Podcasts about edge ai

Sanjit Biswas runs what may be the largest AI deployment in the physical world — and almost nobody in AI talks about it. Samsara (NYSE: IOT), the ~$20B company he co-founded after selling Meraki to Cisco for $1.2B, puts AI on millions of trucks, cranes, and industrial assets: 25 trillion data points a year, 99% of US roads driven every single day, ~$2B in ARR growing 30% profitably. In this episode we go through the entire physical AI stack — asset tags you can run over with a truck, a paper-thin disposable tracking label, engine fault codes, and dash cams running inference at the edge — then into agents, including the Agent Studio warranty agent that compresses an hour of human work into under a minute. We also get into the uncomfortable part (when AI watches you drive all day, is that coaching or surveillance — and why drivers actually want the cameras), mixed fleets of humans and robots, why autonomous trucking will take far longer than robotaxis, and a startling stat from the field: one utility building 3x more grid capacity in the next five years than it did in the previous 125, with 90% of that demand coming from data centers.(00:00) Intro: The biggest AI deployment nobody talks about(01:16) What is physical AI?(03:04) From IoT dashboards to agentic action(04:36) Why physical AI is harder than software AI(06:07) Safety, cybersecurity, and real-world consequences(07:11) What Samsara does(08:22) $2B ARR, 25 trillion data points, and 380,000 crashes(09:44) How AI can prevent road accidents(11:28) From an MIT research project to Meraki(13:42) Learning physical operations from scratch(15:42) Samsara's stack: sensors, intelligence, and action(16:39) Inside Samsara's industrial asset trackers(18:36) Bluetooth, battery life, and connected infrastructure(19:49) A disposable tracking device built like a sticker(21:08) Vehicle gateways and engine diagnostics(22:16) How AI dash cams coach drivers in real time(23:28) Turning the dash cam into an AI interface(24:49) Organizing physical-world data in the cloud(26:32) Selling AI to traditional industries(27:52) Is Samsara's real-world data its AI moat?(29:22) The network effects of covering 99% of U.S. roads(31:23) Edge AI versus cloud AI(32:35) The models running inside Samsara's devices(33:52) Generative AI and video reasoning(35:57) Which AI models does Samsara use?(36:50) Inside Samsara Agent Studio(37:56) How an AI warranty agent works(38:50) Starting with practical, lower-risk automation(40:10) Combining agents, workflows, rules, and guardrails(42:07) What today's AI agents still cannot do(43:07) AI ride-alongs and the future of driver coaching(45:17) Is workplace AI becoming Big Brother?(46:27) How cameras can protect and exonerate drivers(48:48) When AI becomes the judge of your work(50:37) Robots, humanoids, and mixed human-machine fleets(53:19) Samsara's role in autonomous operations(54:50) How quickly will autonomous trucking arrive?(56:32) AI data centers and America's infrastructure boom(58:16) Should lawyers become plumbers? Demand for tradespeople(59:54) Closing thoughts

Anthony Sar, Co-founder and CEO of Finoverse and the driving force behind Hong Kong Fintech Week, joins Theo on One Vision to share the story behind one of Asia's most iconic innovation gatherings. From a bar conversation about hitting 5,000 attendees (when they had 300) to welcoming 45,000 people from 120+ economies at their 10th anniversary, Anthony reflects on what it takes to build something that genuinely keeps people in the room.At the center of the conversation is Samantha, built in-house by Finoverse. She acts as your personal AI networking agent. She interviews you, learns what you're looking for, and makes introductions at scale. After a successful pilot at last year's FinTech Week as well as the recent Genesis Festival, Samantha will return to Hong Kong FinTech Week 2026 this November.  This year's conference is aptly themed Fintech Nexus: Bridging the World, Building the Future. It is a rallying call to drive connectivity and collaboration across global and Chinese Mainland markets, at a time when the world feels more fragmented than ever.One World, One Planet. This episode invites us to come together to foster collaboration for a more inclusive financial services ecosystem for everyone. 

Send us Fan MailOn this episode of Embedded Insiders, we're discussing the continued evolution of Ambient IoT with Giampaolo Marino, Chief Strategy and Growth Officer at Energous. We're diving into battery dependency, the benefits of continuous, always-on data for AI, and how the company's partnership with e-peas supports battery-free sensor deployments through wireless power and energy harvesting.Next, Rich and Ann Olivo, the Vice-President of Marketing for the Thread Group, discuss the Thread standard. Thread is part of the IEEE 802.15.4 wireless protocol and operates at very low power. The two discuss the importance of the protocol and the newly unveiled app that simplifies the process for developers. For more information, visit embeddedcomputing.com

In this AWS Partner Spotlight, Ingram Micro sits down with Jagdish and Sunil from Prijal to unpack their AWS journey — from Kubernetes and observability solutions in production to cutting-edge Edge AI and conversational business intelligence powered by Amazon QuickSight. Hear how Prijal leveraged Ingram Micro's Accelerated Partner Program to compress their go-to-market timeline and unlock new customer opportunities.Key Takeaways

In this AWS Partner Spotlight, Ingram Micro sits down with Jagdish and Sunil from Prijal to unpack their AWS journey — from Kubernetes and observability solutions in production to cutting-edge Edge AI and conversational business intelligence powered by Amazon QuickSight. Hear how Prijal leveraged Ingram Micro's Accelerated Partner Program to compress their go-to-market timeline and unlock new customer opportunities.Key Takeaways

Anthony Sar, Co-founder and CEO of Finoverse and the driving force behind Hong Kong Fintech Week, joins Theo on One Vision to share the story behind one of Asia's most iconic innovation gatherings. From a bar conversation about hitting 5,000 attendees (when they had 300) to welcoming 45,000 people from 120+ economies at their 10th anniversary, Anthony reflects on what it takes to build something that genuinely keeps people in the room.At the center of the conversation is Samantha, built in-house by Finoverse. She acts as your personal AI networking agent. She interviews you, learns what you're looking for, and makes introductions at scale. After a successful pilot at last year's FinTech Week as well as the recent Genesis Festival, Samantha will return to Hong Kong FinTech Week 2026 this November. This year's conference is aptly themed Fintech Nexus: Bridging the World, Building the Future. It is a rallying call to drive connectivity and collaboration across global and Chinese Mainland markets, at a time when the world feels more fragmented than ever.One World, One Planet. This episode invites us to come together to foster collaboration for a more inclusive financial services ecosystem for everyone.

Airports move more than 10 billion passengers a year, yet most still manage congestion, assets, and energy use on estimates rather than real-time data. Wireless connectivity and Edge AI are starting to close that gap - giving operators a new level of intelligence. So, can real-time operational intelligence actually replace the systems, and guesswork airports have run on for decades - and what does it take to deploy it at scale without disrupting everyday operations and compromising security and privacy? Let's find out.Our guests for today's podcast are:Ken Brizel, CEO of Agereh Technologies, a publicly traded Canadian company building wireless, battery-powered operational intelligence solutions that give operations teams real-time visibility into people, indoor assets, and global cargo. andJohnathan Lewis, Division Director of Innovation at Miami International Airport - one of the busiest international gateways in the US, moving more than 50 million passengers a year.In our discussion today, we'll uncover a few things, such as:How airports are closing that technology gap in establishing scalable operational intelligence  How private cellular (CBRS), Wi-Fi, and public cellular work together inside a live, operating airport to support operational intelligence goals, andWhat it actually took to deploy this across a 50-million-passenger hub without disrupting day-to-day operationsLet us welcome Ken and Johnathan.Contact PrivateLTEand5GFollow us on LinkedIn at https://www.linkedin.com/company/privatelteand5gTweet at https://twitter.com/privateLTEand5GFor more resources on Private Cellular Networks, go to https://www.privatelteand5g.com/Email us at ratika.garg@privatelteand5g.com

The next era of intelligent edge devices is taking shape - but what's driving real-world deployment, and how are hardware and software evolving to keep pace with such a fast-moving ecosystem? Welcome to Edge of Tomorrow – The Edge AI Debate, our spin-off series from The IoT Podcast, created in collaboration with the EDGE AI FOUNDATION. This series brings together leading voices from across the Edge AI ecosystem to debate the toughest questions shaping its future.   In this episode, host Pete Bernard (CEO, EDGE AI FOUNDATION) is joined by Scott Hanson (Founder & CTO at Ambiq) to explore how ultra-low power compute is enabling the next generation of intelligent edge devices. Starting with the journey from research to commercialisation, the conversation traces how advances in low-power processor design have evolved into technologies now powering hundreds of millions of devices. It then moves into the realities of building for edge AI today, from balancing long hardware development cycles with rapid AI innovation, to the growing role of software in bridging that gap. The discussion dives into where edge AI is already delivering value at scale, including wearables, smart home systems, industrial monitoring, and medical devices. It also explores the shift from isolated use cases to widespread adoption, with an increasing number of deployed devices now running some form of intelligence at the edge. This episode offers a grounded perspective on the rise of agentic AI, the relationship between edge and cloud intelligence, and what's needed to support increasingly complex, real-world systems, exploring where edge AI is today and where it's heading next. Expect discussion on… The role of ultra-low power compute in scaling Edge AI Hardware vs. software: bridging development cycles Where Edge AI is already delivering value at scale Wearables, smart homes, industrial, and medical use cases The shift from isolated deployments to widespread adoption Edge vs. cloud: where intelligence actually lives The rise of agentic AI and system-level intelligence Chapters… 00:00 Introductions 01:30 The origin of Ambiq and ultra-low power innovation 03:00 Scaling a chip company and market growth 04:00 Hardware vs software development cycles in AI 06:00 Keeping pace with rapid AI innovation 08:30 General-purpose vs specialised architectures 10:45 Moore's Law and efficiency gains at the edge 13:00 Real-world scale: where edge AI is being deployed 15:30 Wearables, smart homes, and industrial use cases 17:30 Edge AI in everyday life: sensing and inference 19:30 Consumer vs commercial adoption trends 21:30 The rise of “life logging” and contextual data 24:00 Societal and ethical considerations of always-on AI 25:30 Agentic AI and system-level intelligence 27:30 Edge vs cloud: where intelligence actually lives 29:30 Cost, scalability, and moving AI out of the cloud 31:30 Physical AI and robotics opportunities 33:30 Final reflections on the future of edge AI

In der neuen Folge  sprechen Jakob Steinschaden, Mitgründer von ⁠eustella⁠ und Trending Topics, und Clemens Wasner, CEO von ⁠enliteAI⁠ und Vorsitzender von AI Austria, über folgende Themen:Eindrücke von der WeAreDevelopers Conference in Berlin: Aufbruchstimmung unter 10.000+ Developern – KI ist als Arbeitstool angekommen, die Angst vor Ersetzung ist der Erkenntnis gewichen, dass der Output pro Person massiv steigt.Token-Budgets als „virtuelle HR-Budgets": US-Firmen geben Entwicklern bis zu 15.000 $ pro Monat an KI-Token – in Europa sind es eher 500–1.000 €. Statt Token-Maxing zählt Agenten-Orchestrierung; manuelles Coden wird zur Fallback-Lösung.Claude Code, Codex & Co als „Wanderpokal" – und der Aufstieg der Open-Weight-Modelle: Kaum Kundentreue bei Coding-Tools, dafür wachsende Adoption offener Modelle wegen Planbarkeit, Kosten und US-Restriktionen bei Top-Modellen wie Fable 5.Lokale KI statt Cloud: Mit neuen Nvidia-Chips (Vera Rubin), leistungsfähigeren Macs und On-Device-Modellen wandert Inferenz zurück zum User – von lokaler Transkription mit MLX-Whisper bis zur OpenAI-Entwickler-Tastatur mit sieben Tasten.Physical AI und die kommende Robotik-Welle: Edge-Systeme im physischen Raum boomen – von Warehouse-Robotern über Ernte- und Bauroboter bis zu Liquid AIs maßgeschneiderten Edge-Modellen; dazu Thomas Dohmkes neues Startup Entire, das Code, Memories und Prompts gemeinsam versioniert.

Can robots learn from the internet the same way ChatGPT learned from text?In this episode, Andrew Wooten, co-founder of Rhoda AI, explains why his company believes the future of robotics isn't collecting millions of hours of robot data ... it's learning from internet-scale video. Instead of relying on traditional vision-language-action (VLA) models that require enormous training datasets, Rhoda's approach teaches robots physical intuition by predicting the future through video.We also explore why, in Andrew's opinion, warehouses and factories will likely be the first major market for humanoid robots (not homes!), why Rhoda chose a wheel-based humanoid design, how language models fit into physical AI, and how the company's robots can learn complex tasks with just 8–10 hours of training data instead of 10,000+ hours.If you're interested in robotics, AI, automation, or the future of manufacturing, this conversation offers a fascinating look at where physical AI is heading.In this episode:* Why warehouses beat homes as the first market for humanoid robots* Why Rhoda chose wheels instead of legs* The biggest limitation of today's robot AI models* How internet-scale video teaches robots physics* Why predicting the future helps robots manipulate the real world* Edge AI vs. cloud robotics* The role of LLMs in controlling robots* How Rhoda cut robot training from 10,000+ hours to just 8–10 hours* When zero-shot robot learning could become realityGuestAndrew WootenCo-founder, Rhoda AIWebsite: https://rhoda.ai00:00 Why Humanoid Robots Don't Have Wheels00:18 Can Robots Learn From Internet Video?00:42 Best Use Cases for Humanoid Robots02:10 Why Warehouses and Factories Come First04:02 The Economic Impact of Robotics05:00 Home Robots vs. Industrial Robots06:05 Why Rhoda AI Chose Wheels08:00 Building a General-Purpose Robot09:55 Why Full-Stack Robotics Companies Have an Advantage10:40 The Evolution of Physical AI12:20 Why Vision-Language-Action Models Fall Short14:05 Training Robots With Internet-Scale Video16:05 How Rhoda AI's Video-Action Model Works17:25 Edge AI vs. Cloud Computing19:05 How Robots Develop Physical Intuition20:40 Predicting the Near Future in Real Time22:00 Can Robots Build a Subconscious?23:10 Using Language Models to Control Robots25:15 Rhoda AI's Hardware Strategy26:20 The Biggest Problems With Today's Humanoids28:20 When Will Robots Truly Learn on the Job?30:05 Training Complex Tasks in 8–10 Hours31:15 Zero-Shot Robot Learning and What Comes Next

  Join Sundeep Ahluwalia, Chief Product Officer at TDK SensEI, as he explores the future of industrial manufacturing and computer vision. In this webinar, Sundeep introduces edgeRX Vision—a powerful combination of computer vision and edge AI designed to deliver fast, accurate quality control directly on the production line. Currently deployed in TDK manufacturing facilities worldwide, edgeRX Vision can inspect up to 2,000 parts per minute, detecting defects in components as small as 1 mm × 0.5 mm. Discover how edgeRX Vision enables manufacturers to achieve: Enhanced AOI capabilities Higher production throughput Real-time visual feedback Precision driven by AI Reduced human error PRESENTER: Sundeep Ahluwalia Chief Product Officer Presented by TDK SensEI Visit https://advancedmanufacturing.org/webinars for more webinars and an interactive experience with visuals.

In this MHI Cast, Kevin Jeong and Alex Glasmacher explore how Edge AI, computer vision and sensor fusion are transforming safety across warehouses, distribution centers and manufacturing environments. From predictive collision avoidance and real-time hazard detection to autonomous systems and connected safety ecosystems, they discuss how organizations can move beyond reactive safety programs and build a more intelligent, proactive approach to protecting workers while improving operational performance.

Let us know how we're doing - text us feedback or thoughts on episode contentMost companies think of AI's carbon footprint as a data center problem. But the infrastructure powering your AI queries is rapidly diversifying — and each deployment model carries a very different sustainability profile. In this episode, Paul breaks down five emerging AI infrastructure options and what they mean for corporate emissions accounting.Paul makes the case that as AI deployment shifts away from traditional cloud infrastructure, the CSO has to be at the table with the CIO and CFO. The deployment decision is one of the most consequential choices a company can make for its GHG profile — and right now, most organizations are making it blind to the emissions implications.Follow Paul on LinkedIn.

NuNet is building a decentralised compute and orchestration network where people can contribute spare CPU, GPU, RAM and other resources, while developers and organisations can deploy workloads across available infrastructure. In this episode, Peter talks with Jennifer from NuNet about the new NuNet Appliance and why it matters for making decentralised compute more practical for everyday users.The conversation covers how NuNet matches the right compute to the right job, how the Appliance lowers the barrier to onboarding devices, and why use cases like n8n automations, private AI agents, edge AI, Cardano SPO infrastructure and web deployment workflows are a natural fit for the network. Jennifer also explains NuNet's zero-trust security model, pricing approach, organisations, ensembles, deployment templates, and how NTX fits into orchestration fees.If you have spare compute, want to run private AI workloads, or are building in the DePIN and Cardano ecosystem, this episode gives a practical look at how NuNet is moving from concept to usable infrastructure.Key Takeaways:- NuNet is a decentralised compute and orchestration platform that lets people contribute spare compute and lets workloads find suitable resources automatically.- The NuNet Appliance is designed to make onboarding CPUs, GPUs, RAM and other compute resources much easier for non-expert users.- NuNet can support broad workloads, including n8n automation, private AI agents, Qwen-based LLM deployments, edge AI, web builds and Cardano SPO infrastructure.- The network uses a zero-trust model where machines are cryptographically identified and verified at each interaction.- Compute pricing is designed around stable currency values, with automatic conversion into NTX rather than forcing users to price workloads directly in a volatile token.- NuNet organisations can let other DePIN projects bring their own communities and native tokens while still using NuNet's orchestration layer.- Ensembles and templates are intended to simplify deployments so users do not need to manually understand every YAML configuration detail.- NuNet is open source, with docs, GitLab, Discord, Medium and X available for people who want to try the network or contribute.Links & References:- NuNet — Compute Orchestration for a Decentralized World: https://link.learncardano.io/eGKGuZ- What is NuNet? | NuNet Documentation: https://link.learncardano.io/rHu2E4- x.com: https://link.learncardano.io/NIhPKR- https://link.learncardano.io/Tlu7wNWebsite: https://link.learncardano.io/bQ68RcX/Twitter: https://link.learncardano.io/3a1QtvDisclaimer: This content is for educational purposes only. Nothing constitutes financial advice.DISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

What happens when the technology keeping essential services running fails at the worst possible moment? When most people think about workplace technology, they picture laptops, smartphones, and office software. But for millions of workers maintaining power networks, repairing infrastructure, supporting emergency services, managing transport systems, and operating in remote environments, technology has a very different job to do. It has to work every single time, often in conditions where failure is simply not an option. In this episode of Tech Talks Daily, I speak with Alex Gittins from Getac about the changing world of field operations, rugged computing, and the growing role of Edge AI in supporting the deskless workforce. Alex explains why rugged technology is far more than placing a consumer device inside a protective case. From extreme temperatures and harsh weather to vibration, dust, poor connectivity, and demanding working environments, true rugged devices are engineered from the ground up to support people working where most technology struggles. We also discuss the often-overlooked reality that around 80% of the global workforce operates away from a desk. These workers are increasingly dependent on digital tools to receive work orders, access mapping systems, capture field data, complete inspections, and communicate with central teams in real time. The conversation also turns to Edge AI and its growing importance for frontline teams. Rather than relying on constant connectivity and cloud processing, Edge AI enables workers to access intelligence directly on their devices. Whether identifying damaged assets through image recognition, guiding inspections, reducing paperwork, or supporting faster decision-making, AI is becoming a practical tool for improving efficiency and safety in the field. Alex also shares how customer expectations are changing. Organisations are no longer buying devices in isolation. Instead, they are involving technology providers much earlier in the process to help design complete solutions that can support future operational requirements. From defence roots to modern field operations, this episode offers a fascinating look at the technology helping keep critical services running behind the scenes. How will AI, connectivity, and rugged computing continue to reshape the future of work for the billions of people who never sit behind a desk?

Edge AI is exceeding the expectations that I had for the technology, at least at this early point in its ramp-up. I had expected it to take longer to reach fruition than it actually has, although you could probably argue that it hasn't come close to reaching even the beginnings of its potential. To understand where Edge AI stands today, where it's going, and when it could potentially get there, I spoke to John Weil, the Vice President and General Manager for IoT and Edge AI Processors at Synaptics, on this week's Embedded Executives podcast.

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

We're sitting down with Chris Szymanski, Director of Product Marketing and Technology Strategy at Broadcom, for the inside track on Wi-Fi 8, edge AI, and smart home trends. Chris tells us how combining Wi-Fi 8 chipsets with advanced processing units and embedded AI acceleration enables smarter, self-optimizing networks across residential and enterprise environments. We talk about how edge AI is reshaping traffic patterns—driving more uplink demand, ultra-low latency requirements, and the need for deterministic, highly reliable connectivity. Chris also highlights how unified wired and wireless architectures are enabling AI-driven operations, enhanced security, and proactive network performance. In the smart home, we learn about how AI is moving from the cloud into devices to power adaptive, context-aware experiences across IoT, voice, video, and automation, and the critical role Wi-Fi 8 will play as the “nervous system” connecting this intelligent ecosystem. Tune in for key insights into AI-ready networking, evolving infrastructure demands, and how Wi-Fi 8 will support the next generation of connected experiences.For Wi-Fi AllianceFor Membership InfoGeneral Contact

On this episode of What's On Your Mind, host Scott Hennen dives headfirst into the controversial layers of North Dakota's upcoming election cycle. Scott exposes what he views as a deeply sinister motive behind the seemingly wholesome "free school lunch" constitutional ballot measure, tracing its massive financial backing straight to a militant pro-abortion group out of California. Later, Max Cassette, CFO of local ag-tech star 701x, joins the studio to show off their cutting-edge GPS solar ear tags—a brilliant piece of homegrown innovation that is revolutionizing cattle tracking, slashing calf mortality rates, and restoring sanity to ranchers. In the second half, Congresswoman Julie Fedorchak calls in to explain how she is fighting to codify common-sense flexibility into the EPA's burdensome Diesel Exhaust Fluid (DEF) regulations to save American farmers billions in costly repairs. Plus, Michael Mortensen maps out the University of Mary's historic upcoming 5th annual pilgrimage to America's only approved Marian apparition site, and Carter Eisinger of the Cass County Republicans drops by to give a live look at early voting data and outline why local municipal races are the ultimate battleground for property taxes. Standout Moments & Timestamps [00:01:01] The Million-Dollar School Lunch Scheme: Scott unpacks why an out-of-state special interest group is pouring massive money into a "mom and apple pie" free lunch ballot measure to quietly build a massive database of progressive voters. [00:02:12] Wealthy vs. Needy: Scott challenges the lack of means testing in the proposed legislation, arguing that wealthy families shouldn't have their children's meals heavily subsidized by average taxpayers. [00:03:29] Connecting the Ballot Dots: Callers and hosts debate the ultimate end game of the out-of-state funding, warning listeners that this "loss leader" measure is designed to pave the way for reshaping the state's constitutional stance on abortion and state spending. [00:06:22] Breaking News in the Gubernatorial Race: Scott drops live breaking news as the Republican party officially releases its candidates from previous conventions pledges, opening a highly competitive primary path for Lisa Dameth and Ryan Wilson. [00:07:36] 701x High-Tech Cattle Tracking: CFO Max Cassette introduces the XT Pro and XT Light ear tags, explaining how solar-powered cellular and satellite connectivity are changing livestock record-keeping from birth to processing. [00:09:41] Edge AI in the Pasture: Max explains how 701x bypasses the internet by utilizing "Edge Artificial Intelligence" directly on the ear tags to monitor calving behavior and spot sickness days before visual symptoms occur.…

Listen Now to 011 WTFuture Watch 011 WTFuture This week’s show kicks off with the hosts untangling the literal and figurative wires of modern podcasting before nerding out over “Edge AI” running locally on smartphones to save energy and protect privacy. The banter takes a wonderfully weird turn when Al brainstorms an AI assistant specifically designed to intentionally repeat sentences not heard properly in a soothing voice to hearing-impaired friends to save them from social isolation. This quickly spirals into a debate over the origins of tinnitus; Bobby suspects it’s triggered by high-frequency Bluetooth headphones and EMFs, while Al hopefully wonders if the ringing is actually a neural data channel or a precursor to telepathy. The crew then marvels at AL’s one minute cinematic video recreating the exact day a dinosaur-killing asteroid hurled molten glass beads into the gills of paddlefish in North Dakota. Before diving into global politics, they take a delightful detour into inter-species communication, pondering whether a local crow leaving a dead bat as a “gift” is a sign of cross-species neighborliness, which even prompts them to trick the backyard flock by playing crow sounds from an app. The conversation blasts into orbit with a breakdown of recently released footage showing a pod of UFOs swarming a nuclear submarine, but the real fireworks explode during a heated debate over the impending arrival of Artificial General Intelligence (AGI). Bobby and Al take a pragmatic, geopolitical stance, warning that owning personal, localized AI is necessary to defend against global manipulation, specifically citing fears that the CCP wants to win the AGI race to implement the “great firewall of all time”. This triggers a passionate disagreement with Sun, who accuses the guys of falling into a fear-mongering, male-centric “dominate and subjugate” mindset that mirrors a perpetual arms race. Hurt feelings emerge as Sun advocates for trusting our collective intelligence to build an abundant, Star Trek-style utopia rather than focusing on apocalyptic Terminator scenarios, forcing AL to frantically defend himself as a fun “cheerleader for AI” rather than a pessimist. Ultimately, the trio cools down and finds common ground in their hopes for joining a peaceful galactic community, perfectly capped off by Sun referencing Iain M. Banks’ sci-fi Culture series as a brilliant blueprint for a post-scarcity society that has successfully conquered traditional cultural hierarchies. Enjoy!

The United States Navy is taking steps to integrate artificial intelligence into Maritime Operations Centers — balancing the need for operational agility with the rigor required for warfighting, according to Rear Adm. Susan BryerJoyner, director of the Warfighting Integration Directorate in the Office of the Chief of Naval Operations. Speaking with AI GovCast, BryerJoyner said Navy leadership is focused on maximizing taxpayer return on investment through data-informed analysis to determine which AI capabilities are ready for operational use and which require further evaluation. Mitigating AI hallucinations remains a top priority, BryerJoyner added, emphasizing that commanders will always remain part of the decision-making process. She said operational staff must understand how AI-generated recommendations are developed to ensure outputs align with mission requirements, planning assumptions and ethical standards. BryerJoyner also discussed how the Chief of Naval Operations is working with the Naval Postgraduate School to expand AI education through the school's new master's degree program in AI.

In this episode, I sit down with Steven Yates, CTO and co-founder of Federant, to dive deep into the urgent need for runtime governance in edge AI. Drawing on decades of experience in embedded systems and PLC design, Steven reveals why the shift to the edge demands more than just powerful inference—it requires robust, local authority to keep operations safe when connectivity falters. We unpack real-world incidents where lack of governance led to costly mishaps, and explore how new open-source solutions are bridging the gap between cloud convenience and industrial reliability. If you think cloud SLAs are enough for industrial AI, this conversation will make you rethink the fundamentals. Join me as we explore the future of safe, autonomous operations—and why the old rules of industrial control are more relevant than ever.

What if doctors were able to test their treatment plans on a version of their patient before actually trying it on their body?

Healthcare CIOs are under pressure to deliver faster insights, safer care, and sustainable operations—without adding complexity. In this joint NVIDIA and Dell Technologies panel at HIMSS26, leaders from across the ecosystem explored how imaging, connected devices, and clinical edge AI are converging to transform care delivery.Here's a look at our panel of experts:* Rebecca Woods, Former CIO and Founder & CEO | Bluebird Leaders* Yu Liu, Co-Founder and CTO | Heidi* Dan Schneider, Professional Visualization Solution Specialist | NVIDIA* Sandra Colner, GM Global Healthcare & Life Sciences | Dell TechnologiesLearn more about Bluebird Leaders: https://www.bluebirdleaders.org/Learn more about Heidi: https://www.heidihealth.com/en-usLearn more about NVIDIA: https://www.nvidia.com/en-us/industries/healthcare-life-sciences/Learn more about Dell Technologies: https://dell.com/HealthcareHealthcare IT Community: https://www.healthcareittoday.com/

This episode of 100 Year Thinkers brings together Chris Mayer and Ian Cassel for a deep discussion on long-term stock picking, microcap investing, business quality, AI disruption, management teams, and the behavioral skills that separate great investors from great analysts. They explore why the edge in investing may increasingly come from judgment, presence, relationships, patience, and the ability to hold the right businesses through uncertainty.Matt Zeigler and I had the privilege of hosting Ian Cassel and Chris Mayer for a special 100-Year Thinkers Edition of the Excess Returns Podcast.Available now on Excess Returns Podcast and Talking Billions.

This episode of our new showThe 100 Year Thinkers brings together Chris Mayer and Ian Cassel for a deep discussion on long-term stock picking, microcap investing, business quality, AI disruption, management teams, and the behavioral skills that separate great investors from great analysts.They explore why the edge in investing may increasingly come from judgment, presence, relationships, patience, and the ability to hold the right businesses through uncertainty.Subscribe to the 100 Year Thinkers on Spotify⁠⁠⁠⁠Subscribe to the 100 Year Thinkers on AppleTopics CoveredWhy being present with management teams may still be an investor edge in the age of AIHow microcap investing differs from small-cap, mid-cap and large-cap investingWhy talking to management can build conviction but also create biasHow Chris Mayer thinks about vertical market software, mission-critical systems and AI disruptionWhy AI may become table stakes rather than a durable competitive advantageHow small companies can use AI to improve workflows, sales, inventory and productivityWhy many microcaps have short shelf lives and rarely become true long-term compoundersThe role of intelligent fanatics, owner-operators and repeat winners in great investmentsWhy management transitions can create powerful microcap opportunitiesThe difference between being a great analyst and being a great investorWhy execution, position sizing, selling losers and holding winners matter more than hit rateHow Matt and Bogumil apply the lessons to AI, business quality and the limits of small business scalabilityTimestamps00:49 Introducing Chris Mayer, Ian Cassel and 100 Year Thinkers04:59 Ian Cassel's first management meeting and XM Satellite Radio09:00 Why management meetings deepen understanding but can also mislead14:32 Chris Mayer on the real edge in long-term investing18:40 Mission-critical software, systems of record and AI disruption22:45 How microcap companies are using AI in real businesses27:02 AI as table stakes and when disruption creates opportunity31:29 Why most microcaps have short shelf lives35:51 Finding Tom Brady before the market knows he is Tom Brady40:53 Why owner-operators and intelligent fanatics matter45:03 Second-in-command leaders, repeat winners and chips on shoulders49:27 Analyst vs investor and the missing skills of stock picking54:00 Using data to identify investor strengths, weaknesses and decision errors58:14 Position sizing and letting small positions earn the right to grow01:03:00 Peter Lynch, stocks as businesses and learning to think like an owner01:07:00 AI, human judgment and the limits of automation01:11:00 Why not every small business can become the next Facebook01:15:00 Where to follow Bogumil and the 100 Year Thinkers series

Apple (AAPL) earnings are out and the "extraordinary demand" for iPhone 17 products shows its hardware business continues to hold a strong grip on smartphone market share. But for Stephen Sopko, he's waiting to hear more about its AI exposure. "I like to say that Apple is the largest Edge AI company in the world," he says. Stephen wants to know how much AI processing occurs on the iPhone devices themselves and believes the company needs more "breadcrumbs" on newer products. He later addresses Apple's China market and how incoming CEO John Ternus will navigate the company's brand abroad. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

In this episode, Adam King of MediaTek and Francis Sideco of TIRIAS Research explore how MediaTek is scaling edge AI across a wide range of devices and markets, from smartphones and tablets to Chromebooks and AI workstations. They also dive into what's next for physical AI, the hype around AGI, and MediaTek's partnerships with NVIDIA and Google.  

Rajat Monga, CVP AI Frameworks @ Microsoft, joins the podcast to discuss his leadership and founder journey, from Google Brain / Tensorflow to inference.io and back to Microsoft. He dissects what it means to refound vs. start from scratch, the value of the open source community, and strategies for discovering what problem to solve when going the startup route. We also cover how to determine your users' hidden incentives and what that means for both product development & marketing, along with navigating the balance between a product's usefulness and consumers' willingness to pay for it. Additionally, Rajat shares about what he's currently up to at Microsoft and the emerging ML / AI technologies he's most excited about.   ABOUT RAJAT MONGA Rajat Monga is responsible for enabling an efficient AI stack at Microsoft from cloud to the edge. Before joining Microsoft, Rajat was founder and CEO of Inference.io, a smart analytics platform powered by AI. During his decade-long tenure at Google, he co-founded and led TensorFlow, and was a founding member of Google Brain. He's built out and led many engineering teams, and designed large scale distributed systems including web scale crawling and eBay's search engine. Rajat is a graduate of IIT Delhi.   Unblocked: The context engine your coding agents are missing. Give your coding agents the context your best engineers have. Your agents can read code, but they don't know how your team works. Rules and MCPs give access to information but not understanding. That's why you still have to tell them where to look and what to look for. Unblocked gives your agents the history, conventions, and decisions behind your code so they generate mergeable output without the back and forth. It automatically surfaces the right context for every task, so agents stay on track without the set up tax or the correction loops. getunblocked.com/elc   SHOW NOTES: Rajat's journey with Google Brain: Scaling deep learning from single PCs to thousands of machines with Jeff Dean & Andrew Ng (2:57) Moving from Google Brain to TensorFlow: Why new hardware and architectures required a total system rebuild (6:02) The "refounding" question: Choosing between starting from scratch or evolving an existing system (8:33) Why Google open-sourced TensorFlow to set industry standards and avoid supporting external copies (10:16) How open-source enabled global innovation, from Japanese cucumber sorting to African plant health (12:02) Transitioning as a leader: Why Rajat left Google during the height of TensorFlow to found a company (13:57) The discovery phase at inference.io: Navigating the pivot from IoT into solving data analytics gaps (15:31) Lessons on PMF: Moving beyond a "useful" product to one that solves a truly critical customer pain point (16:52) Why habits are harder to change than technology and the challenge of competing with established workflows (21:02) Marketing strategies: Tailoring personas for top-down prestige versus bottom-up personal efficiency (23:19) Deciding when to stop: A founder's framework for re-evaluating bets based on current knowledge (24:57) Rajat's new role at Microsoft: Overseeing Edge infrastructure and large-scale Cloud AI inference (27:46) Dissecting ML edge strategy: Using ONNX Runtime to unify AI performance across Windows, iOS, and Android (30:02) Edge AI trends: Shifting from experimental models to production models optimized for cost and privacy (31:20) The future of Edge: How on-device processing will power AI in robotics, smart glasses, and wearables (33:23) Scaling inference: Treating multi-GPU clusters like a distributed operating system for AI models (34:25) Rapid fire questions (37:45)   LINKS AND RESOURCES Epic Disruptions: 11 Innovations That Shaped Our Modern World - Innovation expert Scott Anthony masterfully weaves together the fascinating stories behind history's most transformative disruptions—from ninth-century China to twenty-first-century Silicon Valley. Through eleven pivotal innovations, including the printing press, mass-produced automobiles, the McDonald's revolutionary food system, and the iPhone, Anthony reveals the hidden patterns behind world-changing breakthroughs.   This episode wouldn't have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/ Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/ Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailIn this episode of Embedded Insiders, Ken sits down with Sakyasingha Dasgupta, PhD., Founder, CEO, & Chairman of Edge Cortix, to discuss Edge AI and the impact both software and hardware have when they work together efficiently at the edge.Next, Rich and Ed Kaste, the Senior Vice President of the Ultra-Low Power Business at GlobalFoundries, discuss power consumption when building an Edge-based device. GlobalFoundries is one vendor that has a lot to say about power and the rules they set around it. But first, we're highlighting some important upcoming events:Register for the 2026 Automotive Technologies Virtual Conference on May 14th. For more information, visit embeddedcomputing.com

For many years, RFID technology has been used by leading retailers such as Walmart, Macy's, and Lowe's. CVS, Zara, and others for theft prevention. But now, it is ready to go beyond this limited use case and transform supply chain logistics. In the episode, I talk to Gagan Luthra, VP of Product and Strategy at RAIN RFID leader Impinj, about the history of technology, how it is currently being used, how it is evolving to support complex supply chain use cases, including Gen2X performance enhancements, new form factors, higher processing power, and more.  We also delve into even more exciting opportunities, including using AI models for better forecasting, analytics, and trend analysis, as well as monetizing data through third-party players. Also, check out my EE Times article about RFID tackling food waste losses, utilizing Avery Dennison food labels: https://bit.ly/47APGav  Index: 00:00 - Intro 02:13 - Guest intro (Gagan Luthra) 04:41 - History and current state of RFID, expanding use cases, beyond theft prevention 06:30 - RFIDs are much more than wireless barcodes - much more information and value, from "cradle-to-grave" of products 09:25 - How RFID works without batteries - Tags are energized by an RF signal, and the receiver "reads" the reflections from the tags for identification 11:32 - Automated and Real-time information, unlike barcodes, with static information, from manual operation 15:03 - Higher initial cost of RFID compared to barcode, but much lower opex, much higher utility, and better ROI 20:30 - RFID cost curve continuing to go down, with economies of scale, wider adoption, and improvement in silicon process technology 22:19 - RAIN Alliance, standards for interoperability, difference between RAIN RFID, and NFC  25:13 - Upgrades needed beyond RFID standards for complex supply chain use cases, details of Impinj's Gen2X enhancements to address those needs, Gen2X traction   29:07 - Full backward compatibility with RAIN standards, how that works 31:30 - Edge processing needs at RFID readers, Impinj's latest announcement about R700 enhancements for readers 33:45 - How AI can power next phase of RFID - real-world, physical data for AI models, Edge AI in readers for smart decisions, utilizing cloud for better forecasting, trend analysis, and more, monetization opportunities for anonymized data for third-party players 37:59 - "Crystal Ball" question, where is RFID headed in the next 3-5 years - wider adoption across many verticals, tagging almost anything, even very low-cost items, and adoption of AI     40:35 - Closing  

Digital imaging is so ubiquitous today that it's easy to forget how improbable it once was. In this episode of TechSurge, guest host Nic Brathwaite sits down with Dr. Eric Fossum, inventor of the CMOS active pixel image sensor, to unpack the breakthrough that made it possible to embed cameras into billions of devices and the deeper lessons behind it.Eric explains how his work began not with consumer electronics, but with a NASA constraint: how to shrink a refrigerator-sized space camera into something small enough for spacecraft. The solution required a fundamental shift in architecture. By moving from CCD-based imaging to CMOS, where sensing and processing could happen on a single chip, he enabled a level of miniaturization and scalability that transformed cameras from standalone systems into embedded infrastructure.But the conversation goes far beyond the invention itself. Nic and Eric explore what it takes to commercialize deep technology, from the early days of Photobit to its acquisition by Micron, and the critical role ecosystems play in turning breakthroughs into global platforms. They discuss why intellectual property is less about protection and more about leverage, and why even the most important inventions require manufacturing scale, capital, and partnerships to succeed.The episode also looks forward. As AI systems increasingly rely on visual and physical data, sensors are shifting from tools designed for human perception to components optimized for machine intelligence. Eric highlights the challenges of pushing intelligence to the edge, the limitations of current architectures, and the growing importance of sensing technologies beyond traditional imaging—including molecular detection and new materials that go beyond silicon.While much of today's investment is concentrated in models and compute, this conversation makes the case that the next wave of innovation may come from deeper layers of the stack, where machines interact directly with the physical world. The future of AI may depend not just on how systems think, but on how they see, detect, and understand their environment.If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform.Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future Season 2 episodes.Episode LinksConnect with Eric and learn more about his work and recognition: https://engineering.dartmouth.edu/community/faculty/eric-fossum Learn more about CMOS image sensors: https://www.spacefoundation.org/space_technology_hal/active-pixel-sensor/Timestamps02:00 From CCD to CMOS: Rethinking How Images Are Captured06:45 The NASA Problem: Shrinking a Camera for Space12:30 From Refrigerator to Coffee Cup and Beyond19:30 From Lab to Market: Founding Photobit26:00 Scaling the Technology: Micron, Manufacturing, and Cost31:00 The Role of IP in Deep Tech: Leverage vs Protection39:30 From Human Vision to Machine Perception44:30 Edge AI vs Centralized Compute: Where Intelligence Lives49:30 Beyond Imaging: Molecular Sensing and New Frontiers53:30 What Comes Next: Materials, Sensors, and the Limits of Silicon

Send us Fan MailOn this episode of Embedded Insiders, Ken and Robert Otręba, Chief Executive Officer at GRINN, discuss the company's edge AI products and full-stack engineering services. They recap the trends and demos showcased at embedded world, highlighting the company's System on Module (SoM) and Edge AI SBC solutions. Next, Rich and Sean Dougherty, a Vice President with Everspin Technologies, discuss the current memory crisis. Specifically, skyrocketing memory costs and the large capacities needed for artificial intelligence. But first, Ken and I are talking about sustainable AI, such as the growing environmental cost of AI and the role of data centers. For more information, visit embeddedcomputing.com

In this episode, we sit down with Alexander Selegenev, Executive Director of TMT Investments, the AIM-listed venture capital firm focused on high-growth technology companies across AI, software, and fintech.Alexander opens with an introduction to TMT's business and investment philosophy, then walks us through the firm's strategy and thesis in detail.We explore how TMT balances genuine excitement about artificial intelligence with the valuation discipline required to generate returns for shareholders.We dig into the numbers, asking why deployed capital fell sharply in 2025 compared to the prior year, and what that tells us about how the team is reading the current opportunity set. Alexander then takes us through the portfolio's core holdings, including the standout story of Scale AI, which delivered a 138% uplift in just eight months following Meta's investment, and what originally attracted TMT to the business.We also look at Bolt, now EBIT positive and active in more than 800 cities globally, and discuss how close the ride-hailing giant might be to an IPO or significant exit event. On the other side of the ledger, Alexander addresses the write-downs seen over the past year and the factors behind them.Alexander provides insight into their thinking around balancing special dividends with share buybacks and what success looks like for TMT Investments. Hosted on Acast. See acast.com/privacy for more information.

What does “AI at the edge” really mean in 2026, and why does it matter now more than ever before? In this episode, we're joined by Brandon Shibley, Edge AI Solutions Engineering Lead at Qualcomm's Edge Impulse, to discuss the current state and future of Edge AI in 2026. We discuss Gen AI, Small Models, and Cascades of Models, along with real-world constraints like latency, power, and privacy. We also dive into the role of MLOps, evolving hardware, and how developers can start building practical edge AI systems today.Featuring:Brandon Shibley – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Read our Ultimate Guide to Edge AIDownload your copy of O'Reilly's AI at the Edge Check out the Edge Impulse blogSign-up for an expert led trial of Edge ImpulseUpcoming Events: Register for upcoming webinars here!

At BIO-Europe Spring 2026 in Lisbon, Portugal, web editor Nicole Raleigh spoke with Dr Olga Nissan, vice president of business development at Evogene, a computational chemistry company, specialising in the generative design of small molecules for the pharmaceutical and agricultural industries. Nissan discusses recent developments at Evogene, including its extended collaboration with Google Cloud to develop and integrate AI agents into Evogene's ChemPass AI platform, as well as its collaboration with Queensland University of Technology in the non-small cell lung cancer (NSCLC) space. She also speaks to where industry is at in its integration of cutting-edge AI into scientific research.

Daniel is joined by Dr. Veer Kheterpal. Veer has founded three technology companies and possesses full-stack expertise spanning software to silicon across edge and datacenter applications. Currently, he is the CEO & co-founder of Quadric, a semiconductor IP licensing company that delivers the blueprints for efficient,… Read More

In this episode of the Electromaker Show, Ian takes you on a fast tour through Embedded World 2026, covering the interviews, demos, and products that stood out most on the show floor. From the new Arduino Ventuno Q and native Zephyr on Arduino Nano Matter, to Nordic's in-house Edge AI tools, Bluetooth channel sounding, and satellite-connected nRF9151 demos, this episode covers a wide spread of embedded tech in one place. At the event we stopped by Silicon Labs, D-Robotics, JetBrains, Texas Instruments, DFRobot, and Epishine to look at robotics platforms, AI-enabled IDEs, sensor demos, x86 single board computers, and indoor solar energy harvesting. If you want a sharp overview of what mattered at Embedded World 2026, this is the place to start.   Watch the show! We publish a new show every week. Subscribe here: https://www.youtube.com/channel/UCiMO2NHYWNiVTzyGsPYn4DA?sub_confirmation=1 We stock the latest products from Adafruit, Seeed Studio, Pimoroni, Sparkfun, and many more! Browse our shop: https://www.electromaker.io/shop Join us on Discord! https://discord.com/invite/w8d7mkCkxj​ Follow us on Twitter: https://twitter.com/ElectromakerIO Like us on Facebook: https://www.facebook.com/electromaker.io/ Follow us on Instagram: https://www.instagram.com/electromaker_io/ Featured in this show: Arduino Ventuno Q Silicon Labs Bluetooth Sounding Demo Arduino Nano Matter DIY Fan Native Zephyr Arduino Nano Matter Factory Demo Shop at the Electromaker Store! Nordic Semiconductor Neuton and Axon AI demos Nordic Fuel Gauge 2.0 nRF9151 SMA NTN demo Nordic Bluetooth Sounding Demo D-Robotics Jetbrains CLion for Embedded Development Texas Instruments CC Studio IDE Texas Instruments Humanoid Robotics DFRobot Alcohol Sensing Demo Unihiker peripheral and usb example Epishine: flexible indoor solar energy Many More on our YouTube Page! %

A wearable that logs your digestion by tracking hydrogen “events,” Hollywood betting big on one-minute vertical soap operas, and Wi‑Fi 7 routers that may not do what the box implies, this hour is packed with the kind of technology news that makes you stop and go, “wait, is that real?” We take each headline and separate the joke from the actual value, because the story behind the gimmick is usually where the truth lives. We also shift into practical mode with a stack of real scam and phishing emails that show how people get trapped by urgency, fake account warnings, and that tempting unsubscribe link. We talk through the easiest tells like mismatched sender domains, scripts that don't match the offer, and why “just click to verify” is still one of the most effective social engineering moves online. If you've got family members who get nervous when they see “final notice,” this segment is worth sharing. From there, we hit modern tech contradictions: Tinder trying to fix dating app fatigue by pushing in-person singles events, and a promising offline AI board that runs local inference without relying on cloud services. Edge AI and offline AI can mean faster responses, fewer privacy risks, and less dependence on internet outages, but it also raises real questions about updates and long-term support. Subscribe for more consumer tech reality checks, share the show with a friend who needs scam-proofing, and leave us a review with the strangest tech headline you've seen lately.Support the show

BrainChip CEO Sean Hehir joins me to unpack where artificial intelligence is actually headed—and why the dominant “everything in the data center” narrative is incomplete.Most AI conversations fixate on massive models, GPU farms, and trillion-dollar infrastructure bets. This episode shifts the frame. Sean and I explore the structural reality that power consumption, latency, and grid constraints are forcing AI to decentralize—and what that means for founders, engineers, and the broader economy.Sean explains how neuromorphic computing and ultra-low-power silicon enable AI inference outside the data center—inside wearables, medical devices, drones, manufacturing systems, and even space applications. We examine why CPUs and GPUs aren't optimized for edge workloads, how custom silicon changes the economics, and why power efficiency isn't a side issue—it's the bottleneck that determines what scales.The conversation expands into workforce displacement, labor fluidity, productivity cycles, and whether technological acceleration inevitably creates unemployment crises—or simply reshuffles value creation again, as history repeatedly shows.This isn't a speculative futurism episode. It's a grounded look at model trends, infrastructure limits, and how companies survive inside a market moving at month-scale rather than decade-scale.The lesson isn't that AI replaces everything.It's that architecture determines outcomes.TL;DR* AI is centralizing in data centers—but it's also rapidly decentralizing to the edge* Power constraints will shape the next phase of AI more than hype cycles* Neuromorphic and event-driven silicon drastically reduce energy per compute* Edge AI enables medical wearables, safety detection, space systems, and industrial automation* Models are getting larger—but optimization techniques will shrink them into smaller form factors* Productivity gains historically displace tasks—not human adaptability* The future isn't about bigger servers—it's about smarter distribution* Lowest power per compute is a strategic advantage, not a marketing lineMemorable Lines* “Don't bet against humanity. We're very creative.”* “The future of AI isn't just in data centers.”* “Power isn't a feature—it's the constraint.”* “If you're the lowest power solution, you will always have customers.”* “Architecture decides what becomes possible.”GuestSean Hehir — CEO of BrainChipTechnology executive leading the commercialization of neuromorphic AI processors focused on ultra-low-power edge inference. Oversees BrainChip's evolution from early engineering innovation to market-driven, customer-focused deployment.

Behnam Bastani, CEO and cofounder of OpenInfer, breaks down why the last two years of AI feel explosive, and why the next wave is not chat, it is action at the edge.We get into always on inference, what actually forces compute to move closer to the data, and the missing layer that makes edge AI scale: the Android like infrastructure that lets devices collaborate instead of living in silos.Key takeaways• The hype spike is real, but the runway is decades, it took compute, sensors, and communication protocols maturing over generations to unlock this moment• AI is shifting from conversational to actionable, which means continuous, always on inference becomes the norm• Edge wins when cost, reliability, and data sovereignty matter, cloud and edge will coexist, but the workload placement changes• The biggest bottleneck is not just silicon, it is the infrastructure layer that makes building and deploying across devices easy, plus a shared fabric so devices can cooperate• Adoption is as much a human story as a technical one, this shift lands faster and broader than previous tech transitions, so anxiety is predictable and needs real attentionTimestamped highlights00:38 OpenInfer's mission, intelligence on every physical surface, and why collaboration matters02:07 Electricity as the earlier revolution, intelligence as the next kind of power, and the control problem05:54 Where we really are on the maturity curve, early products are here, mass adoption and safety take time08:31 When the device boundary disappears, it stops being you versus the agent, it becomes one system11:04 Always on inference, and the three forces pushing compute to the edge: cost, reliability, data sovereignty14:40 The Android moment for edge AI, why the operating system layer unlocks developers, apps, and adoptionA line worth replayingThose are going to be the three pillars that really enforces that edge and cloud are going to live together.Pro tips for builders• If your product needs real time decisions, design for intermittent networks from day one, reliability is not optional• Treat data sovereignty as a product feature, not a compliance afterthought, it is becoming the moat• Push for interoperability early, the fabric that lets devices share the right data is what makes edge feel seamlessCall to actionIf this episode helped you rethink where AI should run and what it takes to ship it in the real world, follow the show and share it with one builder who is working on edge, robotics, devices, or applied AI.

As someone who spends a lot of time covering AI announcements, product launches, and conference stages, it is easy to forget that most AI today is still built for desks, screens, and digital workflows. Yet the reality is that the vast majority of the global workforce operates in the physical world, on roads, construction sites, depots, and job sites where mistakes are measured in injuries, collisions, and lives lost. That gap between where AI innovation happens and where real risk exists is exactly why I wanted to sit down with Amish Babu, CTO at Motive. In this episode, I speak with Amish about what it truly means to build AI for the physical economy. We unpack why designing AI for vehicles, fleets, and safety-critical environments is fundamentally different from building AI for emails, documents, or dashboards. Amish explains why latency, trust, and reliability are non-negotiable when AI is embedded directly into vehicles, and why edge AI, multimodal sensing, and on-device compute are essential when milliseconds matter. This is a conversation about AI that has to work perfectly in messy, unpredictable, real-world conditions. We also explore how Motive approaches AI as a full system, combining hardware, software, and models into a single platform built specifically for life on the road. Amish shares how AI can help prevent collisions, support drivers in the moment, and create measurable safety and operational outcomes for fleets operating across transportation, construction, energy, and public sector environments. Along the way, we challenge common misconceptions around AI in vehicles, including the idea that it is about surveillance rather than protection, or that all AI systems are created equal when lives are on the line. If you are interested in how AI moves beyond productivity tools and into high-stakes environments where safety, accountability, and trust matter most, this episode offers a grounded and practical perspective from someone building these systems every day. I would love to hear your thoughts on this one. How do you see the role of AI evolving as it moves deeper into the physical world? Useful Links Connect with Amish Babu Learn More About Motive How Motive's AI works: Real-time edge intelligence, humans-in-the-loop, and continuous improvement.

Doug Green, Publisher of Technology Reseller News, spoke with Dinakar Munagala, CEO & Co- Dinakar Munagala Founder of Blaize, and Joseph Sulistyo, SVP of Corporate Marketing, about Blaize's push to make AI inference practical outside the data center—and why a new strategic collaboration with Nokia is designed to accelerate that shift, especially across Asia Pacific. Blaize positions itself as an AI computing company built around a purpose-built, fully programmable processor architecture it calls a graph streaming processor, paired with software intended to simplify development of “real-world” AI. Munagala framed the company's focus as practical AI inference for environments like smart factories, smart cities, agriculture, defense, and other edge and hybrid deployments where latency, power, thermal limits, and operating conditions are non-negotiable. A centerpiece of the discussion was Blaize's announcement that Nokia is strengthening edge AI capabilities through a strategic collaboration with Blaize to deliver hybrid inference solutions across APAC. Munagala and Sulistyo described the move as a signal that AI's next phase isn't only about large-scale training in centralized data centers, but about deploying inference where outcomes are realized—near cameras, sensors, machines, and field infrastructure. In their view, Nokia's global reach in networking, automation, and integration creates a path to deliver end-to-end solutions that combine connectivity and compute for real deployments, not demos. Sulistyo emphasized the economics driving hybrid inference: cost-sensitive, power-constrained environments often cannot justify a single “monolithic” compute approach. Instead, he argued, the market is moving toward heterogeneous architectures—mixing different compute types to hit performance targets while controlling total cost of ownership. In APAC, he noted, the scale of deployments makes marginal savings meaningful, and hybrid designs become an operational requirement, not a preference. The conversation also connected edge inference to public-sector and community outcomes. Both executives highlighted smart-city use cases—such as traffic management, tolling, and first-responder automation—where real-time inference can improve accuracy and responsiveness while reducing labor-intensive processes. They extended that point to rural and underserved regions, arguing that “smart city” also includes municipalities and regional governments, where automation and analytics can unlock revenue (e.g., tolls and fines) while improving safety. Doug pushed on definitions and practicality, prompting Munagala to describe edge inference as compute performed as close as possible to the sensor—for example, processing video near a camera mounted on a pole, at a toll booth, or in a factory—so systems can detect events and respond with low latency. He added that some deployments may route inference to nearby on-prem servers or regional data centers, depending on architecture and proximity, and Blaize aims to support these variations with a common hardware/software platform. Blaize also addressed the “AI energy speed bump” impacting communities and operators—particularly where power availability and cost are constrained. Munagala said low power is foundational to Blaize's design goals and argued that purpose-built inference architectures can reduce the burden associated with power-hungry AI approaches. Sulistyo added that the broader infrastructure conversation increasingly includes cooling realities (air and liquid) and the need to match the deployment environment to the right compute profile. To ground “real-world AI” in examples, the guests pointed to deployments including license plate recognition in complex, variable conditions and traffic anomaly detection (identifying behavior that deviates from normal flow). They described these as compute-intensive workloads that must run reliably outdoors and under harsh conditions, where latency and endurance matter as much as accuracy. They also discussed retail analytics as another example of edge inference delivering measurable business outcomes by connecting what happens in-store to revenue-driving decisions. Looking ahead, Munagala described the Nokia collaboration as a model for additional partnerships that bring inference solutions into production environments at scale. Sulistyo noted APAC is the initial focus, with other regions expected to follow based on demand, proof points, and the prioritization of specific use cases. To learn more about Blaize and its technology, visit https://www.blaize.com/.

What if your classroom could adapt to each learner without handing their data to the cloud? That's the promise we dig into with technologist and founder Sebastien Fenelon, whose journey from scarce resources in Haiti to building privacy-first, edge AI tools reframes what “future-ready” really means for educators and instructional designers.We start with the power of resilience—how self-taught coding, late-night study sessions, and community support can outpace limited infrastructure—and move into practical strategies for teaching code with clarity and context. Sebastien shares why AI should compress project timelines, not critical thinking, and offers a simple “100-hour” ramp to acquire new languages fast. From K–12 to higher ed, we outline how to design small, visible wins that build confidence while using AI to scaffold learning rather than replace it.We close with a playbook for staying adaptable: keep learning in focused sprints, plug into communities that share what works, and seek mentors who reveal the path behind the skills. If you're ready to personalize learning, protect student data, and keep your curriculum uniquely yours, this conversation offers a clear blueprint. If it resonates, follow and share with a colleague, and leave a quick review to help more educators find thoughtful, practical guidance on AI in the classroom.

On this episode of That Tech Pod, we talk with Logan Lawler, Senior Director at Dell Technologies, about what it takes to make AI actually work in the real world. Logan shares his 16-year journey at Dell and why his focus today is less on hype and more on practical infrastructure choices that enable AI at scale.We break down Edge AI versus Cloud AI with clear, concrete examples, including how GPU-accelerated desktops, workstations, and hybrid cloud setups can turn “that's impossible” AI problems into manageable ones. Logan also highlights why storage, not compute, is often the biggest bottleneck, and the common mistakes organizations make when data can't keep up with GPUs. The conversation gets into energy and sustainability, from the environmental cost of massive data centers to what it means when nuclear power and AI collide. We also explore the human side of AI: whether instant answers are making us lazier, why struggle is still essential for learning, and how that idea shows up in parenting, education, and work. We close with real-world edge AI success stories, a few cautionary tales, and some lighter moments, making this a grounded discussion on AI, infrastructure, and the tradeoffs we rarely talk about.Logan Lawler works at Dell Technologies, where he leads strategy for Dell Pro Precision AI Solutions. Over his 16-year career at Dell, he's worked across sales, marketing, and e-commerce, and now helps enterprises and creative studios leverage high-performance AI workstations and hybrid cloud infrastructure. A frequent speaker and media guest, Logan explains how GPU-accelerated PCs and storage solutions are transforming industries from film and animation to healthcare research. Logan was raised in Missouri and is a graduate of the University of Missouri. He now lives in Texas with his family.

What happens when the AI race stops being about size and starts being about sense? In this episode of Tech Talks Daily, I sit down with Wade Myers from MythWorx, a company operating quietly while questioning some of the loudest assumptions in artificial intelligence right now. We recorded this conversation during the noise of CES week, when headlines were full of bigger models, more parameters, and ever-growing GPU demand. But instead of chasing scale, this discussion goes in the opposite direction and asks whether brute force intelligence is already running out of road. Wade brings a perspective shaped by years as both a founder and investor, and he explains why today's large language models are starting to collide with real-world limits around power, cost, latency, and sustainability. We talk openly about the hidden tax of GPUs, how adding more compute often feels like piling complexity onto already fragile systems, and why that approach looks increasingly shaky for enterprises dealing with technical debt, energy constraints, and long deployment cycles. What makes this conversation especially interesting is MythWorx's belief that the next phase of AI will look less like prediction engines and more like reasoning systems. Wade walks through how their architecture is modeled closer to human learning, where intelligence is learned once and applied many times, rather than dragging around the full weight of the internet to answer every question. We explore why deterministic answers, audit trails, and explainability matter far more in areas like finance, law, medicine, and defense than clever-sounding responses. There is also a grounded enterprise angle here. We talk about why so many organizations feel uneasy about sending proprietary data into public AI clouds, how private AI deployments are becoming a board-level concern, and why most companies cannot justify building GPU-heavy data centers just to experiment. Wade draws parallels to the early internet and smartphone app eras, reminding us that the playful phase often comes before the practical one, and that disappointment is often a signal of maturation, not failure. We finish by looking ahead. Edge AI, small-footprint models, and architectures that reward efficiency over excess are all on the horizon, and Wade shares what MythWorx is building next, from faster model training to offline AI that can run on devices without constant connectivity. It is a conversation about restraint, reasoning, and realism at a time when hype often crowds out reflection. So if bigger models are no longer the finish line, what should business and technology leaders actually be paying attention to next, and are we ready to rethink what intelligence really means? Useful Links Connect with Wade Myers Learn More About MythWorx Thanks to our sponsors, Alcor, for supporting the show.

Jan. 13, 2026- Assemblymember Alex Bores, a Manhattan Democrat, and State Sen. Andrew Gounardes, a Brooklyn Democrat, discuss the fate of their 2025 bill to regulate cutting-edge artificial intelligence development, which was the subject of intense lobbying and got tweaked by Gov. Kathy Hochul.

In this episode of Eye on AI, Craig Smith sits down with Anurag Dhingra, Senior Vice President and General Manager at Cisco, to explore where AI is actually creating value inside the enterprise. Rather than focusing on flashy demos or speculative futures, this conversation goes deep into the invisible layer powering modern AI: infrastructure. Anurag breaks down how AI is being embedded into enterprise networking, security, observability, and collaboration systems to solve real operational problems at scale.  From self-healing networks and agentic AI to edge computing, robotics, and domain-specific models, this episode reveals why the next phase of AI innovation is less about chatbots and more about resilient systems that quietly make everything work better. This episodeis perfect for enterprise leaders, AI practitioners, infrastructure teams, and anyone trying to understand how AI moves from theory into production. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Why AI Only Matters If the Infrastructure Works (01:22) Cisco's Evolution (04:39) Connecting Networks, People, and Experiences at Scale (09:31) How AI Is Transforming Enterprise Networking (12:00) Edge AI, Robotics, and Real-World Reliability (14:18) Security Challenges in an Agent-Driven Enterprise (15:28) What Agentic AI Really Means (Beyond Automation) (20:51) The Rise of Hybrid AI: Cloud Models vs Edge Models (24:30) Why Small, Purpose-Built Models Are So Powerful (29:19) Open Ecosystems and Agent-to-Agent Collaboration (33:32) How Enterprises Actually Adopt AI in Practice (35:58) Building AI-Ready Infrastructure for the Long Term (40:14) AI in Customer Experience and Contact Centers (44:14) The Real Opportunity of AI and What Comes Next

Welcome to episode 335 of The Cloud Pod, where the forecast is always cloudy! Welcome to the first show of 2026, and it's a full house, too! Justin, Jonathan, Ryan,  and Matt are all here to reflect on 2025, plus bring you their predictions for 2026. Let's get started!  Titles we almost went with this week SQL Me Maybe: AlloyDB Gets Chatty With Your Database **OpenAI SELECT * FROM natural_language WHERE accuracy LIKE ‘100%’ **Anthropic etcd You Were Worried About Database Limits: CloudWatch Has Your Back CSV You Later: Looker Adds Drag-and-Drop Data Uploads AWS Spots an Opportunity to Manage Your Container Costs EKS Network Policies: No More IP Address Whack-a-Mole AWS Security Hub Splits: It’s Not You, It’s CSPM Spot On: ECS Finally Manages Your Cheapest Compute TOON Squad: DigitalOcean’s New Format Makes JSON Look Bloated The Price is Wrong: AWS Breaks Two Decades of Downward Pricing Tradition Show Your Work: Why AI-Generated Code Without Tests is Just Expensive Spam No More Agent Orange: Google Simplifies VM Extension Deployment AWS Discovers Prices Can Go Both Ways, Raises GPU Costs 15 Percent Sovereignty Washing: When Your European Cloud Still Answers to Uncle Sam Agent Builder Gets a Memory Upgrade: Google’s AI Finally Remembers Where It Put Its Keys Ctrl+F for the Future: A year-end Scorecard & Next-Gen Bets AI Agents, GPU Prices, and The best of the Cloud Pod 2025 Beyond the Hype: The Cloud Pods Definitive 2025 Year in Review Apocalypse Now… What? Our 2026 Forecast Follow Up  01:27 RYAN’S PREDICTIONS Prediction Status Notes Quick LLM models for individuals ACCURATE Meta-Llama-3.1-8B-Instruct, GLM-4-9B-0414, and Qwen2.5-VL-7B-Instruct—each chosen for an outstanding balance of performance and computational efficiency, making them ideal for edge AI deployment. A new AI inference application called Inferencer allows even modest Apple Mac computers to run the largest open-source LLMs. AI at the edge natively (Lambda-esque) ACCURATE Akamai launched a new Inference Cloud product for edge AI using Nvidia’s Blackwell 6000 GPUs in 17 cities. AWS IoT Greengrass with Lambda functions for edge logic. “Edge AI allows for instant decision-making where it matters most—close to the data source.” Cloud native security mesh multi-cloud UNCLEAR Service mesh technologies continue to evolve (Istio, Linkerd), but I didn’t find a breakthrough “app-to-app at the edge” security mesh product announcement in 2025. This one needs more specific evidence. Ryan Score: 2/3 02:25 MATTHEW’S PREDICTIONS Prediction Status Notes FOCUS adopted by Snowflake or Databricks ACCURATE FOCUS version 1.2 was ratified on May 29, 2025. Three new providers announced support: Alibaba Cloud, Databricks, and Grafana. Databricks officially adopted FOCUS! AI security/ethical standard (SOC or ISO) ACCURATE ISO 42001 is the first international standard outlining requirements for AI governance. Major companies achieving certification in 2025: Automation Anywhere is among the first 100 companies worldwide to earn ISO/IEC 42001:2023 certification. Anthropic also achieved ISO 42001 certification. Amazon deprecates 5+ services (WorkMail bonus) ACCURATE (no bonus) 19 services are mothballed, four are being sunset, and one is end of its supported life. Deprecated services include CodeCommit, Cloud9, S3 Select, CloudSearch, SimpleDB, Forecast, Data Pipeline, QLDB, Snowball Edge, and more. WorkMail NOT deprecated – WorkDocs was (April 2025), but WorkMail remains active. Matthew Score: 3/3 03:22 JONATHAN’S PREDICTIONS Prediction Status Notes Company claims AGI achieved ACC