lt

You claimed the model can reproduce an image from that training data. That's false, and what the judge dismissed.

  “none of the Stable Diffusion output images provided in response to a 
  particular Text Prompt is likely to be a close match for any specific image in  
  the training data.”

  “I am not convinced that copyright claims based a derivative theory can 
  survive absent ‘substantial similarity’ type allegations,” the ruling stated. 

It has learned to make pixels a particular color to mimic that style, but that's it.

2018 They got funding to research it further,

2020 was a first attempt of publication at Nature that was retracted, further improvements were made until 22/23 were two patents were filled, then suddenly 10 days ago Kwon, one of the co-researchers jumped the gun publishing a paper with the details, on one hand fearing a leak of someone else publishing first as that was too simple to replicate, on the other hand excluding everyone else from the paper and only listing him and Lee/Kim (LK) as authors as a Nobel prize can only be shared by three people. 2.5hrs later LK published again listing other 5 authors but him.

So 512 is really inadequate unless you just want to make search queries using natural language.

First, Reddit's monetization is broken by design. It never made any sense to me why they would charge for reddit gold for an ad-free experience on their website and own mobile app but not on the API. Why would they let third party apps serve their own ads and let them charge to remove them? This would be simple to fix, both technically and in the API's ToS, just serve the same ads regardless of the client. People would be upset, but ultimately I feel it would be entirely fair. But no, it doesn't seem to be a solution considered.

Second, the LLM dataset issue is also attributed to the price hike. Again, I think it's fair if unpopular to charge premium for bulk data. Again, there are technical and ToS solutions for this. They could introduce exponential tiers for bulk data, restrictions on allowed usage, other things that make user-facing usage reasonable but bulk processing expensive, but then again, starting measuring api usage per client id and not per user goes against this point, just making the API extremely expensive for everyone anywhere to the point of being unusable.

Third, all points seem to lead to the fact that what they really want is to kill third party apps and hope a large part of those users move to their app, for what? More tracking, tighter grip, better engagement metrics? Not sure. Even the changes to the extremely hostile mobile site now forcing some users to download the app. Really, I'd figure they'd understand their userbase better than that and how a small fraction on content producers and a even smaller fraction of power users and moderators carry the site, and pissing them off is a really bad idea. But what do I know.

https://twitter.com/XingangP/status/1659483374174584832

The first few releases of model and code have been done by Compvis, and this one by RunwayML. More than permitted by the license, this seems to have been released by the actual developers.

I've seen speculation that it has been released this way to provide distance between Stability.AI from eventual future litigations, but it feels more like an internal ultimatum deadline.

A possible better editorialized title would be

Creating a parallel kernel so we can continually rebase commits instead of every two years

1. unsubscribe to all the big default subreddits, like askreddit, funny, etc (don't worry, you can still check them out if you want).

2. go to r/all and use the filter feature to block the most annoying content or popular stuff that you absolutely don't care about. I blocked politics subreddits, some memes, anime, communities for popular youtubers, some of the worse default ones. Just look at the current /r/all listing and block whatever you don't care about that appears in the first few pages, refresh and do it again a few times. I go back every once in a while to repeat the process.

3. subscribe to specific things you care about. smaller communities are better, some of the large ones are better moderated than others.

4. favorite a few (3-4) subreddits that are about things I want to check often.

My home feed is mostly tailored to my interests, even if there's some fluff. Smaller subreddits I don't check often and appear there. Then I check my favorite subreddits for specific things, and there's r/all for the popular stuff.

I find that general topics like tech, music, sports are usually bad, but more specific, not necessarily niche, are better (a sub about a specific framework, maybe, or about your hometown, favorite band, or favorite team). Moderation style helps a lot.

- temporal, like news, announcements, analysis and discussion on current events. It's usually irrelevant after a while, it gets stale fast.

- atemporal, like essays, history, theory, articles, knowledge in general that while it may get superseded, expanded or invalidated over time, it's interesting content that you learn from, from a current or historical perspective.

While the first kind tends to get more attention due to the clickbaity nature, I much prefer the second kind and wonder if a site focused solely on that would be more interesting.

    “I apologize for such a long letter - I didn't have time to write a short one.”

Maybe someday we'll have a full 3D VR tour, but there's a lot of great stuff there already.

Haven’t taken up on it yet but likely will. Cancelling the test of the trip is going to be tough, financially, so I’m looking on how this progresses.

> The term pilot light is often used to describe a DR scenario in which a minimal version of an environment is always running in the cloud. The idea of the pilot light is an analogy that comes from the gas heater. In a gas heater, a small flame that’s always on can quickly ignite the entire furnace to heat up a house

Technical Debt is about taking shortcuts to have something working now, in exchange for more work maintaining and evolving it in the future. If the software doesn't work, it's not debt, it's a bug.

If you are not changing the software, it doesn't matter that a list is hardcoded instead of parameterizable, that the modules are tightly coupled and can't be reused, or that the build must be done following a particular set of incantations in a specific machine. If you are, these are all examples of debt that will come back charging interest.

Properly planned and managed, technical debt is a tool much like actual debt (a loan) that can accelerate your growth and your time to market. Thing is, most teams don't know that they are taking on a debt and don't realize how much interest they are paying each day.