4 ms·

I use LLMs (like claude-code and codex-cli) the same way accountants use calculators. Without one, you waste all your focus on adding numbers; with one, you jus

by enkrs 1y ago

I use LLMs (like claude-code and codex-cli) the same way accountants use calculators. Without one, you waste all your focus on adding numbers; with one, you just enter values and check if the result makes sense. Programming feels the same—without LLMs, I’m stuck on both big problems (architecture, performance) and small ones (variable names). With LLMs, I type what I want and get code back. I still think about whether it works long-term, but I don’t need to handle every little algorithm detail myself. Of course there are going to be discussions what is real programming (like I'm sure there were discussions what is "real" accounting with the onset of a calculator) The moment we stop treating LLMs like people and see them as big calculators, it all clicks.

janalsncm 1y ago
The issue with your analogy is that calculators do not hallucinate. They do not make mistakes. An accountant is able to fully offload the mental overhead of arithmetic because the calculator is reliable.
- theshrike79 1y ago
  Replace calculator with the modern equivalent: Excel. It does make mistakes and is not reliable[0]. The user still needs to have a "feel" for the data. (to be pedantic "Excel" doesn't make mistakes, people trusting its defaults do) [0] https://timharford.com/2021/05/cautionary-tales-wrong-tools-cost-lives/ https://timharford.com/2021/05/cautionary-tales-wrong-tools-...
  - Jensson 1y ago
    > (to be pedantic "Excel" doesn't make mistakes, people trusting its defaults do) So what is your point? An expert that mastered excel don't have to check that excel calculated things correctly, he just need to check that he gave excel the right inputs and formulas. That is not true for LLM, you do have to check that it actually did what you asked regardless how good you are at prompting. The only thing I trust an LLM to do correctly are translations, they are very reliable at that, other than that I always verify.
    - deleted 1y ago
      [deleted]
    - theshrike79 1y ago
      "Just" check that every cell in the million row xlxs file is correct. See the issue here? Excel has no proper built-in validation or test suite, not sure about 3rd party ones. The last time I checked some years back there was like one that didn't do much. All it takes is one person accidentally or unknowingly entering static data on top of a few formulas in the middle and nobody will catch it. Or Excel "helps" by changing the SEPT1 gene to "September 1. 2025"[0] - this case got so bad they had to RENAME the gene to make Excel behave. "Just" doing it properly didn't work at scale. The point I'm trying to get at here that neither tool is perfect and requires validation afterwards. With agentic coding we can verify the results, we have the tools for it - and the agent can run them automatically. In this case Excel is even worse because one human error can escalate massively as there is no simple way to verify the output, Excel has no unit test equivalents or validators. [0] https://www.progress.org.uk/human-genes-renamed-as-microsoft-excel-reads-them-as-dates/ https://www.progress.org.uk/human-genes-renamed-as-microsoft...
      - janalsncm 1y ago
        You are describing a garbage in, garbage out problem. However, LLMs introduce a new type of issue, the “valid data in, garbage out” problem. The existence of the former doesn’t make the latter less of an issue. “Just” checking a million rows is trivial depending on the types of checks you’re running. In any case, you would never want a check which yields false positives and false negatives, since that defeats the entire purpose of the check.
  - janalsncm 1y ago
    With an LLM there is no learning curve though (or a minimal one at best). No expert can prevent an LLM from hallucinating, even (and especially) the people building them.
- irjustin 1y ago
  > The issue with your analogy is that calculators do not hallucinate. They do not make mistakes. An accountant is able to fully offload the mental overhead of arithmetic because the calculator is reliable. If you've ever done any modeling/serious accounting, you'll find that you feel more like a DBA than a "person punching on a calculator". You ask questions and then you figure out how to get the answers you want by "querying" excel cells. Many times querying isn't in quotes. To me, the analogy of the parent is quite apt.
  - Jensson 1y ago
    But the database doesn't hallucinate data, if always does exactly what you ask it to do and gives you reliable numbers unless you ask it to do a random operation.
    - 360MustangScope 1y ago
      That is why you check your results. If you know what the end outcome should be. Doesn’t matter if it hallucinates. If it does, it probably already got you 90% of the work done which is less work that you have to do now to finish it.
      - commakozzi 1y ago
        Exactly, thank you.
      - janalsncm 1y ago
        This only works for classes of problems where checking the answer is easier than doing the calculation. Things like making a visualization, writing simple functions, etc. For those, it’s definitely easier to use an LLM. But a lot of software isn’t like that. You can introduce subtle bugs along the way, so verifying is at least as hard as writing it in the first place. Likely harder, since writing code is easier than reading for most people.
      - 1718627440 1y ago
        Except verifying code is much harder then writing it.
- rusticpenn 1y ago
  It depends on how much you want the LLM to do. I personally work on function level and can easily verify if it works with a look and few tests.
- lupusreal 1y ago
  That's why you tell claude code to write tests, and use them, use linting tools, etc. And then you test the code yourself. If you're still concerned, /clear then tell claude code that some other idiot wrote the code and it needs to tear it apart and critique it. Hallucination is not an intractable problem, the stochastic nature of hallucinations makes it easy to use the same tools to catch them. I feel like hallucinations have become a cop out, an excuse, for people who don't want to learn how to use these new tools anyway.
  - otabdeveloper4 1y ago
    > you now have to not only review and double-check shitty AI code, but also hallucinated AI tests too Gee thanks for all that extra productivity, AI overlords. Maybe they should replace AI programmers with AI instead?
    - lupusreal 1y ago
      I said to make the chatbot do it, not to do all the reviewing yourself. You can do manual reviews once it makes something that works. In the meantime, you can be working on something else entirely.
      - otabdeveloper4 1y ago
        > In the meantime, you can be working on something else entirely. Like fixing useless and/or broken tests written by an LLM? (Thank you, AI overlords, for freeing me from the pesky algorithmic and coding tedia so I can instead focus on fixing the mountains of technical debt you added!)
  - EdwardDiego 1y ago
    > That's why you tell claude code to write tests, and use them I've seen Python unit tests emitted by LLM that, for a given class under test, start with. def test_foo_can_be_imported(self): try: from a.b.c import Foo except ImportError: self.fail() def test_foo_can_be_instantiated(self): from a.b.c import Foo instance = Foo() self.assertNotNull(instance) self.assertTrue(isinstance(instance, Foo) def test_other_stuff_that_relies_on_importing_and_instantiating_foo(self) ... And I've watched Cursor do multiple rounds of "1: The tests failed! I better change the code. 2: The tests failed! I better change the tests. GOTO 1" until it gets passing tests, sometimes by straight out deleting tests, or hardcoding values to make them pass. So I don't have the same faith in LLM-authored tests as you do. > I feel like hallucinations have become a cop out, an excuse, for people who don't want to learn how to use these new tools anyway. I feel like you've taken that attitude so you can dismiss concerns you don't agree with, without having to engage with them. It's disappointing.
MangoToupe 1y ago
I'm assuming based on the granularity you're referring to autocomplete, and surely that already doesn't feel like dialup.

by enkrs 1y ago

janalsncm 1y ago

The issue with your analogy is that calculators do not hallucinate. They do not make mistakes. An accountant is able to fully offload the mental overhead of arithmetic because the calculator is reliable.

theshrike79 1y ago
Replace calculator with the modern equivalent: Excel. It does make mistakes and is not reliable[0]. The user still needs to have a "feel" for the data. (to be pedantic "Excel" doesn't make mistakes, people trusting its defaults do) [0] https://timharford.com/2021/05/cautionary-tales-wrong-tools-cost-lives/ https://timharford.com/2021/05/cautionary-tales-wrong-tools-...
- Jensson 1y ago
  > (to be pedantic "Excel" doesn't make mistakes, people trusting its defaults do) So what is your point? An expert that mastered excel don't have to check that excel calculated things correctly, he just need to check that he gave excel the right inputs and formulas. That is not true for LLM, you do have to check that it actually did what you asked regardless how good you are at prompting. The only thing I trust an LLM to do correctly are translations, they are very reliable at that, other than that I always verify.
  - deleted 1y ago
    [deleted]
  - theshrike79 1y ago
    "Just" check that every cell in the million row xlxs file is correct. See the issue here? Excel has no proper built-in validation or test suite, not sure about 3rd party ones. The last time I checked some years back there was like one that didn't do much. All it takes is one person accidentally or unknowingly entering static data on top of a few formulas in the middle and nobody will catch it. Or Excel "helps" by changing the SEPT1 gene to "September 1. 2025"[0] - this case got so bad they had to RENAME the gene to make Excel behave. "Just" doing it properly didn't work at scale. The point I'm trying to get at here that neither tool is perfect and requires validation afterwards. With agentic coding we can verify the results, we have the tools for it - and the agent can run them automatically. In this case Excel is even worse because one human error can escalate massively as there is no simple way to verify the output, Excel has no unit test equivalents or validators. [0] https://www.progress.org.uk/human-genes-renamed-as-microsoft-excel-reads-them-as-dates/ https://www.progress.org.uk/human-genes-renamed-as-microsoft...
    - janalsncm 1y ago
      You are describing a garbage in, garbage out problem. However, LLMs introduce a new type of issue, the “valid data in, garbage out” problem. The existence of the former doesn’t make the latter less of an issue. “Just” checking a million rows is trivial depending on the types of checks you’re running. In any case, you would never want a check which yields false positives and false negatives, since that defeats the entire purpose of the check.
- janalsncm 1y ago
  With an LLM there is no learning curve though (or a minimal one at best). No expert can prevent an LLM from hallucinating, even (and especially) the people building them.
irjustin 1y ago
> The issue with your analogy is that calculators do not hallucinate. They do not make mistakes. An accountant is able to fully offload the mental overhead of arithmetic because the calculator is reliable. If you've ever done any modeling/serious accounting, you'll find that you feel more like a DBA than a "person punching on a calculator". You ask questions and then you figure out how to get the answers you want by "querying" excel cells. Many times querying isn't in quotes. To me, the analogy of the parent is quite apt.
- Jensson 1y ago
  But the database doesn't hallucinate data, if always does exactly what you ask it to do and gives you reliable numbers unless you ask it to do a random operation.
  - 360MustangScope 1y ago
    That is why you check your results. If you know what the end outcome should be. Doesn’t matter if it hallucinates. If it does, it probably already got you 90% of the work done which is less work that you have to do now to finish it.
    - commakozzi 1y ago
      Exactly, thank you.
    - janalsncm 1y ago
      This only works for classes of problems where checking the answer is easier than doing the calculation. Things like making a visualization, writing simple functions, etc. For those, it’s definitely easier to use an LLM. But a lot of software isn’t like that. You can introduce subtle bugs along the way, so verifying is at least as hard as writing it in the first place. Likely harder, since writing code is easier than reading for most people.
    - 1718627440 1y ago
      Except verifying code is much harder then writing it.
rusticpenn 1y ago
It depends on how much you want the LLM to do. I personally work on function level and can easily verify if it works with a look and few tests.
lupusreal 1y ago
That's why you tell claude code to write tests, and use them, use linting tools, etc. And then you test the code yourself. If you're still concerned, /clear then tell claude code that some other idiot wrote the code and it needs to tear it apart and critique it. Hallucination is not an intractable problem, the stochastic nature of hallucinations makes it easy to use the same tools to catch them. I feel like hallucinations have become a cop out, an excuse, for people who don't want to learn how to use these new tools anyway.
- otabdeveloper4 1y ago
  > you now have to not only review and double-check shitty AI code, but also hallucinated AI tests too Gee thanks for all that extra productivity, AI overlords. Maybe they should replace AI programmers with AI instead?
  - lupusreal 1y ago
    I said to make the chatbot do it, not to do all the reviewing yourself. You can do manual reviews once it makes something that works. In the meantime, you can be working on something else entirely.
    - otabdeveloper4 1y ago
      > In the meantime, you can be working on something else entirely. Like fixing useless and/or broken tests written by an LLM? (Thank you, AI overlords, for freeing me from the pesky algorithmic and coding tedia so I can instead focus on fixing the mountains of technical debt you added!)
- EdwardDiego 1y ago
  > That's why you tell claude code to write tests, and use them I've seen Python unit tests emitted by LLM that, for a given class under test, start with. def test_foo_can_be_imported(self): try: from a.b.c import Foo except ImportError: self.fail() def test_foo_can_be_instantiated(self): from a.b.c import Foo instance = Foo() self.assertNotNull(instance) self.assertTrue(isinstance(instance, Foo) def test_other_stuff_that_relies_on_importing_and_instantiating_foo(self) ... And I've watched Cursor do multiple rounds of "1: The tests failed! I better change the code. 2: The tests failed! I better change the tests. GOTO 1" until it gets passing tests, sometimes by straight out deleting tests, or hardcoding values to make them pass. So I don't have the same faith in LLM-authored tests as you do. > I feel like hallucinations have become a cop out, an excuse, for people who don't want to learn how to use these new tools anyway. I feel like you've taken that attitude so you can dismiss concerns you don't agree with, without having to engage with them. It's disappointing.

MangoToupe 1y ago

I'm assuming based on the granularity you're referring to autocomplete, and surely that already doesn't feel like dialup.