Having a single perfect product strategy with non-overlapping product categories and understandable names is hard for any organization, particularly in a rapidly evolving space.

Its obviously an issue to have multiple mature products be chaotically names.

At this moment antigravity and gemini cli and are hardly mature. Isn't now the perfect time to consolidate?

[1] https://playground.mujoco.org/

Content is graded on both instant appeal (e.g. rotten tomatoes "popcornmeter") and artistic appeal (e.g. rotten tomatoes "tomatometer").

I firmly believe that AI generated content cannot have any artistic appeal, because I believe art is fundamentally an invocation of human expression. This might be fine in some contexts, but in general I'd prefer consuming content from groups that I trust to strike a good balance between these types of appeal (e.g. A24 movies).

- "removing the kettlebell" led to removing the visual representation of the kettlebell as well the deformation it makes on the pillow

- "removing the hands" removed the childs hands from the tops, but did not then lead to the tops falling over!

Others like the colliding cars are in some weird gray area between the two.

One should note as these tools proliferate, there is a lot of artistic expression that we are giving up to these imprecise natural language parsing engines.

What does this even mean? Who is being "genuine"? This is far to naive a take for a company thats burning through hundreds of millions of dollars, and constantly striving to set the tone of AI and their own supremacy.

I think what you are missing is their annual comp with two commas in it.

"Dont trust google" imo is the wrong response here. We are at the mercy of our institutions, and if they are failing us we need mechanisms to keep them in check.

I think the best way to think about it is that its an engineering hack to deal with a shortcoming of LLMs: for complex queries LLMs are unable to directly compute a SOLUTION given a PROMPT, but are instead able to break down the prompt to intermediate solutions and eventually solve the original prompt. These "orchestrator" / "swarm" agents add some formalism to this and allow you to distribute compute, and then also use specialized models for some of the sub problems.

[1] https://www.deepflow.com/

The fact that tech leaders espouse the brilliance of LLMs and don't use this specific test method is infuriating to me. It is deeply unfortunate that there is little transparency or standardization of the datasets available for training/fine tuning.

Having this be advertised will make more interesting and informative benchmarks. OEM models that are always "breaking" the benchmarks are doing so with improved datasets as well as improved methods. Without holding the datasets fixed, progress on benchmarks are very suspect IMO.

I wish / hope the medical community will address stories like this before people lose trust in them entirely. How frequent are mis-diagnosis like this? How often is "user research" helping or hurting the process of getting good health outcomes? Are there medical boards that are sending PSAs to help doctors improve common mis-diagnosis? Whats the role of LLMs in all of this?

- note that google already has investment in industrial robotics with intrinsic ai (that is the confluence of OSRF, bot and dolly, and a few other robotics/ai companies)

- this partnership specifically seems to be focused on humanoid robotics, which is not taken seriously by industrial manufacturing folks