We’re excited to introduce the Brick by Brick podcast, where we learn how startups were built directly from the people who built them!

The first episode of The New Feynman Lectures, Season 1 is live! In Episode 1, @PratapRanade sits down with Shaun Donovan, Head of Electrical and Mechanical Engineering @anduriltech to talk all things triplex redundancies in autonomous aircraft. Shaun is a wealth of knowledge with incredible stories and lessons learned through his years of experience building the most ambitious UAVs, nuclear reactors, and more. You’ll commonly hear those who work with him, or have worked with him, refer to him as the smartest person they know, and the best engineer they know. He’s built a world-class engineering culture and team @anduriltech that we @arenaphysica are privileged to work with and support. I learn something new every time I’m with Shaun. Watch Episode 1, and I’m sure you will too. youtube.com/watch?v=_AIoes8j…

American manufacturing is one of the hardest problems of our generation. We're here to solve it. Today we announce $19M in seed funding, backed by @khoslaventures, @hanabicapital, @redglassvc, @ZeroShotFund, and all our other incredible investors. AI-first. Software-defined. Just getting started. We're hiring.

We’re excited to announce today that @mastra has raised a $22M Series A led by Spark Capital. This brings our total capital raised to $35M:

Today, we're announcing Heaviside, our foundation model for electromagnetism. Trained on tens of millions of designs and over 20 years of proprietary simulation data, Heaviside predicts electromagnetic behavior from geometry in 13ms, which is 800,000x faster than a commercial solver. Heaviside is not a language model, and it’s not a surrogate model. Heaviside marks a new class of foundation model for physics which understands the fundamental relationships between materials, the geometries and the electromagnetic fields they generate. We’re releasing a research preview of Heaviside in Atlas RF Studio, an interactive agentic sandbox where you describe the EM behavior you want and the model generates the physical structure that produces it. @arenaphysica , we believe the implications of this class of model extend well beyond RF, as the frontier of exquisite hardware is electromagnetically-governed: wireless communication, radar, power delivery, high-speed computing, and the interconnects inside every chip on earth. In the months ahead, we’re excited to scale up Heaviside to broader frequency ranges, design spaces, and to support silicon-level designs, and deploy it with our closest partners and collaborators in service of their biggest design challenges. If you’ve read our thesis, this is just Step 2 in our pursuit of electromagnetic superintelligence. Read the full announcement and try Atlas RF Studio…tell us what you think: arenaphysica.com/publication…

Today, we're announcing Heaviside, our foundation model for electromagnetism. Trained on tens of millions of designs and over 20 years of proprietary simulation data, Heaviside predicts electromagnetic behavior from geometry in 13ms, which is 800,000x faster than a commercial solver. Heaviside is not a language model, and it’s not a surrogate model. Heaviside marks a new class of foundation model for physics which understands the fundamental relationships between materials, the geometries and the electromagnetic fields they generate. We’re releasing a research preview of Heaviside in Atlas RF Studio, an interactive agentic sandbox where you describe the EM behavior you want and the model generates the physical structure that produces it. @arenaphysica , we believe the implications of this class of model extend well beyond RF, as the frontier of exquisite hardware is electromagnetically-governed: wireless communication, radar, power delivery, high-speed computing, and the interconnects inside every chip on earth. In the months ahead, we’re excited to scale up Heaviside to broader frequency ranges, design spaces, and to support silicon-level designs, and deploy it with our closest partners and collaborators in service of their biggest design challenges. If you’ve read our thesis, this is just Step 2 in our pursuit of electromagnetic superintelligence. Read the full announcement and try Atlas RF Studio…tell us what you think: arenaphysica.com/publication…

The future is electromagnetic. One challenge is that there are ~ten people in the world who can deeply intuit electromagnetism. RF engineering is "black magic." Arena Physica thinks machines can intuit EM better. CEO Pratap Ranade & I on AI for EM: notboring.co/p/electromagnet…

A mini-rant abut AI and longevity. They say "Artificial Superintelligence would take only a few years to cure cancer, solve longevity, and defeat death itself'. This is a common claim by pro-AI lobbyists, accelerationists, and naive tech-fetishists. But the claim makes no sense. The recent success of LLMs does NOT suggest that ASIs could easily cure diseases or solve longevity, for at least two reasons. 1) The data problem. Generative AI for art, music, and language succeeded mostly because AI companies could steal billions of examples of art, music, and language from the internet, to build their base models. They weren't just trained on academic papers _about_ art, music, and language. They were trained on real _examples_ of art, music, and language. There are no analogous biomedical data sets with billions of data points that would allow accurate modelling of every biochemical detail of human physiology, disease, and aging. ASIs can't just read academic papers about human biology to solve longevity. They'd need direct access to vast quantities of biomedical data that simply don't exist in any easy-to-access forms. And they'd need very detailed, reliable, validated data about a wide range of people across different ages, sexes, ethnicities, genotypes, and medical conditions. Moreover, medical privacy laws would make it extremely difficult and wildly unethical to collect such a vast data set from real humans about every molecular-level detail of their bodies. 2) The feedback problem. LLMs also work well because the AI companies could refine their output with additional feedback from human brains (through Reinforcement Learning from Human Feedback, RLHF). But there is nothing analogous to that for modeling human bodies, biochemistry, and disease processes. There are no known methods of Reinforcement Learning from Physiological Feedback. And the physiological feedback would have to be long-term, over spans of years to decades, taking into account thousands of possible side-effects for any given intervention. There's no way to rush animal and human clinical trials -- however clever ASI might become at 'drug discovery'. More generally, there would be no fast feedback loops from users about model performance. GenAI and LLMs succeeded partly because developers within companies, and customers outside companies, could give very fast feedback about how well the models were functioning. They could just look at the output (images, songs, text), and then tweak, refine, test, and interpret models very quickly, based on how good they were at generating art, music, and language. In biomedical research, there would be no fast feedback loops from human bodies about how well ASI-suggested interventions are actually affecting human bodies, over the long term, across different lifestyles, including all the tradeoffs and side-effects. It's interesting that most of the people arguing that 'ASI would cure all diseases and aging' are young tech bros who know a lot about computers, but almost nothing about organic chemistry, human genomics, biomedical research, drug discovery, clinical trials, the evolutionary biology of senescence, evolutionary medicine, medical ethics, or the decades of frustrations and failures in longevity research. They think that 'fixing the human body' would be as simple as debugging a few thousand lines of code. Look, I'm all for curing diseases and promoting longevity. If we took the hundreds of billions of dollars per year that are currently spent on trying to build ASI, and we devoted that money instead to longevity research, that would increase the amount of funding in the longevity space by at least 100-fold. And we'd probably solve longevity much faster by targeting it directly than by trying to summon ASI as a magical cure-all. ASIs has some potential benefits (and many grievous risks and downsides). But it's totally irresponsible of pro-AI lobbyists to argue that ASIs could magically & quickly cure all human diseases, or solve longevity, or end death. And it's totally irresponsible of them to claim that anyone opposed to ASI development is 'pro-death'.

Introducing our biggest upgrade to @googlemaps since the original launch, featuring Ask Gemini (with personalization), Immersive Navigation, and much more!! 🗺️