Circuit Mind

Every vendor promises results. The real question is 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘁𝗵𝗲𝘆 𝗵𝗼𝗹𝗱 𝘂𝗽 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘂𝗻𝗱𝗲𝗿 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝘀𝗰𝗿𝘂𝘁𝗶𝗻𝘆. Teams using Circuit Mind in production are turning multi-week timelines into days, and achieving lower BoM costs: ▸ Design 1st: 2 days against a 12-day budget, and 32-43% lower BoM cost ▸ APAG cosyst: RFQ-ready design in 2 days against a 9-day budget (78% faster) ▸ Nextech: 3 days against a 12-day estimate, and 15% lower component costs Last year, Los Alamos National Laboratory ran a peer-reviewed evaluation of the Circuit Mind platform and published report 𝗟𝗔-𝗨𝗥-𝟮𝟱-𝟯𝟬𝟴𝟰𝟮. They concluded that Circuit Mind automated ~90% of a "medium-difficulty" microcontroller design, with auto-generated verification, FMECA, and derating reports produced alongside it. That's #6 in our guide, 𝟲 𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝗳𝗼𝗿 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 𝗳𝗼𝗿 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀 𝗗𝗲𝘀𝗶𝗴𝗻 – validated outcomes. Independent, peer-reviewed validation is the standard to hold vendors to. Also inside the guide: ▸ Why generic AI fails on the physics of circuits ▸ How to guard against hallucinations and produce designs you can defend in review ▸ How to make AI respect your engineering DNA: approved vendor lists, design rules, ECAD libraries Read the full guide. 👉 𝗹𝗶𝗻𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀

Two years ago, AI in electronics design was a curiosity. Today it's a mandate. Boards and executives are now asking hardware engineering leaders to bring AI into the design process – with measurable outcomes. Unlike software, where a wrong answer is a quick patch, in hardware it's measured in respins and schedule slips. So we built the eval guide: 𝟲 𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝗳𝗼𝗿 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 𝗳𝗼𝗿 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀 𝗗𝗲𝘀𝗶𝗴𝗻. It gives engineering leaders a structured way to assess any electronics design automation platform (including ours) plus the specific questions to bring into vendor conversations and RFPs. Inside the guide: ▸ The question every team should answer first ▸ Six criteria to pressure-test with any AI vendor ▸ The questions to ask in vendor evaluations and RFPs ▸ How outcomes hold up under independent, peer-reviewed validation Our co-founder & CEO Tomide Adesanmi shared what he's seen across 1,000+ design teams evaluating AI, which informed our write-up. Read the full guide. 👉 𝗹𝗶𝗻𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀

Today Circuit Mind is on stage at CadenceLIVE Silicon Valley. Our CEO Tomide Adesanmi is presenting alongside August Tuttle from Schneider Electric and Steve Durrill from Cadence - sharing a real enterprise use case: 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗶𝗿𝗰𝘂𝗶𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 in a Schneider Electric design workflow. 📍𝗦𝗮𝗻𝘁𝗮 𝗖𝗹𝗮𝗿𝗮 𝗖𝗼𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻 𝗖𝗲𝗻𝘁𝗲𝗿, 𝗣𝗖𝗕 & 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝘁𝗿𝗮𝗰𝗸 ⏱️ 𝟭𝟭:𝟭𝟱 𝗔𝗠 - 𝟭𝟭:𝟰𝟱 𝗔𝗠 If you're at the event, come see it live! #CadenceLIVE #CircuitMind #ElectronicsDesign #HardwareEngineering

Hardware design is unforgiving. A single mistake in a schematic can cost months, if it slips through to fabrication. 𝗘𝘃𝗲𝗻 𝗮𝗳𝘁𝗲𝗿 𝗹𝗼𝗻𝗴 𝗿𝗲𝘃𝗶𝗲𝘄 𝗰𝘆𝗰𝗹𝗲𝘀, 𝘁𝗲𝗮𝗺𝘀 𝘀𝘁𝗶𝗹𝗹 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲𝗺𝘀𝗲𝗹𝘃𝗲𝘀 𝗱𝗲𝗮𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗿𝗲𝘀𝗽𝗶𝗻𝘀. Schematic review is painstaking work, and all it takes is one tired engineer missing one detail for a problem to fall through the cracks. This week at 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗪𝗼𝗿𝗹𝗱, we’re introducing help. 𝗠𝗲𝗲𝘁 𝗔𝗩𝗘 – 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝘀𝗰𝗵𝗲𝗺𝗮𝘁𝗶𝗰 𝗿𝗲𝘃𝗶𝗲𝘄. AVE reviews schematics against component datasheets, design rules, and system constraints to help teams identify issues earlier and reduce costly respins. It joins 𝗔𝗖𝗘 (𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗼 𝗦𝗰𝗵𝗲𝗺𝗮𝘁𝗶𝗰 𝗶𝗻 𝟲𝟬 𝘀𝗲𝗰𝗼𝗻𝗱𝘀) as part of the 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗠𝗶𝗻𝗱 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺. If you’re attending Embedded World, come see the reveal at 𝗯𝗼𝗼𝘁𝗵 𝟰-𝟰𝟰𝟴 𝗼𝗻 𝗧𝘂𝗲𝘀𝗱𝗮𝘆, 𝗠𝗮𝗿𝗰𝗵 𝟭𝟬! #AIReview #AIDesign #EmbeddedWorld #HardwareDesign #ElectronicsEngineering #EDA #AI

🚨 We’re excited to be speaking at PEDC 2026 in Prague! 𝗔𝗜 𝗶𝗻 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗻𝗴 𝗛𝘆𝗽𝗲 𝗳𝗿𝗼𝗺 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 🗓️ Tuesday, 21 January ⏰ 11:00 – 12:30 📍 Prague, Czech Republic As part of the 𝗔𝗜 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝘁𝗵𝗲𝗺𝗲, Tomide Adesanmi, Co-founder & CEO of Circuit Mind (UK), will share a practical view of how AI is impacting electronics product development today. In this session, 𝗧𝗼𝗺𝗶𝗱𝗲 𝘄𝗶𝗹𝗹 𝗰𝗼𝘃𝗲𝗿: ▶︎ Which parts of the electronics design workflow are truly ripe for automation ▶︎ How AI-powered design automation is delivering real-world value today ▶︎ The tangible value teams gain from adopting AI across the electronic design workflow Whether you’re an engineer, product leader, or technology decision-maker, this talk will help you cut through the noise and focus on where AI can meaningfully accelerate electronics development. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗣𝗘𝗗𝗖 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲: https://pedc.eu/program/ #PEDC2025 #AIinProductDevelopment #ElectronicsEngineering #HardwareDesign #AIforEngineering #Innovation

Today we celebrate our 𝟰𝘁𝗵 𝗗𝗮𝘆 𝗼𝗳 𝗗𝗲𝘀𝗶𝗴𝗻 𝗪𝗶𝗻𝘀, leading up to the holiday, and revisiting our collaboration with Los Alamos National Laboratory. They ran two real PCB designs through AI-powered automation, to benchmark across different levels of complexity. 🔗 Full link to their peer-reviewed report: https://lnkd.in/e7vuewxE Here’s what LANL measured across their “medium-difficulty” and “high-difficulty” boards: “𝗠𝗲𝗱𝗶𝘂𝗺-𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆” 𝗗𝗲𝘀𝗶𝗴𝗻 A microcontroller-based system with memory, power domains, comms interfaces, audio, sensors, and storage. 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: - 𝗧𝗼𝘁𝗮𝗹 𝘁𝗶𝗺𝗲 𝘄𝗶𝘁𝗵 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: 𝟰 𝗵𝗼𝘂𝗿𝘀 𝟭𝟯 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 - 𝗠𝗮𝗻𝘂𝗮𝗹 𝗯𝗮𝘀𝗲𝗹𝗶𝗻𝗲: 𝟲𝟬–𝟴𝟬 𝗵𝗼𝘂𝗿𝘀 - ~𝟵𝟬% 𝗼𝗳 𝘁𝗵𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗮𝘂𝘁𝗼-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 - Automated reports (FMEA, Power, Derating, ICD) recovered 𝗱𝗼𝘇𝗲𝗻𝘀 𝗼𝗳 𝗮𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗵𝗼𝘂𝗿𝘀 This is a >90% reduction in effort for a full schematic + BOM + validation workflow. “𝗛𝗶𝗴𝗵-𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆” 𝗗𝗲𝘀𝗶𝗴𝗻 A complex FPGA-based system with DDR4, power sequencing, high-speed interfaces (HDMI, DP, PCIe), Ethernet PHY, and multiple regulators. 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: - Automated generation of the supported architecture blocks - 𝟲𝟬–𝟴𝟬 𝗵𝗼𝘂𝗿𝘀 𝗼𝗳 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘁𝗶𝗺𝗲 𝘀𝗮𝘃𝗲𝗱 from automated power-tree design, reporting, and component selection Even with some more complex blocks (FPGA, DDR4, HDMI, etc.), LANL still measured meaningful time savings and gained visibility into automation opportunities at higher complexity levels. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: By running 𝘵𝘸𝘰 contrasting designs through the same workflow, LANL validated how automation scales to removing dozens of hours from the critical path. A fantastic collaboration and a great example of rigorous engineering evaluation.

𝗧𝗼𝗱𝗮𝘆, 𝗰𝗲𝗹𝗲𝗯𝗿𝗮𝘁𝗶𝗻𝗴 “𝟰 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗗𝗲𝘀𝗶𝗴𝗻 𝗪𝗶𝗻𝘀”, 𝘄𝗲’𝗿𝗲 𝘀𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁𝗶𝗻𝗴 𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘄𝗲 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲𝗱 𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝗲𝘅𝘁𝗲𝗰𝗵: 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝗶𝗻𝘁𝗼 𝗮 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲𝗱 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝗻 𝗷𝘂𝘀𝘁 𝗮 𝗳𝗲𝘄 𝗱𝗮𝘆𝘀, 𝗻𝗼𝘁 𝘄𝗲𝗲𝗸𝘀. 🔗 Full case study: [https://lnkd.in/eUJ958Jq) 𝗧𝗵𝗲𝗶𝗿 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: Nextech, a trailblazer in digital transformation and product development, was designing a remote monitoring and control system with multiple functional blocks, tight power constraints, and cost sensitivity. Their manual estimate for the initial design cycle was 𝟭𝟮 𝗱𝗮𝘆𝘀. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗔𝗜-𝗮𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: 𝗙𝘂𝗹𝗹 𝗱𝗲𝘀𝗶𝗴𝗻 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝗶𝗻 𝟯 𝗱𝗮𝘆𝘀 🆚 𝗠𝗮𝗻𝘂𝗮𝗹 𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲: 𝟭𝟮 𝗱𝗮𝘆𝘀 That’s a 𝟳𝟱% 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 in early design effort, freeing engineers from weeks of component research, integration checks, and schematic iteration. But the real advantage wasn’t just speed. 𝗜𝗻 𝗺𝗶𝗻𝘂𝘁𝗲𝘀, 𝘁𝗵𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘃𝗮𝗿𝗶𝗮𝗻𝘁𝘀, enabling Nextech to: - Compare design candidates across cost, power, and size - Identify the most balanced architecture early - Understand total component cost before committing - Catch downstream issues before they became expensive Instead of selecting the first feasible design, Nextech evaluated several intelligently generated alternatives – something that’s normally too time-consuming to justify at this stage. The result: 𝗮 𝗯𝗲𝘁𝘁𝗲𝗿, 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗼𝗿𝗺𝗲𝗱 𝗱𝗲𝘀𝗶𝗴𝗻 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝗳𝗮𝘀𝘁, with clearer trade-offs from day one. Huge thanks to Mark Furiate and the Nextech engineering team for their collaboration and for partnering with us on this project. Incredible work all around!

Next up in our “𝟰 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗗𝗲𝘀𝗶𝗴𝗻 𝗪𝗶𝗻𝘀” holiday series, we’re spotlighting a rapid-prototyping sprint we collaborated on with the 𝗗𝗲𝘀𝗶𝗴𝗻 𝟭𝘀𝘁 team. 🔗 Full case study: [https://lnkd.in/evhhKXQA) 𝗧𝗵𝗲𝗶𝗿 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: Design 1st, a leading product design firm, based in Ottawa, Canada, was facing intense time-pressure to deliver cutting-edge solutions, with their clients demanding rapid designs to validate complex electronics systems, while keeping costs low. Their first project with Circuit Mind was a Precision Wellness device. 𝗧𝗵𝗲𝗶𝗿 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Conceptual design completed in 𝟮 𝗱𝗮𝘆𝘀 🆚 Manual baseline of 𝟭𝟮 𝗱𝗮𝘆𝘀 That’s 𝟭𝟬 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗮𝘆𝘀 𝗿𝗲𝗰𝗹𝗮𝗶𝗺𝗲𝗱 – an 𝟴𝟯% 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗲𝗳𝗳𝗼𝗿𝘁 for one of the most time-pressured phases of the project. What changed in the process? - Requirements captured quickly in the platform - Automated schematic generation - Preliminary BOM generated and validated - Early design exploration across multiple architecture variants - Export-ready design assets for Altium By automating the repetitive parts of the conceptual design phase, Design 1st accelerated feasibility checks, opened up more room for iteration, and moved into development with far less manual overhead. Huge thanks to Steve Auger and Donovan Wallace from the Design 1st team for their collaboration and fantastic engineering work on this project!

This week, leading up to the Christmas holiday, we’re celebrating “𝟰 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗗𝗲𝘀𝗶𝗴𝗻 𝗪𝗶𝗻𝘀” to highlight electronics engineering teams who transformed their workflows with AI this year. 𝗧𝗼𝗱𝗮𝘆’𝘀 𝘀𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 𝗛𝗼𝘄 𝗔𝗣𝗔𝗚𝗖𝗼𝗦𝘆𝘀𝘁 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝗮 𝗯𝗶𝗱-𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗘𝗩 𝗹𝗶𝗴𝗵𝘁𝗶𝗻𝗴 𝗱𝗲𝘀𝗶𝗴𝗻 𝗳𝗿𝗼𝗺 𝘄𝗲𝗲𝗸𝘀 𝘁𝗼 𝗱𝗮𝘆𝘀 – 𝗮𝗻𝗱 𝗲𝘅𝗽𝗹𝗼𝗿𝗲𝗱 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. 🔗 Full case study: [https://lnkd.in/exQEGcCQ) 𝗧𝗵𝗲𝗶𝗿 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: APAGCoSyst, a global electronic design and production center, faced a fast-moving RFQ for an EV interior-lighting system and needed to deliver a complete electronics design – architecture, schematic, and BOM – on a tight timeline. Traditionally, this workflow would span multiple weeks. 𝗧𝗵𝗲𝗶𝗿 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Here’s what happened with AI-assisted automation: 𝗙𝘂𝗹𝗹 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝗰𝗸𝗮𝗴𝗲 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝗶𝗻 𝟮 𝗱𝗮𝘆𝘀 🆚 𝗧𝘆𝗽𝗶𝗰𝗮𝗹 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲 𝗼𝗳 𝗮𝗯𝗼𝘂𝘁 𝟮 𝘄𝗲𝗲𝗸𝘀 That’s nearly an 𝟴𝟬% 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝘂𝗿𝗻𝗮𝗿𝗼𝘂𝗻𝗱 𝘁𝗶𝗺𝗲 – critical when bidding windows are tight and response speed determines competitiveness. But the biggest surprise wasn’t just the time saved. It was what APAG could 𝗱𝗼 with that time. 𝗜𝗻 𝗺𝗶𝗻𝘂𝘁𝗲𝘀, 𝗔𝗖𝗘 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝘁𝗵𝗿𝗲𝗲 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀, 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝘃𝗮𝗹𝗶𝗱 𝗕𝗼𝗠 𝗰𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀. This allowed Bernhard Edlinger and the APAG engineering team to: - Compare architecture variants instantly - Identify the most cost-optimized concept - Reduce BoM cost early, before locking in the design - Deliver a more competitive bid package with data-backed justification A huge advantage in an industry where cost, speed, and clarity all make a difference. Massive thanks to Bernhard Edlinger and the APAGCoSyst Electronic Control Systems engineering team for their collaboration and for sharing their results with us – an outstanding example of how automation can reshape competitive design bids! #EngineeringAcceleration #DesignWins #AIInHardwareDevelopment

𝗪𝗲’𝗿𝗲 𝗴𝗼𝗶𝗻𝗴 𝗹𝗶𝘃𝗲 𝘀𝗵𝗼𝗿𝘁𝗹𝘆! 𝗧𝗼𝗱𝗮𝘆 𝗮𝘁 𝟵:𝟬𝟬 𝗔𝗠 𝗣𝗦𝗧 / 𝟭𝟮:𝟬𝟬 𝗣𝗠 𝗘𝗦𝗧 / 𝟱:𝟬𝟬 𝗣𝗠 𝗚𝗠𝗧 Software teams got Cursor and Copilot years ago. Hardware teams finally get theirs. Join us to see what AI means for consumer electronics design teams If you want to stop losing months to design delays and respins… this could be the highest-leverage hour you’ll spend all year. Grab a spot here: https://lnkd.in/eUAG8Scy See you inside! #HardwareEngineering #ElectronicDesign #AIEngineering