This blog post was originally published in expanded form at RapidFire AI’s website. It is reprinted here with the permission of RapidFire AI. The Use Case An AI-forward team at a Fortune 500 enterprise tech company builds intelligent autocomplete for enterprise form data entry: predicting what a user will select next across product dimensions, pricing fields, […]
This blog post was originally published at STMicroelectronics’s website. It is reprinted here with the permission of STMicroelectronics. As technology aims to enable Physical AI, ST is sharing today how collaboration brought our sensors into a Holoscan Sensor Bridge module from Leopard Imaging, enabling developers to feed multi-modal sensing data to the NVIDIA Jetson Thor or
How Rapidflare’s multi-layer safety filter achieved 100% protection against harmful content while maintaining zero false positives on legitimate queries. This blog post was originally published at Rapidflare’s website. It is reprinted here with the permission of Rapidflare. When you deploy an AI agent to a public developer community, the threat model changes completely. In a
How We Built a 100% Effective Multi-Layer Safety Filter for Enterprise AI Agents Read More +
This video was originally published at Peridio’s website. It is reprinted here with the permission of Peridio. In this episode of Beyond the Bench from Peridio, Bill Brock sits down with Robert Otręba, Founder & CEO of Grinn, a Poland-based embedded engineering company operating for nearly 18 years. Robert shares how Grinn grew from a two-person
Beyond the Bench: Reinventing Embedded Hardware with Grinn Read More +
This blog post was originally published at V-Nova’s website. It is reprinted here with the permission of V-Nova. In Industrial and Defense environments, I hear the same story. More cameras. Higher resolutions. Stricter latency targets. Infrastructure that cannot be replaced easily. And increasing pressure around storage, bandwidth, compute, and privacy. This is why MPEG-5 LCEVC is becoming even more relevant. It improves compression
MPEG-5 LCEVC: A practical shift for industrial AI video pipelines Read More +
This blog post was originally published at HTEC’s website. It is reprinted here with the permission of HTEC. The hardware boom is slowing down. What comes next is a software, power, and inference problem—and most of the industry isn’t ready for any of it. AI chips are now 0.2% of all chips manufactured, but
Key Trends Shaping the Semiconductor Industry in 2026 Read More +
TI’s new application brief and companion demo outline how mmWave radar, camera input, FPGA-based sensor bridging and NVIDIA Holoscan can be combined into a low-latency perception pipeline for humanoids and other autonomous machines. Texas Instruments, D3 Embedded, Lattice Semiconductor and NVIDIA are outlining a concrete radar-camera fusion stack for robotics rather than just talking
This blog post was originally published at AMD’s website. It is reprinted here with the permission of AMD. This blog showcases how to deploy power-efficient Ryzen AI perception models with ROS 2 – the Robot Operating System. We utilize the Ryzen AI Max+ 395 (Strix-Halo) platform, which is equipped with an efficient Ryzen AI NPU and
Building Robotics Applications with Ryzen AI and ROS 2 Read More +
LAGUNA HILLS, Calif. — April 6, 2026 — BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, BCHPY), the world’s first commercial producer of ultra-low-power, neuromorphic AI technology, today announced the launch of its Radar Reference Platform. This fully validated hardware and AI stack is designed to provide real-time object classification at the edge, solving the critical “identification gap” that limits traditional radar
BrainChip Unveils Radar Reference Platform to Bridge the ‘Identification Gap’ in Edge AI Read More +
We’re excited to announce Intel’s strategic partnership with Google to deliver optimized Gemma 4 models on Intel hardware from day one. This collaboration enables developers to leverage the power of Google’s latest AI models on Intel hardware: Intel® Core™ Ultra processors, Intel® Xeon® CPUs, and Intel® Arc™ GPUs. Developers can create AI applications that run
Gemma 4 Models Optimized for Intel Hardware: Enabling Instant Deployment from Day Zero Read More +
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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