which is a harder and very different problem.

Even taking the “we’re all 100x more efficient at writing code” argument at face value… there’s still all of the product/market fit, marketing, sales, etc “schlep” which is very much non-trivial.

Are there any agentic sales and marketing offerings?

Because being able to reliably hand off that part of the value chain to an agent would close a real gap. (Not sure this can be done in reality)

While a good post the title is a bit ambiguous. The post is about applications created using AI not applications with AI functionality embedded.

- this would be much more insightful if the author takes the number of submissions to producthunt and the top 10 saas directories as the measure to see how many new apps were created pre AI and post AI era

- product hunt or app sumo is something i believe everyone tries to get a submission to which would truly measure how many new apps are we having per month these days

So far, as sideloaded APKs on my tablet. Most recently one that makes it easier to learn Dutch and quiz myself based on captions from tv shows

I think part of the mismatch is that people are still looking for “more apps” as the output metric.

A lot of the real value shows up as workflow compression instead. Internal tools, one-off automations, bespoke research flows, coding helpers, things that would never have justified becoming a product in the first place.

I AI coded an entire platform for my work. It works great for me. I also recognize that this is not something I want to make into a commercial product because it was so easy that there's just no value.

I think this might be more of an comment on software as a business than AI not coding good apps.

The first 80% is the easy part, and good ol' Visual Basic was fabulous at it, but the last 80% is the time suck.

Same with vibe-coded stuff.

This is just counting pypi packages. Why would I go to the effort of publishing a library or cli tool that took me ten minutes to create? Especially in an environment where open source contributions from strangers are useless. If anything I'd expect useful AI to reduce the number of new pypi packages.

My guess - these are not not on PyPI because of libraries. AI generating is good when you don't care about how your app works, when implementation details does not matter.

When you are developing library it's exact opposite - you really care about how it works and which interface it provides so you end up writing it mostly by hand.

How do packages measure anything? This is a biased sample. Average user of AI/developer would not ever in their life make a package or any open source contribution. They would probably work on the proprietary software. Not to say that conclusions are wrong though.

There are more apps, fewer libraries.

You don't need as many libraries when functionality can be vibe-coded.

You don't need help from the open source community when you have an AI agent.

The apps are probably mostly websites and native apps, not necessarily published to PyPI.

"Show HN" has banned vibe-coded apps because there's been so many.

Internally, we've created such good debugging tools that can aggregate a lot from a lot of sources. We've yet to address the quality of vibecoded critical applications so they aren't merged, but one off tools for incall,alert debugging and internal workflows has skyrocketed.

Title asks where the AI apps are. Analysis looks at Python libraries. Kind of a non-sequitur, no?

They're private, that's the beauty. Code is so cheap now, we can ween ourselves off massive dependency chains.

200 years ago text was much more expensive, and more people memorized sayings and poems and quotations. Now text is cheap, and we rarely quote.

There are actually a lot of new startups coming out with agentic workflows, and they're probably moving fast. But to your point, there's probably still a lot of friction that keeps the average person/dev from launching new companies.

I don't think people are using AI to create new dependencies that they're then submitting to open source package managers (which is what this shows)

This is more useful for discussing what kind of projects AI is being used for than whether it's being used.

As we haven't seen new operating systems or web browsers and the like, I'm guessing the reason is the same the corporation execs still have to find out: producing the code is just a small part of it. The big part is iterating bug fixes, compatibility, maintenance etc.

We’re in a personal software era. Or disposable software era however you want to look at it. I think most people are building for themselves and no longer needing to lean on community to get a lot of things done now.

Self plug, but basically that’s the TL;DR https://robertdelu.ca/2026/02/02/personal-software-era/

One pattern I've noticed: the apps that work best combine multiple models rather than relying on one. Single-model outputs have too much variance for production use cases.

I absolutely hate web development with a passion and haven’t done a new from the ground up web app in 25 years and even since then it was mostly a quick copy and paste to add a feature.

But since late last year even when it’s not part of the requirements leading app dev + cloud consulting projects, I’ll throw in a feature complete internal web admin site to manage everything for a project with a UI that looks like something I would have done 25 years ago with a decent UX.

They are completely vibe coded, authenticated with Amazon Cognito and the only things I verify are that unauthenticated users can’t access endpoints, the permissions of the lambda hosting environment (IAM role) and the database user it’s using permissions.

Only at most 5 people will ever use the website at a time - but yeah I get scalability for free (not that it matters) because it’s hosted on Lambda. (yes with IAC)

The website would not exist at all if it weren’t for AI.

Now just to be clear, if a website is meant for real people and the customer’s customers. I’ll insist on a real web designer and a real web developer be assigned to the project with me.

Is this the best way to measure this? I think the biggest adopters of AI coding has been companies who are building features on existing apps, not building new apps entirely. Wouldn't it make more sense looking at how quickly teams are able to build and ship within companies?

It seems like all tech executives are saying they are seeing big increases in productivity among engineering teams. Of course everyone says they're just [hyping, excusing layoffs, overhired in 2020, etc], but this would be the most relevant metric to look at I think.

A friend of mine who is tech savvy and I would say has novice level coding experience decided to build his dream app. Its really been a disaster. The app is completely broken in many different ways, has functionality gaps, no security, no thought out infrastructure, its pretty much a dumpster fire. The problem is that he doesn't know what he doesn't know, so its impossible for him to actually fix it beyond instructing the AI over and over to simply "fix it". The more this is done, the worst the app becomes. He's tried all the major AI vendors, from scratch, same result, a complete mess of code. He's given up on it now and has moved on with his life.

Im not saying that AI is bad, infact, its the opposite, its one of the most important tools that I have seen introduced in my lifetime. Its like a calculator. Its not going to turn everyone into a mathematician, but it will turn those who have an understanding of math into faster mathematician.

> Okay, so let’s consider a different chart. We start by gathering the 15,000 most downloaded Python packages on PyPI in December 2025.2 Then we split the packages into cohorts based on their birth-year, and for each cohort we plot their median release frequency over time.3 This seems like a reasonable proxy measure of the production of real, actively-used software.

No. Many projects explicitly release on a fixed schedule. Even if you don't, you're going to get into a rhythm.

There's a deeper problem with using PyPi to measure the success of vibecoding: Libraries are more difficult to program then apps. Maybe vibecoding is a good way to create apps that solve some specific problem, but not to create generally useful libraries.

I feel they're largely here, on this platform. Hacker News, currently, could be renamed to AI News, without any loss of generality.

the pypi metric feels off. most of the ai stuff i see shipping is either internal tooling that never hits pypi, or its built on top of existing packages (langchain, openai sdk, etc) rather than creating new ones.

the real growth is in apps that use ai as a feature, not ai-first packages. like every saas just quietly added an llm call somewhere in their stack. thats hard to measure from dependency graphs.

> So where are all the AI apps?

They're in the app stores. Apple's review times are skyrocketing at the moment due to the influx of new apps.

There has been a 2x and sometimes even 10x in PR size, measured in LoC...

But that's not really what we were promised.

Why would package be used as the standard? What person fully leveraging AI is going to put up packages for release? They (their AI model) write the code to leverage it themselves. There is no reason to take on the maintenance of a public package just because you have AI now. If anything, packages are a net drag on new AI productivity because then you'd have to worry about breaking changes, etc. As far as actual apps being built by AI, the same indie hackers that had garbage codebases that worked well enough for them to print money are just moving even faster. There are plenty of stories about that.

If one were to release an AI app - what would be an appropriate license? Genuine question.

We got an article today : "Apple App Store Is Flooded with AI Slop and Legitimate Developers Are Paying" (forbes)

(557,000 new apps to Apple’s App Store; a 24% jump from 2024). Who is right?

https://gethuman.sh

It's silly to think that 'AI apps' must look like the enterprise, centrally-managed SaaS that we are used to. My AI apps are all bespoke, tailored to my exact needs, accessed only via my VPN. They would not be useful to anyone else, so why would I make them public?

Hmmm, my anecdotal experience doesn't match up with this article. Personally I am seeing an explosion of AI-created apps. A number of different subreddits I use for disparate interests have been inundated with them lately. Show HN has experienced the same thing, no?

Apparently everyone has evidence to the complete contrary

"THE APPLE APP STORE IS DROWNING IN AI SLOP" https://x.com/shiri_shh/status/2036307020396241228

i think it's hard to measure this, it's kinda like measuring productivity through number of commits / PRs

Stuck behind Apple's app review process.

It's simple. AI speeds the 80% of development that was never the blocker.

Arguably makes the remaining 20% even harder to handle.

I'm sure that AI can be a huge boost to great, mature developers. Which are insanely rare in an industry that has consistently promoted brainless ivy league coders farming algo quizzes for months.

But those with a huge sensibility and experience can definitely be enabled to produce more.

But the 20% is still there and again, it's easy to make it way harder because you're less intimate with the brittle 80%.

I am worried for people using write ups like this as a huge, much appreciated dose of copium.

Try it out and don't stop trying. If something improves at this rate, even if you think it's not there right now, don't assume it is going to stop. Be honest about the things we were always obviously bad at, that the ai has been getting quickly better at, and assume that it will continue getting better. If this were true, what would that mean for you?

Intel AI denoiser in Blender.

> So what?

As mentioned in a comment here:

> Maybe the top 15,000 PyPi packages isn't the best way to measure this? > Apparently new iOS app submissions jumped by 24% last year

Looks like most LLM generated code is used by amateurs/slop coders to generate end-user apps they hope to sell - these user profiles are not the type of people who contribute to the data/code commons. Hence there's no uptick in libs. So basically a measurement issue.

I have a number of small apps and libraries I've prompted into existence and have never considered publishing. They work great for me, but are slop for someone else. All the cases I haven't used them for are likely incomplete or buggy or weird, the code quality is poor, and documentation is poor (worse than not existing in many cases.)

Plus you all have LLMs at home. I have my version that takes care of exactly my needs and you can have yours.