As AI adoption accelerates and global regulations evolve, digital sovereignty has shifted from policy concept to board-level priority. In this episode of Smart Talks with IBM, Malcolm Gladwell sits down live at VivaTech with IBM’s Ana Paula Assis and AXA CIO Giovanni D’Aniello to explore how organizations can balance innovation, control, trust, and resilience in the age of AI.
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Ryan is joined by Rosemary Wang, Developer Advocate at IBM, to explore what infrastructure as code looks like once AI starts writing and deploying it. They discuss why guardrails still lag adoption, breaks down what it means when “anyone can deploy,” and why deep systems knowledge still matters.
Episode notes:
Try out Bob, IBM’s coding agent that Rosemary talked about in the episode.
Connect with Rosemary on X, LinkedIn, or Bluesky.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
When Dr. Lara Jehi began treating epilepsy patients in the 2000s, critical surgical decisions were driven more by clinician intuition and expertise than data. Today, she is a leader of IBM and Cleveland Clinic’s Discovery Accelerator, using advanced AI and quantum computing to transform how researchers analyze data, simulate molecules, accelerate drug discovery, and develop more precise treatments. Malcolm Gladwell talks with Dr. Jehi about how quantum computing is changing biomedical research, and what these breakthroughs could mean for the future of healthcare and life sciences.
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While many tech companies race to build ever-larger AI models, IBM CEO Arvind Krishna sees the future differently. Speaking with host Bob Safian before a live audience during New York Tech Week, Krishna explains why enterprises are overcomplicating AI adoption, what kinds of risks leaders should be taking right now, and how to weigh AI's costs against its benefits. He also shares why IBM believes quantum computing will reshape the next era of technology.
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IBM's VP of Quantum Systems, Oliver Dial, has spent his career building quantum computers from the ground up, and he's unusually direct about what they can and can't do. In this conversation with Craig Smith, Oliver Dial walks through where the field actually stands in 2026: quantum utility was achieved in 2023, quantum advantage is the target for this year, and a fully error-corrected machine capable of tackling the hard problems is on IBM's roadmap for 2029. That last milestone, Dial says, now feels both achievable and terrifying.
The episode is worth your time because Dial doesn't hype. He explains why IBM built a 1,000-qubit computer and then took it apart almost immediately, why Google's quantum advantage claims remain scientifically contested, and how a new error-correcting code IBM developed just reduced the qubit overhead required for fault-tolerant quantum computing by an order of magnitude. For anyone trying to understand what quantum computing will actually mean for their industry, and when, this is the clearest map of the road ahead available right now.
If this conversation changed how you think about the future of computing, subscribe to Eye on A.I. for weekly conversations with the researchers and builders shaping what comes next.
What if the country that trains the world's engineers finally built the infrastructure to match its talent?
In this episode of Eye on AI, Craig Smith sits down with Amith Singhee, Director of IBM Research India and CTO of IBM India and South Asia, to explore where India actually stands in the global AI race and what it will take to close the gap.
Amith gives an honest, ground-level assessment of why India has been slow to compete. The talent has always been there. But until recently, the investment, the compute infrastructure, and the institutional intent hadn't come together in a sustained, coordinated way. That's changing, and Amith explains exactly what's different now.
He walks through IBM Research India's 27-year presence in the country, the research it's doing on foundation models, hybrid cloud AI deployment, agentic systems, and quantum computing. He also explains why building AI from India doesn't just help India. Working with less data, less compute, and more linguistic diversity forces better engineering and makes IBM's models more generalizable for the entire world.
We also get deep into the technical frontier. Why catastrophic forgetting is one of the key unsolved problems standing between current AI and anything more capable. How IBM is already shipping continual learning in practice through its COBOL modernization tools, helping enterprises decode decades of legacy code before the engineers who wrote it are gone. And why agentic AI, for all the hype, still has a mountain of unglamorous enterprise engineering left to climb before it becomes truly reliable.
Plus, what Amith would tell an 18-year-old engineer in India today about what skills will actually matter in an AI-driven world.
Subscribe for more conversations with the people shaping the future of AI and emerging technology.
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Craig Smith on X: https://x.com/craigss
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(00:00) Introduction and Amith Singhee's Background
(06:26) Why IBM Set Up Research in India
(11:45) Can India Compete in AI
(15:18) How IBM Collaborates With Indian Universities
(19:25) Why India Has Been Slow in AI
(24:50) IBM's Hybrid Cloud AI Research Focus
(27:34) How Data Scarcity in India Makes Better AI
(31:18) Fine-Tuning Models Without Losing General Knowledge
(35:03) Continual Learning and Catastrophic Forgetting
(38:25) COBOL and Legacy Code Modernization
(42:11) Agentic AI Hype vs Enterprise Reality
(48:09) What Young Engineers Should Study Today
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—
Every few decades, software engineering is declared “dead” or on the verge of being automated away. We’ve heard versions of this story before. But what if it’s just the start of a new “golden age” of a different type of software engineering, like it has been many times before?
In this episode of The Pragmatic Engineer, I’m joined once again by Grady Booch, one of the most influential figures in the history of software engineering, to put today’s claims about AI and automation into historical context.
Grady is the co-creator of the Unified Modeling Language, author of several books and papers that have shaped modern software development, and Chief Scientist for Software Engineering at IBM, where he focuses on embodied cognition.
Grady shares his perspective on three golden ages of computing since the 1940s, and how each emerged in response to the constraints of its time. He explains how technical limits and human factors have always shaped the systems we build, and why periods of rapid change tend to produce both real progress and inflated expectations.
He also responds to current claims that software engineering will soon be fully automated, explaining why systems thinking, human judgment, and responsibility remain central to the work, even as tools continue to evolve.
—
Timestamps
(00:00) Intro
(01:04) The first golden age of software engineering
(18:05) The software crisis
(32:07) The second golden age of software engineering
(41:27) Y2K and the Dotcom crash
(44:53) Early AI
(46:40) The third golden age of software engineering
(50:54) Why software engineers will very much be needed
(57:52) Grady responds to Dario Amodei
(1:06:00) New skills engineers will need to succeed
(1:09:10) Resources for studying complex systems
(1:13:39) How to thrive during periods of change
—
The Pragmatic Engineer deepdives relevant for this episode:
• When AI writes almost all code, what happens to software engineering?
• Inside a five-year-old startup’s rapid AI makeover
• Software architecture with Grady Booch
• What is old is new again
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
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API sprawl creates hidden security risks and missed revenue opportunities when organizations lose visibility into the APIs they build. According to IBM’s Neeraj Nargund, APIs power the core business processes enterprises want to scale, making automated discovery, observability, and governance essential—especially when thousands of APIs exist across teams and environments. Strong governance helps identify endpoints, remediate shadow APIs, and manage risk at scale. At the same time, enterprises increasingly want to monetize the data APIs generate, packaging insights into products and pricing and segmenting usage, a need amplified by the rise of AI.
To address these challenges, Nargund highlights “smart APIs,” which are infused with AI to provide context awareness, event-driven behavior, and AI-assisted governance throughout the API lifecycle. These APIs help interpret and act on data, integrate with AI agents, and support real-time, streaming use cases.
IBM’s latest API Connect release embeds AI across API management and is designed for hybrid and multi-cloud environments, offering centralized governance, observability, and control through a single hybrid control plane.
Learn more from The New Stack about smart APIs:
Redefining API Management for the AI-Driven Enterprise
How To Accelerate Growth With AI-Powered Smart APIs
Wrangle Account Sprawl With an AI Gateway
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In this episode of Leaders of Code, Stack Overflow Chief of Product and Technology Jody Bailey chats with Matt Lyteson, CIO of Technology Platform Transformation at IBM, about the processes and challenges of adopting AI within an enterprise environment. They explore IBM's strategic approach to integrating AI into workflows and emphasise the importance of fostering the right behaviours among employees, particularly regarding automation and AI assistance.
The discussion also:
Explores what it means for a company like IBM to truly embrace AI, with Lyteson sharing strategies for integrating AI into every workflow to maximize productivity across the organization.
Highlights key challenges like data privacy, security risks, and the critical need for workforce reskilling in an AI-enabled world.
Notes
Connect with Matt Lyteson on LinkedIn.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
IBM’s recent acquisitions of Red Hat, HashiCorp, and its planned purchase of Confluent reflect a deliberate strategy to build the infrastructure required for enterprise AI. According to IBM’s Sanil Nambiar, AI depends on consistent hybrid cloud runtimes (Red Hat), programmable and automated infrastructure (HashiCorp), and real-time, trustworthy data (Confluent). Without these foundations, AI cannot function effectively.
Nambiar argues that modern, software-defined networks have become too complex for humans to manage alone, overwhelmed by fragmented data, escalating tool sophistication, and a widening skills gap that makes veteran “tribal knowledge” hard to transfer. Trust, he says, is the biggest barrier to AI adoption in networking, since errors can cause costly outages. To address this, IBM launched IBM Network Intelligence, a “network-native” AI solution that combines time-series foundation models with reasoning large language models. This architecture enables AI agents to detect subtle warning patterns, collapse incident response times, and deliver accurate, trustworthy insights for real-world network operations.
Learn more from The New Stack about AI infrastructure and IBM’s approach:
AI in Network Observability: The Dawn of Network Intelligence
How Agentic AI Is Redefining Campus and Branch Network Needs
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Enterprises are racing to deploy AI services, but the teams responsible for running them in production are seeing familiar problems reemerge—most notably, silos between data scientists and operations teams, reminiscent of the old DevOps divide. In a discussion recorded at AWS re:Invent 2025, IBM’s Thanos Matzanas and Martin Fuentes argue that the challenge isn’t new technology but repeating organizational patterns. As data teams move from internal projects to revenue-critical, customer-facing applications, they face new pressures around reliability, observability, and accountability.
The speakers stress that many existing observability and governance practices still apply. Standard metrics, KPIs, SLOs, access controls, and audit logs remain essential foundations, even as AI introduces non-determinism and a heavier reliance on human feedback to assess quality. Tools like OpenTelemetry provide common ground, but culture matters more than tooling.
Both emphasize starting with business value and breaking down silos early by involving data teams in production discussions. Rather than replacing observability professionals, AI should augment human expertise, especially in critical systems where trust, safety, and compliance are paramount.
Learn more from The New Stack about enabling AI with silos:
Are Your AI Co-Pilots Trapping Data in Isolated Silos?
Break the AI Gridlock at the Intersection of Velocity and Trust
Taming AI Observability: Control Is the Key to Success
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IBM was instrumental to the entire 20th century of computing — but it's a lot harder for most of us to see what it's been up to during this century. That's because it's fully an enterprise company, and CEO Arvind Krishna says that business is booming.
But there’s a huge change coming to that business as well, as Watson-style deep learning has given way to LLMs and generative AI. Sure, Arvind says IBM got there a little too early. But he doesn’t seem concerned that IBM would be stuck on the sidelines.
Read the full interview transcript on The Verge.
Links:
Computer wins on ‘Jeopardy!’: Trivial, it’s not | New York Times (2011)
What Ever Happened to IBM’s Watson? | New York Times (2021)
America Forgot About IBM Watson. Is ChatGPT Next? | The Atlantic
IBM acquires Red Hat | The Verge
IBM and Groq Partner to Accelerate Enterprise AI Deployment | IBM
IBM’s Jerry Chow on the future of quantum computing | Decoder
IBM: quantum computing partnership with AMD is bearing fruit | The Verge
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Credits:
Decoder is a production of The Verge and part of the Vox Media Podcast Network.
Our producers are Kate Cox and Nick Statt. Our editor is Ursa Wright. Our editorial director is Kevin McShane.
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DevOps practitioners — whether developers, operators, SREs or business stakeholders — increasingly rely on telemetry to guide decisions, yet face growing complexity, siloed teams and rising observability costs. In a conversation at KubeCon + CloudNativeCon North America, IBM’s Jacob Yackenovich emphasized the importance of collecting high-granularity, full-capture data to avoid missing critical performance signals across hybrid application stacks that blend legacy and cloud-native components. He argued that observability must evolve to serve both technical and nontechnical users, enabling teams to focus on issues based on real business impact rather than subjective judgment.
AI’s rapid integration into applications introduces new observability challenges. Yackenovich described two patterns: add-on AI services, such as chatbots, whose failures don’t disrupt core workflows, and blocking-style AI components embedded in essential processes like fraud detection, where errors directly affect application function.
Rising cloud and ingestion costs further complicate telemetry strategies. Yackenovich cautioned against limiting visibility for budget reasons, advocating instead for predictable, fixed-price observability models that let organizations innovate without financial uncertainty.
Learn more from The New Stack about the latest in observability:
Introduction to Observability
Observability 2.0? Or Just Logs All Over Again?
Building an Observability Culture: Getting Everyone Onboard
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Malcolm Gladwell heads to San Francisco Tech Week to talk with IBM’s new Director of Research Jay Gambetta in front of a live audience. They discuss IBM’s plans to scale quantum computing power, the groundbreaking experiments already underway, and what impact these new computers could have on chemistry, medicine, and even finance.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.
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Malcolm Gladwell sits down with IBM Chairman and CEO Arvind Krishna in a special live episode of Smart Talks with IBM. They discuss the groundbreaking potential of quantum computing, the transformative impact of AI on business, and how Krishna’s visionary predictions from the 90s continue to guide IBM’s innovations.
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The agentic AI space faces challenges around secure, governed connectivity between agents, tools, large language models, and microservices. To address this, Solo.io developed two open-source projects: Kagent and Agentgateway. While Kagent, donated to the Cloud Native Computing Foundation, helps scale AI agents, it lacks a secure way to mediate communication between agents and tools. Enter Agentgateway, donated to the Linux Foundation, which provides governance, observability, and security for agent-to-agent and agent-to-tool traffic. Written in Rust, it supports protocols like MCP and A2A and integrates with Kubernetes Gateway API and inference gateways.
Lin Sun, Solo.io’s head of open source, explained that Agentgateway allows developers to control which tools agents can access—offering flexibility to expose only tested or approved tools. This enables fine-grained policy enforcement and resilience in agent communication, similar to how service meshes manage microservice traffic. Agentgateway ensures secure and selective tool exposure, supporting scalable and secure agent ecosystems. Major players like AWS and Microsoft are also engaging in its development.
Learn more from The New Stack about the latest in open source projects like Agentgateway:
Learn more from The New Stack about the latest in open source projects like Agentgateway:
Why Tech Giants Are Backing the New Agentgateway Project
AI Agents Are Creating a New Security Nightmare for Enterprises and Startups
Five Steps to Build AI Agents that Actually Deliver Business Results
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SaaStr 809: Why Enterprise AI Adoption Is Moving 5-10X Faster Than Cloud with Box's CEO and Co-Founder, IBM's VP for AI and SaaStr's CEO and Founder
This conversation between Aaron Levie, CEO & Co-Founder of Box, Raj Datta, Global Vice President for Software and A.I. Partnerships at IBM and Jason Lemkin, CEO and Founder of SaaStr, covers the evolution from chat interfaces to digital labor models, the integration of AI to automate complex tasks, and the emergence of new paradigms for businesses deploying AI agents. Key topics include the distinction between AI agents and assistants, the development of proprietary data models, and the rapid pace of AI adoption. With real-world examples from companies like IBM and Box, this session offers insights into how AI is reshaping software ecosystems, enhancing enterprise capabilities, and potentially redefining market moats.
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This episode of the SaaStr podcast is sponsored by: Attention.com
Tired of listening to hours of sales calls? Recording is yesterday's game. Attention.com unleashes an army of AI sales agents that auto-update your CRM, build custom sales decks, spot cross-sell signals, and score calls before your coffee's cold. Teams like BambooHR and Scale AI already automate their Sales and RevOps using customer conversations. Step into the future at attention.com/saastr
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Hey everyone, we just hosted 10,000 of you at the SaaStr Annual in the SF Bay Area, and now get ready, because SaaStr AI is heading to London!
On December 2nd and 3rd, we're bringing SaaStr AI to the heart of Europe. This is your chance to connect with 2,500+ SaaS and AI executives, founders, and investors, all sharing the secrets to scaling in the age of AI.
Whether you're a founder, a revenue leader, or an investor, SaaStr AI in London is where the future of SaaS meets the power of AI.
And we just announced tickets and sponsorships, so don't wait! Head to SaaStrLondon.com to grab yours and join us this December in London.
SaaStr AI in London —where SaaS meets AI, and the next wave of innovation begins. See you there!
watsonx.ai is an enterprise-grade AI studio. Developers can get started in the watsonx Developer Hub.
We published a technical behind-the-scenes look at watsonx, as well as a Q&A on why it’s business-ready.
Find Maryam on LinkedIn.
Congrats to Stack Overflow user Michael Kolber, who earned a Lifeboat badge with a straightforward and effective answer to Is it possible to download a website’s entire code, HTML, CSS and JavaScript files?.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Rama Akkiraju, VP of IT for AI and ML at NVIDIA, discusses the transformative power of AI in enterprises. Akkiraju highlights the rapid evolution from perception AI to agentic AI and emphasizes the importance of treating AI as a new layer in the development stack. She also shares insights on the role of AI platform architects and key trends shaping the future of AI infrastructure.
We often judge AI models by leaderboard scores, but what if efficiency matters more? Kate Soule from IBM joins us to discuss how Granite AI is rethinking AI at the edge—breaking tasks into smaller, efficient components and co-designing models with hardware. She also shares why AI should prioritize efficiency frontiers over incremental benchmark gains and how seamless model routing can optimize performance.
Featuring:
Kate Soule – LinkedIn
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Links:
IBM Granite
IBM Granite on Hugging Face
IBM Expands Granite Model Family with New Multi-Modal and Reasoning AI Built for the Enterprise
In this episode of Smart Talks with IBM, Malcolm Gladwell speaks with Ric Lewis, IBM’s Senior Vice President of Infrastructure. They discuss how hardware capability has enabled the matrix math required to run large language models. Furthermore, they delve into some creative examples of how to put AI to work: from your bank to your local coffee shop. Ric underscores the importance of infrastructure in unlocking the potential of AI, helping businesses harness their data to drive transformative outcomes.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.
Visit us at https://ibm.com/smarttalks
See omnystudio.com/listener for privacy information.
To deploy responsible AI and build trust with customers, businesses need to prioritize AI governance. In this episode of Smart Talks with IBM, Malcolm Gladwell and Laurie Santos discuss AI accountability with Christina Montgomery, Chief Privacy and Trust Officer at IBM. They chat about AI regulation, what compliance means in the AI age, and why transparent AI governance is good for business.
Visit us at: ibm.com/smarttalks
Explore watsonx.governance: https://www.ibm.com/products/watsonx-governance
This is a paid advertisement from IBM.
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—
Welcome to The Pragmatic Engineer! Today, I’m thrilled to be joined by Grady Booch, a true legend in software development. Grady is the Chief Scientist for Software Engineering at IBM, where he leads groundbreaking research in embodied cognition.
He’s the mind behind several object-oriented design concepts, a co-author of the Unified Modeling Language, and a founding member of the Agile Alliance and the Hillside Group.
Grady has authored six books, hundreds of articles, and holds prestigious titles as an IBM, ACM, and IEEE Fellow, as well as a recipient of the Lovelace Medal (an award for those with outstanding contributions to the advancement of computing). In this episode, we discuss:
• What it means to be an IBM Fellow
• The evolution of the field of software development
• How UML was created, what its goals were, and why Grady disagrees with the direction of later versions of UML
• Pivotal moments in software development history
• How the software architect role changed over the last 50 years
• Why Grady declined to be the Chief Architect of Microsoft – saying no to Bill Gates!
• Grady’s take on large language models (LLMs)
• Advice to less experienced software engineers
• … and much more!
—
Timestamps
(00:00) Intro
(01:56) What it means to be a Fellow at IBM
(03:27) Grady’s work with legacy systems
(09:25) Some examples of domains Grady has contributed to
(11:27) The evolution of the field of software development
(16:23) An overview of the Booch method
(20:00) Software development prior to the Booch method
(22:40) Forming Rational Machines with Paul and Mike
(25:35) Grady’s work with Bjarne Stroustrup
(26:41) ROSE and working with the commercial sector
(30:19) How Grady built UML with Ibar Jacobson and James Rumbaugh
(36:08) An explanation of UML and why it was a mistake to turn it into a programming language
(40:25) The IBM acquisition and why Grady declined Bill Gates’s job offer
(43:38) Why UML is no longer used in industry
(52:04) Grady’s thoughts on formal methods
(53:33) How the software architect role changed over time
(1:01:46) Disruptive changes and major leaps in software development
(1:07:26) Grady’s early work in AI
(1:12:47) Grady’s work with Johnson Space Center
(1:16:41) Grady’s thoughts on LLMs
(1:19:47) Why Grady thinks we are a long way off from sentient AI
(1:25:18) Grady’s advice to less experienced software engineers
(1:27:20) What’s next for Grady
(1:29:39) Rapid fire round
—
The Pragmatic Engineer deepdives relevant for this episode:
• The Past and Future of Modern Backend Practices https://newsletter.pragmaticengineer.com/p/the-past-and-future-of-backend-practices
• What Changed in 50 Years of Computing https://newsletter.pragmaticengineer.com/p/what-changed-in-50-years-of-computing
• AI Tooling for Software Engineers: Reality Check https://newsletter.pragmaticengineer.com/p/ai-tooling-2024
—
Where to find Grady Booch:
• X: https://x.com/grady_booch
• LinkedIn: https://www.linkedin.com/in/gradybooch
• Website: https://computingthehumanexperience.com
Where to find Gergely:
• Newsletter: https://www.pragmaticengineer.com/
• YouTube: https://www.youtube.com/c/mrgergelyorosz
• LinkedIn: https://www.linkedin.com/in/gergelyorosz/
• X: https://x.com/GergelyOrosz
—
References and Transcripts:
See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
In this episode of Smart Talks with IBM, Malcolm Gladwell speaks with Jason Kelley, GM, Strategic Partners and Ecosystems at IBM, and Kristy Friedrichs, SVP and Chief Partnership Officer at Palo Alto Networks. They discuss the challenges and opportunities that the rapid development of AI brings to the cybersecurity space. Jason and Kristy also underscore how implementing a zero trust strategy can help enterprises enhance cyber resiliency and simplify operations. Together, IBM and Palo Alto Networks are delivering fully integrated, open, end-to-end security solutions to enterprises.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.
Visit us at https://ibm.com/smarttalks
See omnystudio.com/listener for privacy information.
As the scale of artificial intelligence continues to evolve, open technology like many of IBM’s Granite models are helping enhance transparency in AI and improve efficiency across businesses. In this episode of Smart Talks with IBM, Jacob Goldstein sat down with Maryam Ashoori, the Director of Product Management and Head of Product for IBM’s watsonx.ai, where she spearheads the product strategy and delivery of IBM’s watsonx Foundation Models. Together, they explored the shift from large general-purpose AI models to smaller, customizable models tailored to specific needs.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.
Visit us at https://ibm.com/smarttalks
See omnystudio.com/listener for privacy information.
The role of AI in the classroom is evolving rapidly. When students and teachers embrace this technology, it has the ability to democratize access to education through programs like IBM SkillsBuild. In this episode of Smart Talks with IBM, Dr. Laurie Santos, host of Pushkin’s The Happiness Lab podcast, spoke with two innovators in the space. Justina Nixon-Saintil is Vice President and Chief Impact Officer, IBM Corporate Social Responsibility, and April Dawson is an Associate Dean of Technology and Innovation and a professor of law. They discuss the importance of lifelong learning, upskilling, and the ethical implications of AI in education.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.
Visit us at https://ibm.com/smarttalks
See omnystudio.com/listener for privacy information.
From her early days coding on a TI-84 calculator, to working as an engineer at IBM, to pivoting over to her new role in DevRel, speaking, and community, Mrina has seen the world of coding from many angles.
You can follow her on Twitter here and on LinkedIn here.
You can learn more about CK editor here and TinyMCE here.
Congrats to Stack Overflow user NYI for earning a great question badge by asking:
How do I convert a bare git repository into a normal one (in-place)?
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
In this episode of Syntax, Wes and Scott talk with Una Kravetz and Adam Argyle from Google Chrome about the evolution of CSS, new features, and the push toward more advanced UI capabilities on the web. They discuss the introduction of CSS versioning, exciting new properties like text-box-trim, state queries, and scroll state functionalities, select, and more!
Show Notes 00:00 - Welcome to Syntax!.
01:43 - Brought to you by Sentry.io.
02:19 - The evolution of CSS.
04:07 - CSS versioning and spec levels. CSS RFC.
17:49 - Use-cases for allow-discrete.
20:34 - State queries.
24:19 - Where does the baseline data come from?
25:17 - Will the RFC become official? The latest in Web UI (Google I/O ‘24).
27:33 - New features Una is excited about.
29:44 - Select. https://open-ui.org/components/customizableselect.
https://codepen.io/argyleink/pen/YzoEPOG.
38:31 - New features Adam is excited about.
39:24 - text-box-trim.
40:59 - State queries.
54:56 - Sick Picks + Shameless Plugs.
Sick Picks Una: Logitech MX Master 3
Adam: Teenage Engineering K.O. II
Shameless Plugs Una: Una.im
Adam: The CSS Podcast
Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
Major breakthroughs in artificial intelligence research often reshape the design and utility of Al in both business and society. In this special rebroadcast episode of Smart Talks with IBM, Malcolm
Gladwell and Jacob Goldstein explore the conceptual underpinnings of modern Al with Dr. David Cox, VP of Al models at IBM Research. They talk foundation models, self-supervised machine learning, and the practical applications of Al and data platforms like watsonx in business and technology.
When we first aired this episode last year, the concept of foundation models was just beginning to capture our attention. Since then, this technology has evolved and redefined the boundaries of what's possible. Businesses are becoming more savvy about selecting the right models and understanding how they can drive revenue and efficiency.
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In a rapidly evolving world, we need to balance the fear surrounding AI and its role in the workplace with its potential to drive productivity growth. In this special live episode of Smart Talks with IBM, Malcolm Gladwell is joined onstage by Rob Thomas, senior vice president of software and chief commercial officer at IBM, during NY Tech Week. They discuss “the productivity paradox,” the importance of open-source AI, and a future where AI will touch every industry.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.
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