7 ms·

I recently asked Opus to just “Add vector search” to my current hobby project, a topic I know very little about. It set up manticore, pulled an embedding model,

I recently asked Opus to just “Add vector search” to my current hobby project, a topic I know very little about. It set up manticore, pulled an embedding model, wrote a migration tool for my old keyword indices, and built the front end. I’m not exaggerating much either: the prompt was the length of a tweet. I think it would easily have taken me 4+ hours to do that. It ran in 15 minutes while I played Kirby Air Riders and worked on the first try. Afterward, I sort of had to reflect on the fact that I learned essentially nothing about building vector search. I wanted the feature more than I wanted to know how to build the feature. It kept me learning the thing I cared about rather than doing a side quest.

ModernMech 10mo ago
The result of you having worked 4 hours to implement the thing is not just that you have the thing, it's that you have the thing and you understand the thing. Having the thing is next to useless if you don't understand it. At best it plods along as you keep badgering Claude to fix it, until inevitably Claude reaches a point where it can't help. At which time you'll be forced to spend at least the 4 hours you would have originally spent trying to understand it so you can fix it yourself. At worst the thing will actively break other things you do understand in ways you don't understand, and you'll have to spend at least 4 hours cleaning up the mess. Either way it's not clear you've saved any time at all.
- OxfordOutlander 10mo ago
  > inevitably Claude reaches a point where it can't help. Perhaps not. If LLMs keep getting better, more competent models can help him stay on top of it lol.
  - evklein 10mo ago
    You're still captive to a product. Which means that when CloudCo. increases their monthly GenAI price from $50/mo. to $500/mo., you're losing your service or you're paying. By participating in the build process you're giving yourself a fighting chance.
    - pillefitz 10mo ago
      I will quickly forget the details about any given code base within a few months anyway. Having used AI to build a project at least leaves me with very concise and actionable documentation and, as the prompter, I will have a deep understanding of the high-level vision, requirements and functionality.
- eleventen 10mo ago
  Respectfully, I think I’m in a better position to decide a) what value this has to me and b) what I choose to learn vs just letting Opus deal with. You don’t have enough information to say if I’ve saved time because you don’t know what I’m doing or what my goals are.
  - ModernMech 10mo ago
    Respectfully, a) I didn't say anything about what value this has to you but moreover... b) you also don't have enough information to say if it's saved you time because the costs you will bear are in the future. Systems require maintenance, that's a fact you can't get rid of with AI. And often times, maintaining systems require more work than building them in the first place. Maintaining systems tends to require a deep understanding of how they work and the tradeoffs that were decided when they were built. But you didn't build the thing, you didn't even design it as you left that up to Claude. That makes the AI the only thing on the planet that understands the system, but we know actually the AI doesn't understand anything at all. So no one understands the system you built, including the AI you used. And you expect that this whole process will have saved you time, while you play games? I just don't see it working out that way, sorry. The artifact the AI spit out will eventually demand you pay the cost in time to understand it, or you will incur future costs for not understanding it as it fails to act as you expect. You'll pay either way in the end.
    - eleventen 10mo ago
      > And you expect that this whole process will have saved you time, while you play games? The topic in question is “Can AI tools do a task that would take a human 4 hours”. Not whether it can do that in a way that leads to maintainability or sustained learning. I’m noodling on a hobby project as leisure time. I got what I wanted. I had fun. > incur future costs for not understanding it as it fails to act as you expect That is your stronger argument. I’ve seen quality problems with the search results that come from using a smaller embedding model than I should. I don’t know yet if that’s a blocker or tolerable. But I think that argument would be wrong too, because I’m very glad I chose Claude. The biggest limitation might be that I don’t have the compute locally to run an embedding model good enough to achieve decent results. It would have been a huge waste of my time to build it by hand and discover that at the end. I’m not about to pay for a sass vector DB or run this in AWS. At that level of effort I’d just scrap it.
- weitendorf 10mo ago
  You do learn how to control claude code and architect/orient things around getting it to deliver what you want. That's a skill that is both new and possibly going to be part of how we work for a long time (but also overlaps with the work tech leads and managers do). My proto+sqlite+mesh project recently hit the point where it's too big for Claude to maintain a consistent "mental model" of how eg search and the db schemas are supposed to be structured, kept taking hacky workarounds by going directly to a db at the storage layer instead of the API layer, etc. so I hit an insane amount of churn trying to get it to implement some of the features needed to get it production ready. Here's the whackamole/insanity documented in git commit history: https://github.com/accretional/collector/compare/main...feature/backup-policy-and-fixed-dir https://github.com/accretional/collector/compare/main...feat... But now I know some new tricks and intuition for avoiding this situation going forward. Because I do understand the mental model behind what this is supposed to look like at its core, and I need to maintain some kind of human-friendly guard rails, I'm adding integration tests in a different repo and a README/project "constitution" that claude can't change but is accountable for maintaining, and configuring it to keep them in context while working on my project. Kind of a microcosm of startups' reluctance to institute employee handbook/kpis/PRDs followed by resignation that they might truly be useful coordination tools.
  - ModernMech 10mo ago
    Yeah, this is close to my experience with it as well. The AI spits out some tutorial code and it works, and you think all your problems are solved. Then in working with the thing you start hitting problems you would have figured out if you had built the thing from scratch, so you have to start pulling it apart. Then you start realizing some troubling decisions the AI made and you have to patch them, but to do so you have to understand the architecture of the thing, requiring a deep dive into how it works. At the end of the day, you've spent just as much time gaining the knowledge, but one way was inductive (building it from scratch) while the other is deductive (letting the AI build it and then tearing it apart). Is one better than the other? I don't know. But I don't think one saves more time than the other. The only way to save time is to allow the thing to work without any understanding of what it does.
  - latentsea 10mo ago
vachina 10mo ago
Yeah and then it becomes an unmaintainable monolith because at some point the AI also lost track of what code does what. Great for Opus because you’re now a captive customer.
- tokioyoyo 10mo ago
  The point of eventual “all-code-is-written-by-AI” is that it really does not matter if your code is maintainable or not. In the end, most of the products are written to accomplish some sort of a goal or serve a need within a given set of restrictions (cost, speed and etc.). If the goal is achieved within given restrictions, the codebase can be thrown away until the next need is there to just create everything from scratch, if needed.
  - simonw 10mo ago
    I don't buy it. I think that could work, but it can work in the same way that plenty of big companies have codebases that are a giant ball of mud and yet they somehow manage to stay in business and occasionally ship a new feature. Meanwhile their rivals with well constructed codebases who can promptly ship features that work are able to run rings around them. I expect that we'll learn over time that LLM-managed big ball of mud codebases are less valuable than LLM-managed high quality well architected long-term maintained codebases.
    - tokioyoyo 10mo ago
      Fair enough. In my imagination, I can see people writing AI-first framework/architectures and a general trend for people to “migrate to such frameworks”, just like the push towards the microservices architectures in 2010s. A part of these frameworks would be “re-constructibility” by changing contracts in parts where it matters, and somehow the framework would make it easy for the LLM to discover such “parts”. Honestly, i’m making stuff up, as I don’t think it’s feasible right now because of the context sizes. But given how fast things develop, maybe in a couple of years things might change.
      - 10mo ago
Avicebron 10mo ago
> I learned essentially nothing about building vector search. I wanted the feature more than I wanted to know how to build the feature Opus/Anthropic is hands down the best in my experience. But using it feels like intellectual fast food (they all are), I hate the fact that I can build something like a neatly presentable one off spa tool (ty Simon) when I'm barely paying attention. it feels unsatisfying to use. EDIT: because I'm rambling, I like "AI" as much as the next guy, probably more because I was there before it turned into LLMs"R"US, but I also like(d) the practice of sitting around listening to music solving problems with Scala. I don't know why we've decided to make work less fun..
- pastel8739 10mo ago
  “We” didn’t decide to make work less fun, others decided for us.
- fluidcruft 10mo ago
  I sort of disagree. It's somewhat like having hypercard again. You can build fun UI things and make machines do what you want them to do. You can care about the parts you want to care about and not sweat about the parts you don't want to learn in detail (yet). And Claude and codex make great guides/Sherpas. There are just too many parts involved to do anything. For example today I built a simple data collection app to use on my phone that involves inventories with photos for a tedious workflow I have to do. I knew what I wanted but didn't know how to even choose which tools to bother learn. And just even trying things to see if an approach works or not without spending hours learning one thing or another or wading through the hell of web search is really great. Things I learned today that I figure everyone else must know: if you want to take a photo from a webapp I guess you need https. So I decided to try mTLS (knew it existed but never had the time) so asked Claude to write me a short tutorial about setting it up, creating keys, importing them (including a cool single line trick of spinning up a python server and downloading the keys on my phone rather than find a USB stick or whatever). And then helping me figure out a path out of the suffering of Chrome and Firefox hating self-signed CA. But at least I figured out how to make Firefox happy. But it would insist on prompting me for the certificate for every htmx request. But chatting with Claude I learn caddy is pretty cool, it's go. Claude suggests an auth boxcar when I balk at adding auth and user management to my app because I think the webserver should handle all this shit (wtf is a boxcar? Claude clues me in). I tell Claude to use go or rust to build the boxcar because Jesus Christ "yay" build another service just to get a good damn customized CRUD app on my phone that can take a picture. Claude picks go which is fine by me. (Incidentally I can't write go, but I can read it and it's on my "to be learned" agenda and go seems safer than a pile of python for this simple thing) The boxcar was fine but Claude was struggling with getting headers to work in the caddy config. So while Claude is working on that I do a quick Google about whether caddy can have extensions because there has to be a better way to "if someone has authenticated successfully, give them a cookie that will last an hour so they don't have to mash the confirm about using the certificate for every goddamn htmx request" than spin up a web service. Interrupt Claude and suggest an extension instead of a boxcar. Claude's on board so we ditch the boxcar. Have Claude and codex evaluate the extension for security. They find important issues about things a jerk might do, fix them. So successful mTLS connections transition to session cookies. So my dumb CRUD tool doesn't have to worry about auth. Which it didn't have to do anyway except browsers say so etc because my phone is literally only able to access the server via VPN anyway. Other things I have learned today that only wasted 5min of Claude's time rather than hours of mine: Firefox camera access can't control flash, focus or zoom. So call out to the native app instead. This is all quite fun and the tool I'm building is going to really make my own life better. Is there a better way to do this: probably.
simonw 10mo ago
I don't think building it the long way is necessarily a more effective way to learn. You could spend 4 hours (that you don't have) building that feature. Or... you could have the coding agent build it in the background for you in 15 minutes, then spend 30 minutes reading through what it did, tweaking it yourself and peppering it with questions about how it all works. My hunch is that the 30 minutes of focused learning spent with a custom-built version that solves your exact problem is as effective (or even more effective) than four hours spent mostly struggling to get something up and running and going down various rabbit holes of unrelated problem-solving. Especially if realistically you were never going to carve out those four hours anyway.
- ktzar 10mo ago
  It's the same hunch we all have when we think we're going to learn something by watching tutorials. We learn by struggling.
- aabhay 10mo ago
  This feels like the exactly wrong way to think about it IMO. For me “knowledge” is not the explicit recitation of the correct solution, it’s all the implicit working knowledge I gain from trying different things, having initial assumptions fail, seeing what was off, dealing with deployment headaches, etc. As I work, I carefully pay attention to the outputs of all tools and try to mentally document what paths I didn’t take. That makes dealing with bugs and issues later on a lot easier, but it also expands my awareness of the domain, and checks my hubris on thinking I know something, and makes it possible to reason about the system when doing things later on. Of course, this kind of interactive deep engagement with a topic is fast becoming obsolete. But the essence to me of “knowing” is about doing and experiencing things, updating my bayesian priors dialectically (to put it fancily)
  - simonw 10mo ago
    I agree that the only reliable way to learn is to put knowledge into practice. I don't think that's incompatible with getting help from LLMs. I find that LLMs let me try so much more stuff, and at such a faster rate, that my learning pace has accelerated in a material way.
yeasku 10mo ago
Can we see that vector search code or use it?
lordnacho 10mo ago
> I wanted the feature more than I wanted to know how to build the feature This is exactly what LLMs are great for. For instance, I'm looking at trading models. I want to think about buying and selling. I need some charts to look at, but I'm not a chart wizard. I can make basic charts, but it feels tedious to actually learn the model of how the charting software works. LLM will just give me the chart code for the visualization I want, and if I ever care to learn about it, I have it in a form that is relevant to me, not the form of the API documents. In general, a lot of coding is like this. You have some end goal in mind, but there's a bunch of little things that need to be knitted together, and the knitting used to take a lot of time. I like to say the LLM has reduced my toil while getting me to the same place. I can even do multiple projects at once, only really applying myself where there is a decision to be made, and it's all possible because I'm not sorting out the minutiae of some incidental API.
trebligdivad 10mo ago
Well, look through it's log and what it did and if you don't understand anything ask it why it did it/what it does.
exe34 10mo ago
I like having the flexibility. If it's something I want to learn, I'll ask it to write some explanation into an md that I can read, and I can also look at the code diff in more detail. but if it's tedious things like interacting with the android sdk, I'll just let it do whatever it needs to do to get the feature working.

ModernMech 10mo ago

The result of you having worked 4 hours to implement the thing is not just that you have the thing, it's that you have the thing and you understand the thing. Having the thing is next to useless if you don't understand it. At best it plods along as you keep badgering Claude to fix it, until inevitably Claude reaches a point where it can't help. At which time you'll be forced to spend at least the 4 hours you would have originally spent trying to understand it so you can fix it yourself. At worst the thing will actively break other things you do understand in ways you don't understand, and you'll have to spend at least 4 hours cleaning up the mess. Either way it's not clear you've saved any time at all.

OxfordOutlander 10mo ago
> inevitably Claude reaches a point where it can't help. Perhaps not. If LLMs keep getting better, more competent models can help him stay on top of it lol.
- evklein 10mo ago
  You're still captive to a product. Which means that when CloudCo. increases their monthly GenAI price from $50/mo. to $500/mo., you're losing your service or you're paying. By participating in the build process you're giving yourself a fighting chance.
  - pillefitz 10mo ago
    I will quickly forget the details about any given code base within a few months anyway. Having used AI to build a project at least leaves me with very concise and actionable documentation and, as the prompter, I will have a deep understanding of the high-level vision, requirements and functionality.
eleventen 10mo ago
Respectfully, I think I’m in a better position to decide a) what value this has to me and b) what I choose to learn vs just letting Opus deal with. You don’t have enough information to say if I’ve saved time because you don’t know what I’m doing or what my goals are.
- ModernMech 10mo ago
  Respectfully, a) I didn't say anything about what value this has to you but moreover... b) you also don't have enough information to say if it's saved you time because the costs you will bear are in the future. Systems require maintenance, that's a fact you can't get rid of with AI. And often times, maintaining systems require more work than building them in the first place. Maintaining systems tends to require a deep understanding of how they work and the tradeoffs that were decided when they were built. But you didn't build the thing, you didn't even design it as you left that up to Claude. That makes the AI the only thing on the planet that understands the system, but we know actually the AI doesn't understand anything at all. So no one understands the system you built, including the AI you used. And you expect that this whole process will have saved you time, while you play games? I just don't see it working out that way, sorry. The artifact the AI spit out will eventually demand you pay the cost in time to understand it, or you will incur future costs for not understanding it as it fails to act as you expect. You'll pay either way in the end.
  - eleventen 10mo ago
    > And you expect that this whole process will have saved you time, while you play games? The topic in question is “Can AI tools do a task that would take a human 4 hours”. Not whether it can do that in a way that leads to maintainability or sustained learning. I’m noodling on a hobby project as leisure time. I got what I wanted. I had fun. > incur future costs for not understanding it as it fails to act as you expect That is your stronger argument. I’ve seen quality problems with the search results that come from using a smaller embedding model than I should. I don’t know yet if that’s a blocker or tolerable. But I think that argument would be wrong too, because I’m very glad I chose Claude. The biggest limitation might be that I don’t have the compute locally to run an embedding model good enough to achieve decent results. It would have been a huge waste of my time to build it by hand and discover that at the end. I’m not about to pay for a sass vector DB or run this in AWS. At that level of effort I’d just scrap it.
weitendorf 10mo ago
You do learn how to control claude code and architect/orient things around getting it to deliver what you want. That's a skill that is both new and possibly going to be part of how we work for a long time (but also overlaps with the work tech leads and managers do). My proto+sqlite+mesh project recently hit the point where it's too big for Claude to maintain a consistent "mental model" of how eg search and the db schemas are supposed to be structured, kept taking hacky workarounds by going directly to a db at the storage layer instead of the API layer, etc. so I hit an insane amount of churn trying to get it to implement some of the features needed to get it production ready. Here's the whackamole/insanity documented in git commit history: https://github.com/accretional/collector/compare/main...feature/backup-policy-and-fixed-dir https://github.com/accretional/collector/compare/main...feat... But now I know some new tricks and intuition for avoiding this situation going forward. Because I do understand the mental model behind what this is supposed to look like at its core, and I need to maintain some kind of human-friendly guard rails, I'm adding integration tests in a different repo and a README/project "constitution" that claude can't change but is accountable for maintaining, and configuring it to keep them in context while working on my project. Kind of a microcosm of startups' reluctance to institute employee handbook/kpis/PRDs followed by resignation that they might truly be useful coordination tools.
- ModernMech 10mo ago
  Yeah, this is close to my experience with it as well. The AI spits out some tutorial code and it works, and you think all your problems are solved. Then in working with the thing you start hitting problems you would have figured out if you had built the thing from scratch, so you have to start pulling it apart. Then you start realizing some troubling decisions the AI made and you have to patch them, but to do so you have to understand the architecture of the thing, requiring a deep dive into how it works. At the end of the day, you've spent just as much time gaining the knowledge, but one way was inductive (building it from scratch) while the other is deductive (letting the AI build it and then tearing it apart). Is one better than the other? I don't know. But I don't think one saves more time than the other. The only way to save time is to allow the thing to work without any understanding of what it does.
- latentsea 10mo ago

vachina 10mo ago

Yeah and then it becomes an unmaintainable monolith because at some point the AI also lost track of what code does what. Great for Opus because you’re now a captive customer.

tokioyoyo 10mo ago
The point of eventual “all-code-is-written-by-AI” is that it really does not matter if your code is maintainable or not. In the end, most of the products are written to accomplish some sort of a goal or serve a need within a given set of restrictions (cost, speed and etc.). If the goal is achieved within given restrictions, the codebase can be thrown away until the next need is there to just create everything from scratch, if needed.
- simonw 10mo ago
  I don't buy it. I think that could work, but it can work in the same way that plenty of big companies have codebases that are a giant ball of mud and yet they somehow manage to stay in business and occasionally ship a new feature. Meanwhile their rivals with well constructed codebases who can promptly ship features that work are able to run rings around them. I expect that we'll learn over time that LLM-managed big ball of mud codebases are less valuable than LLM-managed high quality well architected long-term maintained codebases.
  - tokioyoyo 10mo ago
    Fair enough. In my imagination, I can see people writing AI-first framework/architectures and a general trend for people to “migrate to such frameworks”, just like the push towards the microservices architectures in 2010s. A part of these frameworks would be “re-constructibility” by changing contracts in parts where it matters, and somehow the framework would make it easy for the LLM to discover such “parts”. Honestly, i’m making stuff up, as I don’t think it’s feasible right now because of the context sizes. But given how fast things develop, maybe in a couple of years things might change.
    - 10mo ago

Avicebron 10mo ago

> I learned essentially nothing about building vector search. I wanted the feature more than I wanted to know how to build the feature Opus/Anthropic is hands down the best in my experience. But using it feels like intellectual fast food (they all are), I hate the fact that I can build something like a neatly presentable one off spa tool (ty Simon) when I'm barely paying attention. it feels unsatisfying to use. EDIT: because I'm rambling, I like "AI" as much as the next guy, probably more because I was there before it turned into LLMs"R"US, but I also like(d) the practice of sitting around listening to music solving problems with Scala. I don't know why we've decided to make work less fun..

pastel8739 10mo ago
“We” didn’t decide to make work less fun, others decided for us.
fluidcruft 10mo ago
I sort of disagree. It's somewhat like having hypercard again. You can build fun UI things and make machines do what you want them to do. You can care about the parts you want to care about and not sweat about the parts you don't want to learn in detail (yet). And Claude and codex make great guides/Sherpas. There are just too many parts involved to do anything. For example today I built a simple data collection app to use on my phone that involves inventories with photos for a tedious workflow I have to do. I knew what I wanted but didn't know how to even choose which tools to bother learn. And just even trying things to see if an approach works or not without spending hours learning one thing or another or wading through the hell of web search is really great. Things I learned today that I figure everyone else must know: if you want to take a photo from a webapp I guess you need https. So I decided to try mTLS (knew it existed but never had the time) so asked Claude to write me a short tutorial about setting it up, creating keys, importing them (including a cool single line trick of spinning up a python server and downloading the keys on my phone rather than find a USB stick or whatever). And then helping me figure out a path out of the suffering of Chrome and Firefox hating self-signed CA. But at least I figured out how to make Firefox happy. But it would insist on prompting me for the certificate for every htmx request. But chatting with Claude I learn caddy is pretty cool, it's go. Claude suggests an auth boxcar when I balk at adding auth and user management to my app because I think the webserver should handle all this shit (wtf is a boxcar? Claude clues me in). I tell Claude to use go or rust to build the boxcar because Jesus Christ "yay" build another service just to get a good damn customized CRUD app on my phone that can take a picture. Claude picks go which is fine by me. (Incidentally I can't write go, but I can read it and it's on my "to be learned" agenda and go seems safer than a pile of python for this simple thing) The boxcar was fine but Claude was struggling with getting headers to work in the caddy config. So while Claude is working on that I do a quick Google about whether caddy can have extensions because there has to be a better way to "if someone has authenticated successfully, give them a cookie that will last an hour so they don't have to mash the confirm about using the certificate for every goddamn htmx request" than spin up a web service. Interrupt Claude and suggest an extension instead of a boxcar. Claude's on board so we ditch the boxcar. Have Claude and codex evaluate the extension for security. They find important issues about things a jerk might do, fix them. So successful mTLS connections transition to session cookies. So my dumb CRUD tool doesn't have to worry about auth. Which it didn't have to do anyway except browsers say so etc because my phone is literally only able to access the server via VPN anyway. Other things I have learned today that only wasted 5min of Claude's time rather than hours of mine: Firefox camera access can't control flash, focus or zoom. So call out to the native app instead. This is all quite fun and the tool I'm building is going to really make my own life better. Is there a better way to do this: probably.

simonw 10mo ago

I don't think building it the long way is necessarily a more effective way to learn. You could spend 4 hours (that you don't have) building that feature. Or... you could have the coding agent build it in the background for you in 15 minutes, then spend 30 minutes reading through what it did, tweaking it yourself and peppering it with questions about how it all works. My hunch is that the 30 minutes of focused learning spent with a custom-built version that solves your exact problem is as effective (or even more effective) than four hours spent mostly struggling to get something up and running and going down various rabbit holes of unrelated problem-solving. Especially if realistically you were never going to carve out those four hours anyway.

ktzar 10mo ago
It's the same hunch we all have when we think we're going to learn something by watching tutorials. We learn by struggling.
aabhay 10mo ago
This feels like the exactly wrong way to think about it IMO. For me “knowledge” is not the explicit recitation of the correct solution, it’s all the implicit working knowledge I gain from trying different things, having initial assumptions fail, seeing what was off, dealing with deployment headaches, etc. As I work, I carefully pay attention to the outputs of all tools and try to mentally document what paths I didn’t take. That makes dealing with bugs and issues later on a lot easier, but it also expands my awareness of the domain, and checks my hubris on thinking I know something, and makes it possible to reason about the system when doing things later on. Of course, this kind of interactive deep engagement with a topic is fast becoming obsolete. But the essence to me of “knowing” is about doing and experiencing things, updating my bayesian priors dialectically (to put it fancily)
- simonw 10mo ago
  I agree that the only reliable way to learn is to put knowledge into practice. I don't think that's incompatible with getting help from LLMs. I find that LLMs let me try so much more stuff, and at such a faster rate, that my learning pace has accelerated in a material way.

yeasku 10mo ago

Can we see that vector search code or use it?

lordnacho 10mo ago

> I wanted the feature more than I wanted to know how to build the feature This is exactly what LLMs are great for. For instance, I'm looking at trading models. I want to think about buying and selling. I need some charts to look at, but I'm not a chart wizard. I can make basic charts, but it feels tedious to actually learn the model of how the charting software works. LLM will just give me the chart code for the visualization I want, and if I ever care to learn about it, I have it in a form that is relevant to me, not the form of the API documents. In general, a lot of coding is like this. You have some end goal in mind, but there's a bunch of little things that need to be knitted together, and the knitting used to take a lot of time. I like to say the LLM has reduced my toil while getting me to the same place. I can even do multiple projects at once, only really applying myself where there is a decision to be made, and it's all possible because I'm not sorting out the minutiae of some incidental API.

trebligdivad 10mo ago

Well, look through it's log and what it did and if you don't understand anything ask it why it did it/what it does.

exe34 10mo ago

I like having the flexibility. If it's something I want to learn, I'll ask it to write some explanation into an md that I can read, and I can also look at the code diff in more detail. but if it's tedious things like interacting with the android sdk, I'll just let it do whatever it needs to do to get the feature working.