What? I’ve already written the design documentation and done all the creative and architectural parts that I consider most rewarding. All that’s left for coding is answering questions like “what exactly does the API I need to use look like?” and writing a bunch of error handling if statements. That’s toil.

Definitely depends on the person. There are definitely people who are getting 90% of their coding done with AI. I’m one of them. I have over a decade of experience and I consider coding to be the easiest but most laborious part of my job so it’s a welcome change.

One thing that’s really changed the game recently is RAG and tools with very good access to our company’s data. Good context makes a huge difference in the quality of the output. For my latest project, I’ve been using 3 internal tools. An LLM browser plugin which has access to our internal data and let’s you pin pages (and docs) you’re reading for extra focus. A coding assistant, which also has access to internal data and repos but is trained for coding. Unfortunately, it’s not integrated into our IDE. The IDE agent has RAG where you can pin specific files but without broader access to our internal data, its output is a lot poorer.

So my workflow is something like this: My company is already pretty diligent about documenting things so the first step is to write design documentation. The LLM plugin helps with research of some high level questions and helps delve into some of the details. Once that’s all reviewed and approved by everyone involved, we move into task breakdown and implementation.

First, I ask the LLM plugin to write a guide for how to implement a task, given the design documentation. I’m not interested in code, just a translation of design ideas and requirements into actionable steps (even if you don’t have the same setup as me, give this a try. Asking an LLM to reason its way through a guide helps it handle a lot more complicated tasks). Then, I pass that to the coding assistant for code creation, including any relevant files as context. That code gets copied to the IDE. The whole process takes a couple minutes at most and that gets you like 90% there.

Next is to get things compiling. This is either manual or in iteration with the coding assistant. Then before I worry about correctness, I focus on the tests. Get a good test suite up and it’ll catch any problems and let you reflector without causing regressions. Again, this may be partially manual and partially iteration with LLMs. Once the tests look good, then it’s time to get them passing. And this is the point where I start really reading through the code and getting things from 90% to 100%.

All in all, I’m still applying a lot of professional judgement throughout the whole process. But I get to focus on the parts where that judgement is actually needed and not the more mundane and toilsome parts of coding.

The language model isn’t teaching anything it is changing the wording of something and spitting it back out. And in some cases, not changing the wording at all, just spitting the information back out, without paying the copyright source.

You could honestly say the same about most “teaching” that a student without a real comprehension of the subject does for another student. But ultimately, that’s beside the point. Because changing the wording, structure, and presentation is all that is necessary to avoid copyright violation. You cannot copyright the information. Only a specific expression of it.

There’s no special exception for AI here. That’s how copyright works for you, me, the student, and the AI. And if you’re hoping that copyright is going to save you from the outcomes you’re worried about, it won’t.

Makes sense to me. Search indices tend to store large amounts of copyrighted material yet they don’t violate copyright. What matters is whether or not you’re redistributing illegal copies of the material.

If I understand correctly they are ruling you can by a book once, and redistribute the information to as many people you want without consequences. Aka 1 student should be able to buy a textbook and redistribute it to all other students for free. (Yet the rules only work for companies apparently, as the students would still be committing a crime)

A student can absolutely buy a text book and then teach the other students the information in it for free. That’s not redistribution. Redistribution would mean making copies of the book to hand out. That’s illegal for people and companies.

ITT: A bunch of people who have never heard of information theory suddenly have very strong feelings about it.

Models are not improving? Since when? Last week? Newer models have been scoring higher and higher in both objective and subjective blind tests consistently. This sounds like the kind of delusional anti-AI shit that the OP was talking about. I mean, holy shit, to try to pass off “models aren’t improving” with a straight face.

Because that’s not what copyright is for. It exists to give the creator exclusive rights over distribution. That’s it. So unless the company is planning to distribute the work and they obtained a copy willingly and legally distributed to them, then copyright is the wrong law to lean on.

I mean, it’s in the name. The right to make copies. Not to be glib, but it really is

A copyright is a type of intellectual property that gives its owner the exclusive legal right to copy, distribute, adapt, display, and perform a creative work, usually for a limited time.

You may notice a conspicuous absence of control over how a copied work is used, short of distributing it. You can reencode it, compress it, decompress it, make a word cloud, statistically analyze its tone, anything you want as long as you’re not redistributing the work or an adaptation (which has a pretty limited meaning as well). “Personal use” and “fair use” are stipulations that weaken a copyright owner’s control over the work, not giving them new rights above and beyond copyright. And that’s a great thing. You get to do whatever you want with the things you own.

You don’t have a right to other people’s work. That’s what copyright enables. But that’s beside the point. The owner doesn’t get to say what you use a work for that they’ve distributed to you.

Training literally is consuming. A copyright license doesn’t get to dictate what computer programs the work is allowed to be used with. There’s a ton a entertainment mega corps that would love for that to be the case, though.

You’re saying that you’re not allowed to do a statistical analysis on a copyrighted work. It’s nonsense. It’s well-established that copyright does not prevent that kind of use.

Copyright licensing allows the owner to control how a work is distributed, not how it’s consumed. “Personal use” just means that you can’t turn around and redistribute a work that you’ve obtained. Not that you’re not allowed to consume it in a corporate setting.

C is just a work around for B and the fact that the technology has no way to identify and overcome harmful biases in its data set and model. This kind of behind the scenes prompt engineering isn’t even unique to diversifying image output, either. It’s a necessity to creating a product that is usable by the general consumer, at least until the technology evolves enough that it can incorporate those lessons directly into the model.

And so my point is, there’s a boatload of problems that stem from the fact that this is early technology and the solutions to those problems haven’t been fully developed yet. But while we are rightfully not upset that the system doesn’t understand that lettuce doesn’t go on the bottom of a burger, we’re for some reason wildly upset that it tries to give our fantasy quasi-historical figures darker skin.

I mean “taking pictures of people who are smiling” is definitely a bias in our culture. How we collectively choose to record information is part of how we encode human biases.

I get what you’re saying in specific circumstances. Sure, a dataset that is built from a single source doesn’t make its biases universal. But these models were trained on a very wide range of sources. Wide enough to cover much of the data we’ve built a culture around.

This is the thing. There’s an incredible number of inaccuracies in the picture, several of which flat out ignore the request in the prompt, and we laugh it off. But the AI makes his skin a little bit darker? Write the Washington Post! Historical accuracy! Outrage!

DVDs (how many people even still own a player?) are not a real alternative to streaming for a number of reasons. Nor is “just watch something else on another platform.” Or, at least, if your claim is that entertainment is interchangeable then you’ve got no real complaint about YouTube. Hell, YouTube has its own ad-free subscription. By your own logic, the ads can’t be enshittificantion because you can just pay more to avoid it!

The enshittification of Netflix goes beyond just charging more. It’s any decision the company makes to make the user experience worse so they can make more money. That’s things like hiding your list and your recently watched shows so they can make you scroll through more recommendations. So then they can autoplay the content they stuck in your way. Recommendations that, like YouTube, are more concerned with what they want to monetize than what you actually want. And it’s restricting the way you used to be able to use the service, like on multiple TVs even within the same house, to get you to wade through a bunch of payment plans.

But my point still stands. Enshittification doesn’t require them to become a monopoly and start producing nothing but reality TV. It just describes the strategy shift that these companies inevitably make from making the platform better to attract more users, to making it worse to extract more money from the user base they’ve built up.

Eh, people have their own tastes in TV. Streaming companies buy exclusive rights to certain content and if that’s where your tastes lie, you’re pretty SOL. It’s about as close to “lock-in” as you can get.

Your definition of enshittificantion is also far too strict. It’s just the shift that companies inevitably make from trying to attract new users quickly by providing a great service, to trying to extract maximum profit by degrading the service quality and cramming in as much revenue generation as they can.