Building better tools for satellite hardware teams. ⚒️ Earlier this month, our Lead Electronics Engineer, @poorawsanhsirk, presented at KiCon Europe 2026 on KiCAD-Prism, an open-source platform developed at Pixxel for teams building hardware with KiCad. KiCAD-Prism makes it easier to review designs, manage components, track changes, and collaborate across hardware projects, while giving teams control over their own infrastructure. Built by engineers, for engineers, and now open for the wider hardware community to build on. 🛰️

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

Today, we’re proud to unveil Heaviside-1, @arenaphysica's second generation foundation model for electromagnetism. Five months since announcing Heaviside-0, today’s launch marks a critical milestone towards building electromagnetic superintelligence, a foundational intelligence that understands EM across the spectrum and is capable of designing next gen electromagnetically-governed systems, from RF to semiconductors to photonics. Why we’re so excited about Heaviside-1, and what today’s announcement represents: > It’s big: Heaviside-1 is a GPT-2-size model, roughly 10x the size of Heaviside-0 and trained on 250k unique designs with >500B unique EM field samples > It’s fast: It generates predictions with 5 orders of magnitude acceleration relative to commercial solvers, with under 1dB of accuracy > Full-3D Encoding: Heaviside-1 natively encodes 3D structures, their material properties, and excitation patterns as input, a major step towards generalizability across design spaces > Understands the underlying physics primitive: Heaviside-1 generates full-3D near-fields at arbitrary locations, not just downstream quantities like s-parameters > Generalization: Most importantly, our bet on fields as the primitive for Heaviside-1 generalizes OOD, proving that it’s learning real EM physics. Heaviside-1 generalizes outside of distribution incredibly well and better than Heaviside-0, down to 0.53 dB s-parameter error from 0.99 dB on Heaviside-0. Read more about Heaviside-1 and the work that went into it in this write-up by our research team, @m__frei, @chris_m_bryant, @nathanmirman, @HaoLiu69889370, Tommaso, Roberto, Ruichen, Noah, Boyuan, and @Trevs_Dev, and our RF team @hk2532_harish, @anrfic, @suyangcn and Zhaoji. arenaphysica.com/publication…

We've combined June/July with a wide series of updates as we move into August: mecha.so/blog/mecha-monsoon-… Highlights - IMX95 PCB (EVT) in production - Keyboard extension re-designed - Gamepad PCB (DVT) in production - Native App Early Demos - Prep-work for Certification

Right. forum.kicad.info/t/post-v10-…