5 ms·

Bad engineers continue being bad, good engineers continue being good. I personally don’t know any colleagues who were good engineers just because they wrote co

by datsci_est_2015 6mo ago

Bad engineers continue being bad, good engineers continue being good. I personally don’t know any colleagues who were good engineers just because they wrote code faster. The best engineers I know were ones who drew on experience and careful consideration and shared critical insights with their team that steered the direction of the system positively. > Claude, engineer a system for me, but do it good. Thanks!

embedding-shape 6mo ago
> I personally don’t know any colleagues who were good engineers just because they wrote code faster Same, if anything, the opposite seems to be true, the ones that I'd call "good engineers" were slower, less panicked when production was down and could reason their way (slowly) through pretty much anything thrown at them. Opposite experience, I've sit next to developers who are trying their fastest to restore production and then making more mistakes to make it even worse, or developers who rush through the first implementation idea they had for a feature, missing to consider so many things and so on.
- ryandrake 6mo ago
  > Same, if anything, the opposite seems to be true, the ones that I'd call "good engineers" were slower Unfortunately, a lot of workplaces are ignoring this, believing their engineers are assembly line workers, and the ones who complete 10 widgets per minute are simply better than the ones who complete 5 widgets per minute.
  - nathan_compton 6mo ago
    It isn't just that they believe this - they want a business model where this is how it works. For a big company a star coder is a liability - they have strong labor power, they can leave and they are hard to replace, etc. Companies want workflows that work with mediocre programmers because they are more like interchangeable parts. This is the real secret to why AI programming will work in a lot of places. If you look at the externalities of employing talented people, shitty code actually looks better than great code.
    - ryandrake 6mo ago
      To these kinds of companies, what's even better than a rack of mediocre programmers? AI agents that you can just conjure up and prompt. They take up no facility space, don't require lunch breaks or vacations, obey all commands and direction, and produce a predictable and consistent amount of output per dollar. This is the earworm the leaders of these companies have allowed into their minds. Like Agent Mulder, they Want To Believe in this so badly...
      - overfeed 6mo ago
        > This is the earworm the leaders of these companies have allowed into their minds. Like Agent Mulder, they Want To Believe in this so badly... If you assume they are not idiots and analyze the FOMO incentives via a little game-theory, it becomes clear why. Assuming the competition has adopted AI, leadership can ignore it, or pursue it. If they adopt it, then they are level with the completion whether AI actually succeeds or fails - they get to keep their executive job. If leadership ignores AI, and it actually delivers the productivity gains to the competition, they will be fired. If they ignore AI and it's a bust, they gain nothing.
        untrust 6mo ago
        What if the outcome is the competition burns their money on LLM usage for little to no gain? If you're an exec and you jumped into LLMs as well then you also lose any advantage you would have had by saving your money or hiring a few more humans.
        overfeed 6mo ago
        > What if the outcome is the competition burns their money on LLM usage for little to no gain? The company does better than the money-burning competition, but the executives personally gain nothing; there are no bonuses just because the competition took a misstep.
        m4x 6mo ago
        If AI turns out to be a bust, ignoring it could become a significant win. One possible outcome of AI adoption is that existing code bases are degraded, and existing programmer capability is allowed to atrophy. In that situation, companies that adopt AI lose out relative to companies that eschew it.
  - sanderjd 6mo ago
    Which workplaces?
- sanderjd 6mo ago
  This is true. But I find AI tools to be a huge help for all of this. Not to do any of it faster, but to remove a bunch of the tedium from the process of testing ideas and iterating on them. Instead of "I wonder if the problem is..." requiring half an hour of research, now I can do an initial check of that theory in less than a minute, and then dig further, or move onto the next one. Or say I estimate it's gonna take me an hour or more to test an idea, I might just decide I don't have time to invest in that. Well now maybe I can get a tentative answer on that by spending a minute laying out the theory and letting an agent spend ten or twenty minutes on it in the background. In this way I can explore space I just would have determined was not worth the effort previously. To me, none of this feels like "going faster", it feels like "opening up possibilities to try more things, with a lot less tedious work".
  - skydhash 6mo ago
    Have you ever wonder how people do it without it being a tedium for them? For things that have a visual elements like UI and UX, you can start with sketches (analog or digital) and eliminate the bad ideas, refine the good ones with higher quality rendering. Then choose one concept and inplement it. By that time, the code is trivial. What I found with LLM usage is that people will settle on the first one, declaring it good enough, and not exploring further (because that is tedious for them). The other type of problem are mostly three categories (mathematical, logical, or data/information/communication). For the first type you have to find the formula, prove it is correct, and translate it faithfully to code. But we rarely have that kind of problem today unless you’re in a research lab or dealing with floating-point issues. The second type is more common where you enacting rules based on some axioms originating from the systems you depend on. That leads to the creation of constraints and invariants. Again I’m not seeing LLM helping there as they lack internal consistency for this type of activity. (Learning Prolog helps in solving that kind of problem) The third type is about modelizing real world elements as data structures and designing how they transform overtime and how they interact with each other. To do it well, you need deep domain knowledge about the problem. If LLM can help you there that means two things: a) Your knowledge is lacking and you ought to talk to the people you’re building the system for; b) The problem is solved and you’d do well to learn from the solution. (Basically what the DDD books are all about) Most problems are a combination of subproblems of those three categories (recursively). But from my (admittedly small amount of) interactions with pro LLM users, they don’t want to solve a problem, they want it to be solved for them. So it’s not about avoiding tediousness, it’s sidestepping the whole thing.
jkaptur 6mo ago
> I personally don’t know any colleagues who were good engineers just because they wrote code faster. However, the best engineers I know are usually among the quickest to open an editor or debugger and use it fluently to try something out. It's precisely that speed that enables a process like "let's try X, hmm, how about Y, no... ok, Z is nice; ok team, here are the tradeoffs...". Then they remember their experience with X, Y, and Z, and use it to shape their thinking going forward. Meanwhile, other engineers have gotten X to finally mostly work and are invested in shipping it because they just want to be done. In my experience, this is how a lot of coding agents seem to act. It's not obvious to me how to apply the expert loop to agentic coding. Of course you can ask your agent to try several different things and pick the best, or ask it to recommend architectural improvements that would make a given change easier...
- skydhash 6mo ago
  > However, the best engineers I know are usually among the quickest to open an editor or debugger and use it fluently to try something out The Pragmatic Programmer book has whole chapters about this. Ultimately, you either solve the problem analogously (whiteboard, deep thinking on a sofa). Or you got fast as trying out stuff AND keeping the good bits.
- datsci_est_2015 6mo ago
  Or: depth-first search of the solution space vs breadth-first (or balanced) search of the solution space. > Of course you can ask your agent to try several different things and pick the best, or ask it to recommend architectural improvements that would make a given change easier The ideal solution increasingly seems to be encoding everything that differentiates a good engineer from a bad engineer into your prompt. But at that point the LLM isn’t really the model as much as the medium. And I have some doubts that LLMs are the ideal medium for encoding expertise.
- beacon294 6mo ago
  As you practice it will be apparent, you simply keep working on the application architecture yourself.
- sanderjd 6mo ago
deleted 6mo ago
[deleted]
truncate 6mo ago
>> Bad engineers continue being bad, good engineers continue being good. I don't know if good engineers can necessarily continue to be good. There is limit to how much careful consideration one can give if everything is on an accelerated timeline. Regardless good or not, there is limit on how much influence you have on setting those timelines. The whole playing field is changing.
- andai 6mo ago
  An old comic I like: - I've taken a controversial new pill that accelerates my brain. -- So you're smart now? - I'm stupid faster! That being said, being stupid faster can work if validation is cheap (and exists in the first place). Turns out "eh close enough" for AGI is just stupidity in an "until done" loop. (Technically referred to as Ralphing.)
  - Salgat 6mo ago
    So the chimpanzees on the keyboard thing is real.
  - sanderjd 6mo ago
    Yep, validation is key. The smartest thing I've heard on this, which has reoriented how I think about this is that the objective function of a piece of software is now more important to get right than the implementation.
    - dspillett 6mo ago
      > the objective function of a piece of software is now more important to get right than the implementation That has always been the case. That is why weeks or even months of programming and other project busy work could replace a couple of days of time getting properly fleshed out requirements down.
      - sanderjd 6mo ago
        Agreed, it has always been the case. But I've never thought of it that way so explicitly. And I might argue that the important distinction is that the objective function is programmatically verifiable (which the word "requirements" has not always implied).
LtWorf 6mo ago
Good engineers need to be allowed to be good. If they are told to pump features or lose their job, they might act like bad engineers as well.
- sanderjd 6mo ago
  Aren't they more likely to leave?
  - LtWorf 6mo ago
    Depends. If they have a good salary, nice coworkers, WFH. If they manage to tolerate having to produce crap they might stick around if other factors are above average. For someone with 3-4 kids who lives far from the city, WFH and time flexibility can be important motivators.
    - sanderjd 6mo ago
      Yep!
nly 6mo ago
The best paid engineers I know seem to be the super fast hackers who write unfathomable amounts of code in short order. Unfortunately thoughtful design and engineering doesn't get recognised
- bdangubic 6mo ago
  in my experience this is because there are very very very very few thoughtful designers and engineers, especially compared to people that are cranking out code.
  - galangalalgol 6mo ago
    Also thoughtful code varies from that library that does that thing you need with an api so intuitive you don't even need autocomplete or docs (though it has docs) to the library that is extensible to every possible use case you will never need but missing the obious ones you do or at least makes them horribly unergonomic in the name of that extensibility and purity with regard to some random paradigm that is self evidently the best one.
notnullorvoid 6mo ago
> Bad engineers continue being bad, good engineers continue being good. Unfortunately I have seen some really good software engineering peers regress into bad engineers through a increasing reliance on AI. Conversely some very bad engineers (undeserving of the title) have been producing better outputs than I ever expected possible of them.
- threethirtytwo 6mo ago
  Which one are you?

embedding-shape 6mo ago

> I personally don’t know any colleagues who were good engineers just because they wrote code faster Same, if anything, the opposite seems to be true, the ones that I'd call "good engineers" were slower, less panicked when production was down and could reason their way (slowly) through pretty much anything thrown at them. Opposite experience, I've sit next to developers who are trying their fastest to restore production and then making more mistakes to make it even worse, or developers who rush through the first implementation idea they had for a feature, missing to consider so many things and so on.

ryandrake 6mo ago
> Same, if anything, the opposite seems to be true, the ones that I'd call "good engineers" were slower Unfortunately, a lot of workplaces are ignoring this, believing their engineers are assembly line workers, and the ones who complete 10 widgets per minute are simply better than the ones who complete 5 widgets per minute.
- nathan_compton 6mo ago
  It isn't just that they believe this - they want a business model where this is how it works. For a big company a star coder is a liability - they have strong labor power, they can leave and they are hard to replace, etc. Companies want workflows that work with mediocre programmers because they are more like interchangeable parts. This is the real secret to why AI programming will work in a lot of places. If you look at the externalities of employing talented people, shitty code actually looks better than great code.
  - ryandrake 6mo ago
    To these kinds of companies, what's even better than a rack of mediocre programmers? AI agents that you can just conjure up and prompt. They take up no facility space, don't require lunch breaks or vacations, obey all commands and direction, and produce a predictable and consistent amount of output per dollar. This is the earworm the leaders of these companies have allowed into their minds. Like Agent Mulder, they Want To Believe in this so badly...
    - overfeed 6mo ago
      > This is the earworm the leaders of these companies have allowed into their minds. Like Agent Mulder, they Want To Believe in this so badly... If you assume they are not idiots and analyze the FOMO incentives via a little game-theory, it becomes clear why. Assuming the competition has adopted AI, leadership can ignore it, or pursue it. If they adopt it, then they are level with the completion whether AI actually succeeds or fails - they get to keep their executive job. If leadership ignores AI, and it actually delivers the productivity gains to the competition, they will be fired. If they ignore AI and it's a bust, they gain nothing.
      - untrust 6mo ago
        What if the outcome is the competition burns their money on LLM usage for little to no gain? If you're an exec and you jumped into LLMs as well then you also lose any advantage you would have had by saving your money or hiring a few more humans.
        overfeed 6mo ago
        > What if the outcome is the competition burns their money on LLM usage for little to no gain? The company does better than the money-burning competition, but the executives personally gain nothing; there are no bonuses just because the competition took a misstep.
      - m4x 6mo ago
        If AI turns out to be a bust, ignoring it could become a significant win. One possible outcome of AI adoption is that existing code bases are degraded, and existing programmer capability is allowed to atrophy. In that situation, companies that adopt AI lose out relative to companies that eschew it.
- sanderjd 6mo ago
  Which workplaces?
sanderjd 6mo ago
This is true. But I find AI tools to be a huge help for all of this. Not to do any of it faster, but to remove a bunch of the tedium from the process of testing ideas and iterating on them. Instead of "I wonder if the problem is..." requiring half an hour of research, now I can do an initial check of that theory in less than a minute, and then dig further, or move onto the next one. Or say I estimate it's gonna take me an hour or more to test an idea, I might just decide I don't have time to invest in that. Well now maybe I can get a tentative answer on that by spending a minute laying out the theory and letting an agent spend ten or twenty minutes on it in the background. In this way I can explore space I just would have determined was not worth the effort previously. To me, none of this feels like "going faster", it feels like "opening up possibilities to try more things, with a lot less tedious work".
- skydhash 6mo ago
  Have you ever wonder how people do it without it being a tedium for them? For things that have a visual elements like UI and UX, you can start with sketches (analog or digital) and eliminate the bad ideas, refine the good ones with higher quality rendering. Then choose one concept and inplement it. By that time, the code is trivial. What I found with LLM usage is that people will settle on the first one, declaring it good enough, and not exploring further (because that is tedious for them). The other type of problem are mostly three categories (mathematical, logical, or data/information/communication). For the first type you have to find the formula, prove it is correct, and translate it faithfully to code. But we rarely have that kind of problem today unless you’re in a research lab or dealing with floating-point issues. The second type is more common where you enacting rules based on some axioms originating from the systems you depend on. That leads to the creation of constraints and invariants. Again I’m not seeing LLM helping there as they lack internal consistency for this type of activity. (Learning Prolog helps in solving that kind of problem) The third type is about modelizing real world elements as data structures and designing how they transform overtime and how they interact with each other. To do it well, you need deep domain knowledge about the problem. If LLM can help you there that means two things: a) Your knowledge is lacking and you ought to talk to the people you’re building the system for; b) The problem is solved and you’d do well to learn from the solution. (Basically what the DDD books are all about) Most problems are a combination of subproblems of those three categories (recursively). But from my (admittedly small amount of) interactions with pro LLM users, they don’t want to solve a problem, they want it to be solved for them. So it’s not about avoiding tediousness, it’s sidestepping the whole thing.

jkaptur 6mo ago

> I personally don’t know any colleagues who were good engineers just because they wrote code faster. However, the best engineers I know are usually among the quickest to open an editor or debugger and use it fluently to try something out. It's precisely that speed that enables a process like "let's try X, hmm, how about Y, no... ok, Z is nice; ok team, here are the tradeoffs...". Then they remember their experience with X, Y, and Z, and use it to shape their thinking going forward. Meanwhile, other engineers have gotten X to finally mostly work and are invested in shipping it because they just want to be done. In my experience, this is how a lot of coding agents seem to act. It's not obvious to me how to apply the expert loop to agentic coding. Of course you can ask your agent to try several different things and pick the best, or ask it to recommend architectural improvements that would make a given change easier...

skydhash 6mo ago
> However, the best engineers I know are usually among the quickest to open an editor or debugger and use it fluently to try something out The Pragmatic Programmer book has whole chapters about this. Ultimately, you either solve the problem analogously (whiteboard, deep thinking on a sofa). Or you got fast as trying out stuff AND keeping the good bits.
datsci_est_2015 6mo ago
Or: depth-first search of the solution space vs breadth-first (or balanced) search of the solution space. > Of course you can ask your agent to try several different things and pick the best, or ask it to recommend architectural improvements that would make a given change easier The ideal solution increasingly seems to be encoding everything that differentiates a good engineer from a bad engineer into your prompt. But at that point the LLM isn’t really the model as much as the medium. And I have some doubts that LLMs are the ideal medium for encoding expertise.
beacon294 6mo ago
As you practice it will be apparent, you simply keep working on the application architecture yourself.
sanderjd 6mo ago

deleted 6mo ago

[deleted]

truncate 6mo ago

>> Bad engineers continue being bad, good engineers continue being good. I don't know if good engineers can necessarily continue to be good. There is limit to how much careful consideration one can give if everything is on an accelerated timeline. Regardless good or not, there is limit on how much influence you have on setting those timelines. The whole playing field is changing.

andai 6mo ago
An old comic I like: - I've taken a controversial new pill that accelerates my brain. -- So you're smart now? - I'm stupid faster! That being said, being stupid faster can work if validation is cheap (and exists in the first place). Turns out "eh close enough" for AGI is just stupidity in an "until done" loop. (Technically referred to as Ralphing.)
- Salgat 6mo ago
  So the chimpanzees on the keyboard thing is real.
- sanderjd 6mo ago
  Yep, validation is key. The smartest thing I've heard on this, which has reoriented how I think about this is that the objective function of a piece of software is now more important to get right than the implementation.
  - dspillett 6mo ago
    > the objective function of a piece of software is now more important to get right than the implementation That has always been the case. That is why weeks or even months of programming and other project busy work could replace a couple of days of time getting properly fleshed out requirements down.
    - sanderjd 6mo ago
      Agreed, it has always been the case. But I've never thought of it that way so explicitly. And I might argue that the important distinction is that the objective function is programmatically verifiable (which the word "requirements" has not always implied).

LtWorf 6mo ago

Good engineers need to be allowed to be good. If they are told to pump features or lose their job, they might act like bad engineers as well.

sanderjd 6mo ago
Aren't they more likely to leave?
- LtWorf 6mo ago
  Depends. If they have a good salary, nice coworkers, WFH. If they manage to tolerate having to produce crap they might stick around if other factors are above average. For someone with 3-4 kids who lives far from the city, WFH and time flexibility can be important motivators.
  - sanderjd 6mo ago
    Yep!

nly 6mo ago

The best paid engineers I know seem to be the super fast hackers who write unfathomable amounts of code in short order. Unfortunately thoughtful design and engineering doesn't get recognised

bdangubic 6mo ago
in my experience this is because there are very very very very few thoughtful designers and engineers, especially compared to people that are cranking out code.
- galangalalgol 6mo ago
  Also thoughtful code varies from that library that does that thing you need with an api so intuitive you don't even need autocomplete or docs (though it has docs) to the library that is extensible to every possible use case you will never need but missing the obious ones you do or at least makes them horribly unergonomic in the name of that extensibility and purity with regard to some random paradigm that is self evidently the best one.

notnullorvoid 6mo ago

> Bad engineers continue being bad, good engineers continue being good. Unfortunately I have seen some really good software engineering peers regress into bad engineers through a increasing reliance on AI. Conversely some very bad engineers (undeserving of the title) have been producing better outputs than I ever expected possible of them.

threethirtytwo 6mo ago
Which one are you?