heliophobicdude

Any word on if this using their diffusion architecture?

This looks way better than mermaid. Is there support for this in GitHub PR code blocks like mermaid?

It's super fast to iterate with? Curious what mistakes are you seeing and how were you using it?

I use the instant edit feature with directions on exact code changes.

Wishing they release the Gemini Diffusion model. It'll quickly replace the default model for Aider.

Might be private repo? I get 404 as well

Question for the researchers, can dLLMs be pinned down with a seed? Can they be made 100% deterministic?

I think the lede is being buried. This is a great and fast InstructGPT. This is absolutely going to be used in spell checks, codemods, and code editors.

Instant edits feature can surgically perform text edits fast without all the extra fluff or unsolicited enhancements.

I copied shadertoys, asked it to rename all variables to be more descriptive and pasted the result to see it still working. I'm impressed.

I've been let off the waitlist. So far, I'm impressed with the Instant Edits. It's crazy fast. I can provide a big HTML file and prompt it to change a color theme and it makes careful edits to just the relevant parts. It seems to be able to parallelize the same instruction to multiple parts of the input. This is incredible for refactoring.

I copied a shader toy example, asked it to rename all the variables to be more descriptive and it edited just the variable names. I was able to compile and run in shader toy.

Thank you for sharing this. I'm amazed! Are there any known emergent abilities of it? I ran my evals and seems to struggle in very similar ways to smaller transformer based LLMs

It's a great language for Application-level software on a single platform (could be Linux).

It has some challenges that it needs to solve to do great as a cross platform "general-purpose" programming language.

It's paradoxically high level with its syntax and ergonomics but is tied down to the same cross platform headaches like in low level languages (e.g. cpp). Linking across cross platforms requires lots of careful thought and testing. Unlike cpp, it's not super portable. It requires a hefty 30 MB runtime for some features of the language to work. Try static executable hello world.

That being said, it's possible. You can build cross platform applications with Swift, but you'd still have some of the same kinds of portability issues like in cpp but with nicer syntax and ergonomics.

> I would actually argue that axing Image Playground would improve Apple Intelligence; its mere existence greatly lowers the expectations for how good the whole thing is.

I was expecting a discount. In the onboarding it says "Prices are the same. You won't pay more for autonomous rides."

If that's the case, I would rather give my money to a human driver than just Uber.

Do you still also need to compile a mac build on a Mac?

Is the date April 2 or February 4?

We should NOT give up on scaling pretraining just yet!

I believe that we should explore pretraining video completion models that explicitly have no text pairings. Why? We can train unsupervised like they did for GPT series on the text-internet but instead on YouTube lol. Labeling or augmenting the frames limits scaling the training data.

Imagine using the initial frames or audio to prompt the video completion model. For example, use the initial frames to write out a problem on a white board then watch in output generate the next frames the solution being worked out.

I fear text pairings with CLIP or OCR constrain a model too much and confuse