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I'd be really curious if you're willing to expand more on how it has helped with those workflows. Do you copy/paste chunks in and ask it to explain them? Have i

I'd be really curious if you're willing to expand more on how it has helped with those workflows. Do you copy/paste chunks in and ask it to explain them? Have it try to refactor them and then clean up?

larve 3y ago
For legacy code: - generate comments (hit or miss, but at least it can rewrite my random notes into consistent notes) - generate type annotations - refactor "broadly" (say, "rename all variables to match the following style" or "turn this class into a dataclass like XXX" or "transform the SQL queries into builder queries using XYZ"). Often requires some manual work but it gets a lot of tedious stuff out of the way - reverse-engineer clean API specs by just pasting in recorded HTTP logs - clean up logs into proper enums by generating the regexps - write CLI tools to probe the system (say, CLI tool to exercise the APIs mentioned above) - generate synthetic test data - transform HTML garbage into using a modern component system / react - transform legacy react/js into consistent redux actions - generate SQL queries at the speed of mouth I could go on forever...
- kjkjadksj 3y ago
  Are you testing chatgpts output in any way? I’ve considered using it for tasks but after hearing all the talk of how it can write good looking code that ends up not working as you might expect, I started wondering if the time savings from generating that block are wasted from interpreting and testing.
  - simonw 3y ago
    I have access to ChatGPT Code Interpreter mode, where it can both write Python and then execute it. I use that to write code all the time, because ChatGPT can write the code, run it, get an error, then re-write the code to address the error. Here are two recent transcripts whereI used it in this way: - https://chat.openai.com/share/b062955d-3601-4051-b6d9-80cef9228233 https://chat.openai.com/share/b062955d-3601-4051-b6d9-80cef9... - https://chat.openai.com/share/b9873d04-5978-489f-8c6b-4b948db7724d https://chat.openai.com/share/b9873d04-5978-489f-8c6b-4b948d...
- aleph_minus_one 3y ago
  > - generate SQL queries at the speed of mouth Because of the points this is the nearest to the work that some colleagues do, I anakyze this point (but you could ask similar questions about many of the other points): In my experience, writing correct SQL queries (which often tend to be quite non-trivial because of the internal complexity of the projects) typically involves a lot of knowledge about the whole system that my colleagues and I work on. Even if I could copy-paste this information, written down once, into the AI chat window: - I seriously doubt that any of these AI chat bots would be able to generate a remotely decent SQL query based on this information, if only because these SQL queries look really different from what you would see in typical CRUD web applications (for a very instructive example think into the direction of ETL for unifying historically separated lines of business where you often have lots of discussions with the respective colleagues to clear up very subtle details what the code is actually supposed to do in some strange boundary cases that exist because of some historical reasons (which one wants to get rid of)) - even explaining what the SQL query is supposed to do would in my opinion take more time than simply writing it down. Even ignoring the previous point: it is very typical that explaining in sufficient detail what the code is supposed to do would take far more time than simply writing it. A lot of programming work is not writing some scaffolding of some CRUD app or implementing a textbook algorithm.
  - simonw 3y ago
    Have you tried this at all? I find that many of the generative AI models (GPT-4, 3.5, even MPT-30B running on my laptop) are really shockingly good at SQL. Paste in a query and ask it for a detailed explanation. I've genuinely not seen it NOT provide a good result for that yet. Generating new SQL queries is a bit harder, because of the context you need to provide - but I've had very strong results from that as well. I've had the best results from providing both the schema and a couple of example rows from each table - which helps it identify things like "the country column contains abbreviations like US and GB". If you've found differently I'd love to hear about it.
    - aleph_minus_one 3y ago
      > Paste in a query and ask it for a detailed explanation. I've genuinely not seen it NOT provide a good result for that yet. [...] If you've found differently I'd love to hear about it. I have not directly tried it (the employer does not allow AI chatbots for any application intended for production (i.e. more sensitive stuff), but only for doing experiments), but working on the code I very rarely had the problem that I could not understand what some single (SQL) line of code does in the "programming sense". The central problem that rather occurs often is understanding why this line does exist and why things are implemented the way they are. Just to give an example: to accelerate some queries, I thought some index would make sense (colleagues principally agreed; it would likely accelerate a particular query that I had in mind). But there exists a good reason why there exists no index at this table (as the respective colleague explained to me). This again implies that for ETL stuff involving particular tables, one should make use of temporary tables where possible instead of JOINs; this is the reason why the code is organized as it is. This is the kind of explanation that I need, which surely no AI can deliver. Or another example: why does some particular function (1) have a rights check for a "more powerful" role and a related one (2) does not need one? The reason is very interesting: principally having this check (for a "more powerful" role) does not make a lot of sense, but for some very red-tape reasons auditors requested that only a particular group of roles shall be allowed to execute (1), but they were perfectly fine with a much larger group of users being allowed to execute (2). Again something that no AI will be able to answer.
- revolvingocelot 3y ago
  >- refactor "broadly" (say, "rename all variables to match the following style" or "turn this class into a dataclass like XXX" or "transform the SQL queries into builder queries using XYZ"). Often requires some manual work but it gets a lot of tedious stuff out of the way Can you go on more about this, please? This sounds, frankly, heavenly, but the second sentence gives me pause. I guess it's not necessarily a question of how reliably it can "broadly" refactor but rather how broadly "broadly" is meant to be taken... >- generate SQL queries at the speed of mouth ...and this? I'm not really a database guy, but I do keep hearing from them about how (eg, a database guy's) stateful knowledge of a database can result much, much more efficient queries than eg a sales guy with a query builder. Are the robut's queries more like the former or the latter?
  - larve 3y ago
    I gave some insights on the SQL thing above. For the refactor broadly, it's useful when I have something that's a bit too squishy for my IDE refactoring tools/multicursor editing/vim macros, but easy enough to do or provide an example for. One thing I mentioned is having consistent variable names. I would highly recommend taking a piece of code (any code) and then just start experimenting. Here's a few prompt ideas: - make this a singleton - use more classes - use less classes - create more functions - use lambdas - rewrite in a functional pipeline style - extract higher order types - use fluent APIs - use a query builder - transform to a state machine - make it async - add cancellation - use a work queue - turn it into a microservice pipeline - turn it into a text adventure - create a declarative DSL to simplify the core logic - list the edge cases - write unit tests for each edge case - transform the unit tests into table-driven tests - create a fuzzing harness - transform into a REST API - write a CLI tool - write a websocket server to stream updates into a graph - generate a HTML frontend - add structured logging - create a CPU architecture to execute this in hardware - create a config file - generate test data - generate a bayesian model to generate test data - generate a HTML frontend to generate a bayesian model to generate test data and download as a csv - etc... If you are not feeling inspired, take a random computer science book, open at a random page, and literally just paste some jargon in there and see what happens. You don't need correct sentences or anything, just random words. There really is nothing that can go wrong, in the worst case the result is gibberish. The code doesn't even need to build or be correct for it to be useful. These models are trained to be plausible, and even more importantly, self-consistent. When prompted with code in-context, these things are amazing at figuring out consistent, plausible, elegant, mainstream APIs. Implementing them correctly is something I usually tend to do manually instead of bludgeoning the LLM.

larve 3y ago

For legacy code: - generate comments (hit or miss, but at least it can rewrite my random notes into consistent notes) - generate type annotations - refactor "broadly" (say, "rename all variables to match the following style" or "turn this class into a dataclass like XXX" or "transform the SQL queries into builder queries using XYZ"). Often requires some manual work but it gets a lot of tedious stuff out of the way - reverse-engineer clean API specs by just pasting in recorded HTTP logs - clean up logs into proper enums by generating the regexps - write CLI tools to probe the system (say, CLI tool to exercise the APIs mentioned above) - generate synthetic test data - transform HTML garbage into using a modern component system / react - transform legacy react/js into consistent redux actions - generate SQL queries at the speed of mouth I could go on forever...

kjkjadksj 3y ago
Are you testing chatgpts output in any way? I’ve considered using it for tasks but after hearing all the talk of how it can write good looking code that ends up not working as you might expect, I started wondering if the time savings from generating that block are wasted from interpreting and testing.
- simonw 3y ago
  I have access to ChatGPT Code Interpreter mode, where it can both write Python and then execute it. I use that to write code all the time, because ChatGPT can write the code, run it, get an error, then re-write the code to address the error. Here are two recent transcripts whereI used it in this way: - https://chat.openai.com/share/b062955d-3601-4051-b6d9-80cef9228233 https://chat.openai.com/share/b062955d-3601-4051-b6d9-80cef9... - https://chat.openai.com/share/b9873d04-5978-489f-8c6b-4b948db7724d https://chat.openai.com/share/b9873d04-5978-489f-8c6b-4b948d...
aleph_minus_one 3y ago
> - generate SQL queries at the speed of mouth Because of the points this is the nearest to the work that some colleagues do, I anakyze this point (but you could ask similar questions about many of the other points): In my experience, writing correct SQL queries (which often tend to be quite non-trivial because of the internal complexity of the projects) typically involves a lot of knowledge about the whole system that my colleagues and I work on. Even if I could copy-paste this information, written down once, into the AI chat window: - I seriously doubt that any of these AI chat bots would be able to generate a remotely decent SQL query based on this information, if only because these SQL queries look really different from what you would see in typical CRUD web applications (for a very instructive example think into the direction of ETL for unifying historically separated lines of business where you often have lots of discussions with the respective colleagues to clear up very subtle details what the code is actually supposed to do in some strange boundary cases that exist because of some historical reasons (which one wants to get rid of)) - even explaining what the SQL query is supposed to do would in my opinion take more time than simply writing it down. Even ignoring the previous point: it is very typical that explaining in sufficient detail what the code is supposed to do would take far more time than simply writing it. A lot of programming work is not writing some scaffolding of some CRUD app or implementing a textbook algorithm.
- simonw 3y ago
  Have you tried this at all? I find that many of the generative AI models (GPT-4, 3.5, even MPT-30B running on my laptop) are really shockingly good at SQL. Paste in a query and ask it for a detailed explanation. I've genuinely not seen it NOT provide a good result for that yet. Generating new SQL queries is a bit harder, because of the context you need to provide - but I've had very strong results from that as well. I've had the best results from providing both the schema and a couple of example rows from each table - which helps it identify things like "the country column contains abbreviations like US and GB". If you've found differently I'd love to hear about it.
  - aleph_minus_one 3y ago
    > Paste in a query and ask it for a detailed explanation. I've genuinely not seen it NOT provide a good result for that yet. [...] If you've found differently I'd love to hear about it. I have not directly tried it (the employer does not allow AI chatbots for any application intended for production (i.e. more sensitive stuff), but only for doing experiments), but working on the code I very rarely had the problem that I could not understand what some single (SQL) line of code does in the "programming sense". The central problem that rather occurs often is understanding why this line does exist and why things are implemented the way they are. Just to give an example: to accelerate some queries, I thought some index would make sense (colleagues principally agreed; it would likely accelerate a particular query that I had in mind). But there exists a good reason why there exists no index at this table (as the respective colleague explained to me). This again implies that for ETL stuff involving particular tables, one should make use of temporary tables where possible instead of JOINs; this is the reason why the code is organized as it is. This is the kind of explanation that I need, which surely no AI can deliver. Or another example: why does some particular function (1) have a rights check for a "more powerful" role and a related one (2) does not need one? The reason is very interesting: principally having this check (for a "more powerful" role) does not make a lot of sense, but for some very red-tape reasons auditors requested that only a particular group of roles shall be allowed to execute (1), but they were perfectly fine with a much larger group of users being allowed to execute (2). Again something that no AI will be able to answer.
revolvingocelot 3y ago
>- refactor "broadly" (say, "rename all variables to match the following style" or "turn this class into a dataclass like XXX" or "transform the SQL queries into builder queries using XYZ"). Often requires some manual work but it gets a lot of tedious stuff out of the way Can you go on more about this, please? This sounds, frankly, heavenly, but the second sentence gives me pause. I guess it's not necessarily a question of how reliably it can "broadly" refactor but rather how broadly "broadly" is meant to be taken... >- generate SQL queries at the speed of mouth ...and this? I'm not really a database guy, but I do keep hearing from them about how (eg, a database guy's) stateful knowledge of a database can result much, much more efficient queries than eg a sales guy with a query builder. Are the robut's queries more like the former or the latter?
- larve 3y ago
  I gave some insights on the SQL thing above. For the refactor broadly, it's useful when I have something that's a bit too squishy for my IDE refactoring tools/multicursor editing/vim macros, but easy enough to do or provide an example for. One thing I mentioned is having consistent variable names. I would highly recommend taking a piece of code (any code) and then just start experimenting. Here's a few prompt ideas: - make this a singleton - use more classes - use less classes - create more functions - use lambdas - rewrite in a functional pipeline style - extract higher order types - use fluent APIs - use a query builder - transform to a state machine - make it async - add cancellation - use a work queue - turn it into a microservice pipeline - turn it into a text adventure - create a declarative DSL to simplify the core logic - list the edge cases - write unit tests for each edge case - transform the unit tests into table-driven tests - create a fuzzing harness - transform into a REST API - write a CLI tool - write a websocket server to stream updates into a graph - generate a HTML frontend - add structured logging - create a CPU architecture to execute this in hardware - create a config file - generate test data - generate a bayesian model to generate test data - generate a HTML frontend to generate a bayesian model to generate test data and download as a csv - etc... If you are not feeling inspired, take a random computer science book, open at a random page, and literally just paste some jargon in there and see what happens. You don't need correct sentences or anything, just random words. There really is nothing that can go wrong, in the worst case the result is gibberish. The code doesn't even need to build or be correct for it to be useful. These models are trained to be plausible, and even more importantly, self-consistent. When prompted with code in-context, these things are amazing at figuring out consistent, plausible, elegant, mainstream APIs. Implementing them correctly is something I usually tend to do manually instead of bludgeoning the LLM.