Which provides way more information than the article

The OpenAI deal almost certainly related purely to GPU capacity, which had little to do with the article. The layoffs would have happened regardless.

IMO - churn, and generalization is the root cause. Engineers are thrown on projects for a year with little prior experience, leave others to pickup the pieces, etc. There's no longer a sense of ownership, and I'm sure the recent wave of layoffs isn't helping with this.

Proper obfuscation libraries exist, typically at the cost of a pretty notable amount of performance that I'd wager most are not willing to sacrifice

And like even the best of client-side DRM, everything can be reverse engineered. All the code has been downloaded to the user's machine. It's one of the (IMO terrible) excuses for the SaaSification of all software

Though for greenfield development, writing the test cases (like the spec) is equally as hard, if not harder than writing the code.

I also observe that LLMs tend to find themselves trapped in local minima. Once the codebase architecture has been solidified, very rarely will it consider larger refactors. In some ways - very similar to overfitting in ML

I'm surprised all the flights in the world can be represented in a <500KB api call

Imagine giving the power to rightsholders to terminate anyone's internet service with e.g, a DMCA takedown. I'm sure that won't be abused at all, and is a very necessary step to protecting "artists"

I had Tidal many years back, and from the Lossless v Regular I only ever noticed a difference when it came to breathy sounds/etc. I did see that Tidal would burn through like 50GB of data monthly though.

Also - you may want to test some more modern recordings, the microphone/mastering quality of things nowadays is far better than what it was 2 decades ago (despite what some audiophiles may claim)

It would be very nice to have a little more details on how this works. Though I guess we can figure it out via code/trialing it out.

I'll give it a go later today!

Realistically a model will never be "compiled" 1:1. Copyrighted data is almost certainly used and even _if_ one could somehow download the petabytes of training data - it's quite likely the model would come out differently.

The article seems to be talking more about the difficulties of fine tuning models though - a setup problem that likely exists in all research, and many larger OSS projects that get more complicated.

For this one - it's tough. It definitely started a conversation. The 'cc' part of the name is sus, but on the internet, its impossible to verify the truth of any post. But the counterpoint is - if for some reason Anthropic wanted to fund a large scale astroturfing op -- why make is so obvious with cc postfixes?

I'd say the bigger problem would be AI posters/commentors, though I've not seen as much of them versus certain subreddits which are just probably more bots than human...

Unfortunately this is the way the world seems to be going... Every SaaS, startup is subscription based, filled with ads, etc... But at the same time with the cost of marketing/outreach, traditional distribution just doesn't exist.