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We welcome back Andrew Sillifant, Solution Director at Pure Storage, for a deep dive into the concept of data gravity. We start with the traditional 2010 definition coined by Dave McCrory—that data accumulates, making it harder to move, and forcing dependent systems to cluster nearby. However, Andrew presents his core thesis, arguing that this foundational principle is no longer sufficient in a world of exploding complexity. Our conversation emphasizes the need to re-examine data gravity through a modern lens, acknowledging the massive shift to cloud computing and the proliferation of interconnected systems over the last decade. Andrew introduces five crucial dimensions that now describe data's impact: Volume, redefined by context and classification; Dependency, now accelerated by API calls, integration points, and AI agents; Criticality, which includes regulations, security, and implicit SLAs; Velocity, measured by how many functions data is used for; and Latency, complicated by geographic requirements that skew response times. These dimensions highlight how non-physical constraints, like egress fees and data sovereignty laws, create artificial friction that compounds the problem beyond sheer data size. Our discussion concludes with a new framework of five sources of data gravity that IT leaders must address: Technical Gravity (the physical component and mobility), Economic Gravity (the costs of hosting and moving data, like egress fees), Regulatory Gravity (compliance and legal restrictions), Institutional Gravity (the dependency on a small number of people who know how to manage old systems), and Measurement Gravity (budgeting and decision-making risks). Finally, Andrew connects these challenges to Pure Storage, noting how platform features like deduplication and continuous innovation are actively working to lessen the effects of data gravity for customers. To learn more, visit https://blog.purestorage.com/purely-technical/the-economics-of-data-gravity/ Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 01:05 Andrew Observations About the USA 04:19 Defining Data Gravity 07:30 Challenges Caused By Data Gravity 09:01 Real World Data Gravity Examples 17:15 Data Gravity Impact Vectors 33:02 New Dimensions of Data Gravity 40:30 Where Pure Helps with Data Gravity
From Pepcom at CES 2026 in Las Vegas, Paul Tan, COO for Keychron, about the new Q Ultra Series keyboards, their best yet. Highlights include an 8K wireless polling rate with ultra-low latency, exceptional battery life measured in months, a premium CNC-milled aluminum build, upgraded switches, and browser-based configuration that eliminates software downloads that allow for deep customization. Show Notes: Chapters: Links: Guests: Support: Become a MacVoices Patron on Patreon http://patreon.com/macvoices Enjoy this episode? Make a one-time donation with PayPal Connect: Web: http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner http://www.twitter.com/macvoices Mastodon: https://mastodon.cloud/@chuckjoiner Facebook: http://www.facebook.com/chuck.joiner MacVoices Page on Facebook: http://www.facebook.com/macvoices/ MacVoices Group on Facebook: http://www.facebook.com/groups/macvoice LinkedIn: https://www.linkedin.com/in/chuckjoiner/ Instagram: https://www.instagram.com/chuckjoiner/ Subscribe: Audio in iTunes Video in iTunes Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss Video: http://www.macvoices.com/rss/macvoicesvideorss
From Pepcom at CES 2026 in Las Vegas, Paul Tan, COO for Keychron, about the new Q Ultra Series keyboards, their best yet. Highlights include an 8K wireless polling rate with ultra-low latency, exceptional battery life measured in months, a premium CNC-milled aluminum build, upgraded switches, and browser-based configuration that eliminates software downloads that allow for deep customization. Show Notes: Chapters: Links: Guests: Support: Become a MacVoices Patron on Patreon http://patreon.com/macvoices Enjoy this episode? Make a one-time donation with PayPal Connect: Web: http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner http://www.twitter.com/macvoices Mastodon: https://mastodon.cloud/@chuckjoiner Facebook: http://www.facebook.com/chuck.joiner MacVoices Page on Facebook: http://www.facebook.com/macvoices/ MacVoices Group on Facebook: http://www.facebook.com/groups/macvoice LinkedIn: https://www.linkedin.com/in/chuckjoiner/ Instagram: https://www.instagram.com/chuckjoiner/ Subscribe: Audio in iTunes Video in iTunes Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss Video: http://www.macvoices.com/rss/macvoicesvideorss
Snow in the forecast, bass in the bloodstream. We kick things off with a little storm prep and then slide straight into three new releases that light up different corners of the pop universe: a girl band debut with real instruments and real restraint, a solo star leaning into neon funk, and a multinational rookie squad ringing the alarm with swagger.First, LATENCY's “It Was Love” trades flash for feel. Five members play and sing, shaping a soft, mid-tempo band sound that grows from quiet reflection to gentle resolve. Clean guitars, steady percussion, and a tasteful piano-synth color create space for melodies that stick without shouting. By the time the guitar solo lifts around the midpoint, the song has earned its momentum—intimate, bittersweet, and clear-eyed.Then Kento Nakajima flips the room with “XTC”. Think syncopated 80s synths, jazzy guitar licks, and a rhythm that snaps you into motion. Kenty's vocal control is the secret weapon, gliding from sultry lows to sharp falsetto as the track dances between playful invitation and seductive tension. It's a confident solo statement that blends disco shimmer with modern polish, perfect for late-night drives or living room dance breaks.Finally, meet ALPHA DRIVE ONE and their adrenaline-spiked “FREAK ALARM.” The intro hums like a clock before exploding into 80s hip-hop charge—reverb bass, crisp drums, and a chorus that stretches like a siren. The lyrics double as a manifesto: they're the “new alien,” inviting you to own your edge and step into the circle. It's a debut with grit and charm, made to wake up your playlist.If you want a soundtrack for the week—reflection, heat, and high-voltage confidence—this one's for you. Listen, save your favorite moments, and tell us which chorus won your day.LATENCY Instagram X YouTube It Was LoveKento Nakajima Instagram X YouTube XTCALPHA DRIVE ONE Instagram X YouTube FREAK ALARMSupport the showPlease help Music Elixir by rating, reviewing, and sharing the episode. We appreciate your support!Follow us on:TwitterInstagram BlueskyIf have questions, comments, or requests click on our form:Music Elixir FormDJ Panic Blog:OK ASIA
Cursive Cursing. A Cat Named Beef. Getting Twitchy. Let's Test the Show's Latency. Sour creme on your chalupa is not a euphemism. Emails dash jugs of pee. A Baja Better Time. You Need A Grommet. Imma seal up my hole. The hoity toity mall. Shrodenger's seating. Panky and the brian. But I would have liked to watch you struggle a little bit. Flower Child Something. Belter Passwords with Tom and more on this episode of The Morning Stream. Hosted on Acast. See acast.com/privacy for more information.
Cursive Cursing. A Cat Named Beef. Getting Twitchy. Let's Test the Show's Latency. Sour creme on your chalupa is not a euphemism. Emails dash jugs of pee. A Baja Better Time. You Need A Grommet. Imma seal up my hole. The hoity toity mall. Shrodenger's seating. Panky and the brian. But I would have liked to watch you struggle a little bit. Flower Child Something. Belter Passwords with Tom and more on this episode of The Morning Stream. Hosted on Acast. See acast.com/privacy for more information.
As constraints on energy, water, and permitting collide with exploding demand for AI and compute, a once-fringe idea is moving rapidly toward the center of the conversation: putting data centers in space. Starcloud believes orbital infrastructure isn't science fiction—it's a necessary extension of the global compute stack if scaling is going to continue at anything close to its current pace.Founded by Philip Johnston, Starcloud is building space-based compute systems designed to compete on cost, performance, and scale with terrestrial data centers. The company has already flown a data center–grade GPU in orbit and is now working toward larger, commercially viable systems that could reshape where and how AI is powered. We discuss:How energy and permitting constraints are reshaping the future of computeWhy space-based data centers may be economically inevitable, not optionalWhat Starcloud proved by running an H100 GPU in orbitHow launch costs, watts-per-kilogram, and chip longevity define the real economicsThe national security implications of who controls future compute capacity • Chapters •00:00 - Intro00:50 - The issue with data centers02:20 - Explosion of the data center debates04:58 - Philip's 5GW data center rendering and early conceptions of data centers in space at YC08:16 - Proving people wrong11:17 - The team at Starcloud today12:29 - Competing against SpaceX's data center14:42 - Sam Altman's beef with Starlink16:52 - Economics of Orbital vs Terrestrial Data Centers by Andrew McCallip21:33 - Where are we putting these things?23:50 - Latency in space25:59 - Political side of building data centers28:36 - Starcloud 130:16 - Space based processors30:51 - Shakespeare in space32:00 - Hardening an Nvidia H100 against radiation and making chips in space economical34:43 - Cooling systems in space36:01 - How Starcloud is thinking about replacing failed GPUs38:46 - The mission for Starcloud 240:05 - Competitors outside of SpaceX40:49 - Getting to economical launch costs44:35 - Will the next great wars be over water and power for data centers?46:25 - What keeps Philip up at night?47:11 - What keeps Mo up at night? • Show notes •Starcloud's website — https://www.starcloud.com/Philip's socials — https://x.com/PhilipJohnstonMo's socials — https://x.com/itsmoislamPayload's socials — https://twitter.com/payloadspace / https://www.linkedin.com/company/payloadspaceIgnition's socials — https://twitter.com/ignitionnuclear / https://www.linkedin.com/company/ignition-nuclear/Tectonic's socials — https://twitter.com/tectonicdefense / https://www.linkedin.com/company/tectonicdefense/Valley of Depth archive — Listen: https://pod.payloadspace.com/ • About us •Valley of Depth is a podcast about the technologies that matter — and the people building them. Brought to you by Arkaea Media, the team behind Payload (space), Ignition (nuclear energy), and Tectonic (defense tech), this show goes beyond headlines and hype. We talk to founders, investors, government officials, and military leaders shaping the future of national security and deep tech. From breakthrough science to strategic policy, we dive into the high-stakes decisions behind the world's hardest technologies.Payload: www.payloadspace.comTectonic: www.tectonicdefense.comIgnition: www.ignition-news.com
"How do I create the 'wow' moment for my end user? And slowness never creates the wow moment." PolarGrid founder and CEO Rade Kovacevic believes GenAI video and voice will be killer apps once they can function in real-time. Enabling real-time GenAI requires uncorking the inference bottleneck that the hyperscalers have helped build. The BetaKit podcast is presented by Fasken Emerging Tech, supporting trailblazing startups, venture capital funds and acquirers of high-growth tech companies for over 30 years. If you're curious about the health of Canada's tech M&A scene, you've got to check out Exit InSights. It's a first-of-its-kind report from Fasken's Emerging Technology & Venture Capital Group that analyses private M&A activity among VC-backed and high-growth tech companies. You'll learn how buyers and sellers are maximizing value, minimizing risk, and navigating one of the most vibrant tech ecosystems out there. Download your free copy of the report.
Software Engineering Radio - The Podcast for Professional Software Developers
In this episode, Sahaj Garg, CTO of wispr.ai, joins SE Radio host Robert Blumen to talk about the challenges of building low-latency AI applications. They discuss latency's effect on consumer behavior as well as interactive applications. The conversation explores how to measure latency and how scale impacts it. Then Sahaj and Robert shift to themes around AI, including whether "AI" means LLMs or something broader, as they look at latency requirements and challenges around subtypes of AI applications. The final part of the episode explores techniques for managing latency in AI: speed vs accuracy trade-offs; speed vs cost; latency vs cost; choosing the right model; reducing quantization; distillation; and guessing + validating.
Healthcare organizations are navigating modernization under intense regulatory, security, and resource constraints. This episode explores how the Microsoft technology stack shows up differently in healthcare. The conversation breaks down hybrid cloud realities, Azure managed services, security and compliance, business resiliency, disaster recovery, and cost optimization, all grounded in real healthcare use cases. The episode also explores at how organizations can measure ROI beyond cost savings, connecting Microsoft investments to patient care, clinician experience, and operational resilience. Speakers: Jennifer Johnson, Director of Healthcare at Connection David Carey and Kevin Paiva, Senior Field Solution Architects at Connection Show Notes: 00:10 Welcome and session overview 01:40 Why healthcare cloud adoption is different 02:10 Defining hybrid cloud in healthcare 03:00 Why hybrid is now the default model 03:55 Latency myths and performance realities 04:45 Which workloads belong on-prem vs. in the cloud 05:45 SaaS, staffing pressure, and infrastructure complexity 06:30 Azure managed services and Connection's approach 07:45 Co-managed Azure vs. fully outsourced models 08:30 Why Azure over other hyperscalers 09:20 Azure security, HIPAA, and Zero Trust 10:30 Azure Health Data Services 11:45 Business continuity vs. business resiliency 14:10 What healthcare leaders worry about most today 15:00 Disaster recovery and Azure Expert MSP 16:30 Post-pandemic resource constraints 17:30 Application sprawl, security, and identity management 18:50 Cost containment and ROI in healthcare IT 21:15 The teams behind Connection's Microsoft practice 24:45 Final takeaways and next steps
In this episode of the Crazy Wisdom podcast, host Stewart Alsop interviews Marcin Dymczyk, CPO and co-founder of SevenSense Robotics, exploring the fascinating world of advanced robotics and AI. Their conversation covers the evolution from traditional "standard" robotics with predetermined pathways to advanced robotics that incorporates perception, reasoning, and adaptability - essentially the AGI of physical robotics. Dymczyk explains how his company builds "the eyes and brains of mobile robots" using camera-based autonomy algorithms, drawing parallels between robot sensing systems and human vision, inner ear balance, and proprioception. The discussion ranges from the technical challenges of sensor fusion and world models to broader topics including robotics regulation across different countries, the role of federalism in innovation, and how recent geopolitical changes are driving localized high-tech development, particularly in defense applications. They also touch on the democratization of robotics for small businesses and the philosophical implications of increasingly sophisticated AI systems operating in physical environments. To learn more about SevenSense, visit www.sevensense.ai.Check out this GPT we trained on the conversationTimestamps00:00 Introduction to Robotics and Personal Journey05:27 The Evolution of Robotics: From Standard to Advanced09:56 The Future of Robotics: AI and Automation12:09 The Role of Edge Computing in Robotics17:40 FPGA and AI: The Future of Robotics Processing21:54 Sensing the World: How Robots Perceive Their Environment29:01 Learning from the Physical World: Insights from Robotics33:21 The Intersection of Robotics and Manufacturing35:01 Journey into Robotics: Education and Passion36:41 Practical Robotics Projects for Beginners39:06 Understanding Particle Filters in Robotics40:37 World Models: The Future of AI and Robotics41:51 The Black Box Dilemma in AI and Robotics44:27 Safety and Interpretability in Autonomous Systems49:16 Regulatory Challenges in Robotics and AI51:19 Global Perspectives on Robotics Regulation54:43 The Future of Robotics in Emerging Markets57:38 The Role of Engineers in Modern WarfareKey Insights1. Advanced robotics transcends traditional programming through perception and intelligence. Dymczyk distinguishes between standard robotics that follows rigid, predefined pathways and advanced robotics that incorporates perception and reasoning. This evolution enables robots to make autonomous decisions about navigation and task execution, similar to how humans adapt to unexpected situations rather than following predetermined scripts.2. Camera-based sensing systems mirror human biological navigation. SevenSense Robotics builds "eyes and brains" for mobile robots using multiple cameras (up to eight), IMUs (accelerometers/gyroscopes), and wheel encoders that parallel human vision, inner ear balance, and proprioception. This redundant sensing approach allows robots to navigate even when one system fails, such as operating in dark environments where visual sensors are compromised.3. Edge computing dominates industrial robotics due to connectivity and security constraints. Many industrial applications operate in environments with poor connectivity (like underground grocery stores) or require on-premise solutions for confidentiality. This necessitates powerful local processing capabilities rather than cloud-dependent AI, particularly in automotive factories where data security about new models is paramount.4. Safety regulations create mandatory "kill switches" that bypass AI decision-making. European and US regulatory bodies require deterministic safety systems that can instantly stop robots regardless of AI reasoning. These systems operate like human reflexes, providing immediate responses to obstacles while the main AI brain handles complex navigation and planning tasks.5. Modern robotics development benefits from increasingly affordable optical sensors. The democratization of 3D cameras, laser range finders, and miniature range measurement chips (costing just a few dollars from distributors like DigiKey) enables rapid prototyping and innovation that was previously limited to well-funded research institutions.6. Geopolitical shifts are driving localized high-tech development, particularly in defense applications. The changing role of US global leadership and lessons from Ukraine's drone warfare are motivating countries like Poland to develop indigenous robotics capabilities. Small engineering teams can now create battlefield-effective technology using consumer drones equipped with advanced sensors.7. The future of robotics lies in natural language programming for non-experts. Dymczyk envisions a transformation where small business owners can instruct robots using conversational language rather than complex programming, similar to how AI coding assistants now enable non-programmers to build applications through natural language prompts.
بزاف ديال المهندسين (Engineers) ف الـ Backend كايغلطو ف هاد المفاهيم الأساسية. واش السيستيم ديالك غيبقى خدام إلا تّشق فيه شي "Fault"؟ واش هاديك الـ Moyenne (الأرقام المتوسطة) اللي كاتشوف ف الـ Dashboard هي اللي كاتشرح الحقيقة؟ف هاد الحلقة من سحابة (S7aba)، غادي نغوصو ف الكتاب اللي دار الروينة ف هاد الدومين: Designing Data-Intensive Applications ديال Martin Kleppmann. غادي نجاوبو على هاد الأسئلة
De Rosalia à Little Simz, en passant par Bad Bunny, Sophian Fanen balaye son année 2025. En cette fin d'année, Sophian vous fait plaisir et sélectionne ses obsessions favorites. Rosalía, Berghain, tiré de l'album Lux (Columbia Records, 2025) Bad Bunny, Weltita (feat. Chuwi), tiré de l'album Debí Tirar Más Fotos (Rimas Entertainment) Theodora, Ils me rient tous au nez, tiré de l'album Mega BBL (Boss Lady, 2025) Tarta Relena, Si veriash a la rana, tiré de l'album És pregunta (Latency, 2024) Ale hop & Titi Bakorta, Bonne année, tiré de l'album Mapambazuko (Nyege Nyege Tapes, 2025) Andrea Laszlo de Simone, Quando, tiré de l'album Una Lunghissima Ombra (Ekler/Hamburger Records, 2025) Blaiz Fayah, Maureen et DJ Glad, Money Pull Up, tiré de l'album Shatta Ting (Creepy Music, 2025) Little Simz, Lion (feat. Obongjayar), tiré de l'album Lotus (Awal, 2025) Miki, Jtm encore, tiré de l'album Industry Plant (Structure, 2025) Xania Monet, How Was I Supposed to Know?, tiré de l'album Unfolded (TMJ/Hallwood, 2025)
De Rosalia à Little Simz, en passant par Bad Bunny, Sophian Fanen balaye son année 2025. En cette fin d'année, Sophian vous fait plaisir et sélectionne ses obsessions favorites. Rosalía, Berghain, tiré de l'album Lux (Columbia Records, 2025) Bad Bunny, Weltita (feat. Chuwi), tiré de l'album Debí Tirar Más Fotos (Rimas Entertainment) Theodora, Ils me rient tous au nez, tiré de l'album Mega BBL (Boss Lady, 2025) Tarta Relena, Si veriash a la rana, tiré de l'album És pregunta (Latency, 2024) Ale hop & Titi Bakorta, Bonne année, tiré de l'album Mapambazuko (Nyege Nyege Tapes, 2025) Andrea Laszlo de Simone, Quando, tiré de l'album Una Lunghissima Ombra (Ekler/Hamburger Records, 2025) Blaiz Fayah, Maureen et DJ Glad, Money Pull Up, tiré de l'album Shatta Ting (Creepy Music, 2025) Little Simz, Lion (feat. Obongjayar), tiré de l'album Lotus (Awal, 2025) Miki, Jtm encore, tiré de l'album Industry Plant (Structure, 2025) Xania Monet, How Was I Supposed to Know?, tiré de l'album Unfolded (TMJ/Hallwood, 2025)
In this episode of The Effortless Podcast, Amit Prakash and Dheeraj Pandey dive deep into one of the most important shifts happening in AI today: the convergence of structured and unstructured data, interfaces, and systems.Together, they unpack how conversations—not CRM fields—hold the real ground truth; why schemas still matter in an AI-driven world; and how agents can evolve into true managers, coaches, and chiefs of staff for revenue teams. They explore the cognitive science behind visual vs conversational UI, the future of dynamically generated interfaces, and the product depth required to build enduring AI-native software.Amit and Dheeraj break down the tension between deterministic and probabilistic systems, the limits of prompt-driven workflows, and why the future of enterprise AI is “both-and” rather than “either-or.” It's a masterclass in modern product, data design, and the psychology of building intelligent tools.Key Topics & Timestamps 00:00 – Introduction02:00 – Why conversations—not CRM fields—hold real ground truth05:00 – Reps as labelers and the parallels with AI training pipelines08:00 – Business logic vs world models: defining meaning inside enterprises11:00 – Prompts flatten nuance; schemas restore structure14:00 – SQL schemas as the true model of a business17:00 – CRM overload and the friction of rigid data entry20:00 – AI agents that debrief and infer fields dynamically23:00 – Capturing qualitative signals: champions, pain, intent26:00 – Multi-source context: transcripts, email threads, Slack29:00 – Why structure is required for math, aggregation, forecasting32:00 – Aggregating unstructured data to reveal organizational issues35:00 – Labels, classification, and the limits of LLM-only workflows38:00 – Deterministic (SQL/Python) vs probabilistic (LLMs) systems41:00 – Transitional workflows: humans + AI field entry44:00 – Trust issues and the confusion of the early AI market47:00 – Avoiding “Clippy moments” in agent design50:00 – Latency, voice UX, and expectations for responsiveness53:00 – Human-machine interface for SDRs vs senior reps56:00 – Structured vs unstructured UI: cognitive science insights59:00 – Charts vs paragraphs: parallel vs sequential processing1:02:00 – The “Indian thali” dashboard problem and dynamic UI1:05:00 – Exploration modes, drill-downs, and empty prompts1:08:00 – Dynamic leaves, static trunk: designing hierarchy1:11:00 – Both-and thinking: voice + visual, structured + unstructured1:14:00 – Why “good enough” AI fails without deep product1:17:00 – PLG, SLG, data access, and trust barriers1:20:00 – Closing reflections and the future of AI-native softwareHosts: Amit Prakash – CEO and Founder at AmpUp, former engineer at Google AdSense and Microsoft Bing, with extensive expertise in distributed systems and machine learningDheeraj Pandey – Co-founder and CEO at DevRev, former Co-founder & CEO of Nutanix. A tech visionary with a deep interest in AI, systems, and the future of work.Follow the Hosts:Amit PrakashLinkedIn – Amit Prakash I LinkedInTwitter/X – https://x.com/amitp42Dheeraj PandeyLinkedIn –Dheeraj Pandey | LinkedIn Twitter/X – https://x.com/dheerajShare your thoughts : Have questions, comments, or ideas for future episodes?Email us at EffortlessPodcastHQ@gmail.comDon't forget to Like, Comment, and Subscribe for more conversations at the intersection of AI, technology, and innovation.
Every few years, the world of product management goes through a phase shift. When I started at Microsoft in the early 2000s, we shipped Office in boxes. Product cycles were long, engineering was expensive, and user research moved at the speed of snail mail. Fast forward a decade and the cloud era reset the speed at which we build, measure, and learn. Then mobile reshaped everything we thought we knew about attention, engagement, and distribution.Now we are standing at the edge of another shift. Not a small shift, but a tectonic one. Artificial intelligence is rewriting the rules of product creation, product discovery, product expectations, and product careers.To help make sense of this moment, I hosted a panel of world class product leaders on the Fireside PM podcast:• Rami Abu-Zahra, Amazon product leader across Kindle, Books, and Prime Video• Todd Beaupre, Product Director at YouTube leading Home and Recommendations• Joe Corkery, CEO and cofounder of Jaide Health • Tom Leung (me), Partner at Palo Alto Foundry• Lauren Nagel, VP Product at Mezmo• David Nydegger, Chief Product Officer at OvivaThese are leaders running massive consumer platforms, high stakes health tech, and fast moving developer tools. The conversation was rich, honest, and filled with specific examples. This post summarizes the discussion, adds my own reflections, and offers a practical guide for early and mid career PMs who want to stay relevant in a world where AI is redefining what great product management looks like.Table of Contents* What AI Cannot Do and Why PM Judgment Still Matters* The New AI Literacy: What PMs Must Know by 2026* Why Building AI Products Speeds Up Some Cycles and Slows Down Others* Whether the PM, Eng, UX Trifecta Still Stands* The Biggest Risks AI Introduces Into Product Development* Actionable Advice for Early and Mid Career PMs* My Takeaways and What Really Matters Going Forward* Closing Thoughts and Coaching Practice1. What AI Cannot Do and Why PM Judgment Still MattersWe opened the panel with a foundational question. As AI becomes more capable every quarter, what is left for humans to do. Where do PMs still add irreplaceable value. It is the question every PM secretly wonders.Todd put it simply: “At the end of the day, you have to make some judgment calls. We are not going to turn that over anytime soon.”This theme came up again and again. AI is phenomenal at synthesizing, drafting, exploring, and narrowing. But it does not have conviction. It does not have lived experience. It does not feel user pain. It does not carry responsibility.Joe from Jaide Health captured it perfectly when he said: “AI cannot feel the pain your users have. It can help meet their goals, but it will not get you that deep understanding.”There is still no replacement for sitting with a frustrated healthcare customer who cannot get their clinical data into your system, or a creator on YouTube who feels the algorithm is punishing their art, or a devops engineer staring at an RCA output that feels 20 percent off.Every PM knows this feeling: the moment when all signals point one way, but your gut tells you the data is incomplete or misleading. This is the craft that AI does not have.Why judgment becomes even more important in an AI worldDavid, who runs product at a regulated health company, said something incredibly important: “Knowing what great looks like becomes more essential, not less. The PM's that thrive in AI are the ones with great product sense.”This is counterintuitive for many. But when the operational work becomes automated, the differentiation shifts toward taste, intuition, sequencing, and prioritization.Lauren asked the million dollar question. “How are we going to train junior PMs if AI is doing the legwork. Who teaches them how to think.”This is a profound point. If AI closes the gap between junior and senior PMs in execution tasks, the difference will emerge almost entirely in judgment. Knowing how to probe user problems. Knowing when a feature is good enough. Knowing which tradeoffs matter. Knowing which flaw is fatal and which is cosmetic.AI is incredible at writing a PRD. AI is terrible at knowing whether the PRD is any good.Which means the future PM becomes more strategic, more intuitive, more customer obsessed, and more willing to make thoughtful bets under uncertainty.2. The New AI Literacy: What PMs Must Know by 2026I asked the panel what AI literacy actually means for PMs. Not the hype. Not the buzzwords. The real work.Instead of giving gimmicky answers, the discussion converged on a clear set of skills that PMs must master.Skill 1: Understanding context engineeringDavid laid this out clearly: “Knowing what LMS are good at and what they are not good at, and knowing how to give them the right context, has become a foundational PM skill.”Most PMs think prompt engineering is about clever phrasing. In reality, the future is about context engineering. Feeding models the right data. Choosing the right constraints. Deciding what to ignore. Curating inputs that shape outputs in reliable ways.Context engineering is to AI product development what Figma was to collaborative design. If you cannot do it, you are not going to be effective.Skill 2: Evals, evals, evalsRami said something that resonated with the entire panel: “Last year was all about prompts. This year is all about evals.”He is right.• How do you build a golden dataset.• How do you evaluate accuracy.• How do you detect drift.• How do you measure hallucination rates.• How do you combine UX evals with model evals.• How do you decide what good looks like.• How do you define safe versus unsafe boundaries.AI evaluation is now a core PM responsibility. Not exclusively. But PMs must understand what engineers are testing for, what failure modes exist, and how to design test sets that reflect the real world.Lauren said her PMs write evals side by side with engineering. That is where the world is going.Skill 3: Knowing when to trust AI output and when to override itTodd noted: “It is one thing to get an answer that sounds good. It is another thing to know if it is actually good.”This is the heart of the role. AI can produce strategic recommendations that look polished, structured, and wise. But the real question is whether they are grounded in reality, aligned with your constraints, and consistent with your product vision.A PM without the ability to tell real insight from confident nonsense will be replaced by someone who can.Skill 4: Understanding the physics of model changesThis one surprised many people, but it was a recurring point.Rami noted: “When you upgrade a model, the outputs can be totally different. The evals start failing. The experience shifts.”PMs must understand:• Models get deprecated• Models drift• Model updates can break well tuned prompts• API pricing has real COGS implications• Latency varies• Context windows vary• Some tasks need agents, some need RAG, some need a small finetuned modelThis is product work now. The PM of 2026 must know these constraints as well as a PM of the cloud era understood database limits or API rate limits.Skill 5: How to construct AI powered prototypes in hours, not weeksIt now takes one afternoon to build something meaningful. Zero code required. Prompt, test, refine. Whether you use Replit, Cursor, Vercel, or sandboxed agents, the speed is shocking.But this makes taste and problem selection even more important. The future PM must be able to quickly validate whether a concept is worth building beyond the demo stage.3. Why Building AI Products Speeds Up Some Cycles and Slows Down OthersThis part of the conversation was fascinating because people expected AI to accelerate everything. The panel had a very different view.Fast: Prototyping and concept validationLauren described how her teams can build working versions of an AI powered Root Cause Analysis feature in days, test it with customers, and get directional feedback immediately.“You can think bigger because the cost of trying things is much lower,” she said.For founders, early PMs, and anyone validating hypotheses, this is liberating. You can test ten ideas in a week. That used to take a quarter.Slow: Productionizing AI featuresThe surprising part is that shipping the V1 of an AI feature is slower than most expect.Joe noted: “You can get prototypes instantly. But turning that into a real product that works reliably is still hard.”Why. Because:• You need evals.• You need monitoring.• You need guardrails.• You need safety reviews.• You need deterministic parts of the workflow.• You need to manage COGS.• You need to design fallbacks.• You need to handle unpredictable inputs.• You need to think about hallucination risk.• You need new UI surfaces for non deterministic outputs.Lauren said bluntly: “Vibe coding is fast. Moving that vibe code to production is still a four month process.”This should be printed on a poster in every AI startup office.Very Slow: Iterating on AI powered featuresAnother counterintuitive point. Many teams ship a great V1 but struggle to improve it significantly afterward.David said their nutrition AI feature launched well but: “We struggled really hard to make it better. Each iteration was easy to try but difficult to improve in a meaningful way.”Why is iteration so difficult.Because model improvements may not translate directly into UX improvements. Users need consistency. Drift creates churn. Small changes in context or prompts can cause large changes in behavior.Teams are learning a hard truth: AI powered features do not behave like typical deterministic product flows. They require new iteration muscles that most orgs do not yet have.4. The PM, Eng, UX Trifecta in the AI EraI asked whether the classic PM, Eng, UX triad is still the right model. The audience was expecting disagreement. The panel was surprisingly aligned.The trifecta is not going anywhereRami put it simply: “We still need experts in all three domains to raise the bar.”Joe added: “AI makes it possible for PMs to do more technical work. But it does not replace engineering. Same for design.”AI blurs the edges of the roles, but it does not collapse them. In fact, each role becomes more valuable because the work becomes more abstract.• PMs focus on judgment, sequencing, evaluation, and customer centric problem framing• Engineers focus on agents, systems, architecture, guardrails, latency, and reliability• Designers focus on dynamic UX, non deterministic UX patterns, and new affordances for AI outputsWhat does changeAI makes the PM-Eng relationship more intense. The backbone of AI features is a combination of model orchestration, evaluation, prompting, and context curation. PMs must be tighter than ever with engineering to design these systems.David noted that his teams focus more on individual talents. Some PMs are great at context engineering. Some designers excel at polishing AI generated layouts. Some engineers are brilliant at prompt chaining. AI reveals strengths quickly.The trifecta remains. The skill distribution within it evolves.5. The Biggest Risks AI Introduces Into Product DevelopmentWhen we asked what scares PMs most about AI, the conversation became blunt and honest. Risk 1: Loss of user trustLauren warned: “If people keep shipping low quality AI features, user trust in AI erodes. And then your good AI product suffers from the skepticism.”This is very real. Many early AI features across industries are low quality, gimmicky, or unreliable. Users quickly learn to distrust these experiences.Which means PMs must resist the pressure to ship before the feature is ready.Risk 2: Skill atrophyTodd shared a story that hit home for many PMs. “Junior folks just want to plug in the prompt and take whatever the AI gives them. That is a recipe for having no job later.”PMs who outsource their thinking to AI will lose their judgment. Judgment cannot be regained easily.This is the silent career killer.Risk 3: Safety hazards in sensitive domainsDavid was direct: “If we have one unsafe output, we have to shut the feature off. We cannot afford even small mistakes.”In healthcare, finance, education, and legal industries, the tolerance for error is near zero. AI must be monitored relentlessly. Human in the loop systems are mandatory. The cycles are slower but the stakes are higher.Risk 4: The high bar for AI compared to humansJoe said something I have thought about for years: “AI is held to a much higher standard than human decision making. Humans make mistakes constantly, but we forgive them. AI makes one mistake and it is unacceptable.”This slows adoption in certain industries and creates unrealistic expectations.Risk 5: Model deprecation and instabilityRami described a real problem AI PMs face: “Models get deprecated faster than they get replaced. The next model is not always GA. Outputs change. Prompts break.”This creates product instability that PMs must anticipate and design around.Risk 6: Differentiation becomes hardI shared this perspective because I see so many early stage startups struggle with it.If your whole product is a wrapper around an LLM, competitors will copy you in a week. The real differentiation will not come from using AI. It will come from how deeply you understand the customer, how you integrate AI with proprietary data, and how you create durable workflows.6. Actionable Advice for Early and Mid Career PMsThis was one of my favorite parts of the panel because the advice was humble, practical, and immediately useful.A. Develop deep user empathy. This will become your biggest differentiator.Lauren said it clearly: “Maintain your empathy. Understand the pain your user really has.”AI makes execution cheap. It makes insight valuable.If you can articulate user pain precisely.If you can differentiate surface friction from underlying need.If you can see around corners.If you can prototype solutions and test them in hours.If you can connect dots between what AI can do and what users need.You will thrive.Tactical steps:• Sit in on customer support calls every week.• Watch 10 user sessions for every feature you own.• Talk to customers until patterns emerge.• Ask “why” five times in every conversation.• Maintain a user pain log and update it constantly.B. Become great at context engineeringThis will matter as much as SQL mattered ten years ago.Action steps:• Practice writing prompts with structured context blocks.• Build a library of prompts that work for your product.• Study how adding, removing, or reordering context changes output.• Learn RAG patterns.• Learn when structured data beats embeddings.• Learn when smaller local models outperform big ones.C. Learn eval frameworksThis is non negotiable.You need to know:• Precision vs recall tradeoffs• How to build golden datasets• How to design scenario based evals for UX• How to test for hallucination• How to monitor drift• How to set quality thresholds• How to build dashboards that reflect real world input distributionsYou do not need to write the code.You do need to define the eval strategy.D. Strengthen your product senseYou cannot outsource product taste.Todd said it best: “Imagine asking AI to generate 20 percent growth for you. It will not tell you what great looks like.”To strengthen your product sense:• Review the best products weekly.• Take screenshots of great UX patterns.• Map user flows from apps you admire.• Break products down into primitives.• Ask yourself why a product decision works.• Predict what great would look like before you design it.The PMs who thrive will be the ones who can recognize magic when they see it.E. Stay curiousRami's closing advice was simple and perfect: “Stay curious. Keep learning. It never gets old.”AI changes monthly. The PM who is excited by new ideas will outperform the PM who clings to old patterns.Practical habits:• Read one AI research paper summary each week.• Follow evaluation and model updates from major vendors.• Build at least one small AI prototype a month.• Join AI PM communities.• Teach juniors what you learn. Nothing accelerates mastery faster.F. Embrace velocity and side projectsTodd said that some of his biggest career breakthroughs came from solving problems on the side.This is more true now than ever.If you have an idea, you can build an MVP over a weekend. If it solves a real problem, someone will notice.G. Stay close to engineeringNot because you need to code, but because AI features require tighter PM engineering collaboration.Learn enough to be dangerous:• How embeddings work• How vector stores behave• What latency tradeoffs exist• How agents chain tasks• How model versioning works• How context limits shape UX• Why some prompts blow up API costsIf you can speak this language, you will earn trust and accelerate cycles.H. Understand the business deeplyJoe's advice was timeless: “Know who pays you and how much they pay. Solve real problems and know the business model.”PMs who understand unit economics, COGS, pricing, and funnel dynamics will stand out.7. Tom's Takeaways and What Really Matters Going ForwardI ended the recording by sharing what I personally believe after moderating this discussion and working closely with a variety of AI teams over the past 2 years.Judgment becomes the most valuable PM skillAs AI gets better at analysis, synthesis, and execution, your value shifts to:• Choosing the right problem• Sequencing decisions• Making 55 45 calls• Understanding user pain• Making tradeoffs• Deciding when good is good enough• Defining success• Communicating vision• Influencing the orgAgents can write specs.LLMs can produce strategies.But only humans can choose the right one and commit.Learning speed becomes a competitive advantageI said this on the panel and I believe it more every month.Because of AI, you now have:• Infinite coaches• Infinite mentors• Infinite experts• Infinite documentation• Infinite learning loopsA PM who learns slowly will not survive the next decade. Curiosity, empathy, and velocity will separate great from goodMany panelists said versions of this. The common pattern was:• Understand users deeply• Combine multiple tools creatively• Move quickly• Learn constantlyThe future rewards generalists with taste, speed, and emotional intelligence.Differentiation requires going beyond wrapper appsThis is one of my biggest concerns for early stage founders. If your entire product is a wrapper around a model, you are vulnerable.Durable value will come from:• Proprietary data• Proprietary workflows• Deep domain insight• Organizational trust• Distribution advantage• Safety and reliability• Integration with existing systemsAI is a component, not a moat.8. Closing ThoughtsHosting this panel made me more optimistic about the future of product management. Not because AI will not change the job. It already has. But because the fundamental craft remains alive.Product management has always been about understanding people, making decisions with incomplete information, telling compelling stories, and guiding teams through ambiguity and being right often.AI accelerates the craft. It amplifies the best PMs and exposes the weak ones. It rewards curiosity, empathy, velocity, and judgment.If you want tailored support on your PM career, leadership journey, or executive path, I offer 1 on 1 career, executive, and product coaching at tomleungcoaching.com.OK team. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com
The Data Center Boom: Five Trends Engineering Firms Need to Know The data center market is experiencing unprecedented growth, driven by artificial intelligence adoption and changing infrastructure demands. For ACEC member firms, this represents both a substantial business opportunity and a chance to shape critical national infrastructure. ACEC's latest Market Intelligence Brief reveals a market poised to reach $62 billion in design and construction spending by 2029, with implications that extend far beyond traditional data center engineering. The launch of ChatGPT in 2022 marked an inflection point. What began as voice assistants has evolved into sophisticated language learning models that consume dramatically more energy. A standard AI query uses about 0.012 kilowatt-hours, while generating a single high-quality image requires 2.0 kWh—roughly 20 times the daily consumption of a standard LED lightbulb. As weekly ChatGPT users surged from 100 million to 700 million between November 2023 and August 2025, the infrastructure implications became impossible to ignore. AI-driven data center power demand, which stood at just 4 gigawatts in 2024, is projected to reach 123 gigawatts by 2035. Even more striking: 70 percent of data center power demand will be driven by AI workloads. This explosive growth requires engineering solutions at unprecedented scale, from power distribution and backup systems to advanced cooling technologies and grid integration strategies. Public perception about data center water consumption often overlooks important nuances in cooling technology. While mechanical cooling systems have historically consumed significant water resources, newer approaches could dramatically reduce water use. Free air cooling, closed-loop systems, and liquid immersion technologies offer low-water use alternatives, with some methods reducing freshwater consumption by 70 percent or more compared to traditional systems. As Thom Jackson, mechanical engineer and partner at Dunham Engineering, notes: "Most data centers utilize closed loop cooling systems requiring no makeup water and minimal maintenance." The "big four" hyperscale operators—Amazon Web Services, Microsoft Azure, Google Cloud Platform, and Meta—have all committed to becoming water-positive by 2030, replenishing more water than they consume. These commitments are driving innovation in cooling system design and creating opportunities for engineering firms with expertise in sustainable mechanical systems. The days of one-size-fits-all data centers are over. Latency requirements, scalability needs, and proximity to end users are accelerating adoption of diverse building types. Edge data centers bring computing closer to users for real-time applications like IoT and 5G. Hyperscale facilities support massive cloud and AI workloads with 100,000-plus servers. Colocation models enable scalable shared environments for enterprises, while modular designs—prefabricated with integrated power and cooling—offer rapid, cost-effective deployment. Each model presents distinct engineering challenges and opportunities, from specialized HVAC systems and high floor-to-ceiling ratios for hyperscale facilities to distributed infrastructure planning for edge networks. Two emerging trends deserve particular attention. First, the Department of Energy has selected four federal sites to host AI data centers paired with clean energy generation, including small modular reactors (SMRs). The Nuclear Regulatory Commission anticipates at least 25 SMR license applications by 2029, signaling strong demand for nuclear co-location expertise. Second, developers are increasingly exploring adaptive reuse of underutilized office spaces, Brownfield sites, and historical buildings. These locations offer existing utility infrastructure that can reduce construction time and costs, making them attractive alternatives despite some design constraints. Recent federal policy changes are streamlining data center deployment. Executive Order 14318 directs agencies to accelerate environmental reviews and permitting, while revisions to New Source Review under the Clean Air Act could allow construction to begin before air permits are issued. ACEC recently formed the Data Center Task Force to advocate for policies that balance speed, affordability, and national security in data center development, complimenting EO 14318. For engineering firms, site selection expertise has become increasingly valuable. Success hinges on sales and use tax exemptions, existing power and fiber connectivity, effective community engagement, and thorough environmental risk assessment. AI-driven planning tools like UrbanFootprint and ESRI ArcGIS are helping developers evaluate site suitability, identifying opportunities for firms. The data center market offers engineering firms a chance to lead in sustainable design, infrastructure innovation, and strategic planning at a moment when digital infrastructure has become as critical as traditional utilities.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
In this week's episode of Hands-On Tech, Lance asks Mikah Sargent about the pros and cons of using powerline ethernet adapters, and Mikah shares his strong thoughts on these devices. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
The Crew and some new and old friends, take on the Mass Driver at Morro Rock. They've made it past the barge and into the compound, now to split up, plant the virus and get out without a bang. Hopefully...Jade, Evan and Peter are joined by Dayeanne Hutton and Anais R Morgan (Infinite Sided Dice) live on stage at San Diego Comic Con 2025!Check out our Youtube Channel for more live panels! HereBTS and art posts will come out for all our patrons to peruse next week!More info can be found here: linktr.ee/NoLatencyIf you'd like to support us, We now have a Patreon! Patreon.com/nolatencyEven more information and MERCH is on our website! www.nolatencypodcast.comTwitter: @nolatencypodInstagram: @nolatencypodLogo & Map Art By Paris ArrowsmithCharacter Art by: Doodlejumps, Saint and Paris ArrowsmithProducing and Editing by Paris ArrowsmithMusic and Sound sfx by Epidemic Sound.Find @SkullorJade, @Miss_Magitek and @Binary_Dragon, @retrodatv on twitch, for live D&D, TTRPGs and more.#cyberpunkred #actualplay #ttrpg #radioplay #scifi #cyberpunk #drama #comedy #LIVE #SDCC25
How do we know through atmospheres? How can being affected by an atmosphere give rise to knowledge? What role does somatic, nonverbal knowledge play in how we belong to places? Atmospheric Knowledge takes up these questions through detailed analyses of practices that generate atmospheres and in which knowledge emerges through visceral intermingling with atmospheres. From combined musicological and anthropological perspectives, Birgit Abels and Patrick Eisenlohr investigate atmospheres as a compelling alternative to better-known analytics of affect by way of performative and sonic practices across a range of ethnographic settings. With particular focus on oceanic relations and sonic affectedness, Atmospheric Knowledge centers the rich affordances of sonic connections for knowing our environments. A free ebook version of this title is available through Luminos, University of California Press's Open Access publishing program. Visit www.luminosoa.org to learn more. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
How do we know through atmospheres? How can being affected by an atmosphere give rise to knowledge? What role does somatic, nonverbal knowledge play in how we belong to places? Atmospheric Knowledge takes up these questions through detailed analyses of practices that generate atmospheres and in which knowledge emerges through visceral intermingling with atmospheres. From combined musicological and anthropological perspectives, Birgit Abels and Patrick Eisenlohr investigate atmospheres as a compelling alternative to better-known analytics of affect by way of performative and sonic practices across a range of ethnographic settings. With particular focus on oceanic relations and sonic affectedness, Atmospheric Knowledge centers the rich affordances of sonic connections for knowing our environments. A free ebook version of this title is available through Luminos, University of California Press's Open Access publishing program. Visit www.luminosoa.org to learn more. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/anthropology
How do we know through atmospheres? How can being affected by an atmosphere give rise to knowledge? What role does somatic, nonverbal knowledge play in how we belong to places? Atmospheric Knowledge takes up these questions through detailed analyses of practices that generate atmospheres and in which knowledge emerges through visceral intermingling with atmospheres. From combined musicological and anthropological perspectives, Birgit Abels and Patrick Eisenlohr investigate atmospheres as a compelling alternative to better-known analytics of affect by way of performative and sonic practices across a range of ethnographic settings. With particular focus on oceanic relations and sonic affectedness, Atmospheric Knowledge centers the rich affordances of sonic connections for knowing our environments. A free ebook version of this title is available through Luminos, University of California Press's Open Access publishing program. Visit www.luminosoa.org to learn more. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sociology
How do we know through atmospheres? How can being affected by an atmosphere give rise to knowledge? What role does somatic, nonverbal knowledge play in how we belong to places? Atmospheric Knowledge takes up these questions through detailed analyses of practices that generate atmospheres and in which knowledge emerges through visceral intermingling with atmospheres. From combined musicological and anthropological perspectives, Birgit Abels and Patrick Eisenlohr investigate atmospheres as a compelling alternative to better-known analytics of affect by way of performative and sonic practices across a range of ethnographic settings. With particular focus on oceanic relations and sonic affectedness, Atmospheric Knowledge centers the rich affordances of sonic connections for knowing our environments. A free ebook version of this title is available through Luminos, University of California Press's Open Access publishing program. Visit www.luminosoa.org to learn more. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/geography
How do we know through atmospheres? How can being affected by an atmosphere give rise to knowledge? What role does somatic, nonverbal knowledge play in how we belong to places? Atmospheric Knowledge takes up these questions through detailed analyses of practices that generate atmospheres and in which knowledge emerges through visceral intermingling with atmospheres. From combined musicological and anthropological perspectives, Birgit Abels and Patrick Eisenlohr investigate atmospheres as a compelling alternative to better-known analytics of affect by way of performative and sonic practices across a range of ethnographic settings. With particular focus on oceanic relations and sonic affectedness, Atmospheric Knowledge centers the rich affordances of sonic connections for knowing our environments. A free ebook version of this title is available through Luminos, University of California Press's Open Access publishing program. Visit www.luminosoa.org to learn more. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sound-studies
The Crew and some new and old friends, take on the Mass Driver at Morro Rock. Domino has discovered a plot to take out the crew's communication satellite and time is ticking before it's blown out of the sky.Jade, Evan and Peter are joined by Dayeanne Hutton and Anais R Morgan (Infinite Sided Dice) live on stage at San Diego Comic Con 2025!Check out our Youtube Channel for more live panels! HereBTS and art posts will come out for all our patrons to peruse, after Part 2 is released next week!More info can be found here: linktr.ee/NoLatencyIf you'd like to support us, We now have a Patreon! Patreon.com/nolatencyEven more information and MERCH is on our website! www.nolatencypodcast.comTwitter: @nolatencypodInstagram: @nolatencypodFind @SkullorJade, @Miss_Magitek and @Binary_Dragon, @retrodatv on twitch, for live D&D, TTRPGs and more.#cyberpunkred #actualplay #ttrpg #radioplay #scifi #cyberpunk #drama #comedy #LIVE #SDCC25
In this week's vBrownBag, Principal Software Engineer Dominik Wosiński takes us on a deep dive into Amazon Nova Sonic — AWS's latest speech-to-speech AI model. Dominik explores how unified voice models like Nova Sonic are reshaping customer experience, DevOps workflows, and real-time AI interaction, with live demos showing just how natural machine-generated speech can sound. We cover what makes speech-to-speech difficult, how latency and turn-detection affect conversational design, and why this technology marks the next frontier for AI-driven customer support. Stick around for audience Q&A, live experiments, and insights on where AWS Bedrock and generative AI are headed next.
Bill Severn of 1623 Farnam joins JSA TV from DCD>Connect Virginia to discuss how #GenerativeAI is reshaping network infrastructure. He shares insights on latency limits, #edge inference, #hyperscaler-driven metro upgrades, and what an AI-ready interconnect looks like. Plus, a look ahead at 1623 Farnam's expansion plans and investments for the next 12–24 months.
“AI is hungry — for bandwidth, for speed, and for talent.” — Jean-Philippe Avelange, Chief Information Officer, Expereo Jean-Philippe Avelange, CIO of Expereo, joined Doug Green, Publisher of Technology Reseller News, to discuss findings from Expereo's Horizon Telecom Report—revealing how U.S. organizations are losing millions to network failures and struggling to find skilled professionals in cybersecurity, networking, and data automation. Avelange explained that as companies digitize everything from collaboration to customer experience, connectivity interruptions now directly halt business operations, making network reliability as vital as cybersecurity. “Modern enterprises are building their products and services on connectivity. When it stops, business stops,” he noted. The AI multiplier AI adoption is compounding the challenge. “AI is not just another workload—it's a new kind of demand,” Avelange said. AI-driven automation, real-time data flows, and low-latency interactions place unprecedented pressure on legacy network architectures. Organizations can no longer treat networking as a commodity; they must rethink it as a strategic platform requiring redesign and intelligent automation. The human factor According to Avelange, the real shortage isn't people—it's adaptability. The industry needs professionals skilled in network automation, data flow optimization, and problem solving, not just hardware management. “AI won't solve your problem if you don't understand the problem,” he said, advocating for upskilling internal teams alongside strong partnerships with managed service providers (MSPs) that bring intelligence, not just infrastructure. Latency by design Latency, Avelange warned, must be addressed before deployment. “You can always add bandwidth, but you can't add speed after the fact. Latency has to be engineered from the start.” A new mindset For Expereo, the future of networking lies in intelligent connectivity—solutions that merge automation, analytics, and agility to keep enterprises resilient in the AI era. “We're not selling boxes,” Avelange said. “We're helping companies design the networks their digital business runs on.” Read more in the Horizon Telecom Report or visit expereo.com.
In this Mission Matters session hosted by Adam Torres, Eraj Akhtar (CTO & Co-Founder, Excite Capital LLC) and Namuun Battulga (CEO, Jenko Tour JSC & Igo Hotel and Resorts) discuss physics-based, quantum-inspired AI trading and Mongolia's emergence as a cost-efficient, secure data center location powered by a new 70MW plant. They share partner criteria, address security considerations, and outline a mission to scale globally distributed compute and real-economy growth across Asia. Follow Adam on Instagram at https://www.instagram.com/askadamtorres/ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: https://missionmatters.lpages.co/podcastguest/ Visit our website: https://missionmatters.com/ More FREE content from Mission Matters here: https://linktr.ee/missionmattersmedia Learn more about your ad choices. Visit podcastchoices.com/adchoices
In this Mission Matters session hosted by Adam Torres, Eraj Akhtar (CTO & Co-Founder, Excite Capital LLC) and Namuun Battulga (CEO, Jenko Tour JSC & Igo Hotel and Resorts) discuss physics-based, quantum-inspired AI trading and Mongolia's emergence as a cost-efficient, secure data center location powered by a new 70MW plant. They share partner criteria, address security considerations, and outline a mission to scale globally distributed compute and real-economy growth across Asia. Follow Adam on Instagram at https://www.instagram.com/askadamtorres/ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: https://missionmatters.lpages.co/podcastguest/ Visit our website: https://missionmatters.com/ More FREE content from Mission Matters here: https://linktr.ee/missionmattersmedia Learn more about your ad choices. Visit podcastchoices.com/adchoices
Show Notes:Chapters 00:00 Introduction and Background of Lighter02:26 The Launch of Lighter and Its Features05:25 Transition from Private to Public Beta07:56 Trading Volume and Metrics10:37 Open Interest and Volume Dynamics13:31 Incentive Programs and User Engagement15:56 Points System and User Behavior18:42 Future Developments and Season Two21:27 Verifiable Matching and Liquidations24:09 Fee Structure and Token Philosophy24:45 Retail vs. Professional Trading27:12 Fee Structures and Trading Tiers29:00 Latency and Advantages for Premium Accounts32:43 Order Flow and System Verification35:40 Single Sequencer Challenges38:46 Auto-Deleveraging and Liquidation Processes41:33 Criteria for Asset Listings43:25 Community-Driven Regional Strategies If you like this episode, you're welcome to tip with Ethereum / Solana / Bitcoin:如果喜欢本作品，欢迎打赏ETH/SOL/BTC：ETH: 0x83Fe9765a57C9bA36700b983Af33FD3c9920Ef20SOL: AaCeeEX5xBH6QchuRaUj3CEHED8vv5bUizxUpMsr1KytBTC: 3ACPRhHVbh3cu8zqtqSPpzNnNULbZwaNqG Important Disclaimer: All opinions expressed by Mable Jiang, or other podcast guests, are solely their opinion. This podcast is for informational purposes only and should not be construed as investment advice. Mable Jiang may hold positions in some of the projects discussed on this show. 重要声明：Mable Jiang或嘉宾在播客中的观点仅代表他们的个人看法。此播客仅用于提供信息，不作为投资参考。Mable Jiang有时可能会在此节目中讨论的某项目中持有头寸。
A fantastic episode with one of the founding minds behind Broadband, Jason Livingood and I chat about luck, experience, the shifting landscape of broadband speed and content, and the good work of people like the late Dave Taht that has changed the way we treat the “old ideas” of how Internet services should be delivered.
Join Alex Golding as he sits down with Austin Federa, Co-founder of DoubleZero, to explore how they're building permissionless high-performance fiber infrastructure that could revolutionize blockchain performance. Austin shares the technical vision behind creating a parallel internet for distributed systems, starting with Solana validators as their initial market.DoubleZero: https://doublezero.xyz
Doug Madory joins us to unpack the recent Red Sea submarine cable cuts and how Kentik's Cloud Latency Map revealed the global impact in real-time, offering critical insight into cloud performance, interconnectivity, and internet resilience.
Send us a textWhat if AI could tap into live operational data — without ETL or RAG? In this episode, Deepti Srivastava, founder of Snow Leopard, reveals how her company is transforming enterprise data access with intelligent data retrieval, semantic intelligence, and a governance-first approach. Tune in for a fresh perspective on the future of AI and the startup journey behind it.We explore how companies are revolutionizing their data access and AI strategies. Deepti Srivastava, founder of Snow Leopard, shares her insights on bridging the gap between live operational data and generative AI — and how it's changing the game for enterprises worldwide.We dive into Snow Leopard's innovative approach to data retrieval, semantic intelligence, and governance-first architecture.04:54 Meeting Deepti Srivastava 14:06 AI with No ETL, no RAG 17:11 Snow Leopard's Intelligent Data Fetching 19:00 Live Query Challenges 21:01 Snow Leopard's Secret Sauce 22:14 Latency 23:48 Schema Changes 25:02 Use Cases 26:06 Snow Leopard's Roadmap 29:16 Getting Started 33:30 The Startup Journey 34:12 A Woman in Technology 36:03 The Contrarian View
Send us a textWhat if AI could tap into live operational data — without ETL or RAG? In this episode, Deepti Srivastava, founder of Snow Leopard, reveals how her company is transforming enterprise data access with intelligent data retrieval, semantic intelligence, and a governance-first approach. Tune in for a fresh perspective on the future of AI and the startup journey behind it.We explore how companies are revolutionizing their data access and AI strategies. Deepti Srivastava, founder of Snow Leopard, shares her insights on bridging the gap between live operational data and generative AI — and how it's changing the game for enterprises worldwide.We dive into Snow Leopard's innovative approach to data retrieval, semantic intelligence, and governance-first architecture.04:54 Meeting Deepti Srivastava 14:06 AI with No ETL, no RAG 17:11 Snow Leopard's Intelligent Data Fetching 19:00 Live Query Challenges 21:01 Snow Leopard's Secret Sauce 22:14 Latency 23:48 Schema Changes 25:02 Use Cases 26:06 Snow Leopard's Roadmap 29:16 Getting Started 33:30 The Startup Journey 34:12 A Woman in Technology 36:03 The Contrarian View
Jimmy Bogard joins Pod Rocket to talk about making monoliths more modular, why boundaries matter, and how to avoid turning systems into distributed monoliths. From refactoring techniques and database migrations at scale to lessons from Stripe and WordPress, he shares practical ways to balance architecture choices. We also explore how tools like Claude and Lambda fit into modern development and what teams should watch for with latency, transactions, and growing complexity. Links Website: https://www.jimmybogard.com X: https://x.com/jbogard Github: https://github.com/jbogard LinkedIn: https://www.linkedin.com/in/jimmybogard/ Resources Modularizing the Monolith - Jimmy Bogard - NDC Oslo 2024: https://www.youtube.com/watch?v=fc6_NtD9soI Chapters We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! Let us know by sending an email to our producer, Em, at emily.kochanek@logrocket.com (mailto:emily.kochanek@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Special Guest: Jimmy Bogard.
Monitoring and troubleshooting latency can be tricky. If it’s in the network, was it the IP stack? A NIC? A switch buffer? A middlebox somewhere on the WAN? If it’s the application, can you, the network engineer, bring receipts to the app team? And what if you need to build and operate a network that’s... Read more »
Monitoring and troubleshooting latency can be tricky. If it’s in the network, was it the IP stack? A NIC? A switch buffer? A middlebox somewhere on the WAN? If it’s the application, can you, the network engineer, bring receipts to the app team? And what if you need to build and operate a network that’s... Read more »
Monitoring and troubleshooting latency can be tricky. If it’s in the network, was it the IP stack? A NIC? A switch buffer? A middlebox somewhere on the WAN? If it’s the application, can you, the network engineer, bring receipts to the app team? And what if you need to build and operate a network that’s... Read more »
Today we are joined by Gorkem and Batuhan from Fal.ai, the fastest growing generative media inference provider. They recently raised a $125M Series C and crossed $100M ARR. We covered how they pivoted from dbt pipelines to diffusion models inference, what were the models that really changed the trajectory of image generation, and the future of AI videos. Enjoy! 00:00 - Introductions 04:58 - History of Major AI Models and Their Impact on Fal.ai 07:06 - Pivoting to Generative Media and Strategic Business Decisions 10:46 - Technical discussion on CUDA optimization and kernel development 12:42 - Inference Engine Architecture and Kernel Reusability 14:59 - Performance Gains and Latency Trade-offs 15:50 - Discussion of model latency importance and performance optimization 17:56 - Importance of Latency and User Engagement 18:46 - Impact of Open Source Model Releases and Competitive Advantage 19:00 - Partnerships with closed source model developers 20:06 - Collaborations with Closed-Source Model Providers 21:28 - Serving Audio Models and Infrastructure Scalability 22:29 - Serverless GPU infrastructure and technical stack 23:52 - GPU Prioritization: H100s and Blackwell Optimization 25:00 - Discussion on ASICs vs. General Purpose GPUs 26:10 - Architectural Trends: MMDiTs and Model Innovation 27:35 - Rise and Decline of Distillation and Consistency Models 28:15 - Draft Mode and Streaming in Image Generation Workflows 29:46 - Generative Video Models and the Role of Latency 30:14 - Auto-Regressive Image Models and Industry Reactions 31:35 - Discussion of OpenAI's Sora and competition in video generation 34:44 - World Models and Creative Applications in Games and Movies 35:27 - Video Models' Revenue Share and Open-Source Contributions 36:40 - Rise of Chinese Labs and Partnerships 38:03 - Top Trending Models on Hugging Face and ByteDance's Role 39:29 - Monetization Strategies for Open Models 40:48 - Usage Distribution and Model Turnover on FAL 42:11 - Revenue Share vs. Open Model Usage Optimization 42:47 - Moderation and NSFW Content on the Platform 44:03 - Advertising as a key use case for generative media 45:37 - Generative Video in Startup Marketing and Virality 46:56 - LoRA Usage and Fine-Tuning Popularity 47:17 - LoRA ecosystem and fine-tuning discussion 49:25 - Post-Training of Video Models and Future of Fine-Tuning 50:21 - ComfyUI Pipelines and Workflow Complexity 52:31 - Requests for startups and future opportunities in the space 53:33 - Data Collection and RedPajama-Style Initiatives for Media Models 53:46 - RL for Image and Video Models: Unknown Potential 55:11 - Requests for Models: Editing and Conversational Video Models 57:12 - VO3 Capabilities: Lip Sync, TTS, and Timing 58:23 - Bitter Lesson and the Future of Model Workflows 58:44 - FAL's hiring approach and team structure 59:29 - Team Structure and Scaling Applied ML and Performance Teams 1:01:41 - Developer Experience Tools and Low-Code/No-Code Integration 1:03:04 - Improving Hiring Process with Public Challenges and Benchmarks 1:04:02 - Closing Remarks and Culture at FAL
Yonatan Sompolinsky is an academic in the field of computer science, best known for his work on the GHOST protocol (Greedy Heaviest Observed Subtree, which was cited in the Ethereum whitepaper) and the way he applied his research to create Kaspa. In this episode, we talk about scaling Proof of Work and why Kaspa might be a worthy contender to process global payments. –––––––––––––––––––––––––––––––––––– Time stamps: 00:01:22 - Debunking rumors: Why some think Yonatan is Satoshi Nakamoto 00:02:52 - Candidates for Satoshi: Charles Hoskinson, Charlie Lee, Zooko, and Alex Chepurnoy 00:03:41 - Alex Chepurnoy as a Satoshi-like figure 00:04:07 - Kaspa overview: DAG structure, no orphaned blocks, generalization of Bitcoin 00:04:55 - Similarities between Kaspa and Bitcoin fundamentals 00:06:12 - Why Kaspa couldn't be built directly on Bitcoin 00:08:05 - Kaspa as generalization of Nakamoto consensus 00:11:55 - Origins of GHOST protocol and early DAG concepts for Bitcoin scaling 00:13:16 - Academic motivation for GHOST and transitioning to computer science 00:13:50 - Turtle pet named Bitcoin 00:15:22 - Increasing block rate in Bitcoin and GHOST protocol 00:16:57 - Meeting Gregory Maxwell and discovering GHOST flaws 00:20:00 - Yonatan's views on drivechains and Bitcoin maximalism 00:20:36 - Defining Bitcoin maximalism: Capital B vs lowercase b 00:23:18 - Satoshi's support for Namecoin and merged mining 00:24:12 - Bitcoin culture in 2013-2018: Opposing other functionalities 00:26:01 - Vitalik's 2014 article on Bitcoin maximalism 00:26:13 - Andrew Poelstra's opposition to other assets on Bitcoin 00:26:38 - Bitcoin culture: Distaste for DeFi, criticism of Ethereum as a scam 00:28:03 - Bitcoin Cash developments: Cash tokens, cash fusion, contracts 00:28:39 - Rejection of Ethereum in Bitcoin circles 00:30:18 - Ethereum's successful PoS transition despite critics 00:35:04 - Ethereum's innovation: From Plasma to ZK rollups, nurturing development 00:37:04 - Stacks protocol and criticism from Luke Dashjr 00:39:02 - Bitcoin culture justifying technical limitations 00:41:01 - Declining Bitcoin adoption as money, rise of altcoins for payments 00:43:02 - Kaspa's aspirations: Merging sound money with DeFi, beyond just payments 00:43:56 - Possibility of tokenized Bitcoin on Kaspa 00:46:30 - Native currency advantage and friction in bridges 00:48:49 - WBTC on Ethereum scale vs Bitcoin L2s 00:53:33 - Quotes: Richard Dawkins on atheism, Milton Friedman on Yap Island money 00:55:44 - Story of Kaspa's messy fair launch in 2021 01:14:08 - Tech demo of Kaspa wallet experience 01:28:45 - Kaspa confirmation times & transaction fees 01:43:26 - GHOST DAG visualizer 01:44:10 - Mining Kaspa 01:55:48 - Data pruning in Kaspa, DAG vs MimbleWimble 02:01:40 - Grin & the fairest launch 02:12:21 - Zcash scaling & ZKP OP code in Kaspa 02:19:50 - Jameson Lopp, cold storage & self custody elitism 02:35:08 - Social recovery 02:41:00 - Amir Taaki, DarkFi & DAO 02:53:10 - Nick Szabo's God Protocols 03:00:00 - Layer twos on Kaspa for DeFi 03:13:09 - How Kaspa's DeFi will resemble Solana 03:24:03 - Centralized exchanges vs DeFi 03:32:05 - The importance of community projects 03:37:00 - DAG KNIGHT and its resilience 03:51:00 - DAG KNIGHT tradeoffs 03:58:18 - Blockchain vs DAG, the bottleneck for Kaspa 04:03:00 - 100 blocks per second? 04:11:43 - Question from Quai's Dr. K 04:17:03 - Doesn't Kaspa require super fast internet? 04:23:10 - Are ASIC miners desirable? 04:33:53 - Why Proof of Work matters 04:35:55 - A short history of Bitcoin mining 04:44:00 - DAG's sequencing 04:49:09 - Phantom GHOST DAG 04:52:47 - Why Kaspa had high inflation initially 04:55:10 - Selfish mining 05:03:00 - K Heavy Hash & other community questions 06:33:20 - Latency settings in DAG KNIGHT for security 06:36:52 - Aviv Zohar's involvement in Kaspa research 06:38:07 - World priced in Kaspa after hyperinflation 06:39:51 - Kaspa's fate intertwined with crypto 06:40:29 - Kaspa contracts vs Solana, why better for banks 06:42:53 - Cohesive developer experience in Kaspa like Solana 06:45:22 - Incorporating ZK design in Kaspa smart contracts 06:47:22 - Heroes: Garry Kasparov 06:48:12 - Shift in attitude from academics like Hoskinson, Buterin, Back 06:53:07 - Adam Back's criticism of Kaspa 06:55:57 - Michael Jordan and LeBron analogy for Bitcoiners' mindset 06:58:02 - Can Kaspa flip Bitcoin in market cap 07:00:34 - Gold and USD market cap comparison 07:06:06 - Collaboration with Kai team 07:10:37 - Community improvement: More context on crypto 07:13:43 - Theoretical maximum TPS for Kaspa 07:16:05 - Full ZK on L1 improvements 07:17:45 - Atomic composability and logic zones in Kaspa 07:23:12 - Sparkle and monolithic UX feel 07:26:00 - Wrapping up: Beating podcast length record, final thoughts on Bitcoin and Kaspa 07:27:31 - Why Yonatan called a scammer despite explanations 07:32:29 - Luke Dashjr's views and disconnect 07:33:01 - Hope for Bitcoin scaling and revolution
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