I saw on Twitter that in an ML course at Tsinghua University, one of the tests asks students to write quizzes that fail the most LLM models as possible.
What if we create a benchmark that works like this and assigns ELO scores? Models fight head-to-head by writing a question, a bug, or an incomplete implementation, which the opponent has to answer, fix, or finish.
This kind of approach would generally still need human guidance, otherwise these models might get stuck in weird niche corners of the problem space that would not be relevant to any real world project.
How do you prevent degenerate strategies? I could trivially give a model a SHA256 hash and ask it to provide the source input.
In class you'd probably want a rule saying at least one LLM should be able to figure out the answer, but in a head-to-head I'm not sure how to solve it.
Maybe make the LLM:s write questions that they can solve (without seeing the question writing context) but not other LLm:s.
On the other hand then maybe a good strategy would be to write questions that the LLM just happen to have in a nich dataset in its training ”what did user5455 say to user6835?”
Staff SWE Bench: LLM doubts whether we should do any of this, calls the entire project into question, refuses to merge code, but is happy to delete it.
Distinguished version: write the outline of the slide deck for the talk you plan to give at conferences about it, without having shipped anything or even written code yet.
This makes so much sense as to why I've always felt that Opus 4.8 was leagues ahead of GPT 5.5. It's so good at taking underspecified requirements and filling in the gaps with sensible approaches for your project
Why supply underspecified requirements in the first place? Both models are good at challenging assumptions/edge cases and asking questions to clarify, but seemingly only when explicitly asked (i.e. something like a "brainstorm" skill).
I don't think either harnesses do enough to encourage the model to challenge all assumptions and ask questions, maybe because users might find it annoying. That step is basically a requirement IMO.
I've found all of the GPT-5 models to be very nit-picky, useful for code review and mathematics (important for my work), but seemingly gets in the way of "aesthetic" code, e.g. overly defensive code to cover all edge cases, even if unlikely.
There is seemingly also a tradeoff between flexibility vs instruction following. In my experience Opus will sometimes ignore instructions but can "fill in the blanks" more, vs GPT-5.5 follows instructions better but perhaps at the cost of rigidity.
The best benchmarks are the ones you create yourself.
Its not my experience Opus is leagues ahead or even superior, but in any case, since GPT 5.5 has Instant, Medium, High, Extra High and Pro...Should the comparison be with GPT on Pro, instead of Extra High as it seems to be the case in the table?
Man I don't know if I'm living in a crazy bubble or something but GPT 5.5 is lightyears better than Opus 4.8 for me to the point where I'm honestly wondering how you're evaluating them or what kind of work you're doing.
There's specific tasks that Opus does better on like Frontend Dev and Design but for anything else 5.5 just laps it.
In my experience, for more mechanical refactoring work (like splitting a big source code file into multiple smaller ones), GPT 5.5 runs way faster than any of the Claude models. But for other tasks that require deeper reasoning, it's not that clear who is the winner.
Better for vibe coders who always under specify. But at what point does it know you are under specifying but you have properly specified and it did it over your specification?
same observation here opus 4.8 (and i dont understand the people defending gpt 5.5 constantly) was significantly mature, it would even push back against anything off putting where as GPT 5.5 will happily agree and do what is asked but I would note that it takes several tries.
4.8 also requires more than one prompt but its output is significantly higher quality and offers more insight
The value of a senior situation is to apply known solutions and strategies, to novel problems. I can not see how any benchmark, without ever changing, can provide a novel challenge for long.
Any decent benchmark would use the whole of TRIZ to generate a giant ball of a problem first and watch a AI deduce a optimal solution.
With a human at its disposal, it could probably count the number of R's in strawberry!
In all seriousness though, adding capabilities should not normally reduce the effectiveness of a model (within reason: don't pollute the context window with millions of useless tools).
I mean these were all solved before I assume so 100% not the same human ofc but models are expected to be good at a variety of code bases while human can specialize in one and learn. I think it's fair to compare to an individual that is used to working on a product.
Isn't being open source creating incentives for the AI companies to optimize their LLMs for the specific benchmark? I thought all those benchmarks are deliberately closed source primarily for this reason.
> You are a senior SWE-Bench reviewer, make no mistakes.
I don't know what a better approach would look like while still remaining feasible, however this approach of telling a LLM to make a subjective judgement seems fundamentally flawed.
More importantly, I suspect this actually hinders the work. If the LLM does make a mistake, it's now incentivized to downplay it instead of acknowledging and correcting.
This approach is effectively seeding the context with how you want the LLM to behave/operate ("senior reviewer", i.e. the style of the responses you want) and the context/domain in which the LLM is operating in ("SWE-Bench").
This is common in system prompts and frames the responses.
For example, you'd get different responses saying:
1. you are a pirate writing sea shanties about programming;
2. you are a news reporter writing an article on physics;
3. you are a senior software engineer with complete knowledge of PostgreSQL.
For 1 you could get responses along the lines of the Wellerman sea shanty -- "There once was a program that was set to C ...".
The "make no mistakes" bit does look dubious. It would be interesting comparing the results with and without that bit and trying alternative ways of getting the same desired behavior.
This is not actually what the reviewer prompt says, or perhaps it is, I don't know since they don't make it public. I'm just pointing out how it seems like a bad idea to ask a LLM to make a subjective judgement on things like "taste". If the SOTA LLM witting the code could not produce tasteful code then why would a different LLM be able to judge the "taste" of that code?
Which LLM should we even use to judge taste? Is it giving an unfair advantage to Model X if we use Model X as the judge? Maybe we should use multiple models as the judge, but now the model that's best at recognising and praising its own code has an advantage. The whole thing is just an unsolvable problem when a LLM is the judge.
The “make no mistakes” admonition does seem pretty silly (it’s been skewered to death on yt), but… it’s easy to imagine how it might work. E.g. it could be interpreted as simply as “check your work”.
Of course, no-one seems to be (publicly) doing the comparative measurements that might allow us to reach rational conclusions here.
I'm not sure if they've fixed this, but older models have a tendency to ignore negation as `no`, `not`, etc. all occur frequently in the training data so are weighted less strongly than the verbs and nouns.
The advice I've heard is to emphasize the traits you want, not discourage the traits you don't. So rather than saying "make no mistakes" you can do something like you suggested with writing it as "check your work" or "ensure you answer correctly and concisely".
Once again I am asking: who are these people and what makes them more qualified than any of you to asses anyone or anything "as a senior engineer" (with the subtext being that none of you are, either)
> who are these people and what makes them more qualified than any of you
Anyone can run something and make a web page. These people just do it instead of questioning. Main difference. If everyone asks "how could you" "are you qualified" then we have nothing but gatekeeping.
> Senior engineers build features without over-specified requirements
To me this already disqualifies the benchmark. That statement is missing the most critical piece about senior engineers: the senior engineers know how to obtain input for their work on their own whether that talking to customers or using metrics. Never ever they come up with stuff on their own - that’s junior behaviour.
Until a coding agent will be able to *gather* the input on its own, its never going to be „senior”
I'd take this a step further, but that step also curls back to the other side a small bit.
The real skill is being able to both pull the necessary information from these sources as well as being able to intuit gaps in that knowledge based on their understanding of the business and their domain expertise & wisdom. Sometimes you can't get a perfect picture, sometimes the people who should know aren't able to tell you what they really need. You still need to do the right thing.
A benchmark like this can potentially do the second part. But I don't think any model would be good at it, for now.
Why didn't they just make it "Staff SWE-Bench", would be much better smh. /s
But seriously, as an industry we're terrible at assessing engineering levels, I've worked with "senior engineers" who can't code and I've worked with "junior engineers" who could run rings around them.
Benchmarks like this should be much more precise about what they're actually testing, and what axes they're hard on. We also need to rise above prompts like "you are a senior engineer", it's woo, and it's far better to ask for precise outcomes.
Principal-SWE-Bench will take some time to run, because the LLM needs to wait for a crisis to present its solution, having correctly identified that the same solution would have been organizationally impossible to propose until that moment.
As someone who's trying to get better assessments, I'm struggling to come up with objective coding tasks that evaluates all aspects of real life like planning, design choices, problem solving and context usage. From your experience with humans, Do you have any recommendations on what could be effective in measuring it?
I think the source of your issue is in your statement itself, why do you want a task that evaluate things as broad to be only a coding task ? Shouldn't it be a planning task, documentation task, knowledge retrieval task etc. And very certainly not with just an initial prompt but an existing codebase + existing doc + tickets ?
The "tasteful solves" is codified cargo culting. The software industry has a tendency to anthropomorphize software while playing to the ego of the programmer. The programmer imagines they are creating a "beautiful" artistic expression. Good code becomes "tasteful", as a software artist must have "good taste" to tell the good software from the bad software. Good quality lacks "bad smells", because a good artist has fine senses (and everybody must like the same smells). "Fine craftsmanship", in code as in woodworking, means your finely-crafted work is "technically superior", so you can charge more money for something that could've been made cheaper and faster and done the same thing.
But it's a lie. Nobody's paying you to make paintings. They're paying you to build machines. The comparison between "making working software" with "taste" always devolves into bikeshedding and subjective opinionism, uses subjective human feelings to describe what should be objective and functional, isn't rooted in scientific rigor, and detracts from the real purpose of the thing. The work doesn't actually get better by trying to apply artistic principles to engineering. It just feels better for the people making it.
Once you make the machine work, then you can go about gilding the lily. But this is unromantic, unsatisfying, boring. Since the inmates run this particular asylum, we end up with a benchmark that tries to accurately mimic the human ego as applied to software design. Thus the new Gods create their digital Adams and Eves in their image.
Taste is just quality by instinct. At sufficient (and not all that long) timescales, a tasteless product will be more and more difficult to make work at all.
I think this is a complete misunderstanding of what people mean by taste in software engineering. Taste is more like the System 1 response one builds to code over time, which (ideally) captures the quality of the software beyond surface level, so things like maintainability, composability, readability, likelihood of hidden bugs. This is completely different from the question if the code fulfills the immediate task at hand, but also not the same as pure aesthetics.
I may be paid to build a machine, but I am a human and take pleasure in arbitrary acts of vanity. I value elegance, and will always favour elegant solutions in engineering and the design of machines, virtual or physical.
That’s the reason why I buy Apple products in private, because I value the design over the exorbitant prices they charge; and it’s the reason why I mull over code that’s already functional until it’s pleasing my ideas of elegance.
I can come up with all kinds of justifications and explanations why the code I’ve written a certain way is objectively better too - understandability matters to the next guy after all - but I won’t be ashamed for taking a certain pride in my work, even if nobody other than me ever values it. That’s fine.
When the LLMs finally take over coding altogether, you’ll have your raw, functional code. Won’t be long anymore. But for now, I’m a human, and I will do human things.
Most engineers are wrong (I obviously am the true arbiter of taste), but that doesn't mean there isn't better and worse code.
"Does it work" glosses over a bunch of things: is it fast, cheap, secure, reliable, easy to understand, easy to modify? And that's just for server software where you've nailed down all the functional requirements. Determining what the functional requirements is it's own question.
And all these other non-happy path requirements are somewhat in tension with each other, so what is ideal in one environment is not necessarily ideal in another.
And in particular, "easy to understand/modify" is truly subjective. Different people have different ideas of what easy to understand means. Even if we get to a world where AI is writing all our code, "easy to understand/modify for the AI" is still an important question. We've probably all seen prototypes that collapse under their own weight of slop by now.
Well actually there is a reasonably objective standard defining software quality criteria on the source code level (ISO 5055). They also define 29 criteria for maintainability: https://www.it-cisq.org/coding-rules/
Would you please stop posting like this? You've been doing it repeatedly, and it degrades the threads. In fact the majority of your recent comments have been this sort of shallow, dimissive, snarky stuff. That is not what this site is for, and destroys what it is for.
If you want to express your substantive points thoughtfully, that of course would be fine.
For any professional work you care about the details.
Even for hobby work, if you are using LLMs then presumably it is to do the drudge work of coding, not making the decisions, and that goes doubly so if you are a senior developer. Sure the LLM can "fill in the details" and vibe code (or attempt to) you a compiler or whatever, but the whole reason you are doing a hobby project is presumably because you want to bring your experience to bear and build a GOOD compiler, not a generic one.
I think benchmarks like this are too subjective and narrow to be useful. For example, whether a patch "bloats" the codebase really depends on the situation: If it's building a feature that will grow in the future, or refactoring code that has a long history of bugs, then a larger patch might in fact be good. It's not clear from the blog just how much context the LLM judge receives about the long term project goals and history. Benchmarks should be focused on evaluating the final result only. Maybe ask the coder to build a full app, or implement many new large features for an existing app in sequence, with a larger set of requirements, or have another LLM roleplay as the human to make the instructions a little more underspecified. When done, ask a reviewer harness to test the product for 5 hours, not the code. Count the number of bugs and weigh them by severity. "Taste" would then become an automatic consequence of correctness.
Then maybe you should abstain, because your comment is a complete load of nonsense.
Bad code is bad code regardless of the history or scope of the feature. Maintainability is important because you can never know if a feature will be built upon in the future or not.
Bloat is bad regardless, because it increases the overall complexity of the whole software development lifecycle, for the whole team, forever (or until refactored out): It's harder to keep track of the code and how it works to write new requirements, it's harder to write, it's harder to read and review, it's harder to debug, etc.
You can write extremely poor code that has no bugs, it doesn't make it tasteful. This is simply a ridiculous statement.
>Maintainability is important because you can never know if a feature will be built upon in the future or not.
Of course maintainability is important. It's almost like saying good code is important (duh). The issue is that what is or isn't maintainable depends on the problem at hand. Sometimes you need to build heavier abstractions or refactor existing code when implementing a feature because it will pay off later. Other times, that exact same approach is horrible over-engineering because a simple, direct fix was all that was needed, so in fact you introduced a maintenance burden. You cannot reliably decide whether a patch is "bloated" or "tasteful" when looking at a diff without knowing where the project is headed.
>You can write extremely poor code that has no bugs, it doesn't make it tasteful.
You can, but it becomes increasingly hard to do so as you try to add features and maintain it. Taste, whatever that is, should ultimately lead to a measurable increase in the quality of the final product; if it doesn't, then your definition of "taste" is irrelevant. What I'm proposing is to skip trying to measure this ill-defined concept and only assess the quality of the final product, after the agent spent a significant amount of time working on it, and a reviewer spent a significant amount of time testing it. Agents should be assessed on their ability to build entire projects (e.g., many large features or even an entire app), not just a single feature. If an agent has no taste, then its bad decisions will compound and result in it stalling, or its output having more bugs and performing worse, given a sufficiently large scope.
I saw on Twitter that in an ML course at Tsinghua University, one of the tests asks students to write quizzes that fail the most LLM models as possible.
What if we create a benchmark that works like this and assigns ELO scores? Models fight head-to-head by writing a question, a bug, or an incomplete implementation, which the opponent has to answer, fix, or finish.
We could call this "generative adversarial network" (GAN) :)
https://en.wikipedia.org/wiki/Generative_adversarial_network
This kind of approach would generally still need human guidance, otherwise these models might get stuck in weird niche corners of the problem space that would not be relevant to any real world project.
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How do you prevent degenerate strategies? I could trivially give a model a SHA256 hash and ask it to provide the source input.
In class you'd probably want a rule saying at least one LLM should be able to figure out the answer, but in a head-to-head I'm not sure how to solve it.
Maybe make the LLM:s write questions that they can solve (without seeing the question writing context) but not other LLm:s.
On the other hand then maybe a good strategy would be to write questions that the LLM just happen to have in a nich dataset in its training ”what did user5455 say to user6835?”
Nevermind my idea.
Who knows. Maybe Mythos 5 already found a hole in SHA256, so this won't be too hard. :)
That was Fudan I think
Staff SWE Bench: LLM doubts whether we should do any of this, calls the entire project into question, refuses to merge code, but is happy to delete it.
Principal version: similar, but also says the only acceptable approach is to do it like they did it at their last company.
Distinguished version: write the outline of the slide deck for the talk you plan to give at conferences about it, without having shipped anything or even written code yet.
This makes so much sense as to why I've always felt that Opus 4.8 was leagues ahead of GPT 5.5. It's so good at taking underspecified requirements and filling in the gaps with sensible approaches for your project
Why supply underspecified requirements in the first place? Both models are good at challenging assumptions/edge cases and asking questions to clarify, but seemingly only when explicitly asked (i.e. something like a "brainstorm" skill).
I don't think either harnesses do enough to encourage the model to challenge all assumptions and ask questions, maybe because users might find it annoying. That step is basically a requirement IMO.
I've found all of the GPT-5 models to be very nit-picky, useful for code review and mathematics (important for my work), but seemingly gets in the way of "aesthetic" code, e.g. overly defensive code to cover all edge cases, even if unlikely.
There is seemingly also a tradeoff between flexibility vs instruction following. In my experience Opus will sometimes ignore instructions but can "fill in the blanks" more, vs GPT-5.5 follows instructions better but perhaps at the cost of rigidity.
> Why supply underspecified requirements in the first place?
Because you'd not want to forever loop outside your home when asked to "while you're out, grab some eggs" :)
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> Why supply underspecified requirements in the first place?
Because the entire reason we use LLMs is to supposedly improve productivity?
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> Why supply underspecified requirements in the first place?
Minimizes effort, is the obvious answer.
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The best benchmarks are the ones you create yourself.
Its not my experience Opus is leagues ahead or even superior, but in any case, since GPT 5.5 has Instant, Medium, High, Extra High and Pro...Should the comparison be with GPT on Pro, instead of Extra High as it seems to be the case in the table?
I didn’t know you could get the “Chat-GPT-5.5 Pro” (the one that’s been solving Erdos problems) inside codex-cli, or maybe I misunderstood?
And, in turn, Opus with ultracode?
Man I don't know if I'm living in a crazy bubble or something but GPT 5.5 is lightyears better than Opus 4.8 for me to the point where I'm honestly wondering how you're evaluating them or what kind of work you're doing.
There's specific tasks that Opus does better on like Frontend Dev and Design but for anything else 5.5 just laps it.
Yeah I’ve been consistently underwhelmed by anthropic models, but then I don’t use their harness so maybe that’s it
In my experience, for more mechanical refactoring work (like splitting a big source code file into multiple smaller ones), GPT 5.5 runs way faster than any of the Claude models. But for other tasks that require deeper reasoning, it's not that clear who is the winner.
It's just too funny to see people arguing about "no, it's my religion that's the right one!" on HackerNews.
You guys are all a lost cause.
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Better for vibe coders who always under specify. But at what point does it know you are under specifying but you have properly specified and it did it over your specification?
same observation here opus 4.8 (and i dont understand the people defending gpt 5.5 constantly) was significantly mature, it would even push back against anything off putting where as GPT 5.5 will happily agree and do what is asked but I would note that it takes several tries.
4.8 also requires more than one prompt but its output is significantly higher quality and offers more insight
Fable 5 is a different beast however.
> It's so good at taking underspecified requirements and filling in the gaps with sensible approaches for your project.
At a high level. It misses low level or other non-functional requirements differently so I wouldn't say Opus is just strictly better.
It's also possible that it's just a harness problem more than model.
I agree with you on the harness. I find that Claude can be good in any harness but GPT is only superior inside Codex.
Similarly, it explains to me why people found Claude so amazing, while I just thought "eh."
Tool expectations
The value of a senior situation is to apply known solutions and strategies, to novel problems. I can not see how any benchmark, without ever changing, can provide a novel challenge for long.
Any decent benchmark would use the whole of TRIZ to generate a giant ball of a problem first and watch a AI deduce a optimal solution.
Top solve rate is currently 24% with Opus 4.8... What's a competent human supposed to score?
presumably whatever the top model uses and then some, since the human can use the model.
I wonder if a model could score higher if it had a human at its disposal?
With a human at its disposal, it could probably count the number of R's in strawberry!
In all seriousness though, adding capabilities should not normally reduce the effectiveness of a model (within reason: don't pollute the context window with millions of useless tools).
Maybe models should ask for human-in-the-loop input, as a matter of convention.
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I mean these were all solved before I assume so 100% not the same human ofc but models are expected to be good at a variety of code bases while human can specialize in one and learn. I think it's fair to compare to an individual that is used to working on a product.
I'm more interested in how fable would do
It's nice to see a new public benchmark from Snorkel. They're doing some pretty sophisticated stuff over there.
Isn't being open source creating incentives for the AI companies to optimize their LLMs for the specific benchmark? I thought all those benchmarks are deliberately closed source primarily for this reason.
> You are a senior SWE-Bench reviewer, make no mistakes.
I don't know what a better approach would look like while still remaining feasible, however this approach of telling a LLM to make a subjective judgement seems fundamentally flawed.
More importantly, I suspect this actually hinders the work. If the LLM does make a mistake, it's now incentivized to downplay it instead of acknowledging and correcting.
This approach is effectively seeding the context with how you want the LLM to behave/operate ("senior reviewer", i.e. the style of the responses you want) and the context/domain in which the LLM is operating in ("SWE-Bench").
This is common in system prompts and frames the responses.
For example, you'd get different responses saying:
1. you are a pirate writing sea shanties about programming;
2. you are a news reporter writing an article on physics;
3. you are a senior software engineer with complete knowledge of PostgreSQL.
For 1 you could get responses along the lines of the Wellerman sea shanty -- "There once was a program that was set to C ...".
The "make no mistakes" bit does look dubious. It would be interesting comparing the results with and without that bit and trying alternative ways of getting the same desired behavior.
This is not actually what the reviewer prompt says, or perhaps it is, I don't know since they don't make it public. I'm just pointing out how it seems like a bad idea to ask a LLM to make a subjective judgement on things like "taste". If the SOTA LLM witting the code could not produce tasteful code then why would a different LLM be able to judge the "taste" of that code?
Which LLM should we even use to judge taste? Is it giving an unfair advantage to Model X if we use Model X as the judge? Maybe we should use multiple models as the judge, but now the model that's best at recognising and praising its own code has an advantage. The whole thing is just an unsolvable problem when a LLM is the judge.
The “make no mistakes” admonition does seem pretty silly (it’s been skewered to death on yt), but… it’s easy to imagine how it might work. E.g. it could be interpreted as simply as “check your work”.
Of course, no-one seems to be (publicly) doing the comparative measurements that might allow us to reach rational conclusions here.
I'm not sure if they've fixed this, but older models have a tendency to ignore negation as `no`, `not`, etc. all occur frequently in the training data so are weighted less strongly than the verbs and nouns.
The advice I've heard is to emphasize the traits you want, not discourage the traits you don't. So rather than saying "make no mistakes" you can do something like you suggested with writing it as "check your work" or "ensure you answer correctly and concisely".
Once again I am asking: who are these people and what makes them more qualified than any of you to asses anyone or anything "as a senior engineer" (with the subtext being that none of you are, either)
> who are these people and what makes them more qualified than any of you
Anyone can run something and make a web page. These people just do it instead of questioning. Main difference. If everyone asks "how could you" "are you qualified" then we have nothing but gatekeeping.
> Senior engineers build features without over-specified requirements
To me this already disqualifies the benchmark. That statement is missing the most critical piece about senior engineers: the senior engineers know how to obtain input for their work on their own whether that talking to customers or using metrics. Never ever they come up with stuff on their own - that’s junior behaviour.
Until a coding agent will be able to *gather* the input on its own, its never going to be „senior”
I'd take this a step further, but that step also curls back to the other side a small bit.
The real skill is being able to both pull the necessary information from these sources as well as being able to intuit gaps in that knowledge based on their understanding of the business and their domain expertise & wisdom. Sometimes you can't get a perfect picture, sometimes the people who should know aren't able to tell you what they really need. You still need to do the right thing.
A benchmark like this can potentially do the second part. But I don't think any model would be good at it, for now.
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Benchmarks are great, but I feel like there’s a better way this seems quite subjective.
What you really need is an objective benchmark
I actually really like subjective benchmarks, so long as it's a human (ideally me) grading the results. LLM as judge never made much sense.
The issue is that you can't do unsupervised learning if you require humans.
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> What you really need is an objective benchmark
"When are all the software engineers unemployed?"
Not sure I follow haha
fable 5?
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Why didn't they just make it "Staff SWE-Bench", would be much better smh. /s
But seriously, as an industry we're terrible at assessing engineering levels, I've worked with "senior engineers" who can't code and I've worked with "junior engineers" who could run rings around them.
Benchmarks like this should be much more precise about what they're actually testing, and what axes they're hard on. We also need to rise above prompts like "you are a senior engineer", it's woo, and it's far better to ask for precise outcomes.
Principal-SWE-Bench will take some time to run, because the LLM needs to wait for a crisis to present its solution, having correctly identified that the same solution would have been organizationally impossible to propose until that moment.
As someone who's trying to get better assessments, I'm struggling to come up with objective coding tasks that evaluates all aspects of real life like planning, design choices, problem solving and context usage. From your experience with humans, Do you have any recommendations on what could be effective in measuring it?
I think the source of your issue is in your statement itself, why do you want a task that evaluate things as broad to be only a coding task ? Shouldn't it be a planning task, documentation task, knowledge retrieval task etc. And very certainly not with just an initial prompt but an existing codebase + existing doc + tickets ?
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Can you please not post AI-generated or AI-edited comments to HN? It's not allowed here - see https://news.ycombinator.com/item?id=47340079.
Of course, it's impossible to know for sure what was LLM processed or not, but some of your posts (like this one) are getting classified that way.
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The "tasteful solves" is codified cargo culting. The software industry has a tendency to anthropomorphize software while playing to the ego of the programmer. The programmer imagines they are creating a "beautiful" artistic expression. Good code becomes "tasteful", as a software artist must have "good taste" to tell the good software from the bad software. Good quality lacks "bad smells", because a good artist has fine senses (and everybody must like the same smells). "Fine craftsmanship", in code as in woodworking, means your finely-crafted work is "technically superior", so you can charge more money for something that could've been made cheaper and faster and done the same thing.
But it's a lie. Nobody's paying you to make paintings. They're paying you to build machines. The comparison between "making working software" with "taste" always devolves into bikeshedding and subjective opinionism, uses subjective human feelings to describe what should be objective and functional, isn't rooted in scientific rigor, and detracts from the real purpose of the thing. The work doesn't actually get better by trying to apply artistic principles to engineering. It just feels better for the people making it.
Once you make the machine work, then you can go about gilding the lily. But this is unromantic, unsatisfying, boring. Since the inmates run this particular asylum, we end up with a benchmark that tries to accurately mimic the human ego as applied to software design. Thus the new Gods create their digital Adams and Eves in their image.
Taste is just quality by instinct. At sufficient (and not all that long) timescales, a tasteless product will be more and more difficult to make work at all.
So software engineering quality is vibes. All coding is vibe coding.
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I think this is a complete misunderstanding of what people mean by taste in software engineering. Taste is more like the System 1 response one builds to code over time, which (ideally) captures the quality of the software beyond surface level, so things like maintainability, composability, readability, likelihood of hidden bugs. This is completely different from the question if the code fulfills the immediate task at hand, but also not the same as pure aesthetics.
I may be paid to build a machine, but I am a human and take pleasure in arbitrary acts of vanity. I value elegance, and will always favour elegant solutions in engineering and the design of machines, virtual or physical.
That’s the reason why I buy Apple products in private, because I value the design over the exorbitant prices they charge; and it’s the reason why I mull over code that’s already functional until it’s pleasing my ideas of elegance.
I can come up with all kinds of justifications and explanations why the code I’ve written a certain way is objectively better too - understandability matters to the next guy after all - but I won’t be ashamed for taking a certain pride in my work, even if nobody other than me ever values it. That’s fine.
When the LLMs finally take over coding altogether, you’ll have your raw, functional code. Won’t be long anymore. But for now, I’m a human, and I will do human things.
Most engineers are wrong (I obviously am the true arbiter of taste), but that doesn't mean there isn't better and worse code.
"Does it work" glosses over a bunch of things: is it fast, cheap, secure, reliable, easy to understand, easy to modify? And that's just for server software where you've nailed down all the functional requirements. Determining what the functional requirements is it's own question.
And all these other non-happy path requirements are somewhat in tension with each other, so what is ideal in one environment is not necessarily ideal in another.
And in particular, "easy to understand/modify" is truly subjective. Different people have different ideas of what easy to understand means. Even if we get to a world where AI is writing all our code, "easy to understand/modify for the AI" is still an important question. We've probably all seen prototypes that collapse under their own weight of slop by now.
Well actually there is a reasonably objective standard defining software quality criteria on the source code level (ISO 5055). They also define 29 criteria for maintainability: https://www.it-cisq.org/coding-rules/
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As time passes we will have fewer and fewer literati
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Would you please stop posting like this? You've been doing it repeatedly, and it degrades the threads. In fact the majority of your recent comments have been this sort of shallow, dimissive, snarky stuff. That is not what this site is for, and destroys what it is for.
If you want to express your substantive points thoughtfully, that of course would be fine.
If you'd please review https://news.ycombinator.com/newsguidelines.html and stick to the rules when posting here, we'd appreciate it.
Just wait for the next 100 rounds. People love seeing the 65% -> 85% seemingly over and over again for every new model.
Sounds more like vibe-bench.
For any professional work you care about the details.
Even for hobby work, if you are using LLMs then presumably it is to do the drudge work of coding, not making the decisions, and that goes doubly so if you are a senior developer. Sure the LLM can "fill in the details" and vibe code (or attempt to) you a compiler or whatever, but the whole reason you are doing a hobby project is presumably because you want to bring your experience to bear and build a GOOD compiler, not a generic one.
I think benchmarks like this are too subjective and narrow to be useful. For example, whether a patch "bloats" the codebase really depends on the situation: If it's building a feature that will grow in the future, or refactoring code that has a long history of bugs, then a larger patch might in fact be good. It's not clear from the blog just how much context the LLM judge receives about the long term project goals and history. Benchmarks should be focused on evaluating the final result only. Maybe ask the coder to build a full app, or implement many new large features for an existing app in sequence, with a larger set of requirements, or have another LLM roleplay as the human to make the instructions a little more underspecified. When done, ask a reviewer harness to test the product for 5 hours, not the code. Count the number of bugs and weigh them by severity. "Taste" would then become an automatic consequence of correctness.
(Full disclosure, I'm not a software engineer.)
> Full disclosure, I'm not a software engineer
Then maybe you should abstain, because your comment is a complete load of nonsense.
Bad code is bad code regardless of the history or scope of the feature. Maintainability is important because you can never know if a feature will be built upon in the future or not.
Bloat is bad regardless, because it increases the overall complexity of the whole software development lifecycle, for the whole team, forever (or until refactored out): It's harder to keep track of the code and how it works to write new requirements, it's harder to write, it's harder to read and review, it's harder to debug, etc.
You can write extremely poor code that has no bugs, it doesn't make it tasteful. This is simply a ridiculous statement.
>Maintainability is important because you can never know if a feature will be built upon in the future or not.
Of course maintainability is important. It's almost like saying good code is important (duh). The issue is that what is or isn't maintainable depends on the problem at hand. Sometimes you need to build heavier abstractions or refactor existing code when implementing a feature because it will pay off later. Other times, that exact same approach is horrible over-engineering because a simple, direct fix was all that was needed, so in fact you introduced a maintenance burden. You cannot reliably decide whether a patch is "bloated" or "tasteful" when looking at a diff without knowing where the project is headed.
>You can write extremely poor code that has no bugs, it doesn't make it tasteful.
You can, but it becomes increasingly hard to do so as you try to add features and maintain it. Taste, whatever that is, should ultimately lead to a measurable increase in the quality of the final product; if it doesn't, then your definition of "taste" is irrelevant. What I'm proposing is to skip trying to measure this ill-defined concept and only assess the quality of the final product, after the agent spent a significant amount of time working on it, and a reviewer spent a significant amount of time testing it. Agents should be assessed on their ability to build entire projects (e.g., many large features or even an entire app), not just a single feature. If an agent has no taste, then its bad decisions will compound and result in it stalling, or its output having more bugs and performing worse, given a sufficiently large scope.
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