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Curated tech Twitter/X posts on AI, startups, and product development—signal without the doomscroll, updated on weekdays.
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Robots learn slowly because trying something takes a whole afternoon in a lab Antioch from @antiochrobotics compresses the loop, hundreds of runs while the hardware sits still Physical AI has always been short on tries Smarter models help, more attempts help more
Today, we’re announcing Antioch’s $32 million Series A, led by @GreylockVC with participation from @A_StarVC, @Category_VC, @BoxGroup, @IcehouseVenture, and angels. While AI has drastically accelerated software development, physical autonomy has been constrained by slow, expensive hardware-based development. Antioch enables physical AI teams to build, test, and validate systems at the speed of software. We’re proud to be working with leading teams including @amazon @ring, @NVIDIARobotics, and @nebiusai as we make scaled, high-fidelity simulation the standard for physical AI development. Our sincere thanks to the legion of investors, customers, partners, and advisors who are making this step change possible. We’re scaling rapidly and hiring across simulation, infrastructure, machine learning, 3D graphics, go-to-market, and operations.