151–160 of 382 posts
So I'm not sure this is a good rule of thumb. AI is better at doing some things than others, but the boundary is not that simple.
Earlier quoted context omitted.
Its license washing. The code is great because its already a problem solved by someone else. The AI can spit out the solution with no license and no attribution and somehow its legal. I hope American tech legislation holds that same energy once others start taking American IP and spitting it back out with no license or attribution.
I've seen many discussions stating patent hoarding has gone too far, and also that copyright for companies have gone way too far (even so much that Amazon can remove items from your purchase library if they lose their license to it). Then AI begins to offer a method around this over litigious system, and this becomes a core anti-AI argument. I do think it's silly to think public code (as in, code published to the pub…
2. GPL does not allow you to take the code, compress it in your latent space, and then sell that to consumers without open sourcing your code.
Earlier quoted context omitted.
I've seen many discussions stating patent hoarding has gone too far, and also that copyright for companies have gone way too far (even so much that Amazon can remove items from your purchase library if they lose their license to it). Then AI begins to offer a method around this over litigious system, and this becomes a core anti-AI argument. I do think it's silly to think public code (as in, code published to the pub…
1. Equality under the law is important in its own right. Even if a law is wrong, it isn’t right to allow particular corporations to flaunt it in a way that individuals would go to prison for. 2. GPL does not allow you to take the code, compress it in your latent space, and then sell that to consumers without open sourcing your code.
No one goes to prison for this. They might get sued, but even that is doubtful.
Earlier quoted context omitted.
That doesn't make any sense to me. When the code is written, it's all laid out nicely for the reader to understand quickly and verify. Everything is pre-organized, just for you the reader. But in order to write the code, you might have to try 4 different top-level approaches until you figure out the one that works, try integrating with a function from 3 different packages until you find the one that works properly, h…
> But in order to write the code, you might have to try 4 different top-level approaches until you figure out the one that works , try integrating with a function from 3 different packages until you find the one that works properly If you haven’t spent the time to try the different approaches yourself, tried the different packages etc., you can’t really judge if the code you’re reading is really the appropriate thing…
You're just making sure it works correctly and that you understand how. Not superficially, but thinking through it indeed. That the tests are covering it. It doesn't take that long.
What you're describing sounds closer to studying the Talmud than to reading and reviewing most code.
Like, the kind of stuff you're describing is not most code. And when it is, then you've got code that requires design documents where the approach is described in great detail. But again, as a reader you just read those design documents first. That's what they're there for, so other people don't have to waste time trying out all the false starts and dead ends and incorrect architectures. If the code needs this massive understanding, then that understanding needs to be documented. Fortunately, most functions don't need anything like that.
Earlier quoted context omitted.
Its license washing. The code is great because its already a problem solved by someone else. The AI can spit out the solution with no license and no attribution and somehow its legal. I hope American tech legislation holds that same energy once others start taking American IP and spitting it back out with no license or attribution.
The models need to get burned down and retrained with these considerations baked in.
Earlier quoted context omitted.
Its license washing. The code is great because its already a problem solved by someone else. The AI can spit out the solution with no license and no attribution and somehow its legal. I hope American tech legislation holds that same energy once others start taking American IP and spitting it back out with no license or attribution.
I've seen many discussions stating patent hoarding has gone too far, and also that copyright for companies have gone way too far (even so much that Amazon can remove items from your purchase library if they lose their license to it). Then AI begins to offer a method around this over litigious system, and this becomes a core anti-AI argument. I do think it's silly to think public code (as in, code published to the pub…
So any argument that posting stuff online provides an implicit license is severely flawed.
Earlier quoted context omitted.
I've seen many discussions stating patent hoarding has gone too far, and also that copyright for companies have gone way too far (even so much that Amazon can remove items from your purchase library if they lose their license to it). Then AI begins to offer a method around this over litigious system, and this becomes a core anti-AI argument. I do think it's silly to think public code (as in, code published to the pub…
1. Equality under the law is important in its own right. Even if a law is wrong, it isn’t right to allow particular corporations to flaunt it in a way that individuals would go to prison for. 2. GPL does not allow you to take the code, compress it in your latent space, and then sell that to consumers without open sourcing your code.
Sure, that's what the paper says. Most people don't care what that says until some ramifications actually occur. E.g. a cease and desist letter. Maybe people should care, but companies have been stealing IP from individuals long before GPL, and they still do.
I think AI is just a massive force multiplier. If your codebase has bad foundation and going in the wrong direction with lots of hacks, it will just write code which mirrors the existing style... And you get exactly was OP is suggesting. If however, your code foundations are good and highly consistent and never allow hacks, then the AI will maintain that clean style and it becomes shockingly good; in this case, the p…
I agree completely. I just did my first “AI native coding project”. Both because for now I haven’t run into any quotas using Codex CLI with my $20/month ChatGPT subscription and the company just gave everyone an $800/month Claude allowance. Before I even started the implementation I: 1. Put the initial sales contract with the business requirements. 2. Notes I got from talking to sales 3. The transcript of the initial…
yes. the linux kernel and it's extensive mailing lists come to mind. in fact, any decent project which was/is built in a remote-only scenario tends to have extensive documentation along these lines, something like gitlab comes to mind there.
personally i've included design documents with extensive notes, contracts, meeting summaries etc etc in our docs area / repo hosting at $PREVIOUS_COMPANY. only thing from your list we didn't have was transcripts because they're often less useful than a summary of "this is what we actually decided and why". edit -- there were some video/meeting audio recordings we kept around though. at least one was a tutoring session i did.
maybe this is the first time you've felt able to do something like this in a short amount of time because of these GenAI tools? i don't know your story. but i was doing a lot of this by hand before GenAI. it took time, energy and effort to do. but your project is definitely not the first to have this level of detailed contextual information associated with it. i will, however, concede that these tools can make it it easier/faster to get there.
I vibe coded a retro emulator and assembler with tests. Prompts were minimal and I got really great results (Gemini 3). I tried vibe coding the tricky proprietary part of an app I worked on a few years ago; highly technical domain (yes vague don’t care to dox myself). Lots of prompting and didn’t get close. There are literally thousands of retro emulators on github. What I was trying to do had zero examples on GitHub…
I call these "embarrassingly solved problems". There are plenty of examples of emulators on GitHub, therefore emulators exist in the latent spaces of LLMs. You can have them spit one out whenever you want. It's embarrassingly solved. There are no examples of what you tried to do.
I saved the blank file as wordle.py to start the coding while explaining ideas.
That was enough context for github copilot to suggest the entire `for` loop body after I just typed "for"
Not much learning by doing happened in that instance.
Before this `for` loop there were just two lines of code hardcoding some words ..that too were heavily autocompleted by copilot including string constants.
``` answer="cigar" guess="cigar" ```