You can't unit test for taste
dev.karltryggvason.com
dev.karltryggvason.com
Follow this line of thinking, and the AI-friendly answer is easy: we just have to externalize everything we know, so Claude can implement what I want.
Except that I can't fully externalize myself. Debugging a system takes more resources than running the system. If I could write down everything I know and hand it to a machine, I'd do that, but it impossible.
People aren't books or hashmaps. If you want to build something, you need to use the tools, not teach the tools to use you.
[edit: I'm trying to figure out if there's something to be done about this. Email me if you want to chat -- tr at tern dot sh]
I am more familiar with taste in coding and it can at best be described—that the resulting code is too subtly different from something else in the codebase, that you're masking a different bug, that you're not following what the code tells you. The good part is that while this cannot be unit tested, you can write documentation and code comments about it that tell people what they need to know.
But for taste of the kind described in the article there's not even a definition. The logic ended up being "trust a bunch of opaque weights the most"
The usability of iPhones and iPads is a great example of how he was right. They're very easy to use and no functionality was hidden in a right click menu: it had to be visible somewhere.
Right click was still always available as a shortcut for advanced users.
When the context menu was introduced, it was initially designed as a shortcut to actions that were already available elsewhere in the UI.
Just because the feature is available somewhere else in the UI doesn't mean that the shortcut for it must be a two-handed one.
But I remember noticing years ago a large room of tech professionals and 100% of the Windows users had mice plugged into their laptops, and zero percent of Mac users did. It was a failure of the Windows ecosystem that people needed those imho.
It's not 'no support', it was an insane default. For all the talk of 'easy to use', there's a reason context menus exist. You can't just cram every context-specific interaction into an omnibar or a leftclick. Non-trivial software is complicated. Adding that friction to its use does nobody any favours.
Yes, in the decades since... Trackpads have gotten a lot better, but at the time Jobs was pushing for that nonsense, they simply weren't good enough. (And didn't exist at all for non-laptop computers.)
Note that in non-trivial or professional software it's typical to have a hand on the keyboard, because not even a second mouse button is enough. Hold 'q' while dragging to adjust exposure in capture one, etc. Or they have dedicated input hardware like mixing consoles. Or they plug in a speciality mouse.
I'd say there are "simple" simple things you can do though, like take automated screenshots and detect colours for jarring colourschemes.
I had this experience doing a port from Big Query to Postgres using Opus. I had unit tests to guarantee parity with the original code, and Opus insisted on building this bespoke query builder (e.g. `def _where(very_complicated_params)`) on top of sqlglot.
Even with the original code being straightforward and legible and repeated instructions to match, I had to fight with it to get close.
In the end, I ended up doing things the "old fashion way" where I copied chunks code into Claude proper and gave explicit instructions for each piece.
I clearly had externalized the requirements, and yet that wasn't sufficient. The only way to unit test further would be to use an AST to evaluate the output against metrics I couldn't even encode.
Unit test runs, waits for human input before passing or failing, which might seem out of the norm, but we already have QA do manual testing.
If you can externalize it, you only captured the small part of taste that can be externalized in concrete rules.
You can of course pretend anything else doesn't exist, like a person denying anything that can't be measured by their instruments.
He couldn't articulate why but they trusted his gut and it did collapse.
A lot of software engineering relies on that kind of intuition and on a good team you can integrate it and benefit from it and avoid all manner of floor collapses.
I'd argue that transformers are a pretty good indication that intelligence isn't "encodable" in the way we think it means. Usually, most "model" vocabulary means that we can explain and constrain the "data" from the "rules". Except the mere "data" is trillions of interacting weights.
That may be encoding in a physical sense, but that still doesn't explain the intuition in any legible way to humans.
Cynically, we've been able to encode everything already by just saying everything's a transition in a huge lookup table. Not very informative though.
I'm not so sure. For instance, you can write down what it means for a program to be free of XSS and other injection vulnerabilities. Now, how would you unit test for that property?
And that's why it's so hard to get a model to reproduce the specific taste of a person or an organization. My taste is different than yours, so if we dump our aggregate preferences into RL, in averages out to nothing interesting.
For the code-writing case, this means you end up reviewing every line of code, looking for places where you'd thumbs-down the code. Not every line of code contains a real decision, though, so it feels like a waste of time.
LLMs are built for scale so they've given up on the kind of online learning / "long term memory" processes that would individualize them.
The LLM is permanently locked to being a really cracked engineer on their first day at your company, looking at your codebase for the first time.
You can scaffold a bit with .md files, but at the moment they lack the ability to do what humans do: go to sleep, encode things from short to long term memory, and wake up the next day with more specific knowledge baked in.
IMHO this is where code review goes until we fix the individualized model thing: you need to review the decisions the agent made, where you didn't steer. Most will be right. A few will be disastrously wrong. But decision-by-decision is a lot less to review than line-by-line of code.
I wonder if this is even desirable from a product perspective. You probably don't want online learning in a product that you are selling because you can't guarantee a consistent quality of the product.
And to be fair, the ability to fire employees and hire new ones is pretty important for that reason. In cases where you can't easily fire employees (e.g. unions), you encounter the very problem you're describing, and it often leads to companies preferring more consistent automations.
If I were to ask you - what convention you want to follow for your database columns - camelcase or snakecase? There's no correct global answer. There's no overarching truth that should apply to all databases in existence (even if you'll focus on a certain type of database). Hence the no.
But yes, because in the context of existing system there is a convention. If it's snakecase, you create new tables with snakecase column names.
LLMs will generally follow conventions, but sometimes they will not, because indeed - global truths (or at least, the "last article it read" truths) sometimes win over (I assume)
Outside of AI, I run into this issue when taking basic personality tests. A question may be written for a specific reason, which influences the results, but the reason for my answer may be completely unrelated to the reason intended by the person who made the test.
The co-occurence thing is often not a bug of the algorithm but a genuine part of the stochastic landscape that must be solved. Evolution isn't "failing" when sickle cell vulnerability is ported along with malaria resistance; it's just a real tradeoff being made in the current biological landscape.
Want to follow certain pattern, or convention - define it, ie active record vs repository pattern, stick is as an ADR! You don't know what you want? Look at what Claude produces and then acquire taste, mark this as convetion that future sessions will follow, but stick to *one* convention!
Treat your LLMs as junior developers willing to apply various patterns willy nilly, caring only about fulfilling the ACs of given task and not about the longevity or well being of the system in general. They will not look at bigger picture to check if given pattern applies globally, or even if there are any other patterns.
Yes, it is called accepting the concept of "good enough".
If you go for perfection, with the help of AI or not - you will never be done, at least not if your concept of perfect is like mine.
And more concretely here, well you can feed the LLM with enough context about you, so it can better guess what you want. And in some years maybe use a brain computer interface. But I doubt there is a magic bullet here. Just better tools, that we can build. But they won't be perfect either (hard for me to write that, as I set out building the perfect tools).
We can quantize some of the basics, and make a not half bad style guide, but we'll never be able to fully actualize a set of rules to match what humans find generally tasteful. Its too contextual and a moving target.
The right approach is more work out what shared patterns are, make sure a bunch of reasonable ones are post trained into the models so that it's easy to refer to them by name (e.g. "tim pope / chris beams style commit messages", or "make invalid state unrepresentable") and then you're in a world where you can define your personal tasted through labels rather than repetition of the core arguments.
> Follow this line of thinking, and the AI-friendly answer is easy: we just have to externalize everything we know, so Claude can implement what I want.
First you have to have it, and if you think this is a tasteful solution, then you didn't.
And maybe that's just our limits with philosophy, modeling, assumptions, whatever. The danger is not realizing when we're in that zone.
(Fwiw I think unfalsifiability is a limit with any system - "you didn't compile in my syntax/semantics" is an gotcha that's actually valid and useful, but nobody can really determine the hard line)
> QRank is a ranking signal for Wikidata entities. It gets computed by aggregating page view statistics for Wikipedia, Wikitravel, Wikibooks, Wikispecies and other Wikimedia projects
from https://github.com/brawer/wikidata-qrank/blob/main/doc/desig...
Human: well-scoped argument that does just enough to get the job done with minimal risk.
AI: Extremely clever and correct legal argument that almost any lawyer would have said not to file (at least as written). It tries to burn the world and seriously risks pissing off the judge.
This has been my experience, as well, but it’s a really big support. It just needs adult supervision. I can’t understand how vibe-coded apps, actually work.
As far as “taste,” goes, I test my stuff constantly, checking for even minor “friction points,” sometimes, refactoring back to design, in order to resolve issues that many folks would ship. I’m pretty anal, and want my work to be the best experience possible.
I can’t see any LLM coming close to being able to evaluate the user experience, like I can.
With a better process. e.g. plan->revision cycles, better instructions/docs like an ADR system.
I don't think vibe-coding is relegated to "build me reddit but with blockchain" and then it's done.
I think it instead describes the workflow where the software impl stays opaque but you evaluate the end product as an end user to step the product forward. It basically centers you as the tastemaker.
I'd say I vibe-code all of my personal projects now since December where AI had a breakthrough where it required less babysitting and developed good "taste" like smart sum types without being prompted to do so.
I've accumulated my own best practices like a heavy plan->revise cycle where plans ultimately promote into ./plans/impl/YYYY-MM-DD-{slug}.md, and an ADR system in ./docs/design/*.md that encodes arch/design invariants that accumulate over time, and new decisions/principles are folding back into it as they are discovered (by the AI).
During the plan revision cycles, the LLMs may ask me a multiple choice question about which decision branch to take, and lately I've just been responding with "take the ideal option" with good results -- either way it will take a well-reasoned position that I can't really argue with.
Meanwhile, my role is mainly to evaluate the end product and steer it directionally. How much I decide to prescribe and inject myself into technical decisions is a function of how serious the project is, but it's easy to notice that LLMs are simply better and better at arriving at well-reasoned decisions, and my interjections are more and more limited to technical/directional taste rather than necessity.
I have not encountered anything like that, with my Swift (native iOS) apps, but am pretty close to it, with my backend PHP.
I suspect that it depends on the tech stack. So far, the Swift output closely resembles that of a very inexperienced, but smart, engineer; One that has read up on all the "tips and advanced tricks" you can do with Swift, but has never shipped anything substantial. I need to really keep a close eye on it.
But overall I agree, LLMs are currently awful at being beta testers. They miss the most basic stuff that any human would immediately catch as being poor UX, and for all their visual prowess they are terrible at auditing UI.
There is a reason conference talks are always about plain algorithms and data structures.
The only thing that the business seems to care about is top-down UI testing. This is also convenient because you can leave it until the very end after the customer has already seen several prototypes.
I do think TDD makes sense in isolated scopes (prove this specific custom parser works at the edges), but as the general policy for the entire product it's definitely not a viable practice. Much of the time if comes off as an ego trip to see just how cleverly we can mock something so that we can say we technically tested it.
Or to put it differently: a test is an assertion that no matter what, for all time this should never change again. Even if customer requirements change in the future they won't change in such a way as to break this test (this isn't always true, but you should believe it is true).
A test is most valuable when it alerts you to a real problem when it fails. If the test fails but there isn't a real problem (either because customer requirements have changed, or it is flaky) it was needless cost to investigate it. If the test passes that gives some hope of correctness, but you can never be sure it is really correct vs a bug in the test (even if you use TDD and so the test failed when you wrote it that doesn't mean a refactoring since didn't make this an always pass test).
Part of the problem is if I tell you to write sort() or your new toy language's list type you have an intuitive idea of what it should look like and probably will get them right the first time (other than bugs you want the tests so you catch). These should have tiny micro tests. These things also are really easy to use as examples of how to do TDD - which they are, but they are not representative: this type of code is generally in your standard library already and you are not writing it.
Instead you are writing code that isn't well defined with lots of industry experience. It is not clear what the exact interface should be (or more likely it is clear customer requirements will change but you don't know how yet). You have no idea what the best implementation is. You don't know if this will be used in this one place, or if it will become a useful key part that many future projects depend on. You have to make guesses.
You would have the same problem if you wrote tests like that after the code.
TDD has no opinion about the level at which you wrote your test, it just assumes it's the correct one.
This is the number one biggest misconception about TDD which I keep seeing repeated on hacker news.
it follows the definition of TDD and it works really well (with some caveats) but again some people get hung up on what their impression of TDD is (e.g. unit tests checking to see if a car object has a steering wheel or whatever...) rather than what it actually is and what about it is that actually works.
Or, probably more likely a group of snapshots.
Some TDD-obsessed companies will write tests in a way that requires you to spend a half hour understanding the web of mocks in order to update the tests to account for even a minor datastructure change. Coincidentally, your code change would cause those same tests to fail if they weren't mocked out, but they all pass until you make your changes to the mocks. This shreds the "if the tests pass, the change is probably correct" confidence that's most of the reason for having automated tests.
I am not a fan of this style of test writing.
set up a rendering profile and preconditions that generates a minimal snippet of images/video using a predefined GPU profile.
then test for either a pixel perfect reproduction of the correct behaviour or for the properties you're looking for (if it doesnt reproduce deterministically).
this is one way. i also subscribe to the view that if the type system is modified to become stricter in such a way that it can fail reliably in the presence of this type of bug that this is also good enough.
some people might argue that these arent "strictly" TDD by some definition but they set out a path to follow red green refactor and confer identical benefits so my view is who gives a duck?
I don't have enough domain expertise to know which variant of these approaches is best but I'm enough of a TDD expert to know that what you're implying isnt possible is actually something you would would probably derive a lot of value from if you did it.
That's just an extended red where you get feedback from elsewhere.
Isn't red-green-refactor pretty ingrained in TDD?
Only write code to make a failing test pass; then refactor while making sure the tests still pass?
Then write a test that fails, repeat?
I had a quick look at godot tests, and seems to me they cover some parts of the shaders?
Anyway, I was more wondering who/how people are dogmatic about TDD, and manage to leave out one out of three core concepts from red/green/refactor ?
It also takes zero consideration for the interactive nature of games/graphics development.
I don't generally test css code to check that a background is now indeed set to "a more mauvey shade of pinky-russet" after a change - but I might want to.
I might at least want to run a test with browser automation to check that any text is readable on the background.
I could at least find an example of looking at the rendered page for text (as opposed to in the DOM); Google AI had some ideas of how to check the contrast in a screenshot - but no idea if that would actually work as written.
https://medium.com/@dzianisv/vibe-engineering-testing-browse...
There is often a tension between delivering fast and high quality/bug free and what is necessary for medical software or financial calculations might not be necessary for games.
The question of whether to write tests at all is not really about TDD though.
Perhaps unsurprisingly it found that vibe coded tests suck. As a card carrying member of the "church of TDD" (I do think it is practical), this is an empirical result I certainly would agree with.
Substitute static typing for TDD in your comment, and it will remain equally valid statement.
Here I am talking about the basic static typing, and maybe some generics use occasionally, but obviously people also go overboard sometimes with type features and that hinders understanding for newcomers to the codebase.
I mean there are people who go nuts with very complicated types/type systems so there is that, and then you have very complicated programs, maybe that is what you mean?
Using static typing all the time is just using the tools. Using TDD for everything feels a bit suboptimal to me and so needs some obsession to do that. It only becomes a church then if they keep pestering everyone else to do it.
I do it too because it's a common expression, and a marathon is of course longer than a sprint, but both have in common that properly raced, they are absolutely brutal efforts that leave you without a single additional drop at the end. The effort length and instantaneous power output changes, of course. Maybe "it's a marathon build, not the race" would be more precise at the loss of nearly all its expressive power (but with a lot more pedanticism points) :-p .
Nice project !
but that's what the phrase is meant to convey, right?
Don't run through consumable X (energy/money/etc) like there's no tomorrow - even though there's <some big important milestone> now, we've got dozens more of those that we need to meet, so you're better off getting this one done at 75% than committing 100% to it and failing on all the others.
A half is more intense but way easier, you’re just sub threshold but for a time short enough that you cannot really not make it.
I wrote about this a few months back. Rick Rubin is famous for this. I do think it is something that can be trained though, it just needs a lot more context. Taste builds over time through lots of unit tests, through lots of content writing, through an accumulation of product decisions. It’s hard to put it in the individual spec, but it can be teased out of 100 project specs. And when you get to that scale the AI starts to do it pretty well.
If you watch his interview on Rick Beato's channel, this myth will fall apart. He plays guitar, had his own punk rock band and his guitar playing is featured on some high-profile records he produced. Also, he has a lot of practical experience with all kinds of studio equipment.
Well, you can package it up, otherwise Rick wouldn't exist.
But if you break off parts of that - eg. by looking at what is codified out there as “good” design, what’s considered best practice etc - you can create tools the agent can call on that let it get critiques of its own work.
What’s really cool about this is those tools can be code, written by agents and committed to your repo. Put together a script that for example makes sure your brand colors are enforced (eg. https://github.com/cadamsdotcom/CodeLeash/blob/main/scripts/...) and then put it in your pre-commit checks (https://github.com/cadamsdotcom/CodeLeash/blob/main/.pre-com...), and the agent will get feedback on its use of tasteless defaults and adjust accordingly (partly because you blocked commits that contain said tasteless defaults!)
> Verification becomes hard to reason about because there is no ground truth for points of interest, there are no red/green unit tests for taste. I’m sure these are familiar challenges to data scientists and that there are frameworks and evals for working on them. This will require more iteration and manual overrides. Hopefully with feedback and collaboration from the community. But for now I’ve shipped V1…
I suspect LLMs may be able to help us quantify our taste because they can keep track of so many data points all at once, where we have to lossily abstract these details away.
Even if I write down every single thing it did wrong and how I’d do it, and even if I turn those into rules, it will know how to follow these specific rules, but for some reason it can’t seem to generalize beyond that. And the real list of rules seem truly infinite.
Working, useful, delightful, in that order. Testing can make things more likely to work, that's it.
After this line all the references becomes *we*. I can't help but be a little disturbed by that
> To begin with we downloaded ... For instance we excluded ... We also selected ... We used this as a notoriety ... <and many more>
I am increasingly concerned about how LLMs are anthropomorphizing and how that affects our judgement?
> This is my first time writing up a project that I worked on using an AI agent. I kept writing “we” because the project felt like a collaboration.[...] On reading it back, saying we feels like an accountability dodge, because of course I’m fully and solely responsible for any errors in this write-up or code. But just using I/me also feels dishonest, because so much of the implementation here isn’t fully mine so I feel like I’m taking too much credit for my collaboration with the machines. I figure this is a new kind of pronouns debate we’ll be having for the foreseeable future.
I think it is an interesting topic.
The footnote however does re-enforce my concern - in what other ways do we alter our behavior when it feels like we're interacting with another human?
What I was getting at with the "we" in the post is more how we talk and think about work like this. I think it is different in kind to previous projects I've done where a relied on google, stack overflow and elbow grease. Programming has always been "standing on the shoulders of giants" kind of work, but doing it with agents feels different from that. Maybe it was a poor stylistic choice, but I think we need a way to talk about it in an honest way.
I think people have been trying for the written word, with some degree of success (anti-slop skills). I have been trying for visuals, and it's pretty meh. It's easy to get a multimodal LLM to follow a style guide, but a style guide doesn't capture everything that accounts for taste. And anything that is dynamic (not a screenshot test) seems really hard or really expensive.
You absolutely can unit test for taste, just put an agent into loop, and write into prompt what you like. Then do scoring...
Iceland is really bad example, it basically has one populated site (capital) and circular road that goes around the island.