Nvidia is investing $1 billion in Poolside and paying $6 billion to license its technology and hire most of its engineers.
They are the elephant in the room, with their CDS sky-high. Sounds like they are opinionated on the whole AI thing, trying to drive this with open-source models, countering OpenAI using Jalapeno.
Looks zero-sum for the players.
All the while Google silently planning to get milk from all layers.
They do reach out pretty quick when your usage takes a huge dive after you move to one of the single provider plans that offer hard to believe possibly unsustainable value.
"Google" doesn't understand anything, as it isn't one being.
Google has also as many average employees as any other company, and they will also come up with bad, corporate, ideas.
Honestly the worst part about the LLM age is that everyone is suddenly an "expert", and that is why we get shitty things like A2A or MCP or whatever the next "shiny" overengineered thing is.
Just like with any new technology, people will crawl out the woodwork to establish "standards" just so they can claim fame, money and attention that comes with it.
A2A is a very high level protocol that's meant to be an entry point for the actual network protocols that allow agents to communicate with others like HTTP, gRPC, and others. The promise of no-code means the concept has to exist as an abstraction.
There's a Kaggle course going on where Google discusses A2A and their thoughts on it. If you're a pure vibe coder who doesn't know how to code, A2A is for you. If you know the basics of programming and could even do some web crawling, there are many accessible options.
Google and the industry is honing in on these vibe coders who will look at 10 million tokens consumed to make a checklist application and think nothing of it. The agent to agent (A2A) protocol is for them. Personally, I think it's useful to describe what I'm already doing to people who aren't experts.
Full wave inversion uses all of the information from the wave and more intense computational tomography to image structures that pulse wave B mode cannot, though gases are still a problem. Computationally, if you squint, it's similar to the work Midjourney does with AI image generation, as it progressively generates a structure that fits the data.
Ultrasonic waves can penetrate most structures in humans, including the brain. For example, with focused ultrasound (as they mentioned with MRgFUS) you can burn specific structures in the middle of the brain without any incision.
To use this for imaging, you need lots of transducers (MRgFUS typically uses 1024 for ablation, and Midjourney is proposing 358,000 for imaging) and massive advances in computational tomography capabilities. There will still likely be pockets of low confidence where there's a lot of air, like in the lungs. But with sufficient information on what's happening around those areas, you'd still have something that's medically useful.
Ultrasound researcher here: this work is almost certainly not full wave inversion. You're right that FWI can be done in this kind of setup. Notably, a lot of people are trying to use circular arrays of ultrasound sensors to do FWI, particularly in the brain or the breast. But, it's a challenging inverse problem to solve and as far as I know, it's still extremely slow - nowhere near realtime.
The data they show is almost certainly a normal beamforming algorithm like delay and sum, possibly with some simple speed of sound correction. The most similar paper I know of is here in Nature Biomedical Engineering from a team at Caltech. https://www.nature.com/articles/s41551-026-01660-4
I'm curious how you're addressing any legal aspects about this:
> No black-box AI. Every candidate gets a detailed match receipt explaining exactly why they scored an 85%, complete with contextual evidence from their CV.
HR teams like to play dead when they actually have a file with detailed feedback on a candidate. Yet, they choose to keep that to themselves out of baseless legal fear. I wonder how that works out when somebody proves a company's filter consistently proves a specific bias gets rejected systematically.
and
> Automated assignment validation
which is particularly troubling for devs: companies scaling assignments as first screen. How do you get around "AI evaluating AI" loops especially about assignments ?
Recruiters aren’t afraid to give feedback, and there are often no legal grounds for withholding it in Europe (I’m not counting FAANG companies or certain sectors like fintech). Usually, they simply don’t have the time (and enough motivation) to provide individual feedback to every applicant. This leads to ghosting and transitively to the brand reputational damage. Hiring Method allows you to send feedback to almost everyone!
In our software, the candidate being assessed does not know all the assessment criteria. Furthermore, this assessment is merely a starting point for discussion during the technical round. I need to update the description of this feature.
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