7 ms·

How AI coding companions will change the way developers work

29 points by kiyanwang 3y ago

asah 3y ago
ChatGPT tldr: Based on the information available so far, here are the key takeaways from the interview on how AI coding companions will change the way developers work: 1. Generative AI is significantly impacting developer productivity and speed, particularly through the use of coding companions like Amazon CodeWhisperer, which uses a large language model (LLM) trained on open-source projects, technical documentation, and AWS services. This tool handles much of the heavy lifting associated with creating new applications and services【5†source】. 2. Large Language Models (LLMs) not only predict the next line of code but also understand the developer's intent and infer context from what's already been written, including comments, thus generating syntactically valid, idiomatic code. They also simplify mundane and time-consuming tasks, such as writing unit tests or translating code from one language to another【6†source】. 3. CodeWhisperer and similar AI tools are not meant to replace developers but to assist them in focusing on the more complex and interesting aspects of their work, such as problem-solving【7†source】. 4. The plugin of these tools is capable of understanding the developer's context, the programming language being used, and the specific task being carried out. It can provide timely code recommendations and has advanced features like a reference tracker to aid the developer in making the best decisions for their applications【14†source】. 5. LLMs are trained on a vast amount of data from the public internet, cleaned and organized to understand the vocabulary and structure of the language, and to generate meaningful sentences and paragraphs in that language【15†source】. 6. The models built on transformer architecture will soon allow for automatic translation from one programming language to another. This will be particularly useful for teams looking to transition from legacy languages to more recent or efficient ones, such as Rust for high-performance applications【16†source】. I encountered a few issues while trying to access parts of the interview, so there might be additional important points not included in this summary.
- Kiro 3y ago
  What are those brackets and why can't I copy them? They behave like it's some kind of RTL character when trying to select on mobile.
m348e912 3y ago
Sandeep Pokkunuri: "Don’t get me wrong, ultimately the developer is in control. They are the ones who decide whether this code is good. They’re the ones that will run and verify that it is working as expected. They’re the ones that will ship." Sandeep is being very optimistic. AI coding companions open up coding capabilities to even less experienced coders who mostly care of the code works. Whether the code is good will be questionable.
- LouisSayers 3y ago
  Until they try to do something slightly novel and find they need an actual developer to help them along. Not to mention security or other concerns.
  - hef19898 3y ago
    In a sense, ChatGPT for coding is the ultimate "build something for other start-ups" business idea isn't it? It allows barely tech literate, read people knowing more about actual coding than I do but nowhere near enough to qualify as a developer, to found tech start-ups without a tech co-founder. Considering how often this question gets asked, well, ChatGPT is just genious. Not that I am convinced it'll actually serve a purpose other than allowing peiple to be lazy enough to not study and learn stuff themselves.
skummetmaelk 3y ago
I have tried to get useful code out of ChatGPT with GPT-4 and so far been unable to find a case where it was easier for me to verify that chatGPT was right than to just do it myself. This was for toy examples. For more complicated things it's downright useless. Everyone seems to be raving about its ability to write code. What am I doing wrong?
- MichaelRazum 3y ago
  Depends what you are doing and how much code. Especially if you work with new frameworks or apis ChatGPT is extremly helpful. I think especially on small code snippets it is strong. Guess you would have to give an example, what you tried and what you got...
  - skummetmaelk 3y ago
    I can see the argument for using it with frameworks and libraries as it can discover the library functions that do what your prompt describes for you. It seems to have a lot of trouble with building code that works in a certain way. For example if you ask it to build a simple graph container with access functions that give you iterators over neighbourhoods of nodes or whatever it makes a mess. Sometimes it technically works, but the underlying graph datastructure is just ripped out of randomseocodingsite.com using nodes owning pointers to their connected nodes. If you ask it to use a specific underlying structure like a vector of nodes and indexes to represent edges it doesn't quite know what to do. So yes, I guess the initial implementation "works", but not well.
  - hutzlibu 3y ago
    Unless it is too new. For wgsl and webgpu, ChatGPT4 is quite useless. To be fair, the spec is still changing, but this means, that ChatGPT4 will mix in various incompatible bits. I hope they handtune it.
    - danielvaughn 3y ago
      I started learning Rust last week, and figured I could let copilot help me out a bit. Spent a while trying to get a very simple example to compile, with no luck. So even for a 5 year old language, it derps it up.
ilaksh 3y ago
Has anyone compared Google's new Codey, Code Whisperer, and GPT-4 for code generation? For my service to work at all, I feel like I really need GPT-4 or something better if that ever comes along. But aside from GPT-4 trying to use APIs from two years ago, it can definitely make simple software without necessarily needing an actual programmer. Especially when you put it in a closed loop. As the models get better with larger context windows and more up-to-date training, software engineers will probably defer more and more to the AI. And non-programmers will be able to build software with it also. The time where this is just minor code completion or something is going to be pretty limited. In 3-5 years, I would be very surprised it was considered good software engineering to have humans writing code at all.
- moltar 3y ago
  I find that GPT-4 always wins in task specific, clear prompts. E.g. I used it on a project recently to translate code from one version to another, and keep in mind that the syntax was very different A comparable example would be translating old class based React code to functional TSX with hooks. And it does extremely well there. Other coding tools can’t even do that as there’s no way to load a whole prompt and the entire original file as a prompt.
andersa 3y ago
I finally decided to install Copilot recently now that they have the business subscription with a more sensible privacy policy, and it's so much better than expected. Even on internal code it hasn't been trained against it often just works anyway based on the context and other nearby code. If something requires even a slight bit of boilerplate that has a pattern to it, it writes it much faster than I could. I find it almost uncanny sometimes how well it knows what I wanted to do next even without writing any comment telling it what to do. Not to mention the hundreds of random small suggestions like writing similar method definitions, completing all of the parameters I was going to pass to something, writing out the rest of some math formula after I typed the name of the variable for the result I want to compute, etc. People weren't kidding about the productivity boost. On the other hand, I found trying to get ChatGPT to write large chunks of code almost completely useless - it would always be incorrect in some subtle way or failing to use the style I gave it as an example, unless the task is to write some simple python scripts.
- moltar 3y ago
  I kind of agree with you and I followed almost the same path. Excerpt one thing I noticed. That the times it is subtly wrong I waste a ton of time finding the bug. It’s like looking for bugs in someone else’s code. I lack the context and muscle memory of the code written by the assistant. I’m still not sure if it’s net positive. I wish there was an objective way to measure the effect on a personal, project and org level.
  - cageface 3y ago
    This was my experience as well. The time it saved me didn't add up to the time I spent in the debugger fixing subtle bugs in code that looked fine but didn't quite work. I think it would be very easy to ship hard to find bugs in production code with copilot.
    - macNchz 3y ago
      I think there’s a “eyes glaze over” effect with generating lots of boilerplate, where things look generally correct, but have issues you need to be paying attention to notice and correct. One example recently: I watched a developer scaffolding a database schema with an ORM. Plenty of boilerplate defining the fields and stuff, nice to have Copilot running, however the suggestions were baking in bad design that would work now, but almost certainly cause pain later: nullable foreign keys, text fields with inappropriate max lengths, redundant FK relationships where a relationship could be ambiguous depending on whether you joined directly on the FK or through another related table, etc. Obviously all of that is on the developer to notice and be responsible for, but it’s really easy to just tab through the completions, see that it works (right now) and move on.
pylua 3y ago
I use it to ask questions on how to do things that I am not clear about in frameworks I am not familiar with. For instance the best way to implement paging using a spring webclient. You have to chat with it to get it to provide the most optimal answer— the first solution was recursive. I think it would slow me down in areas I already know about. The IntelliJ tools are already pretty good.
pmoriarty 3y ago
My experience with coding-oriented LLMs has been that they fail pretty spectacularly on non-popular languages and are incapable of fixing errors in their own code. I've wrestled for many hours trying to get them to write me a short, simple function in a non-popular language and they failed again and again. I also suspect these LLMs are going to have huge problems when APIs and language definitions change, because the data they're trained on usually doesn't have API/language version metadata attached to it, so they have no way of knowing which version is applicable to whatever you're trying to do. They may spit out code in an obsolete API version, for example, or even give you code containing mixed API versions. This will be a nightmare to debug.
- sureglymop 3y ago
  Where they do shine for me is for repetitive tasks. Say I am starting to write a list and start it out with "item 1", "item 2".. it will then let me tab complete some more items like value completion in e.g. excel. It has definitely saved me time.
agentgumshoe 3y ago
I think this will become one of those the first 80% is easy, last 20% is hard and expensive and it all falls away. Microsoft is really trying to hear up how awesome copilot is going to be, but the real world doesn't operate in an MS Lab.
xwdv 3y ago
Every version I’ve seen of GPT has shown me that if GPT can write most of your code, then you’re not really doing difficult work anyway. Real world problems are far too complicated for GPT to make serious impact beyond being some idea generator or stack overflow alternative.
5350-uiop-1130 3y ago
I spent 30mins trying to get chatGPT to add a feature in some code it provided, which was not working. It kept apologizing and giving slightly tweaked code, which I could see was not actually addressing the issue. Then when I asked it if the feature was actually even possible, it said No.
- _justinfunk 3y ago
  What kind of feature?
  - 5350-uiop-1130 3y ago
    synchronised scrolling between two windows in a chrome extension
jonstewart 3y ago
I manage a dev team and have some team members trying different coding assistants. AWS Code Whisperer is getting good marks — while it’s impressive when CoPilot or ChatGPT can spit out an entire example webapp, that’s not actually useful. Code Whisperer provides small suggestions but this seems to be more of an aid, and it’s easier to ignore/adjust when incorrect. Our AWS rep has also said that they’ll keep our code private and won’t add them to the training set, you know, actually paying attention to legal issues like grown ups rather than creating a yawning chasm of risk.
sovietmudkipz 3y ago
I already jokingly call myself a cyborg. I think I’m in the perfect position to take advantage of AI in my daily workflow. Why? I research all the time because I find what I do fun. I read books, listen to conference talks, practice synthesizing, and project to explore ideas. (I will say there is a lot of conflicting advise out there so it’s important to try to mentalize the perspective to understand why the particular advise is offered. Anyways, I digress…) I also have ~10 years programming as an IC. Because of what I stated above I know the high level “strategy” of what I’ll want to do and most (if not some) of the implementation details. AI helps me with the “tactics” of implementation. I can ask AI or allow copilot to make implementation suggestions and I know enough to tailor the suggestions to my project (and to my coding preferences). Typically I still cross reference using documentation but using AI has helped reduce the amount of cognitive load required to do something interesting. We live in exciting times as programmers. It’s only going to get better as our tooling becomes more sophisticated.
danesparza 3y ago
I guess I'm old enough to remember when developers regularly used regular text editors and had to compile and link in two different steps. I also remember how much just having an integrated development environment helped with the process. And then intellisense! Wow -- I thought ANYbody could be a programmer then, and got worried for my job. :-) I think this is just the next iteration. Yes -- I would expect AI large language models and general purpose AI to change the landscape of how developers do their work. It will be uncomfortable for a while -- but go to your user groups, listen to your podcasts, share the adventure with other people. We get to make it fun.
JCM9 3y ago
Corporate security will be all over this when they realize that all it takes to get really bad malware or vulnerabilities inserted into your codebase is to have some “AI” that got trained nasty stuff insert something really bad into a critical application. I see the value in these companions in early training, but it could get really dangerous after that and it’s only a matter of time until some really nasty breach was caused by terrible code some AI came up with. This will be like Solar Winds 10x by letting the attackers right into the most sacred parts of your organization… writing the code that will make or break your company. But can’t we just filter the training set for bad things? No. The whole point of zero days is that bad folks know what they need to get inserted into your codebase before you (or companies training an AI) know it’s bad.
- danesparza 3y ago
  "insert something really bad into a critical application" Dear god. Please let me know that you need to have code reviews, unit tests, and a QA staff for "critical applications". An AI doesn't replace any of that. I need to hear you say it.
anotherpaulg 3y ago
I have been coding collaboratively with GPT-4 extensively for the last few weeks using aider [1]. Prior to that I had been using an ad hoc workflow to code with GPT-3.5. Here's are some thoughts on my experience collaborating with AI on code: - Use GPT-4. It's vastly better than 3.5. - Use an integrated workflow like copilot or aider. Cutting and pasting code between your editor and a chat window/tool is tedious, and doesn't give GPT any context about the rest of your code. It forces you to do extra up front work to prepare your code to call a bite-sized function that can be easily cut/pasted. Asking GPT for revisions is slow with more cut/paste at every step. - Coding with AI lowers the activation energy to start a new change, since you expect it to be easy. - GPT is a champ at boilerplate, and can quickly prepare the ground for the interesting work. - Typing a few terse, informal sentences describing the task to GPT can help organize my own thinking. - I spend so much less time fiddling with annoying details of syntax, method names, parameter names/order, regex syntax, etc. - It's important to break down a big change into a series of smaller, self contained steps. - Watching GPT type out code is good "think time" for me. - GPT is great at "busy work" like updating docs, adding small quality of life features, writing tests, etc. I accomplish 10x as much of this work now because it's so easy to just ask for it. - GPT has astounding knowledge about available libraries and packages that can simplify your task. - Expect to iterate rapidly with GPT on more complex code changes, offering it feedback/direction. I'm not sure I can go back to coding the old fashioned way. I realized this when GPT-4 went down for an hour or so last week. [1] https://github.com/paul-gauthier/aider https://github.com/paul-gauthier/aider
jacknews 3y ago
I've just restarted writing Ruby on Rails for an interview take-home after a couple of years away from it, and literally every single step, I had to look up the exact syntax/methods, or more commonly things just didn't work out of the box, or as they should have or as expected/remembered and I had to look things up. Often the online answers are wrong (yeah go ahead and use 'first()' and 'reverse' in your model scopes to get reverse ordering, forcing rails to load the entire set from db). Oh yes, you have to run rspec init after you bundle install it. Don't forget factorybot needs this little config file. Oh didn't I mention, form_with breaks error messages? And on and on. I knew exactly what I wanted (I used to know it pretty in-depth), but just experienced constant shin-scrapes and paper-cuts getting back into it. This is where I imagine GPT shines - though I don't know because I can't get an account.

29 points by kiyanwang 3y ago

asah 3y ago

ChatGPT tldr: Based on the information available so far, here are the key takeaways from the interview on how AI coding companions will change the way developers work: 1. Generative AI is significantly impacting developer productivity and speed, particularly through the use of coding companions like Amazon CodeWhisperer, which uses a large language model (LLM) trained on open-source projects, technical documentation, and AWS services. This tool handles much of the heavy lifting associated with creating new applications and services【5†source】. 2. Large Language Models (LLMs) not only predict the next line of code but also understand the developer's intent and infer context from what's already been written, including comments, thus generating syntactically valid, idiomatic code. They also simplify mundane and time-consuming tasks, such as writing unit tests or translating code from one language to another【6†source】. 3. CodeWhisperer and similar AI tools are not meant to replace developers but to assist them in focusing on the more complex and interesting aspects of their work, such as problem-solving【7†source】. 4. The plugin of these tools is capable of understanding the developer's context, the programming language being used, and the specific task being carried out. It can provide timely code recommendations and has advanced features like a reference tracker to aid the developer in making the best decisions for their applications【14†source】. 5. LLMs are trained on a vast amount of data from the public internet, cleaned and organized to understand the vocabulary and structure of the language, and to generate meaningful sentences and paragraphs in that language【15†source】. 6. The models built on transformer architecture will soon allow for automatic translation from one programming language to another. This will be particularly useful for teams looking to transition from legacy languages to more recent or efficient ones, such as Rust for high-performance applications【16†source】. I encountered a few issues while trying to access parts of the interview, so there might be additional important points not included in this summary.

Kiro 3y ago
What are those brackets and why can't I copy them? They behave like it's some kind of RTL character when trying to select on mobile.

m348e912 3y ago

Sandeep Pokkunuri: "Don’t get me wrong, ultimately the developer is in control. They are the ones who decide whether this code is good. They’re the ones that will run and verify that it is working as expected. They’re the ones that will ship." Sandeep is being very optimistic. AI coding companions open up coding capabilities to even less experienced coders who mostly care of the code works. Whether the code is good will be questionable.

LouisSayers 3y ago
Until they try to do something slightly novel and find they need an actual developer to help them along. Not to mention security or other concerns.
- hef19898 3y ago
  In a sense, ChatGPT for coding is the ultimate "build something for other start-ups" business idea isn't it? It allows barely tech literate, read people knowing more about actual coding than I do but nowhere near enough to qualify as a developer, to found tech start-ups without a tech co-founder. Considering how often this question gets asked, well, ChatGPT is just genious. Not that I am convinced it'll actually serve a purpose other than allowing peiple to be lazy enough to not study and learn stuff themselves.

skummetmaelk 3y ago

I have tried to get useful code out of ChatGPT with GPT-4 and so far been unable to find a case where it was easier for me to verify that chatGPT was right than to just do it myself. This was for toy examples. For more complicated things it's downright useless. Everyone seems to be raving about its ability to write code. What am I doing wrong?

MichaelRazum 3y ago
Depends what you are doing and how much code. Especially if you work with new frameworks or apis ChatGPT is extremly helpful. I think especially on small code snippets it is strong. Guess you would have to give an example, what you tried and what you got...
- skummetmaelk 3y ago
  I can see the argument for using it with frameworks and libraries as it can discover the library functions that do what your prompt describes for you. It seems to have a lot of trouble with building code that works in a certain way. For example if you ask it to build a simple graph container with access functions that give you iterators over neighbourhoods of nodes or whatever it makes a mess. Sometimes it technically works, but the underlying graph datastructure is just ripped out of randomseocodingsite.com using nodes owning pointers to their connected nodes. If you ask it to use a specific underlying structure like a vector of nodes and indexes to represent edges it doesn't quite know what to do. So yes, I guess the initial implementation "works", but not well.
- hutzlibu 3y ago
  Unless it is too new. For wgsl and webgpu, ChatGPT4 is quite useless. To be fair, the spec is still changing, but this means, that ChatGPT4 will mix in various incompatible bits. I hope they handtune it.
  - danielvaughn 3y ago
    I started learning Rust last week, and figured I could let copilot help me out a bit. Spent a while trying to get a very simple example to compile, with no luck. So even for a 5 year old language, it derps it up.

ilaksh 3y ago

Has anyone compared Google's new Codey, Code Whisperer, and GPT-4 for code generation? For my service to work at all, I feel like I really need GPT-4 or something better if that ever comes along. But aside from GPT-4 trying to use APIs from two years ago, it can definitely make simple software without necessarily needing an actual programmer. Especially when you put it in a closed loop. As the models get better with larger context windows and more up-to-date training, software engineers will probably defer more and more to the AI. And non-programmers will be able to build software with it also. The time where this is just minor code completion or something is going to be pretty limited. In 3-5 years, I would be very surprised it was considered good software engineering to have humans writing code at all.

moltar 3y ago
I find that GPT-4 always wins in task specific, clear prompts. E.g. I used it on a project recently to translate code from one version to another, and keep in mind that the syntax was very different A comparable example would be translating old class based React code to functional TSX with hooks. And it does extremely well there. Other coding tools can’t even do that as there’s no way to load a whole prompt and the entire original file as a prompt.

andersa 3y ago

I finally decided to install Copilot recently now that they have the business subscription with a more sensible privacy policy, and it's so much better than expected. Even on internal code it hasn't been trained against it often just works anyway based on the context and other nearby code. If something requires even a slight bit of boilerplate that has a pattern to it, it writes it much faster than I could. I find it almost uncanny sometimes how well it knows what I wanted to do next even without writing any comment telling it what to do. Not to mention the hundreds of random small suggestions like writing similar method definitions, completing all of the parameters I was going to pass to something, writing out the rest of some math formula after I typed the name of the variable for the result I want to compute, etc. People weren't kidding about the productivity boost. On the other hand, I found trying to get ChatGPT to write large chunks of code almost completely useless - it would always be incorrect in some subtle way or failing to use the style I gave it as an example, unless the task is to write some simple python scripts.

moltar 3y ago
I kind of agree with you and I followed almost the same path. Excerpt one thing I noticed. That the times it is subtly wrong I waste a ton of time finding the bug. It’s like looking for bugs in someone else’s code. I lack the context and muscle memory of the code written by the assistant. I’m still not sure if it’s net positive. I wish there was an objective way to measure the effect on a personal, project and org level.
- cageface 3y ago
  This was my experience as well. The time it saved me didn't add up to the time I spent in the debugger fixing subtle bugs in code that looked fine but didn't quite work. I think it would be very easy to ship hard to find bugs in production code with copilot.
  - macNchz 3y ago
    I think there’s a “eyes glaze over” effect with generating lots of boilerplate, where things look generally correct, but have issues you need to be paying attention to notice and correct. One example recently: I watched a developer scaffolding a database schema with an ORM. Plenty of boilerplate defining the fields and stuff, nice to have Copilot running, however the suggestions were baking in bad design that would work now, but almost certainly cause pain later: nullable foreign keys, text fields with inappropriate max lengths, redundant FK relationships where a relationship could be ambiguous depending on whether you joined directly on the FK or through another related table, etc. Obviously all of that is on the developer to notice and be responsible for, but it’s really easy to just tab through the completions, see that it works (right now) and move on.

pylua 3y ago

I use it to ask questions on how to do things that I am not clear about in frameworks I am not familiar with. For instance the best way to implement paging using a spring webclient. You have to chat with it to get it to provide the most optimal answer— the first solution was recursive. I think it would slow me down in areas I already know about. The IntelliJ tools are already pretty good.

pmoriarty 3y ago

My experience with coding-oriented LLMs has been that they fail pretty spectacularly on non-popular languages and are incapable of fixing errors in their own code. I've wrestled for many hours trying to get them to write me a short, simple function in a non-popular language and they failed again and again. I also suspect these LLMs are going to have huge problems when APIs and language definitions change, because the data they're trained on usually doesn't have API/language version metadata attached to it, so they have no way of knowing which version is applicable to whatever you're trying to do. They may spit out code in an obsolete API version, for example, or even give you code containing mixed API versions. This will be a nightmare to debug.

sureglymop 3y ago
Where they do shine for me is for repetitive tasks. Say I am starting to write a list and start it out with "item 1", "item 2".. it will then let me tab complete some more items like value completion in e.g. excel. It has definitely saved me time.

agentgumshoe 3y ago

I think this will become one of those the first 80% is easy, last 20% is hard and expensive and it all falls away. Microsoft is really trying to hear up how awesome copilot is going to be, but the real world doesn't operate in an MS Lab.

xwdv 3y ago

Every version I’ve seen of GPT has shown me that if GPT can write most of your code, then you’re not really doing difficult work anyway. Real world problems are far too complicated for GPT to make serious impact beyond being some idea generator or stack overflow alternative.

5350-uiop-1130 3y ago

I spent 30mins trying to get chatGPT to add a feature in some code it provided, which was not working. It kept apologizing and giving slightly tweaked code, which I could see was not actually addressing the issue. Then when I asked it if the feature was actually even possible, it said No.

_justinfunk 3y ago
What kind of feature?
- 5350-uiop-1130 3y ago
  synchronised scrolling between two windows in a chrome extension

jonstewart 3y ago

I manage a dev team and have some team members trying different coding assistants. AWS Code Whisperer is getting good marks — while it’s impressive when CoPilot or ChatGPT can spit out an entire example webapp, that’s not actually useful. Code Whisperer provides small suggestions but this seems to be more of an aid, and it’s easier to ignore/adjust when incorrect. Our AWS rep has also said that they’ll keep our code private and won’t add them to the training set, you know, actually paying attention to legal issues like grown ups rather than creating a yawning chasm of risk.

sovietmudkipz 3y ago

I already jokingly call myself a cyborg. I think I’m in the perfect position to take advantage of AI in my daily workflow. Why? I research all the time because I find what I do fun. I read books, listen to conference talks, practice synthesizing, and project to explore ideas. (I will say there is a lot of conflicting advise out there so it’s important to try to mentalize the perspective to understand why the particular advise is offered. Anyways, I digress…) I also have ~10 years programming as an IC. Because of what I stated above I know the high level “strategy” of what I’ll want to do and most (if not some) of the implementation details. AI helps me with the “tactics” of implementation. I can ask AI or allow copilot to make implementation suggestions and I know enough to tailor the suggestions to my project (and to my coding preferences). Typically I still cross reference using documentation but using AI has helped reduce the amount of cognitive load required to do something interesting. We live in exciting times as programmers. It’s only going to get better as our tooling becomes more sophisticated.

danesparza 3y ago

I guess I'm old enough to remember when developers regularly used regular text editors and had to compile and link in two different steps. I also remember how much just having an integrated development environment helped with the process. And then intellisense! Wow -- I thought ANYbody could be a programmer then, and got worried for my job. :-) I think this is just the next iteration. Yes -- I would expect AI large language models and general purpose AI to change the landscape of how developers do their work. It will be uncomfortable for a while -- but go to your user groups, listen to your podcasts, share the adventure with other people. We get to make it fun.

JCM9 3y ago

Corporate security will be all over this when they realize that all it takes to get really bad malware or vulnerabilities inserted into your codebase is to have some “AI” that got trained nasty stuff insert something really bad into a critical application. I see the value in these companions in early training, but it could get really dangerous after that and it’s only a matter of time until some really nasty breach was caused by terrible code some AI came up with. This will be like Solar Winds 10x by letting the attackers right into the most sacred parts of your organization… writing the code that will make or break your company. But can’t we just filter the training set for bad things? No. The whole point of zero days is that bad folks know what they need to get inserted into your codebase before you (or companies training an AI) know it’s bad.

danesparza 3y ago
"insert something really bad into a critical application" Dear god. Please let me know that you need to have code reviews, unit tests, and a QA staff for "critical applications". An AI doesn't replace any of that. I need to hear you say it.

anotherpaulg 3y ago

I have been coding collaboratively with GPT-4 extensively for the last few weeks using aider [1]. Prior to that I had been using an ad hoc workflow to code with GPT-3.5. Here's are some thoughts on my experience collaborating with AI on code: - Use GPT-4. It's vastly better than 3.5. - Use an integrated workflow like copilot or aider. Cutting and pasting code between your editor and a chat window/tool is tedious, and doesn't give GPT any context about the rest of your code. It forces you to do extra up front work to prepare your code to call a bite-sized function that can be easily cut/pasted. Asking GPT for revisions is slow with more cut/paste at every step. - Coding with AI lowers the activation energy to start a new change, since you expect it to be easy. - GPT is a champ at boilerplate, and can quickly prepare the ground for the interesting work. - Typing a few terse, informal sentences describing the task to GPT can help organize my own thinking. - I spend so much less time fiddling with annoying details of syntax, method names, parameter names/order, regex syntax, etc. - It's important to break down a big change into a series of smaller, self contained steps. - Watching GPT type out code is good "think time" for me. - GPT is great at "busy work" like updating docs, adding small quality of life features, writing tests, etc. I accomplish 10x as much of this work now because it's so easy to just ask for it. - GPT has astounding knowledge about available libraries and packages that can simplify your task. - Expect to iterate rapidly with GPT on more complex code changes, offering it feedback/direction. I'm not sure I can go back to coding the old fashioned way. I realized this when GPT-4 went down for an hour or so last week. [1] https://github.com/paul-gauthier/aider https://github.com/paul-gauthier/aider

jacknews 3y ago

I've just restarted writing Ruby on Rails for an interview take-home after a couple of years away from it, and literally every single step, I had to look up the exact syntax/methods, or more commonly things just didn't work out of the box, or as they should have or as expected/remembered and I had to look things up. Often the online answers are wrong (yeah go ahead and use 'first()' and 'reverse' in your model scopes to get reverse ordering, forcing rails to load the entire set from db). Oh yes, you have to run rspec init after you bundle install it. Don't forget factorybot needs this little config file. Oh didn't I mention, form_with breaks error messages? And on and on. I knew exactly what I wanted (I used to know it pretty in-depth), but just experienced constant shin-scrapes and paper-cuts getting back into it. This is where I imagine GPT shines - though I don't know because I can't get an account.