Did you actually fix the issue, or did you fix the issue and introduce new bugs?
The problem is the asymmetry of effort. You verified you fixed your issue. The maintainers verified literally everything else (or are the ones taking the hit if they're just LGTMing it).
Sorry, I am sure your specific change was just fine. But I'm speaking generally.
How many times have I at work looked at a PR and thought "this is such a bad way to fix this I could not have come up with such a comically bad way if I tried." And naturally couldn't say this to my fine coworker whose zeal exceeded his programming skills (partly because someone else had already approved the PR after "reviewing" it...). No, I had to simply fast-follow with my own PR, which had a squashed revert of his change, with the correct fix, so that it didn't introduce race conditions into parallel test runs.
And the submitter of course has no ability to gauge whether their PR is the obvious trivial solution, or comically incorrect. Therein lies the problem.
This is why open source projects need good architecture and high test coverage.
I'd even argue we need a new type of test coverage, something that traces back the asserts to see what parts of the code are actually constrained by the tests, sort of a differential mutation analysis.
Except we know how these work. There's no number sense. It's predicting tokens. It is able to recount the mathematical foundations because in its training dataset, that often happens, both in instructional material and in proofs.
"When was the last time you reviewed the machine code produced by a compiler?"
Compilers will produce working output given working input literally 100% of my time in my career. I've never personally found a compiler bug.
Meanwhile AI can't be trusted to give me a recipe for potato soup. That is to say, I would under no circumstances blindly follow the output of an LLM I asked to make soup. While I have, every day of my life, gladly sent all of the compiler output to the CPU without ever checking it.
The compiler metaphor is simply incorrect and people trying to say LLMs compile English into code insult compiler devs and English speakers alike.
> Compilers will produce working output given working input literally 100% of my time in my career.
In my experience this isn't true. People just assume their code is wrong and mess with it until they inadvertently do something that works around the bug. I've personally reported 17 bugs in GCC over the last 2 years and there are currently 1241 open wrong-code bugs.
These are still deterministic bugs, which is the point the OP was making. They can be found and solved once. Most of those bugs are simply not that important, so they never get attention.
LLMS on the other hand are non-deterministic and unpredictable and fuzzy by design. That makes them not ideal when trying to produce output which is provably correct - sure you can output and then laboriously check the output - some people find that useful, some are yet to find it useful.
It's a little like using Bitcoin to replace currencies - sure you can do that, but it includes design flaws which make it fundamentally unsuited to doing so. 10 years ago we had rabid defenders of these currencies telling us they would soon take over the global monetary system and replace it, nowadays, not so much.
Sure bitcoin is at least deterministic, but IMO (an that of many in the finance industry) it's solving entirely the wrong problem - in practice people want trust and identity in transactions much more than they want distributed and trustless.
In a similar way LLMs seem to me to be solving the wrong problem - an elegant and interesting solution, but a solution to the wrong problem (how can I fool humans into thinking the bot is generally intelligent), rather than the right problem (how can I create a general intelligence with knowledge of the world). It's not clear to me we can jump from the first to the second.
Bitcoin transactions rely on mining to notarize, which is by design (due to the nature of the proof-of-work system) incredibly non-deterministic.
So when you submit a transaction, there is no hard and fast point in the future when it is "set in stone". Only a geometrically decreasing likelihood over time that a transaction might get overturned, improving by another geometric notch with every confirmed mined block that has notarized your transaction.
A lot of these design principles are compromises to help support an actually zero-trust ledger in contrast to the incumbent centralized-trust banking system, but they definitely disqualify bitcoin transactions as "deterministic" by any stretch of the imagination. They have quite a bit more in common with LLM text generation than one might have otherwise thought.
Not sure I agree, the only axis on which Bitcoin is non-deterministic is that of time - the time to confirmation is not set in stone. Outcomes are still predictable though and follow strict rules.
It’s a fundamentally different product, LLMs are fuzzy word matchers and produce different outcomes even for the same input every time, they inject variance to make them seem more human. I think we’re straying off topic here though.
> I've personally reported 17 bugs in GCC over the last 2 years
You are an extreme outlier. I know about two dozen people who work with C(++) and not a single one of them has ever told me that they've found a compiler bug when we've talked about coding and debugging - it's been exclusively them describing PEBCAK.
I've been using c++ for over 30 years. 20-30 years ago I was mostly using MSVC (including version 6), and it absolutely had bugs, sometimes in handling the language spec correctly and sometimes regarding code generation.
Today, I use gcc and clang. I would say that compiler bugs are not common in released versions of those (i.e. not alpha or beta), but they do still occur. Although I will say I don't recall the last time I came across a code generation bug.
I knew one person reporting gcc bugs, and iirc those were all niche scenarios where it generated slightly suboptimal machine code but not otherwise observable from behavior
Right - I'm not saying that it doesn't happen, but that it's highly unusual for the majority of C(++) developers, and that some bugs are "just" suboptimal code generation (as opposed to functional correctness, which the GP was arguing).
I'm not arguing that LLMs are at a point today where we can blindly trust their outputs in most applications, I just don't think that 100% correct output is necessarily a requirement for that. What it needs to be is correct often enough that the cost of reviewing the output far outweighs the average cost of any errors in the output, just like with a compiler.
This even applies to human written code and human mistakes, as the expected cost of errors goes up we spend more time on having multiple people review the code and we worry more about carefully designing tests.
If natural language is used to specify work to the LLM, how can the output ever be trusted? You'll always need to make sure the program does what you want, rather than what you said.
Just create a very specific and very detailed prompt that is so specific that it starts including instructions and you came up with the most expensive programming language.
This is a non-sequitur. Almost all programming languages are Turing complete, but I think we'd all agree they vary in expressivity (e.g. x64 assembly vs. TypeScript).
By expressivity I mean that you can say what you mean, and the more expressive the language is, the easier that is to do.
It turns out saying what you mean is quite easy in plain English! The hard part is that English allows a lot of ambiguity. So the tradeoffs of how you express things are very different.
I also want to note how remarkable it is that humans have built a machine that can effectively understand natural language.
>"You'll always need to make sure the program does what you want, rather than what you said."
Yes, making sure the program does what you want. Which is already part of the existing software development life cycle. Just as using natural language to specify work already is: It's where things start and return to over and over throughout any project. Further: LLM's frequently understand what I want better than other developers. Sure, lots of times they don't. But they're a lot better at it than they were 6 months ago, and a year ago they barely did so at all save for scripts of a few dozen lines.
That's exactly my point, it's a nice tool in the toolbox, but for most tasks it's not fire-and-forget. You still have to do all the same verification you'd need to do with human written code.
You trust your natural language instructions thousand times a day. If you ask for a large black coffee, you can trust that is more or less what you’ll get. Occasionally you may get something so atrocious that you don’t dare to drink, but generally speaking you trust the coffee shop knows what you want. It you insist on a specific amount of coffee brewed at a specific temperature, however, you need tools to measure.
AI tools are similar. You can trust them because they are good enough, and you need a way (testing) to make sure what is produced meet your specific requirements. Of course they may fail for you, doesn’t mean they aren’t useful in other cases.
What’s to stop the barista putting sulphuric acid in your coffee? Well, mainly they don’t because they need a job and don’t want to go to prison. AIs don’t go to prison, so you’re hoping they won’t do it because you’ve promoted them well enough.
The person I'm replying to believes that there will be a point when you no longer need to test (or review) the output of LLMs, similar to how you don't think about the generated asm/bytecode/etc of a compiler.
That's what I disagree with - everything you said is obviously true, but I don't see how it's related to the discussion.
I don't necessarily think we'll ever reach that point and I'm pretty sure we'll never reach that point for some higher risk applications due to natural language being ambiguous.
There are however some applications where ambiguity is fine. For example, I might have a recipe website where I tell a LLM to "add a slider for the user to scale the number of servings". There's a ton of ambiguity there but if you don't care about the exact details then I can see a future where LLMs do something reasonable 99.9999% of the time and no one does more than glance at it and say it looks fine.
How long it is until we reach that point and if we'll ever reach that point is of course still up for debate, but I dnt think it's completely unrealistic.
The challenge not addressed with this line of reasoning is the required sheer scale of output validation on the backend of LLM-generated code. Human hand-developed code was no great shakes at the validation front either, but the scale difference hid this problem.
I’m hopeful what used to be tedious about the software development process (like correctness proving or documentation) becomes tractable enough with LLM’s to make the scale more manageable for us. That’s exciting to contemplate; think of the complexity categories we can feasibly challenge now!
Or the argument that "well, at some point we can come up with a prompt language that does exactly what you want and you just give it a detailed spec." A detailed spec is called code. It's the most round-about way to make a programming language that even then is still not deterministic at best.
Exactly the point. AI is absolutely BS that just gets peddled by shills.
It does not work. It might work for some JS bullcrao. But take existing code and ask it to add capsicum next to an ifdef of pledge. Watch the mayhem unfold.
This is obviously besides the point but I did blindly follow a wiener schnitzel recipe ChatGPT made me and cooked for a whole crew. It turned out great. I think I got lucky though, the next day I absolutely massacred the pancakes.
I genuinely admire your courage and willingness (or perhaps just chaos energy) to attempt both wiener schnitzel and pancakes for a crew, based on AI recipes, despite clearly limited knowledge of either.
Recent experiments with LLM recipes (ChatGPT): missed salt in a recipe to make rice, then flubbed whether that type of rice was recommended to be washed in the recipe it was supposedly summarizing (and lied about it, too)…
Probabilistic generation will be weighted towards the means in the training data. Do I want my code looking like most code most of the time in a world full of Node.js and PHP? Am I better served by rapid delivery from a non-learning algorithm that requires eternal vigilance and critical re-evaluation or with slower delivery with a single review filtered through an meatspace actor who will build out trustable modules in a linear fashion with known failure modes already addressed by process (ie TDD, specs, integration & acceptance tests)?
I’m using LLMs a lot, but can’t shake the feeling that the TCO and total time shakes out worse than it feels as you go.
There was a guy a few months ago who found that telling the AI to do everything in a single PHP file actually produced significantly better results, i.e. it worked on the first try. Otherwise it defaulted to React, 1GB of node modules, and a site that wouldn't even load.
>Am I better served
For anything serious, I write the code "semi-interactively", i.e. I just prompt and verify small chunks of the program in rapid succession. That way I keep my mental model synced the whole time, I never have any catching up to do, and honestly it just feels good to stay in the driver's seat.
Pro-tip: Do NOT use LLMs to generate recipes, use them to search the internet for a site with a trustworthy recipe, for information on cooking techniques, science, or chemistry, or if you need ideas about pairings and/or cooking theory / conventions. Do not trust anything an LLM says if it doesn't give a source, it seems people on the internet can't cook for shit and just make stuff up about food science and cooking (e.g. "searing seals in the moisture", though most people know this is nonsense now), so the training data here is utterly corrupt. You always need to inspect the sources.
I don't even see how an LLM (or frankly any recipe) that is a summary / condensation of various recipes can ever be good, because cooking isn't something where you can semantically condense or even mathematically combine various recipes together to get one good one. It just doesn't work like that, there is just one secret recipe that produces the best dish, and the way to find this secret recipe is by experimenting in the real world, not by trying to find some weighting of a bunch of different steps from a bunch of different recipes.
Plus, LLMs don't know how to judge quality of recipes at all (and indeed hallucinate total nonsense if they don't have search enabled).
If you have lots of experience from years of serious cooking, like I do, almost everything the LLM suggests or outputs re: cooking is false, bad or at best incredibly sub-par, and you will spend far more time correcting it and/or pushing it toward what you already know for it to be actually helpful / productive in getting you anything actually true. I also think it just messes up incredibly basic stuff all the time. I re-iterate it is only good for the things I said.
Whether or not you think you can get "good" recipes out of it will also depend on your experience with cuisine and cooking, and your own pickiness. I am sure amateurs or people who cook only occasionally can get use out of it, but it is not useful for me.
Cooking is a very different world from coding: recipes aren't composable like code (within-recipe ratios need to be maintained, i.e. recipes written in bakers ratios/proportions, steps are almost always sequentially dependent, and ingredients need to complement each other) and most sources besides the few good empirical ones actually verify anything they make, which is a problem, because the training data for cooking is far more poisoned.
I also cook daily at home, for fun (though I have catered a couple times for some large 50+ people family events too). Just, in my case, cooking is my passion, and has been more than just a minor hobby for me. I.e. there have been many years of my life where I spent 3-5 hours of every day cooking, and this has been the case for about 15 years now. If "professional home cook" was a thing, I'd be that, but, alas.
So my standards are admittedly probably a bit deranged relative to most...
Everything more complex than a hello-world has bugs. Compiler bugs are uncommon, but not that uncommon. (I must have debugged a few ICEs in my career, but luckily have had more skilled people to rely on when code generation itself was wrong.)
I had a fun bug while building a smartwatch app that was caused by the sample rate of the accelerometer increasing when the device heated up. I had code that was performing machine learning on the accelerometer data, which would mysteriously get less accurate during prolonged operation. It turned out that we gathered most of our training data during shorter runs when the device was cool, and when the device heated up during extended use, it changed the frequencies of the recorded signals enough to throw off our model.
I've also used a logic analyzer to debug communications protocols quite a few times in my career, and I've grown to rather like that sort of work, tedious as it may be.
Just this week I built a VFS using FUSE and managed to kernel panic my Mac a half-dozen times. Very fun debugging times.
I remember the time I spent hours debugging a feature that worked on Solaris and Windows but failed to produce the right results on SGI. Turns out the SGI C++ compiler silently ignored the `throw` keyword! Just didn’t emit an opcode at all! Or maybe it wrote a NOP.
All I’m saying is, compilers aren’t perfect.
I agree about determinism though. And I mitigate that concern by prompting AI assistants to write code that solves a problem, instead of just asking for a new and potentially different answer every time I execute the app.
> Meanwhile AI can't be trusted to give me a recipe for potato soup.
This just isn't true any more. Outside of work, my most common use case for LLMs is probably cooking. I used to frequently second guess them, but no longer - in my experience SOTA models are totally reliable for producing good recipes.
I recognize that at a higher level we're still talking about probabilistic recipe generation vs. deterministic compiler output, but at this point it's nonetheless just inaccurate to act as though LLMs can't be trusted with simple (e.g. potato soup recipe) tasks.
Just to nitpick - compilers (and, to some extent, processors) weren't deterministic a few decades ago. Getting them to be deterministic has been a monumental effort - see build reproducibility.
There's also no canonical way to write software, so in that sense generating code is more similar to coming up with a potato soup recipe than compiling code.
That is not the issue, any potato soup recipe would be fine, the issue is that it might fetch values from different recipes and give you an abomination.
This exactly, I cook as passion, and LLMs just routinely very clearly (weighted) "average" together different recipes to produce, in the worst case, disgusting monstrosities, or, in the best case, just a near-replica of some established site's recipe.
At least with the LLM, you don't have to wade through paragraph after paragraph of "I remember playing in the back yard as a child, I would get hungry..."
In fact LLMs write better and more interesting prose than the average recipe site.
It's not hard to scroll to the bottom of a page, IMO, but regardless, sites like you are mentioning have trash recipes in most cases.
I only go with resources where the text is actual documentation of their testing and/or the steps they've made, or other important details (e.g. SeriousEats, Whats Cooking America / America's Test Kitchen, AmazingRibs, Maangchi for Korean, vegrecipesofindia, Modernist series, etc) or look for someone with some credibility (e.g. Kenji Lopez, other chef on YouTube). In this case the text or surrounding content is valuable and should not be skipped. A plain recipe with no other details is generally only something an amateur would trust.
If you need a recipe, you don't know how to make it by definition, so you need more information to verify that the recipe is done soundly. There is also no reason to assume / trust that the LLMs summary / condensation of various recipes is good, because cooking isn't something where you can semantically condense or even mathematically combine various recipes together to get one good one. It just doesn't work like that, there is just one secret recipe that produces the best dish, and LLMs don't know how to judge quality of recipes, mostly.
I've never had an LLM produce something better or more trustworthy than any of those sites I mentioned, and have had it just make shit up when dealing with anything complicated (i.e. when trying to find the optimal ratio of starch to flour for Korean fried chicken, it just confidently claimed 50/50 is best, when this is obviously total trash to anyone who has done this).
The only time I've ever found LLMs useful for cooking is when I need to cook something obscure that only has information in a foreign language (e.g. icefish / noodlefish), or when I need to use it for search about something involving chemistry or technique (it once quickly found me a paper proving that baking soda can indeed be used to tenderize squid - but only after I prompted it further to get sources and go beyond its training data, because it first hallucinated some bullshit about baking soda only working on collagen or something, which is just not true at all).
So I would still never trust or use the quantities it gives me for any kind of cooking / dish without checking or having the sources, instead I would rely on my own knowledge and intuitions. This makes LLMs useless for recipes in about 99% of cases.
The difference is that I have a few sites and resources I already know that are NOT useless. With an LLM output, I have to check and verify every time, and, since LLMs are based on junk, almost always produce junk. But with the trusted sites, I do not have to check, and almost always get something decent and/or close to authentic!
The difference is between a trusted source that is good most of the time, vs. an LLM recipe that is trash 99% of the time.
EDIT: If you haven't visited any / all of the sites / sources I mentioned, check them out! They are really good, especially SeriousEats if the recipe is from Kenji Lopez. Maybe just avoid AmazingRibs, unless you have uBlock installed: they were way ahead of their time, but haven't updated in forever, and clearly have become desperate...
I think things can only be called revolutions in hindsight - while they are going on it's hard to tell if they are a true revolution, an evolution or a dead-end. So I think it's a little premature to call Generative AI a revolution.
AI will get there and replace humans at many tasks, machine learning already has, I'm not completely sure that generative AI will be the route we take, it is certainly superficially convincing, but those three years have not in fact seen huge progress IMO - huge amounts of churn and marketing versions yes, but not huge amounts of concrete progress or upheaval. Lots of money has been spent for sure! It is telling for me that many of the real founders at OpenAI stepped away - and I don't think that's just Altman, they're skeptical of the current approach.
What I don't understand about these arguments is that the input to the LLMs is natural language, which is inherently ambiguous. At which point, what does it even mean for an LLM to be reliable?
And if you start feeding an unambiguous, formal language to an LLM, couldn't you just write a compiler for that language instead of having the LLM interpret it?
Compilers are deterministic (modulo bugs), but most things in life are not, but can still be reliable.
The opposite also holds: "npm install && npm run build" can work today and fail in a year (due to ecosystem churn) even though every single component in that chain is deterministic.
2) Reliability is a continuum, not a discreet yes/no. In practice, we want things to be reliable enough (where "enough" is determined per domain).
I don't presume this will immediately change your mind, but hopefully will open your eyes to looking at this a bit differently.
> I don't presume this will immediately change your mind
I'm not saying that AI isn't useful. I'm just claiming it's not analogous to a compiler. If it was, you would treat your prompts as source code, and check them into source control. Checking the output of an LLM into source control is analogous to committing the machine code output from a compiler into source control.
My question still stands though. What does it mean for a tool to be reliable when the input language is ambiguous? This isn't just about the LLM being nondeterministic. At some point those ambiguities need to be resolved, either by the prompter, or the LLM. But the resolution to those ambiguities doesn't exist in the original input.
Hasn't AI been annoying the piss out of us for well over a year now? I've definitely been hearing "10x productivity" for that long.
So - where is it all? Where's all the 10 years of software dev that happened over the last year? Where's the companies blowing their competitors out of the water by compressing a decade of production into a year?
The proof is in the pudding. Or lack thereof. If the claims were anything like they say, we'd see something by now.
I'd use digital ocean over AWS for any SMB or lean startup (so... anyone not attached to an infinite money hose that has to either scale to NEED AWS, or die trying) just because of 1) their UI not being broken glass you have to crawl over and 2) not having eight trillion features that make doing simple things hard and 3) pricing
This is more because the barrier to entry is so much lower.
Android: have laptop that can do virtualization (...so basically ever laptop that can also do this:) and have enough ram to do run Android studio. Then you theoretically also need an Android device but even that's just because I assume you want to use the app you're making. That's it.
iOS: $100/yr entry fee, plus you need Apple hardware, plus a "server" mode Apple hardware (Mac mini?) if you want to alt store and I assume your main device is a laptop.
Just the money thing and the hardware thing is a huge stumbling block. I know it's rounding error for any even semi serious business but also let's be real, a ton of very important software is basically run on the budget of "the software devs main job and/or EU welfare state benefits".
The www wins. All you need is something that can run a browser. You edit a line, save, refresh and there it is, the real finished product, not emulation.
Apps have terrible reliability too. I just wanted to order a pizza, the restaurant website offered a button for the play store and app store.
There it said the app was for an outdated version of Android.
Perhaps it had been like that for a long time? But lets imagine it happened today. Where are you to get your orders from? Ahh yes, the website.
If apps didn't get the icon on the home screen 90% wouldn't have a reason to exist.
Bunch of pictures with descriptions and an add to cart button. One shouldn't even need to write code, it should be as simple and obvious as serving a document. In stead you need a full time carpenter to keep the store running. The counter and shelves spontaneously collapse, doors regularly get stuck, light fixtures rain down from the ceiling.
People trying to sell pizza deserve better, we can do better.
The good news here is that their code is of such a poor quality it doesn't properly work anyway.
I have recently tried to blindly create a small .dylib consolidation tool in JS using Claude Code, Opus 4.5 and AskUserTool to create a detailed spec. My god how awful and broken the code was. Unusable. But it faked* working just good enough to pass someone who's got no clue.
> The good news here is that their code is of such a poor quality it doesn't properly work anyway.
This is just wishful thinking. In reality it works just well enough to be dangerous. Just look at the latest RCE in OpenCode. The AI it was vibe-coded with allowed any website with origin * to execute code, and the Prompt Engineer™ didn't understand the implications.
I'm watching the voters around the world electing charismatic leaders and then cheering the consequences.
Thus companies electing to replace software developers with AI slop are not of a much surprise to me.
It doesn't matter whether people will die because of AI slop. What matters is keeping Microsoft shareholders happy and they are only happy when there is a growing demand for slop.
Because of the fraud scandal and the concentration of cases among the Somali community there (that those people are generally citizens and not immigrants or refugees is besides the point to the administration). The MN fraud story is a huge big deal in conservative media.
No, but the premise of the ICE/CBP flood to MN is that the fraud is being conducted by deportable people. Note that ICE/CBP has no statutory authority to enforce fraud laws.
My understanding is that the "flood" is due to the state not assisting and arguably impeding ICE, vs states like Florida where the state is cooperating so they don't need as much ICE to do the work. It sounds like the fraud is being used as a red herring by detractors.
AFAIK, committing fraud does not protect illegal immigrants from deportation, which seems to be the implied conclusion here. If ICE deports illegal immigrants who are also committing fraud, I can't see how that is a minus rather than a plus.
Might have to do with the size of the city - I've heard through the grapevine that even Minneapolis is too big and they're thinking of shifting to some city in Maine or New Hampshire.
"Too big" supposedly meaning orchestrating something that allows them to have the optics without the potential for fallout. This is really speculation though.
They found a weakness to justify an increase in violence to their base: the day care corruption. Despite the fact that most of that was found and prosecuted years ago, right-wing influencers were successfully able to bring it back to the forefront, and the administration jumped on it to justify an increased ICE presence, naturally leading to the violence we see. They didn't get the same thing in Chicago, where ICE avoided most of the areas likely to see violence in the first place. And they didn't leave Chicago, they just aren't publicizing it like they were.
I'm not especially in the know about such things. Is there a Chicago politician that crossed Trump such that revenge against Chicago would be in the cards? I assume this is about Walz running against him. It would be California (due to Harris) but they're probably in a better position to fight back than Michigan is.
I think the main problem with online recipes is that there's a lot of stuff that "cooks" and "people who cook" learn that carries over VERY STRONGLY from dish to dish that is just totally absent from online tutorials. Things like what done (but not dried out) chicken looks like, and how to position chicken in a pan so the thicker parts get more heat, and why your chicken went right from "looks plain" to burned with no maillard reaction.
I think foods/culinary courses should be mandatory in high school. I took one as an elective, expecting it to be a blow-off class, but I ended up being shocked by how much I - honor student and all that - didn't know about browning hamburger, much less actual cooking. I ended up taking the subsequent 3 classes in the "foods" line.
For all the effort we put into science education, cooking is applied science we do every day. We should start in elementary school and keep at it through high school, in my opinion.
The problem is the asymmetry of effort. You verified you fixed your issue. The maintainers verified literally everything else (or are the ones taking the hit if they're just LGTMing it).
Sorry, I am sure your specific change was just fine. But I'm speaking generally.
How many times have I at work looked at a PR and thought "this is such a bad way to fix this I could not have come up with such a comically bad way if I tried." And naturally couldn't say this to my fine coworker whose zeal exceeded his programming skills (partly because someone else had already approved the PR after "reviewing" it...). No, I had to simply fast-follow with my own PR, which had a squashed revert of his change, with the correct fix, so that it didn't introduce race conditions into parallel test runs.
And the submitter of course has no ability to gauge whether their PR is the obvious trivial solution, or comically incorrect. Therein lies the problem.
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