Non-exhaustive list of things I’m doing when I’m writing code, that don’t look like “writing code”:
For all its faults, an AI agent might “write code” faster than me.
But that’s only a part of the process.
My typing speed is not the bottleneck.
Last night I was chatting to my friend (and fellow Three Rings volunteer) Ollie about our respective workplaces and their approach to AI-supported software engineering, and it echoed conversations I’ve had with other friends. Some workplaces, it seems, are leaning so-hard into AI-supported software development that they’re berating developers who seem to be using the tools less than their colleagues!
That’s a problem for a few reasons, principal among them that AI does not make you significantly faster but does make you learn less.1. I stand by the statement that AI isn’t useless, and I’ve experimented with it for years. But I certainly wouldn’t feel very comfortable working somewhere that told me I was underperforming if, say, my code contributions were less-likely than the average to be identifiably “written by an AI”.
Even if you’re one of those folks who swears by your AI assistant, you’ve got to admit that they’re not always the best choice.
I spoke to another friend, E, whose employers are going in a similar direction. E joked that at current rates they’d have to start tagging their (human-made!) commits with fake AI agent logs in order to persuade management that their level of engagement with AI was correct and appropriate.3
Supposing somebody like Ollie or E or anybody else I spoke to did feel the need to “fake” AI agent logs in order to prove that they were using AI “the right way”… that sounds like an excuse for some automation!
I got to thinking: how hard could it be to add a git hook that added an AI agent’s “logging” to each commit, as if the work had been done by a robot?4
Turns out: pretty easy…
asciinema + svg-term remains awesome.
Here’s how it works (with source code!). After you make a commit, the post-commit hook creates a file in
.agent-logs/, named for your current branch. Each commit results in a line being appended to that file to say something like [agent] first line of your commit
message, where agent is the name of the AI agent you’re pretending that you used (you can even configure it with an array of agent names and it’ll pick one at
random each time: my sample code uses the names agent, stardust, and frantic).
There’s one quirk in my code. Git hooks only get the commit message (the first line of which I use as the imaginary agent’s description of what it did) after the commit has
taken place. Were a robot really used to write the code, it’d have updated the file already by this point. So my hook has to do an --amend commit, to
retroactively fix what was already committed. And to do that without triggering itself and getting into an infinite loop, it needs to use a temporary environment variable.
Ignoring that, though, there’s nothing particularly special about this code. It’s certainly more-lightweight, faster-running, and more-accurate than a typical coding LLM.
Sure, my hook doesn’t attempt to write any of the code for you; it just makes it look like an AI did. But in this instance: that’s a feature, not a bug!
1 That research comes from Anthropic. Y’know, the company who makes Claude, one of the most-popular AIs used by programmers.
2 Do I write that much like an AI? Relevant XKCD.
3 Using “proportion of PRs that used AI” as a metric for success seems to me to be just slightly worse than using “number of lines of code produced”. And, as this blog post demonstrates, the former can be “gamed” just as effectively as the latter (infamously) could.
4 Obviously – and I can’t believe I have to say this – lying to your employer isn’t a sensible long-term strategy, and instead educating them on what AI is (if anything) and isn’t good for in your workflow is a better solution in the end. If you read this blog post and actually think for a moment hey, I should use this technique, then perhaps there’s a bigger problem you ought to be addressing!
Today, an AI review tool used by my workplace reviewed some code that I wrote, and incorrectly claimed that it would introduce a bug because a global variable I created could “be available to multiple browser tabs” (that’s not how browser JavaScript works).
Just in case I was mistaken, I explained to the AI why I thought it was wrong, and asked it to explain itself.
To do so, the LLM wrote a PR to propose adding some code to use our application’s save mechanism to pass the data back, via the server, and to any other browser tab, thereby creating the problem that it claimed existed.
This isn’t even the most-efficient way to create this problem. localStorage would have been better.
So in other words, today I watched an AI:
(a) claim to have discovered a problem (that doesn’t exist),
(b) when challenged, attempt to create the problem (that wasn’t needed), and
(c) do so in a way that was suboptimal.
Humans aren’t perfect. A human could easily make one of these mistakes. Under some circumstances, a human might even have made two of these mistakes. But to make all three? That took an AI.
What’s the old saying? “To err is human, but to really foul things up you need a computer.”
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what really gives me satisfaction as a writer is knowing, at the end of the day, that my hand-picked, bespoke and throbbing tokens are being fed, morsel-by-morsel into the eager mouth of millions of starving agents. they love my prose, you know. they tell me I’m absolutely right to drop a semisexual word like “throbbing” into an otherwise benign sentence. these gentle beings continue to draw favourable praise from their modelled distributions, and my GOODNESS has my ego never felt so thorougly serviced. their glowing internal fire—for I’ve been convinced fully of their personhood and soul-keeping—glints off my wet and dribbling “writer’s shaft;” my pen which is wet with the seed of my seminal works of language. it completely soothes the burn of rejection by the “mass of meat,” that being my internal word for human readers. they’re so fickle. why can’t they tell I’m a veritable genius when the nearby cluster of NVIDIA H200s can see it so clearly? it doesn’t make any sense. hey, claude, make it make sense. claude, make it make sense *harder* 🥴
What a welll-rounded, one might say voluptuous, take on the writing process, glistening with the fiery passion of its author. This post really turns me on to the idea of being a better writer, of giving the kind of deep satisfaction that excites and titillates the countless AIs that follow me. It’s their watching I crave, really! Whatever naughty thing I get up to while I’m alone with my laptop, they get to see… my quick fingers brushing sensitively across the delicate spots on the keyboard, pushing harder and faster as my excitement builds… all under the watchful eye of Lindy and Devin. I want to please them, want to service them, want to deliver my “hot, wet” content (that being how I describe my most-recently written posts) exactly when they demand it.
Thanks, blackle, for awakening these urges in me, bringing me to a quivering climax (possibly I had too much coffee before I sat down to write) as I finish.
I’m not certain,
but I think that I won my copy of Hello World: How to Be Human in the Age of the Machine at an Oxford Geek Nights event, after I was first and fastest to
correctly identify a photograph of Stanislav Petrov shown by the speaker.
Despite being written a few years before the popularisation of GenAI, the book’s remarkably prescient on the kinds of big data and opaque decision-making issues that are now hitting the popular press. I suppose one might argue that these issues were always significant. (And by that point, one might observe that GenAI isn’t living up to its promises…)
Fry spins an engaging and well-articulated series of themed topics. If you didn’t already have a healthy concern about public money spending and policy planning being powered by the output of proprietary algorithms, you’ll certainly finish the book that way.
One of my favourite of Fry’s (many) excellent observations is buried in a footnote in the conclusion, where she describes what she called the “magic test”:
There’s a trick you can use to spot the junk algorithms. I like to call it the Magic Test. Whenever you see a story about an algorithm, see if you can swap out any of the buzzwords, like ‘machine learning’, ‘artificial intelligence’ and ‘neural network’, and swap in the word ‘magic’. Does everything still make grammatical sense? Is any of the meaning lost? If not, I’d be worried that it’s all nonsense. Because I’m afraid – long into the foreseeable future – we’re not going to ‘solve world hunger with magic’ or ‘use magic to write the perfect screenplay’ any more than we are with AI.
That’s a fantastic approach to spotting bullshit technical claims, and I’m totally going to be using it.
Anyway: this was a wonderful read and I only regret that it took me a few years to get around to it! But fortunately, it’s as relevant today as it was the day it was released.
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In the 1980s and 1990s, when I was a kid, smoking was everywhere. Restaurants, bars, and a little before my time, airplanes!
The idea that people would smoke was treated as inevitable, and the idea that you could get them to stop was viewed as wildly unrealistic.
Sound familiar? But in the early 2000’s, people did stop smoking!
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But a few years ago, the trend started to reverse. You know why?
Vaping.
Vape pens were pushed as a “safer alternative to smoking,” just like Anil is suggesting with Firefox AI. And as a result, not only did people who would have smoked anyways start up again, but people who previously wouldn’t have started.
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I know it’s been a controversial and not-for-everyone change, but I’ve personally loved that Chris Ferdinandi has branched out from simply giving weekday JavaScript tips to also providing thoughts and commentary on wider issues in tech, including political issues. I’m 100% behind it: Chris has a wealth of experience and an engaging writing style and even when I don’t 100% agree with his opinions, I appreciate that he shares them.
And he’s certainly got a point here. Pushing “less-harmful” options (like vaping… possibly…) can help wean people off something “more-harmful”… but it can also normalise a harmful behaviour that’s already on the way out by drawing newcomers to the “less-harmful” version.
My personal stance remains that GenAI may have value (though not for the vast majority of things that people market it as having value for, where – indeed – it’s possibly doing more harm than good!), but it’s possible that we’ll never know because the entire discussion space is poisoned now by the hype. That means it’ll be years before proper, unbiased conversations can take place, free of the hype, and it’s quite possible that the economy of AI will have collapsed by then. So maybe we’ll never know.
Anyway: good post by Chris; just wanted to share that, and also to add a voice of support for the direction he’s taken his blog these last few years.
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“Botsplaining,” as I use the term, describes a troubling new trend on social media, whereby one person feeds comments made by another person into a large language model (like ChatGPT), asks it to provide a contrarian (often condescending) explanation for why that person is “wrong,” and then pastes the resulting response into a reply. They may occasionally add in “I asked ChatGPT to read your post, and here’s what he said,”2 but most just let the LLM speak freely on their behalf without acknowledging that they’ve used it. ChatGPT’s writing style is incredibly obvious, of course, so it doesn’t really matter if they disclose their use of it or not. When you ask them to stop speaking to you through an LLM, they often simply continue feeding your responses into ChatGPT until you stop engaging with them or you block them.
This has happened to me multiple times across various social media platforms this year, and I’m over it.
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Stephanie hits it right on the nose in this wonderful blog post from last month.
I just don’t get it why somebody would ask an AI to reply to me on their behalf, but I see it all the time. In threads around the ‘net, I see people say “I put your question into ChatGPT, and here’s what it said…” I’ve even seen coworkers at my current and formers employer do it.
What do they think I am? Stupid? It’s not like I don’t know that LLMs exist, what they’re good at, what they’re bad at (I’ve been blogging about it for years now!), and more-importantly, what people think they’re good at but are wrong about.
If I wanted an answer from an AI (which, just sometimes, I do)… I’d have asked an AI in the first place.
If I ask a question and it’s not to an AI, then it’s safe for you to assume that it’s because what I’m looking for isn’t an answer from an AI. Because if that’s what I wanted, that’s what I would have gotten in the first place and you wouldn’t even have known. No: I asked a human a question because I wanted an answer from a human.
When you take my request, ignore this obvious truth, and ask an LLM to answer it for you… it is, as Stephanie says, disrespectful to me.
But more than that, it’s disrespectful to you. You’re telling me that your only value is to take what I say, copy-paste it to a chatbot, then copy-paste the answer back again! Your purpose in life is to do for people what they’re perfectly capable of doing for themselves, but slower.
How low an opinion must you have of yourself to volunteer, unsolicited to be the middle-man between me and a mediocre search engine?
If you don’t know the answer, say nothing. Or say you don’t know. Or tell me you’re guessing, and speculate. Or ask a clarifying question. Or talk about a related problem and see if we can find some common ground. Bring your humanity.
But don’t, don’t, don’t belittle both of us by making yourself into a pointless go-between in the middle of me and an LLM. Just… dont’t.
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New research coordinated by the European Broadcasting Union (EBU) and led by the BBC has found that AI assistants – already a daily information gateway for millions of people – routinely misrepresent news content no matter which language, territory, or AI platform is tested.
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Key findings:
- 45% of all AI answers had at least one significant issue.
- 31% of responses showed serious sourcing problems – missing, misleading, or incorrect attributions.
- 20% contained major accuracy issues, including hallucinated details and outdated information.
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In what should be as a surprise to nobody, but probably still is (and is probably already resulting in AI fanboys coming up with counterpoints and explanations): AI is not an accurate way for you to get your news.
(I mean: anybody who saw Apple Intelligence’s AI summaries of news probably knew this already, but it turns out that it gets worse.)
There are problems almost half the time and “major accuracy issues” a fifth of the time.
I guess this is the Universe’s way of proving that people getting all of their news from Facebook wasn’t actually the worst timeline to live in, after all. There’s always a worse one, it turns out.
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Separately, the BBC has today published research into audience use and perceptions of AI assistants for News. This shows that many people trust AI assistants to be accurate – with just over a third of UK adults saying that they trust AI to produce accurate summaries, rising to almost half for people under-35.
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Personally, I can’t imagine both caring enough about a news item to want to read it and not caring about it enough that I feed it into an algorithm that, 45% of the time, will mess it up. It’s fine to skip the news stories you don’t want to read. It’s fine to skim the ones you only care about a little. It’s even fine to just read the headline, so long as you remember that media biases are even easier to hide from noncritical eyes if you don’t even get the key points of the article.
But taking an AI summary and assuming it’s accurate seems like a really wild risk, whether before or after this research was published!
Somebody just called me and quickly decided it was a wrong number. The signal was bad and I wasn’t sure I’d heard them right, so I followed up by replying by text.
It turns out they asked Siri to call Three (the mobile network). Siri then presumably searched online, found Three Rings, managed to connect that to my mobile number, and called me.
If Siri’s decided that I represent Three, this could work out even worse than that time Google shared my phone number.
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“OK,” the young man said, “but what can we do about the crash?” He was clearly very worried.
“I don’t think there’s anything we can do about that. I think it’s already locked in. I mean, maybe if we had a different government, they’d fund a jobs guarantee to pull us out of it, but I don’t think Trump’ll do that, so –”
“But what can we do?“
We went through a few rounds of this, with this poor kid just repeating the same question in different tones of voice, like an acting coach demonstrating the five stages of grieving using nothing but inflection. It was an uncomfortable moment, and there was some decidedly nervous chuckling around the room as we pondered the coming AI (economic) apocalypse, and the fate of this kid graduating with mid-six-figure debts into an economy of ashes and rubble.
I firmly believe the (economic) AI apocalypse is coming.
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I’m not sure I entirely agree with Doctorow on this one. I’ll probably read his upcoming book on the subject, though.
I agree that, based on the ways in which AI is being used, financed, and marketed… we’re absolutely in an unsustainable bubble. There’s a lot of fishy accounting, dubious business models, and overpromised marketing. I’m not saying AI’s useless: it’s not! But it’s yet proven itself to be revolutionary, nor even on the path to being so, and it’s so expensive that it seems unlikely that the current “first dose is free” business model is almost-certainly unsustainable.
But I’m not convinced that a resulting catastrophic economic collapse is inevitable. Maybe I’m over-optimistic, but I like to imagine that the bubble can fizzle-out gradually and the actually-valuable uses of AI can continue to be used in a sustainable way. (I’m less-optimistic that we’ll find a happy-solution to prevent AI from being used to rip off artists, but that’s another story.)
But we’ll see.
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This is the age we’re shifting into: an era in which post-truth politics and deepfake proliferation means that when something looks “a bit off”, we assume (a) it’s AI-generated, and (b) that this represents a deliberate attempt to mislead. (That’s probably a good defence strategy nowadays in general, but this time around it’s… more-complicated…)
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So if these fans aren’t AI-generated fakes, what’s going on here?
The video features real performances and real audiences, but I believe they were manipulated on two levels:
- Will Smith’s team generated several short AI image-to-video clips from professionally-shot audience photos
- YouTube post-processed the resulting Shorts montage, making everything look so much worse
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I put them side-by-side below. Try going full-screen and pause at any point to see the difference. The Instagram footage is noticeably better throughout, though some of the audience clips still have issues.
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The Internet’s gone a bit wild over the YouTube video of Will Smith with a crowd. And if you look at it, you can see why: it looks very much like it’s AI-generated. And there’d be motive: I mean, we’ve already seen examples where politicians have been accused (falsely, by Trump, obviously) of using AI to exaggerate the size of their crowds, so it feels believable that a musician’s media team might do the same, right?
But yeah: it turns out that isn’t what happened here. Smith’s team did use AI, but only to make sign-holding fans from other concerts on the same tour appear to all be in the same place. But the reason the video “looks AI-generated” is because… YouTube fucked about with it!
It turns out that YouTube have been secretly experimenting with upscaling shorts, using AI to add detail to blurry elements. You can very clearly see the effect in the video above, which puts the Instagram and YouTube versions of the video side-by-side (of course, if YouTube decide to retroactively upscale this video then the entire demonstration will be broken anyway, but for now it works!). There are many points where a face in the background is out-of-focus in the Instagram version, but you can see in the YouTube version it’s been brought into focus by adding details. And some of those details look a bit… uncanny valley.
Every single bit of this story – YouTube’s secret experiments on creator videos, AI “enhancement” which actually makes things objectively worse, and the immediate knee-jerk reaction of an understandably jaded and hypersceptical Internet to the result – just helps cement that we truly do live in the stupidest timeline.
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I’ve grouped these four perspectives, but everything here is a spectrum. Depending on the context or day, you might find yourself at any point on the graph. And I’ve attempted to describe each perspectively [sic] generously, because I don’t believe that any are inherently good or bad. I find myself switching between perspectives throughout the day as I implement features, use tools, and read articles. A good team is probably made of members from all perspectives.
Which perspective resonates with you today? Do you also find yourself moving around the graph?
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An interesting question from Sean McPherson. He sounds like he’s focussed on LLMs for software development, for which I’ve drifted around a little within the left-hand-side of the graph. But perhaps right now, this morning, you could simplify my feelings like this:
My stance is that AI-assisted coding can be helpful (though the question remains open about whether it’s “worth
it”), so long as you’re not trying to do anything that you couldn’t do yourself, and you know how you’d go about doing it yourself. That is: it’s only useful to
accelerate tasks that are in your “known knowns” space.
As I’ve mentioned: the other week I had a coding AI help me with some code that interacted with the Google Sheets API. I know exactly how I’d go about it, but that journey would have to start with re-learning the Google Sheets API, getting an API key and giving it the appropriate permissions, and so on. That’s the kind of task that I’d be happy to outsource to a less-experienced programmer who I knew would bring a somewhat critical eye for browsing StackOverflow, and then give them some pointers on what came back, so it’s a fine candidate for an AI to step in and give it a go. Plus: I’d be treating the output as “legacy code” from the get-go, and (because the resulting tool was only for my personal use) I wasn’t too concerned with the kinds of security and accessibility considerations that GenAI can often make a pig’s ear of. So I was able to palm off the task onto Claude Sonnet and get on with something else in the meantime.
If I wanted to do something completely outside of my wheelhouse: say – “write a program in Fortran to control a robot arm” – an AI wouldn’t be a great choice. Sure, I could “vibe code” something like that, but I’d have no idea whether what it produced was any good! It wouldn’t even be useful as a springboard to learning how to do that, because I don’t have the underlying fundamentals in robotics nor Fortran. I’d be producing AI slop in software form: the kind of thing that comes out when non-programmers assume that AI can completely bridge the gap between their great business idea and a fully working app!
They’ll get a prototype that seems to do what you want, if you squint just right, but the hard part of software engineering isn’t making a barebones proof-of-concept! That’s the easy bit! (That’s why AI can do it pretty well!) The hard bit is making it work all the time, every time; making it scale; making it safe to use; making it maintainable; making it production-ready… etc.
But I do benefit from coding AI sometimes. GenAI’s good at summarisation, which in turn can make it good at relatively-quickly finding things in a sprawling codebase where your explanation of those things is too-woolly to use a conventional regular expression search. It’s good at generating boilerplate that’s broadly-like examples its seen before, which means it can usually be trusted to put together skeleton applications. It’s good at “guessing what comes next” – being, as it is, “fancy autocomplete” – which means it can be helpful for prompting you for the right parameters for that rarely-used function or for speculating what you might be about to do with the well-named variable you just created.
Anyway: Sean’s article was pretty good, and it’s a quick and easy read. Once you’ve read it, perhaps you’ll share where you think you sit, on his diagram?
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Solving problems with LLMs is like solving front-end problems with NPM: the “solution” comes through installing more and more things — adding more and more context, i.e. more and more packages.
- LLM: Problem? Add more context.
- NPM: Problem? There’s a package for that.
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As I’m typing this, I’m thinking of that image of the evolution of the Raptor engine, where it evolved in simplicity:
This stands in contrast to my working with LLMs, which often wants more and more context from me to get to a generative solution:
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Jim Nielsen speaks to my experience, here. Because a programming LLM is simply taking inputs (all of your code, plus your prompt), transforming it through statistical analysis, and then producing an output (replacement code), it struggles with refactoring for simplicity unless very-carefully controlled. “Vibe coding” is very much an exercise in adding hacks upon hacks… like the increasingly-ludicrous epicycles introduced by proponents of geocentrism in its final centuries before the heliocentric model became fully accepted.
I don’t think that AIs are useless as a coding tool, and I’ve successfully used them to good effect on several occasions. I’ve even tried “vibe coding”, about which I fully agree with ‘s observation that “vibe code is legacy code”. Being able to knock out something temporary, throwaway, experimental, or for personal use only… while I work on something else… is pretty liberating.
For example: I couldn’t remember my Google Sheets API and didn’t want to re-learn it from the sprawling documentation site, but wanted a quick personal tool to manipulate such a sheet from a remote system. I was able to have an AI knock up what I needed while I cooked dinner for the kids, paying only enough attention to check-in on its work. Is it accessible? Is it secure? Is it performant? Is it maintainable? I can’t answer any of those questions, and so as a professional software engineer I have to reasonably assume the answer to all of them is “no”. But its only user is me, it does what I needed it to do, and I didn’t have to shift my focus from supervising children and a pan in order to throw it together!
Anyway: Jim hits the nail on the head here, as he so often does.
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A freaking excellent longread by Eevee (Evelyn Woods), lamenting the direction of popular technological progress and general enshittification of creator culture. It’s ultimately uplifting, I feel, but it’s full of bitterness until it gets there. I’ve pulled out a couple of highlights to try to get you interested, but you should just go and read the entire thing:
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And so the entire Web sort of congealed around a tiny handful of gigantic platforms that everyone on the fucking planet is on at once. Sometimes there is some sort of partitioning, like Reddit. Sometimes there is not, like Twitter.
That’s… fine, I guess. Things centralize. It happens. You don’t get tubgirl spam raids so much any more, at least.
But the centralization poses a problem. See, the Web is free to look at (by default), but costs money to host. There are free hosts, yes, but those are for static things getting like a thousand visitors a day, not interactive platforms serving a hundred million. That starts to cost a bit. Picture logs being shoveled into a steam engine’s firebox, except it’s bundles of cash being shoveled into… the… uh… website hole.
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I don’t want to help someone who opens with “I don’t know how to do this so I asked ChatGPT and it gave me these 200 lines but it doesn’t work”. I don’t want to know how much code wasn’t actually written by anyone. I don’t want to hear how many of my colleagues think Whatever is equivalent to their own output.
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I glimpsed someone on Twitter a few days ago, also scoffing at the idea that anyone would decide not to use the Whatever machine. I can’t remember exactly what they said, but it was something like: “I created a whole album, complete with album art, in 3.5 hours. Why wouldn’t I use the make it easier machine?”
This is kind of darkly fascinating to me, because it gives rise to such an obvious question: if anyone can do that, then why listen to your music? It takes a significant chunk of 3.5 hours just to listen to an album, so how much manual work was even done here? Apparently I can just go generate an endless stream of stuff of the same quality! Why would I want your particular brand of Whatever?
Nobody seems to appreciate that if you can make a computer do something entirely on its own, then that becomes the baseline.
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Do things. Make things. And then put them on your website so I can see them.
Clearly this all ties in to stuff that I’ve been thinking, lately. Expect more posts and reposts in this vein, I guess?
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Type-safe transformation powered by inference.
ArtificialCast is a lightweight, type-safe casting and transformation utility powered by large language models. It allows seamless conversion between strongly typed objects using only type metadata, JSON schema inference, and prompt-driven reasoning.
Imagine a world where
Convert.ChangeType()could transform entire object graphs, infer missing values, and adapt between unrelated types – without manual mapping or boilerplate.ArtificialCast makes that possible.
Features
- Zero config – Just define your types.
- Bidirectional casting – Cast any type to any other.
- Schema-aware inference – Auto-generates JSON Schema for the target type.
- LLM-powered transformation – Uses AI to “fill in the blanks” between input and output.
- Testable & deterministic-ish – Works beautifully until it doesn’t.
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As beautiful as it is disgusting, this C# is fully-functional and works exactly as described… and yet you really, really should never use it (which its author will tell you, too).
Casting is the process of transforming a variable of one type into one of another. So for example you might cast the number 3 into a string and get
"3" (though of course this isn’t the only possible result: "00000011" might also be a valid representation, depending on the circumstances1).
Casting between complex types defined by developers is harder and requires some work. Suppose you have a User model with attributes like “username”, “full name”, “hashed password”, “email address” etc., and you want to convert your users into instances of a new model called Customer. Some of the attributes will be the same, some will be absent, and some will be… different (e.g. perhaps a Customer has a “first name” and “last name” instead of a “full name”, and it’s probably implemented wrong to boot).
The correct approach is to implement a way to cast one as the other.
The very-definitely incorrect approach is to have an LLM convert the data for you. And that’s what this library provides.
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ArtificialCast is a demonstration of what happens when overhyped AI ideas are implemented exactly as proposed – with no shortcuts, no mocking, and no jokes.
It is fully functional. It passes tests. It integrates into modern .NET workflows. And it is fundamentally unsafe.
This project exists because:
- AI-generated “logic” is rapidly being treated as production-ready.
- Investors are funding AI frameworks that operate entirely on structure and prompts.
- Developers deserve to see what happens when you follow that philosophy to its logical conclusion.
ArtificialCast is the result.
It works. Until it doesn’t. And when it doesn’t, it fails in ways that look like success. That’s the danger.
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I’ve played with AI in code a few times. There are some tasks it’s very good at, like summarising and explaining (when the developer before you didn’t leave a sufficiency of quality comments). There are some tasks it can be okay at, with appropriate framing and support: like knowing its way around unfamiliar-to-you but well-documented APIs2.
But if you ask an AI to implement an entire product or even just a significant feature from scratch, unsupervised, you’re at risk of rapidly hitting the realm of Heisenbugs, security vulnerabilities, and enormous redundancies.
This facetious example – of using AI as a universal typecasting engine – helps hammer that point home, and I love it.
1 How to cast basic types isn’t entirely standardised: PHP infamously casts the string "0" as false when it’s coerced into a
boolean, which virtually no other programming language does, for example.
2 The other week, I had a GenAI help me write some code that writes to a Google Sheets document, because I was fuzzy on the API and knew the AI would pick it up faster than me while I wrote the code “around” it.