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> AI is a tool like any other. Autocomplete on steroids No, AI is a shitty tool that has yet to prove its utility. Autocomplete works by analyzing the official

> AI is a tool like any other. Autocomplete on steroids No, AI is a shitty tool that has yet to prove its utility. Autocomplete works by analyzing the official API and interface, it's completely different than AI which hallucinates meaning between words and also stuff that it was fed before it met you. > variable names (a hard part of computer science) Naming is for software engineering, not CS. One more confusion by people who want to sell us AI at all cost.

dagw 2y ago
Autocomplete works by analyzing the official API and interface, it's completely different than AI You can (and should) give the AI access to your existing codebase and any relevant documentation to use as context if you want good results. If you give the AI zero context for the problem it is trying to solve, of course it will struggle. If you give it all the necessary context, it will do much better. I've found that just uploading the documentation of the API or library you are working with before asking the AI questions about it makes a huge difference in the quality of its output.
criley2 2y ago
At some point, you become the luddite. Maybe you have no experience with modern AI dev tools, maybe you work in a language that is underrepresented in models meaning off the shelf tools don't work well, or maybe you're just an old curmudgeon who will die on a hill. But modern AI tools are far beyond "auto complete". (I actually turn off those in-line completions, I feel they ruin flowstate). The tools now are fully prompted, with multi-file editing, with full codebase context, with web/search and doc integration, and for "on the rails" development are producing high quality code for "easier" tasks. These modern models and tools can solve nearly every single leet code problem faster than you. They can do every single Advent of Code problem likely 10X-100X faster than you can. In my professional, high standards, very legal and contract driven web app world, AI tools are still very useful for doing "on the rails" development. Is it architecting entire systems? No of course not (yet). Is it emulating existing patterns and extending them for new functionality 10X faster than a Jr or Mid? Yes it is. Is it writing nearly perfect automated tests based on examples? Yes it is. It is scaffolding new ideas and putting down a great starting point? Yep. And it's even able to iterate on featurework pretty well, and much faster than Jr/Mid. The kind of work I'd give to a Jr/Mid and expect to take 2-3 days before they need serious feedback up and down the change, these AI are doing in about 30 seconds, maybe 90 seconds if you need to iterate a few times on the prompt. I get that "AI" is a buzzword that is pumping valuations and making business people see $$$. But coding assistants are not that. For many programmers, they are quickly becoming valuable tools that do in fact speed up development.
- otabdeveloper4 2y ago
  > Our modern model and tools can solve nearly every single leet code problem faster than you. That's expected, since all the leetcode problems have ready-to-use solutions on the internet. (In fact, the reason they ask leetcode questions isn't to test your IQ, it's to know if you've read the obvious and available literature.)
  - criley2 2y ago
    Two points: >That's expected, since all the leetcode problems have ready-to-use solutions on the internet. 1) If the implication is "The model knows the answer and regurgitates it like lyrics to a song" then I would push back. Put a leet code problem into deepseek r1 chain-of-reasoning model and watch it spend 2 minutes spitting out 5000 words thinking through every single facet of the problem and genuinely solving it at a level that is higher than 95% of programmers. And point 2) If you do believe it's fundamentally about how much the model has been trained on, then it has seen your CRUD app and has already seen 10,000 times the feature or system you're about to write -- so it should be a foregone conclusion that it can also do all of that development work too. Only the higher order architecting and proprietary domains should be challenging for it, as there would be far less examples to train on (scarcity) or the model doesn't understand a complex solution (architecting systems at scale is something it can't do). (I also point out how well these models did for Advent of Code 2024, when there were zero examples in the training data for it).
    - zeroonetwothree 2y ago
      Is it “thinking” or is it regurgitating analysis of the problem it found somewhere on the internet?
      - criley2 2y ago
        Are you "thinking" or are you regurgitating analysis of the problem based on what you read on the internet, too? This one is funny because for something like leet code, nearly everyone just reads the best answers, learns them and learns how to regurgitate them in an interview environment.
sixstringtheory 2y ago
>> variable names (a hard part of computer science) >Naming is for software engineering, not CS. I figured they were referencing the “two hard problems of computer science”, those two being naming things, cache invalidation and off by one errors. Everybody knows the hardest problems in software engineering are assembling promo packets and building consensus on number of spaces per indent.
- nailer 2y ago
  ‘Number of spaces per indent’ begs the question: why spaces? The proper indentation character is the corn emoji.
lordmoma 2y ago
AI is good with Rust programming, because the compiler will keep it from hallucinating!

dagw 2y ago

Autocomplete works by analyzing the official API and interface, it's completely different than AI You can (and should) give the AI access to your existing codebase and any relevant documentation to use as context if you want good results. If you give the AI zero context for the problem it is trying to solve, of course it will struggle. If you give it all the necessary context, it will do much better. I've found that just uploading the documentation of the API or library you are working with before asking the AI questions about it makes a huge difference in the quality of its output.

criley2 2y ago

At some point, you become the luddite. Maybe you have no experience with modern AI dev tools, maybe you work in a language that is underrepresented in models meaning off the shelf tools don't work well, or maybe you're just an old curmudgeon who will die on a hill. But modern AI tools are far beyond "auto complete". (I actually turn off those in-line completions, I feel they ruin flowstate). The tools now are fully prompted, with multi-file editing, with full codebase context, with web/search and doc integration, and for "on the rails" development are producing high quality code for "easier" tasks. These modern models and tools can solve nearly every single leet code problem faster than you. They can do every single Advent of Code problem likely 10X-100X faster than you can. In my professional, high standards, very legal and contract driven web app world, AI tools are still very useful for doing "on the rails" development. Is it architecting entire systems? No of course not (yet). Is it emulating existing patterns and extending them for new functionality 10X faster than a Jr or Mid? Yes it is. Is it writing nearly perfect automated tests based on examples? Yes it is. It is scaffolding new ideas and putting down a great starting point? Yep. And it's even able to iterate on featurework pretty well, and much faster than Jr/Mid. The kind of work I'd give to a Jr/Mid and expect to take 2-3 days before they need serious feedback up and down the change, these AI are doing in about 30 seconds, maybe 90 seconds if you need to iterate a few times on the prompt. I get that "AI" is a buzzword that is pumping valuations and making business people see $$$. But coding assistants are not that. For many programmers, they are quickly becoming valuable tools that do in fact speed up development.

otabdeveloper4 2y ago
> Our modern model and tools can solve nearly every single leet code problem faster than you. That's expected, since all the leetcode problems have ready-to-use solutions on the internet. (In fact, the reason they ask leetcode questions isn't to test your IQ, it's to know if you've read the obvious and available literature.)
- criley2 2y ago
  Two points: >That's expected, since all the leetcode problems have ready-to-use solutions on the internet. 1) If the implication is "The model knows the answer and regurgitates it like lyrics to a song" then I would push back. Put a leet code problem into deepseek r1 chain-of-reasoning model and watch it spend 2 minutes spitting out 5000 words thinking through every single facet of the problem and genuinely solving it at a level that is higher than 95% of programmers. And point 2) If you do believe it's fundamentally about how much the model has been trained on, then it has seen your CRUD app and has already seen 10,000 times the feature or system you're about to write -- so it should be a foregone conclusion that it can also do all of that development work too. Only the higher order architecting and proprietary domains should be challenging for it, as there would be far less examples to train on (scarcity) or the model doesn't understand a complex solution (architecting systems at scale is something it can't do). (I also point out how well these models did for Advent of Code 2024, when there were zero examples in the training data for it).
  - zeroonetwothree 2y ago
    Is it “thinking” or is it regurgitating analysis of the problem it found somewhere on the internet?
    - criley2 2y ago
      Are you "thinking" or are you regurgitating analysis of the problem based on what you read on the internet, too? This one is funny because for something like leet code, nearly everyone just reads the best answers, learns them and learns how to regurgitate them in an interview environment.

sixstringtheory 2y ago

>> variable names (a hard part of computer science) >Naming is for software engineering, not CS. I figured they were referencing the “two hard problems of computer science”, those two being naming things, cache invalidation and off by one errors. Everybody knows the hardest problems in software engineering are assembling promo packets and building consensus on number of spaces per indent.

nailer 2y ago
‘Number of spaces per indent’ begs the question: why spaces? The proper indentation character is the corn emoji.

lordmoma 2y ago

AI is good with Rust programming, because the compiler will keep it from hallucinating!