lol no they're not

ngl that felt like reading a lot of fluff

"If the founder/CEO and best engineers at Windsurf don't believe in that product/company, why should you?"

^ Any company that competes with them could say that and it would create some pause.

I know it'll probably get better over time but it's pretty awful compared to what you can do with v0 right now.

you know how the endorsements work right? some comms intern writes a quote, emails it to someone at the other companies for the go ahead/approval, and that's how you get dozens of companies all spouting BS that kinda sounds the same.

agents are just the rebranded version of automated workflows and sometimes its an LLM doing the validation

Llama is not an image generating model. Any interface that uses Llama and generates images is calling out to a separate image generator as a tool, like OpenAI used to do with ChatGPT and DALL-E up until a couple of weeks ago: https://simonwillison.net/2023/Oct/26/add-a-walrus/

GPT 4o images is the future of all image gen.

Every other player: Black Forest Labs' Flux, Stability.ai's Stable Diffusion, and even closed models like Ideogram and Midjourney, are all on the path to extinction.

Image generation and editing must be multimodal. Full stop.

Google Imagen will probably be the first model to match the capabilities of 4o. I'm hoping one of the open weights labs or Chinese AI giants will release a model that demonstrates similar capabilities soon. That'll keep the race neck and neck.

One very important distinction between image models is the implementation: 4o is autogressive, slow, and extremely expensive.

Although the Ghibli trend is market validation, I suspect that competitors may not want to copy it just yet.

Extremely expensive in what since? In that it costs $.03 instead of $.00003c? Yeah it's relatively far more expensive than other solutions, but from an absolute standpoint still very cheap for the vast majority of use cases. And it's a LOT better.

Dall-E is already 4-8 cents per image. Afaik this is not in the API yet but I wouldn't be surprised if it's $1 or more.

> 4o is autogressive, slow, and extremely expensive.

If you factor in the amount of time wasted with prompting and inpainting, it's extremely well worth it.

This feels like an MWAA issue but I understand how that often gets conflated with it being an Airflow issue.

You're right, it doesn't happen when developing locally, only in MWAA. This was the answer given by the Airflow team as well and I figured they would punt before I asked.

I realize Amazon is taking an open source project and making a ton of money on it (the instance prices are ridiculous for what you get) and the incentives are misaligned for the Airflow team to help AWS make it better unless AWS paid them to help fix it.

It's crap all around, and Airflow gets a bad rap from AWS's terrible MWAA product based on it.

Temporal’s great! That being said, there is something about being able to orchestrate LLMs and agents using what many already use to orchestrate their data workflows because there’s already proven out reliability, scalability, observability, etc. I’m sure there are boundary conditions for really advanced agentic workflows though…

Temporal is absolutely not for a static graph, idempotent nodes yes. Please explain your argument more

> Temporal is absolutely not for a static graph

I'd clarify this to say "Temporal is absolutely not limited to a static graph." It can certainly handle a static graph, but it can also handle a dynamic one. Here is an example in Go (https://github.com/temporalio/samples-go/tree/main/choice-mu...), there are similar ones for other languages.

I think the confusion might stem from the determinism requirement in Temporal (and other replay-based Durable Execution platforms). It's not the Workflow Definition (i.e., the code) that must be deterministic, it's the Workflow Execution (i.e., a specific running instance of that code) that must be deterministic. Each running instance is allowed to take a different path through that code, so long as it does so consistently when executed with the same input.

i worked at MongoDB for nearly 10 years... when the heck did they have booth babes at conferences?