I've had a ring camera for several years. Historically I've been mostly satisfied with it, but lately they are adding some features that are pretty annoying.

The worst is that they've been adding neighborhood alerts and other proximity alerts, with categories for traffic and weather and lost pets and things like that. Today I got a "community alert" which was actually an advertisement for a local animal shelter. I don't have anything against animal shelters, but my motion detector camera alter is not the correct venue for this message. It's clear that amazon is trying to muscle in on Nextdoor. I don't use Nextdoor. I find it to be like facebook, full of cranks and advertisements and nosey annoying people.
So now I had to wade through a few pages of menus to find where to turn of this new annoyance. Obviously, if I could I would opt out of all new features.

The other annoying thing is that they turned on some AI evaluation of what the camera sees. So I was getting messages like "there's someone with a garden hose on your lawn" or "a person is carrying a cardboard box". There were a few things wrong with this

• I didn't sign up to have this and it slows down the alerts so they are up to 30 seconds after the motion is detected
• The AI sometimes made errors, especially at certain times of day where it misidentified different things in the yard (for example, some place marked by shadow was interpreted as a sidewalk when there isn't a sidewalk there). This happens of course because the AI doesn't know anything about my property, it evaluates everything from scratch each time it looks at an image.
• The ring app started bugging me with upselling messages to pay extra for the AI messages

So yeah. I just wanted to vent about the enshittification of this thing. I'm also aware of the privacy issues of ring cameras and how they're going to use the "pet finder" functionality to keep track of everyone. But this rant isn't really about that more important stuff, just the frustration of how these tech companies won't just leave anything alone because they have different goals than us.

I probably triggered some PTSD right there.

Was just in a meeting at work, where we listed off everything that makes software development hard and slow. An excersize for the thread would be to replicate that list. It turned out that Claude helps with like 1/5th or less of it....especially in a collaborative environment.

So, the situation we're now encountering is that random business areas can vibe code out something, tell nobody, throw it in AWS, have it become a critical part of a business process that fails when they quit, and nobody even has access to look at what was made.

It gives me comfort that in about 5 years there will be a new surge in demand for programmers to reign in all the rogue applications that need shutdown because of the immense risk to continual operation of a company, from data leaks to broken payroll.

It'll be Y2K all over again.

For a long time programmers have had two types of program verification tools, static analysis (like a compiler's checks) and dynamic analysis (running a test suite). I find myself using LLMs to analyze newly written code more and more. Even when they spit out a lot of false positives, I still find them to be a massive help. My workflow is something like this:

1. Commit my changes
2. Ask Claude Opus "Find problems with my latest commit"
3. Look though its list and skip over false positives.
4. Fix the true positives.
5. git add -A && git commit --amend --no-edit
6. Clear Claude's context
7. Back to step 2.

I repeat this loop until all of the issues Claude raises are dismissable. I know there are a lot of startups building a SaaS for things like this (CodeRabbit is one I've seen before, I didn't like it too much) but I feel just doing the above procedure is plenty good enough and catches a lot of issues that could take more time to uncover if raised by manual testing.

It's also been productive to ask for any problems in an entire repo. It will of course never be able to perform a completely thorough review of even a modestly sized application, but highlighting any problem at all is still useful.

Someone recently mentioned to me that they use vision-capable LLMs to perform "aesthetic tests" in their CI. The model takes screenshots of each page before and after a code change and throws an error if it thinks something is wrong.

I've found myself replying to different people about the early 2025 METR study kind of often. So I thought I'd try posting a top level thread, consider it an unsolicitied public service announcement.

You might be familiar with the study because it has been showing up alongside discussions about AI and coding for about a year. It found that LLMs actually decreased developer productivity and so people love to use it to suggest that the whole AI coding thing is really a big lie and the people who think it makes them more productive are hallucinating.

Here's the thing about that study... No one seems to have even glanced at it!

First, it's from early 2025, they used Claude Sonnet 3.5 or 3.7. Those models are no way comparable to current gen coding agents. The commonly cited inflection point didn't happen until later in 2025 with, depending on who you ask, Sonnet 4.5 or Opus 4.5

The study was comprised of 16 people! If those 16 were even vaguely representative of the developer population at the time most of them wouldn't have had significant experience with LLMs for coding.

These are not tools that just work out of the box, especially back then. It takes time and experimentation, or instruction, to use them well.

It was cool that they did the study, trying to understand LLMs was a good idea. But it's not what anyone would consider a representative, or even well thought out, study. 16 people!

But wait! They did a follow up study later in 2025.

This time with about 60 people and newer models and tools. In that study they found the opposite effect, AI tools sped developers up (which is a shock to no one who has used these tools long enough to get a feel for them). They also mentioned:

However the true speedup could be much higher among the developers and tasks which are selected out of the experiment.

In addition they had some, kind of entertaining, issues:

Due to the severity of these selection effects, we are working on changes to the design of our study.

Back to the drawing board, because:

Recruitment and retention of developers has become more difficult. An increased share of developers say they would not want to do 50% of their work without AI, even though our study pays them $50/hour to work on tasks of their own choosing. Our study is thus systematically missing developers who have the most optimistic expectations about AI’s value.

And...

Developers have become more selective in which tasks they submit. When surveyed, 30% to 50% of developers told us that they were choosing not to submit some tasks because they did not want to do them without AI. This implies we are systematically missing tasks which have high expected uplift from AI.

And so...

Together, these effects make it likely that our estimate reported above is a lower-bound on the true productivity effects of AI on these developers.

[...]

Some developers were less likely to complete tasks that they submitted if they were assigned to the AI-disallowed condition. One developer did not complete any of the tasks that were assigned to the AI-disallowed condition.

[...]

Altogether, these issues make it challenging to interpret our central estimate, and we believe it is likely a bad proxy for the real productivity impact of AI tools on these developers.

So to summarize, the new study showed a productivity increase and they estimate it's larger than the ~20% increase the study found. Cheers to them for being honest about the issues they encountered. For my part I know for sure that the increase is significantly more than 20%. The caveat, though, is that is only true after you've had some experience with the tools.

The truth is that we don't need a study for this, any experienced engineer can readily see it for themselves and you can find them talking about it pretty much everywhere. It would be interesting, though, to see a well designed study that attempted to quantify how big the average productivity increase actually is.

For that the participants using AI would need to be experienced with it and allowed to use their existing setups.

I want to add that this is not an attempt to evangelize for AI. I find the tools useful but I'm not selling anything. I'm interested in them and I stay up to date on the conversations surrounding them and the underlying technology. I use them frequently both for my own projects and to help less technical people improve their business productivity.

Whether AI agents are a good thing or not, from a larger perspective, is a very different, and complicated, conversation. The important thing is that utility and impact are two different conversations. There isn't a debate anymore about utility.

I know this probably won't stop people from continuing to derail conversations with the claim that developers are wrong about utility, but I had to try. It's just hard to let it pass by when someone claims the sky is green.

I understand that AI makes people angry and I think they have good reason to be angry. There are a lot of aspects of the AI revolution that I'm not thrilled about. The hype foremost, the FOMO as part of the hype, the potential for increased wealth consolidation really sucks, though I lay that at the feet of systems that existed before LLMs came along.

It's messy, but let's consider giving the benefit of the doubt to professionals who say a tool works instead of claiming they're wrong. Let them enjoy it. We can still be angry at AI at the same time.

I recently came across an interesting-looking indie game, Idols of Ash. Basically, you have to use a simple grapple-and-swing mechanic to descend through an eldritch underground complex while being pursued by a dangerous "murderpede" monster.

I first played it on what I thought was the official site, idolsofash.fun. It's a pretty spiffy design, with a playable web version, extensive FAQs, strategy guides, and embedded images and video of the game. But I ran into some bugs while playing -- no sound effects, weird lighting. When I mentioned these flaws on the developer's Itch.io page, they responded that they had nothing to do with the site.

Turns out it has a disclaimer at the very bottom: "Unofficial fan site. Not affiliated with or endorsed by Leafy Games." Buying and installing the actual version solved my tech issues. And in playing the game more, I noticed that the various guides on the site were subtly wrong in a lot of ways. The About page claims it's maintained by a big fan of the game, but in hindsight the whole thing seems AI-written and full of hallucinations.

Thing is, I don't get the angle here. There's no advertising on the site. It prominently links directly to the game's official Steam and Itch pages, so they're not trying to deliver malware or intercept the developer's sales. I assume the glitches are from a poor decompilation and rehosting of the original Godot engine game, but there's nothing to be gained from that. The presence of images and video suggests some level of human involvement in the site design, meaning it's not some cheap fire-and-forget thing. The URL and content are far too specific to flip into something else after gaining SEO rank. It presents (and acts) exactly like a non-commercial labor-of-love fansite (albeit one that shares the paid game for free in a broken state).

Could this be a genuine, if misguided, attempt by an actual fan to share the game using AI tools? Or is there some kind of scam I'm not seeing? Is this sort of fake AI fansite with embedded versions of the game a widespread problem with indie titles now?

I've been thinking a lot about LLM assisted development, and in particular why I keep dropping the available tools after a few attempts at using them.

I realized recently that it's taking away the part of software development I enjoy: the creative problem solving that comes with writing code. What's left is code review tasks, testing, security checks, etc. Important tasks, but they all primarily involve heavy concentration, and much less creativity.

Why aren't agents focused on handling the mundane tasks instead? Tell me if I've just introduced a security vulnerability or a runtime bug. Generate realistic test data and give me info on what the likely output would be. Tell me that the algorithm I just wrote is O(n^2).

Those tasks are so much more applicable to matching against existing data, something LLMs should be extremely good at, rather than trying to get them to write something novel, which so far they've been mostly bad at, at least in my experience.

I used to really value the art of essay writing. There seemed to be such a richness in the different ways people would construct arguments, structure those arguments, then deliver those arguments stylistically, not just from the perspective of being persuaded as a reader but also from the perspective of seeing how a given writer thinks, relates to the living tradition of language, and understands the world conceptually. But it's basically lost most of its meaning to me in this age of LLMs. The reality is, LLMs are capable of writing texts that, if you gave them to a seasoned reader 5 years ago, they'd say it was well written and indicative of a truly thoughtful mind. Even if there currently exist certain tells with LLMs, those styles certainly existed in different ways in real human writing beforehand. Now, those perfectly reasonable set of styles are verboten and we have to dedicate half our deep focus to figuring out whether, or to what extent, an essay or article was written by AI. It's difficult to enjoy, let alone care, about essay writing and the writers behind them now.

I can still find value in books, though, because they were written in the past and I don't mind never reading any non-scientific book published after 2022 if it comes down to it.