Boring is good(jenson.org)
jenson.org
Boring is good
https://jenson.org/boring/
76 comments
What you describe is the difference between tedious and simple.
Boring is good. I don't want to be excited by technology, i want to be bored in the sense that it's simple and out of my way.
Same for KISS. I tend to tell people to not only keep things simple, but boring even. Some new code i need to read and fix or extend? I want to be bored. Bored means it's obvious and simple.
The difference? There are many complex libraries. By definition they are not simple technology.
For example a crypto library. Probably one of the most complex tasks. I would consider it a good library if it's boring to use/extend/read.
Boring is good. I don't want to be excited by technology, i want to be bored in the sense that it's simple and out of my way.
Same for KISS. I tend to tell people to not only keep things simple, but boring even. Some new code i need to read and fix or extend? I want to be bored. Bored means it's obvious and simple.
The difference? There are many complex libraries. By definition they are not simple technology.
For example a crypto library. Probably one of the most complex tasks. I would consider it a good library if it's boring to use/extend/read.
> The author of "Choose boring technology" regretted the choice of the word "boring"
Well, yes, but only in the sense that people kept giving him beef about how boring is a bad word in their mind, not because it was a bad word for this context per se. Which is somewhat ironic given your comment!
I suppose what you're getting at is the difference between boring, and "boooooriiiiiing".
Well, yes, but only in the sense that people kept giving him beef about how boring is a bad word in their mind, not because it was a bad word for this context per se. Which is somewhat ironic given your comment!
I suppose what you're getting at is the difference between boring, and "boooooriiiiiing".
If “boring” coveys the wrong, and at times even opposite meaning in listeners ears, then “boring” was a bad word choice, even in context.
There is no good choice of words, because you can not control how people interpret language.
Everything at some point will be interpreted incorrectly.
Everything at some point will be interpreted incorrectly.
By that logic all communication is impossible.
In reality, a choice of word that is interpreted close to correctly by most people is a better choice of words than a choice of word that is interpreted close to incorrectly by most people.
In reality, a choice of word that is interpreted close to correctly by most people is a better choice of words than a choice of word that is interpreted close to incorrectly by most people.
> By that logic all communication is impossible
I interpreted it as implying all communication is imperfect, more or less. Each speaker and listener uses language from their own personal context, and reads meaning into each word from their experience, education, even personal preferences.
As for "boring", I feel it was the right word at the time. It's a rhetorical trick, maybe a click bait, to use a word in an unusual way to get attention. Here the word boring, typically negative, is used in a positive encouraging manner. That makes me want to read more to find out why.
I interpreted it as implying all communication is imperfect, more or less. Each speaker and listener uses language from their own personal context, and reads meaning into each word from their experience, education, even personal preferences.
As for "boring", I feel it was the right word at the time. It's a rhetorical trick, maybe a click bait, to use a word in an unusual way to get attention. Here the word boring, typically negative, is used in a positive encouraging manner. That makes me want to read more to find out why.
Some people will interpret any negativity about things they're a fan of as a personal insult. You can't fix that, as people can always choose to be offended.
An optimum might exist, but it might not be meaningfully better than any other option.
An optimum might exist, but it might not be meaningfully better than any other option.
Why conflate boring with old? "Boring" in this context means: proven and stable. Yes, that would take some time to become apparent, but the converse is not necessarily the case: a tech does not become "boring" in a good way simply because it is old.
All this was my understanding before, so not sure why you think "boring" was meant to be equivalent to "old"
All this was my understanding before, so not sure why you think "boring" was meant to be equivalent to "old"
Problem is official definition is „not interesting, tedious” with synonym „dull”.
As much as I do get the idea, I can see how promoting use of tedious to use and dull tools is something that really misses the mark.
Well known and mature tools are still sharp and lots of them are not tedious to use.
As much as I do get the idea, I can see how promoting use of tedious to use and dull tools is something that really misses the mark.
Well known and mature tools are still sharp and lots of them are not tedious to use.
What is the opposite of dull? Exiting?
I do not want my browser to be exiting. I do not want for it to change every week. Say moving buttons to different places. Changing how address bar operates. Maybe trying new short cut keys...
Same goes for most useful software. I actually do want them to be dull. And do their job and not get in between and make my day more interesting by having to fight against it.
I do not want my browser to be exiting. I do not want for it to change every week. Say moving buttons to different places. Changing how address bar operates. Maybe trying new short cut keys...
Same goes for most useful software. I actually do want them to be dull. And do their job and not get in between and make my day more interesting by having to fight against it.
Well known and mature tools are still sharp and lots of them are not tedious to use..
I picked "sharp" not "exciting".
Dull knife doesn't do its job, you want tools that do the job efficiently. "Boring" as it seems was interpreted as picking tools that don't do the job efficiently. That is why original idea creator found "Choose boring technology" most likely misunderstood.
I picked "sharp" not "exciting".
Dull knife doesn't do its job, you want tools that do the job efficiently. "Boring" as it seems was interpreted as picking tools that don't do the job efficiently. That is why original idea creator found "Choose boring technology" most likely misunderstood.
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What you just described fits my definition of boring, which is some function of (time passed, individual at keyboard)
Cobol was (and for some, still is) exciting at first, but _becomes_ boring once you master it, and the ecosystem evolves to fix or work around its shortcomings. Believe it or not, even UX/UI testers can deal with and find happiness in clicking through UIs for the tenth thousand time (sure, last time I saw such Tester, was at around 2010).
This doesn't mean the technology itself becomes bad or stays good. It just means the understanding (and usage patterns) solidifies, so it becomes less exciting, hence: "boring".
But you can't sell a book with the title "Choose well-established technology". Because people would be like, no sht, Sherlock, I don't need a book to know that.
Cobol was (and for some, still is) exciting at first, but _becomes_ boring once you master it, and the ecosystem evolves to fix or work around its shortcomings. Believe it or not, even UX/UI testers can deal with and find happiness in clicking through UIs for the tenth thousand time (sure, last time I saw such Tester, was at around 2010).
This doesn't mean the technology itself becomes bad or stays good. It just means the understanding (and usage patterns) solidifies, so it becomes less exciting, hence: "boring".
But you can't sell a book with the title "Choose well-established technology". Because people would be like, no sht, Sherlock, I don't need a book to know that.
> Go's error handling is boring in a bad way. Manually clicking through screens again and again because you failed to write UI tests is boring in a bad way.
Go's error handling is useful if your system needs to be correct more than it needs to be available, and you should be manually clicking through screens even if you wrote UI tests.
Looking at why something is boring and who it's boring for can be helpful, especially in a team setting. It's entirely possible to automate away a bottleneck that exists for a good reason while sitting across from someone who'd love to handle the issue correctly.
Go's error handling is useful if your system needs to be correct more than it needs to be available, and you should be manually clicking through screens even if you wrote UI tests.
Looking at why something is boring and who it's boring for can be helpful, especially in a team setting. It's entirely possible to automate away a bottleneck that exists for a good reason while sitting across from someone who'd love to handle the issue correctly.
between HTTP/1.1 to HTTP/2+ interoperability issues, HTTP/2+ configuration, TLS configuration, HTTPS is hardly boring and does what it says on the tin every time.
I tend to think that the reason people over-index on complex use-cases for LLMs is actually reliability, not a lack of interest in boring projects.
If an LLM can solve a complex problem 50% of the time, then that is still very valuable. But if you are writing a system of small LLMs doing small tasks, then even 1% error rates can compound into highly unreliable systems when stacked together.
The cost of LLMs occasionally giving you wrong answers is worth it for answers to harder tasks, in a way that it is not worth it for smaller tasks. For those smaller tasks, usually you can get much closer to 100% reliability, and more importantly much greater predictability, with hand-engineered code. This makes it much harder to find areas where small LLMs can add value for small boring tasks. Better auto-complete is the only real-world example I can think of.
If an LLM can solve a complex problem 50% of the time, then that is still very valuable. But if you are writing a system of small LLMs doing small tasks, then even 1% error rates can compound into highly unreliable systems when stacked together.
The cost of LLMs occasionally giving you wrong answers is worth it for answers to harder tasks, in a way that it is not worth it for smaller tasks. For those smaller tasks, usually you can get much closer to 100% reliability, and more importantly much greater predictability, with hand-engineered code. This makes it much harder to find areas where small LLMs can add value for small boring tasks. Better auto-complete is the only real-world example I can think of.
>If an LLM can solve a complex problem 50% of the time, then that is still very valuable
I'd adjust that statement - If an LLM can solve a complex problem 50% of the time and I can evaluate correctness of the output, then that is still very valuable. I've seen too many people blindly pass on LLM output - for a short while it was a trend in the scientific literature to have LLMs evaluate output of other LLMs? Who knows how correct that was. Luckily that has ended.
I'd adjust that statement - If an LLM can solve a complex problem 50% of the time and I can evaluate correctness of the output, then that is still very valuable. I've seen too many people blindly pass on LLM output - for a short while it was a trend in the scientific literature to have LLMs evaluate output of other LLMs? Who knows how correct that was. Luckily that has ended.
> I've seen too many people blindly pass on LLM output
I misread this the first time and realised both interpretations are happening. I've seen people copy-paste out of ChatGPT without reading, and I've seen people "pass on" or reject content simply because it has been AI generated.
I misread this the first time and realised both interpretations are happening. I've seen people copy-paste out of ChatGPT without reading, and I've seen people "pass on" or reject content simply because it has been AI generated.
True! This is what has me more excited about LLMs producing Lean proofs than written maths proofs. The Lean proofs can be proved to be correct, whereas the maths proofs require experts to verify them and look for mistakes.
That said, I do think there are lots of problems where verification is easier than doing the task itself, especially in computer science. I think it is easier to list tasks that aren't easier to verify than to do from scratch actually. Security is one major one.
That said, I do think there are lots of problems where verification is easier than doing the task itself, especially in computer science. I think it is easier to list tasks that aren't easier to verify than to do from scratch actually. Security is one major one.
Even there it's risky. LLMs are good at subtly misstating the problem, so it's relatively easy to make them prove things which look like the thing you wanted but which are mostly unrelated.
Yes, Lean only lets you be confident in the contents of the proof, not how it was formed. But, I still think that's pretty cool and valuable.
> for a short while it was a trend in the scientific literature to have LLMs evaluate output of other LLMs? Who knows how correct that was.
Highly reliable. So much so that is basically how modern LLMs work internally. Also speaking from personal experience in the projects I work on, it is the chief way to counteract hallucination, poisoned context windows, and scaling beyond the interaction limit.
LLMs evaluating LLM output works surprisingly well.
Highly reliable. So much so that is basically how modern LLMs work internally. Also speaking from personal experience in the projects I work on, it is the chief way to counteract hallucination, poisoned context windows, and scaling beyond the interaction limit.
LLMs evaluating LLM output works surprisingly well.
> Who knows how correct that was. Luckily that has ended.
What do you mean it ended? I still see tons of NLP papers with this methodology.
What do you mean it ended? I still see tons of NLP papers with this methodology.
Yeah. Is it even proven that LLMs don't hallucinate for smaller tasks? The author seems to imply that. I fail to see how it could be true.
No? That is trivially not the case. Ask an LLM something outside its training data and it will hallucinate the answer. How can it do anything else? Maybe its hallucination ends up being correct, but not all of the time.
I feel that with LLMs and AI, people are furiously trying to argue the reality they desire into existence. I've never read more articles predicting the future than on this topic (I am guilty of it, too.)
Predicting AGI and all those "complete replacement of jobs" are boring example of this. What's more annoying to me are people who do that arguing into existence about reality they want where AI is completely useless and fake and can't do anything and make you stupid and ten more loudest clickbait titles of recent year
>I feel that with LLMs and AI, people are furiously trying to argue the reality they desire into existence.
The emperor's new clothes ...
The emperor's new clothes ...
In that story no one said anything until some kid. With this one there's a chorus saying the emperor doesn't have clothes, won't have clothes, see my 2018 paper Emperor's Clothes: A Critical Appraisal and so on.
hee hee hee, tim333!
take your non-existent 2018 "paper" and shove it up your 2025 ass, you troll, if you even have an ass, that is.
and see my critical appraisal of your so-called "critical appraisal", just above.
check out that rudyard kipling poem (or some such racist colonial brit dick), where he says ... something something ... and you'll be a man, my son.
you'll never be a man, troll. you don't have the gumption to ever be one.
gesundheit! bless you, coz u really need it ... boy.
take your non-existent 2018 "paper" and shove it up your 2025 ass, you troll, if you even have an ass, that is.
and see my critical appraisal of your so-called "critical appraisal", just above.
check out that rudyard kipling poem (or some such racist colonial brit dick), where he says ... something something ... and you'll be a man, my son.
you'll never be a man, troll. you don't have the gumption to ever be one.
gesundheit! bless you, coz u really need it ... boy.
here is the reference, for your reading displeasure:
https://en.m.wikipedia.org/wiki/If%E2%80%94
https://en.m.wikipedia.org/wiki/If%E2%80%94
what is wrong with you?
"LLMs are not intelligent and they never will be."
If he means they will never outperform humans at cognitive or robotics tasks, that's a strong claim!
If he just means they aren't conscious... then let's don't debate it any more here. :-)
I agree that we could be in a bubble at the moment though.
If he means they will never outperform humans at cognitive or robotics tasks, that's a strong claim!
If he just means they aren't conscious... then let's don't debate it any more here. :-)
I agree that we could be in a bubble at the moment though.
I have heard people on both ends of the spectrum:
- LLM's are too limited in capabilities and make too many mistakes - We're still in the DOS era of LLM's
I'm leaning more towards the the 2nd, but in either case pandora's box has been opened and you can already see the effects of the direction our civilization is moving towards with this technology.
- LLM's are too limited in capabilities and make too many mistakes - We're still in the DOS era of LLM's
I'm leaning more towards the the 2nd, but in either case pandora's box has been opened and you can already see the effects of the direction our civilization is moving towards with this technology.
The word “never” is a dangerous one. I remember that computers would “never” beat humans at chess. And people would never use the internet for banking.
I think this is, essentially, a wishful take. The biggest barrier to models being able to do more advanced knowledge work is creating appropriately annotated training data, followed by a few specific technical improvements the labs are working on. Models have already nearly maxed out "work on a well-defined puzzle that can be feasibly solved in a few hours" -- stunning! -- and now labs will turn to expanding other dimensions.
There are plenty of ways of writing more capable software stacks using LLMs which don’t rely on reinforcement learning. If anything, the AI labs have too much of a focus on building larger models with bigger or better sets of labeled data, where algorithmic changes will let you do more with the same tools.
To slightly amend my original take, much of this data is specifically for evals. That is, the labs need some sort of data set and grading scheme by which they can measure and direct their progress.
LLMs are useful in contexts where fuzzy and hazily accurate is acceptable. A developer trying to hack together some solution through trial and error for example.
They are less useful in contexts where accuracy is expected or legally required. An audit log for example.
Many businesses have made bad judgements of where the distinction is, some don't even recognise a distinction. This will improve over time.
Many businesses have made bad judgements of where the distinction is, some don't even recognise a distinction. This will improve over time.
OT: Since the author is a former Apple UX designer who worked on the Human Interface Guidelines, I hope he shares his thoughts on the recent macOS 26 and iOS updates - especially on Liquid Glass.
https://jenson.org/about-scott/
https://jenson.org/about-scott/
I like this article, and I didn't expect to because there's been volumes written about how you should be boring and building things in an interesting way just for the hell of it, is bad (something I don't agree with).
Small models doing interesting (boring to the author) use-cases is a fine frontier!
I don't agree at all with this though:
> "LLMs are not intelligent and they never will be."
LLMs already write code better than most humans. The problem is we expect them to one-shot things that a human may spend many hours/days/weeks/months doing. We're lacking coordination for long-term LLM work. The models themselves are probably even more powerful than we realize, we just need to get them to "think" as long as a human would.
Small models doing interesting (boring to the author) use-cases is a fine frontier!
I don't agree at all with this though:
> "LLMs are not intelligent and they never will be."
LLMs already write code better than most humans. The problem is we expect them to one-shot things that a human may spend many hours/days/weeks/months doing. We're lacking coordination for long-term LLM work. The models themselves are probably even more powerful than we realize, we just need to get them to "think" as long as a human would.
> LLMs already write code better than most humans.
If you mean better than most humans considering the set of all humans, sure. But they write code worse than most humans who have learned how to write code. That's not very promising for them developing intelligence.
If you mean better than most humans considering the set of all humans, sure. But they write code worse than most humans who have learned how to write code. That's not very promising for them developing intelligence.
I have been writing code for nearly 30 years. I have my name on a couple of ingenious patents.
I have fully integrated LLMs into May daily workflow, and am routinely surprised at the innovative solutions that Claude sometimes comes up with. There is a lot of generated code that is far beyond my own capabilities, even as an expert in the field in which I’m working.
I have fully integrated LLMs into May daily workflow, and am routinely surprised at the innovative solutions that Claude sometimes comes up with. There is a lot of generated code that is far beyond my own capabilities, even as an expert in the field in which I’m working.
The issue is one that's been stated here before: LLMs are language models. They are not world models. They are not problem models. They do not actually understand world or the underlying entities represented by language, or the problems being addressed. LLMs understand the shape of a correct answer, and how the components of language fit together to form a correct answer. They do that because they have seen enough language to know what correct answers look like.
In human terms, we would call that knowing how to bullshit. But just like a college student hitting junior year, sooner or later you'll learn that bullshitting only gets you so far.
That's what we've really done. We've taught computers how to bullshit. We've also managed to finally invent something that lets us communicate relatively directly with a computer using human languages. The language processing capabilities of an LLM are an astonishing multi-generational leap. These types of models will absolutely be the foundation for computing interfaces in the future. But they're still language models.
To me it feels like we've invented a new keyboard, and people are fascinated by the stories the thing produces.
In human terms, we would call that knowing how to bullshit. But just like a college student hitting junior year, sooner or later you'll learn that bullshitting only gets you so far.
That's what we've really done. We've taught computers how to bullshit. We've also managed to finally invent something that lets us communicate relatively directly with a computer using human languages. The language processing capabilities of an LLM are an astonishing multi-generational leap. These types of models will absolutely be the foundation for computing interfaces in the future. But they're still language models.
To me it feels like we've invented a new keyboard, and people are fascinated by the stories the thing produces.
Is it bullshitting to perform nearly perfect language to language translation or to generate photorealistic depictions from text quite reliably? or to reliably perform named entity extraction or any of the other millions of real-world tasks LLMs already perform quite well?
Picking another task like translation which doesn't really require any knowledge outside of language processing is not a particularly good way to convince me that LLMs are doing anything other than language processing. Additionally, "near perfect" is a bit overselling it, IMX, given that they still struggle with idioms and cultural expressions.
Image generation is a bit better, except it's still not really aware of what the picture is, either. It's aware of what images are described as by others, let alone the truth of the generated image. It makes pictures of dragons quite well, but if you ask it for a contour map of a region, is it going to represent it accurately? It's not concerned about truth, it's concerned about truthiness or the appearance of truth. We know when that distinction is important. It doesn't.
Image generation is a bit better, except it's still not really aware of what the picture is, either. It's aware of what images are described as by others, let alone the truth of the generated image. It makes pictures of dragons quite well, but if you ask it for a contour map of a region, is it going to represent it accurately? It's not concerned about truth, it's concerned about truthiness or the appearance of truth. We know when that distinction is important. It doesn't.
I could make the exact same argument about the activation loops happening in your brain when you typed this out.
Transformer architectures are not replicas of human brain architrcture, but they are not categorically different either.
Transformer architectures are not replicas of human brain architrcture, but they are not categorically different either.
You could, but it would be a wrong argument. Animals, and presumably early enough humans for at least a while, had no language, yet still manage to interact with and understand the world. We don't learn solely by reading with ingestion of text being our only experience of anything.
For what it's worth, this is not some kind of slam dunk fundamental permanent limitation of AI that is constructed from LLMs. Multi-modal learning gets you part of the way. Tool use gets you more of the way, enabling interaction with at least something. Embodiment and autonomy would get you further, but at some point, you need a true online learner that can update its own weights and not just simulate that with a very large context window.
Whether or not this entails any limitation in capability (as in, there is anything a human or animal can actually do cognitively that a LLM AI can't) is an open question, but it is a real difference. The person you're responding to, no matter how similar their activation loops may be to software, didn't develop all behaviors and predictive modeling they currently do by reading and then had their brain frozen in time.
For what it's worth, this is not some kind of slam dunk fundamental permanent limitation of AI that is constructed from LLMs. Multi-modal learning gets you part of the way. Tool use gets you more of the way, enabling interaction with at least something. Embodiment and autonomy would get you further, but at some point, you need a true online learner that can update its own weights and not just simulate that with a very large context window.
Whether or not this entails any limitation in capability (as in, there is anything a human or animal can actually do cognitively that a LLM AI can't) is an open question, but it is a real difference. The person you're responding to, no matter how similar their activation loops may be to software, didn't develop all behaviors and predictive modeling they currently do by reading and then had their brain frozen in time.
Bullshitting generally gets people farther than anything else.
THIS !
"think" is doing a lot of heavy lifting there. Let's remember that neural networks are designed primarily not as an accurate brain models but as matrix models that can be calculated on in a feasible time scale on GPUs.
Great take. I personally find the thought of spec-driven development tedious and boring. But maybe that’s a good thing.
This might be slightly of topic but this article mentions the stat that keeps going around that refutes productivity gains given by LLM’s, but honestly I think this is because this is reported by companies that are either measuring time to deliverable completion, or value in dollars generated by their workers using the technology.
They always seem to miss another metric, saved human brain power. My experience hasn’t been that I can complete tasks faster from go to woe, it’s been that they’re far less mentally taxing and I can do other things whilst the ai churns some of the tedium away. When I’m feeling real tired, it’s a lot easier to review ai code than write my own from scratch. As any writer will tell you, editing is easier than writing from a blank page.
A good analogy might be that I can vacuum the house way faster than my robot vacuum can, probably in less than 1/3rd of the time! But letting the robot do it is still way better for quality of life and also it frees me up to do other things. This has been the true gain from using coding LLM’s in my lived experience.
They always seem to miss another metric, saved human brain power. My experience hasn’t been that I can complete tasks faster from go to woe, it’s been that they’re far less mentally taxing and I can do other things whilst the ai churns some of the tedium away. When I’m feeling real tired, it’s a lot easier to review ai code than write my own from scratch. As any writer will tell you, editing is easier than writing from a blank page.
A good analogy might be that I can vacuum the house way faster than my robot vacuum can, probably in less than 1/3rd of the time! But letting the robot do it is still way better for quality of life and also it frees me up to do other things. This has been the true gain from using coding LLM’s in my lived experience.
"This is mostly for proofreading and condensing my rambling voice notes. These rather boring uses have significantly reduced drudgery and improved the overall quality of my writing. Best yet, they work pretty well (well, most of the time) But I don’t ask them to do any of the writing."
I find I struggle with this. If an LLM is being used to improve the overall quality of my writing, I feel like it is then doing the writing and that doesn't sit well with me. Same with having it write my code (though I don't write code for a living).
Maybe it would be similar to playing a guitar solo and then having an LLM fix all the missed notes. Is that still my solo? I tend to feel it isn't.
Just my personal struggle with this new, admittedly incredible, paradigm.
I find I struggle with this. If an LLM is being used to improve the overall quality of my writing, I feel like it is then doing the writing and that doesn't sit well with me. Same with having it write my code (though I don't write code for a living).
Maybe it would be similar to playing a guitar solo and then having an LLM fix all the missed notes. Is that still my solo? I tend to feel it isn't.
Just my personal struggle with this new, admittedly incredible, paradigm.
The investment fund that acquired the company that acquired our company requests all that all companies it owns go big on cloud and AI, no matter what, because this raises valuation and they can sell them for bigger profits.
I have nothing against cloud or AI per se, but I still believe in the right tool for the right job and in not doing things just for the sake of it. While raising valuation is a good thing, raising costs, delaying more useful features and adding complexity should also be taken into account.
I have nothing against cloud or AI per se, but I still believe in the right tool for the right job and in not doing things just for the sake of it. While raising valuation is a good thing, raising costs, delaying more useful features and adding complexity should also be taken into account.
Easy, just quit. Go work for a serious company instead of these grifters.
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This is a good article, but
> We keep asking them to do “intelligent things” and find out a) they really aren’t that good at it, and b) replacing that human task is far more complex than we originally thought
I never thought that. From the beginning on this were easy to uncover marketing statements.
> We keep asking them to do “intelligent things” and find out a) they really aren’t that good at it, and b) replacing that human task is far more complex than we originally thought
I never thought that. From the beginning on this were easy to uncover marketing statements.
> Whenever there is hype, we shuffled into the easy path, forcing the tech into the product without understanding its weaknesses. We are more worried about being left behind than actually doing something of value.
> He uses the example of the dynamo, an old-fashioned term for a powerful electric motor.
um, dynamo is a generator, it takes mechanical energy and turns into to electricity.
um, dynamo is a generator, it takes mechanical energy and turns into to electricity.
While technically correct (the best kind of correct), they are or can be literally the same mechanism. Apply a mechanical force and you will create DC current. Apply a voltage and you will generate motion.
> We’re here to solve problems, not look cool.
Shots fired
Shots fired
Hot take in regards to the good old statement taken literally: the boring<->exciting dimension is irrelevant in terms of something being good or bad.
Yes I know, stable things are stable
Yes I know, stable things are stable
> So in this final part, I want to answer the question: why should we still care? The tech is problematic, and signs point to the bubble bursting.
because "for every 1% unemployment goes up 40,000 people die." bubbles bursting hurt people and there's tens of billions in this bubble.
because "for every 1% unemployment goes up 40,000 people die." bubbles bursting hurt people and there's tens of billions in this bubble.
I also agree that boring is good, but in our current society you won't get a job for being boring, and when you get a job, it's is guaranteed you are not being paid to solve problems.
> and when you get a job, it's is guaranteed you are not being paid to solve problems
That's just your experience, based on your geolocation and chain of events.
That's just your experience, based on your geolocation and chain of events.
> but in our current society you won't get a job for being boring,
One can argue that every other field of engineering outside of Software Engineering, specializes in making complex things into boring things.
We are the unique snowflakes that take business use cases and build castle in the clouds that may or may not actually solve the business problem at hand.
One can argue that every other field of engineering outside of Software Engineering, specializes in making complex things into boring things.
We are the unique snowflakes that take business use cases and build castle in the clouds that may or may not actually solve the business problem at hand.
We're everything from the Architect to the concrete guy,to the framer, carpenter, sparky, and plumber.
... and if it all falls down, don't blame us - you clicked the EULA /s
... and if it all falls down, don't blame us - you clicked the EULA /s
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Some early retirees who started learning Cobol just 8 years ago would very much disagree with you :-)
One of my main job functions is to watch out for and solve problems.
The computer is not boring, per se, it's neutral when unplugged. The idea it accelerates binary primarily for prediction purposes in extracting values from the arbitrary, this is not only exciting, it's illusory.
The writer fails to grasp what rabbit hole we've gone down since the 70s/90s when we began applying the principles of prediction to computation, then horizontalized them via web. This was the most exciting time because it added a vast illusory value to the arbitrary, it's a time of massive piracy that posed as corporatocracy.
Once this prediction became automated by AI, yes, now the piracy becomes boring, and in turn reveals what was going on all along.
The writer fails to grasp what rabbit hole we've gone down since the 70s/90s when we began applying the principles of prediction to computation, then horizontalized them via web. This was the most exciting time because it added a vast illusory value to the arbitrary, it's a time of massive piracy that posed as corporatocracy.
Once this prediction became automated by AI, yes, now the piracy becomes boring, and in turn reveals what was going on all along.
Anyway, boring is bad. Boring is what spends your attention on irrelevant things. Cobol's syntax is boring in a bad way. Go's error handling is boring in a bad way. Manually clicking through screens again and again because you failed to write UI tests is boring in a bad way.
What could be "boring in a good way" is something that gets things done and gets out of your way. Things like HTTPS, or S3, or your keyboard once you have leaned touch typing, are "boring in a good way". They have no concealed surprises, are well-tested in practice, and do what they say on the tin, every time.
New and shiny things can be "boring in the good way", e.g. uv [2]. Old and established things can be full of (nasty) surprises, and, in this regard, the opposite of boring, e.g. C++.
[1]: https://boringtechnology.club/#30
[2]: https://github.com/astral-sh/uv