Bryn Dole

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here: https://lnkd.in/ga6tD5rt

6,432

199 Comments

As Jensen mentioned with Brad Gerstner & Bill Gurley, something that few people know is that NVIDIA is becoming the American open-source leader in AI, with over 400 contributions of models, datasets and apps on Hugging Face in the 12 months. And I have a feeling they're just getting started!

765

29 Comments

Barbarians at the Gate (https://lnkd.in/gkEBX79s) is a very interesting new paper from some great folks at Berkeley, with some exciting results showing the potential for AI in systems research. But I think the authors aren't quite asking the hardest problem about where this takes systems as a field. I wrote a new blog post about it: https://lnkd.in/gxS3s4v4 The core point of the paper is that AI can be effective at automatically driving the 'number go up' part of systems research. Given an evaluator, AI can automate progress on implementation. I believe this is 100% correct. The paper also talks about how more focus will have to go into developing evaluators. Again, I 100% agree, but think they're understating their case. Building evaluators (basically tests) is, I believe, going to become the focus of systems and software over the next decade. Which leaves researchers with the hard problem of choosing which problems to go after. That's never been easy in academic systems, and is only going to become more of a challenge. But the best systems research is driven by choosing problems well! This vision for AI could lead to lots more of the least interesting systems research. The "number go up" kind. Which could make it harder than ever to identify the truly innovative and visionary work. We're already seeing that elsewhere in science. So, mostly, I think the paper is right, but not yet asking the biggest question: what should systems research be?

276

12 Comments

🚀 Breaking Down Memory Walls for AI and Science! Fascinating read from Pacific Northwest National Laboratory (PNNL) on their groundbreaking "active memory" computing architecture, purpose-built for massive AI and scientific workloads. At the heart of this system is technology from XConn Technologies: a high-performance CXL (Compute Express Link) switch. This isn't just an incremental upgrade—it's a paradigm shift. The XConn CXL switch enables a revolutionary pool of shared memory, allowing CPUs and GPUs to access a massive, unified memory space at incredible speeds. This directly tackles the biggest bottleneck in high-performance computing: data movement. For national labs like PNNL, this means unlocking unprecedented scale for scientific discovery, from energy research to advanced materials science. A powerful example of how core infrastructure innovation (CXL) is enabling the next leap in computational science. https://lnkd.in/gCGCtgx6 #AI #HPC #HighPerformanceComputing #CXL #ComputeExpressLink #DataCenter #Innovation #GPU #ScientificComputing #NationalLabs #XConnTechnologies #PNNL

22

Did you hear it? … the song of the AI Agents dominating #awsreinvent2025 Nearly every other booth you walk to is demoing an #aiagent platform. In a nutshell, we’re talking about simple ai agents. I’d call them baby agents. A prompt, a RAG, and a few MCPs here and there to collect data, all governed by an agent studio to design agent workflows. Most platforms are promoting an open model framework where you can pick and choose your preferred LLM. Bedrock is one of the key options available. What’s missing? The business application. A lot of those platforms are in their very early stages, many are not even GA yet. What’s common: build from scratch mentality, and my ai agent platform can do everything, everywhere, all at once. IT buyers? Torn down between all of these platforms not knowing if it’s the right time to start choosing one or many. The potential? It’s going to be insanely huge, with businesses and organizations reaching incredible levels of automation, while also providing superior quality and experience to their customers. Get ready as 2026 will be exciting! [sounds surreal to say so, nearly have to apologize that my post did not use any single AI, not even for brainstorming or improving my style. WDYT: Better with or without AI?]

16

4 Comments