Podcasts about Apis

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop explores the complex world of context and knowledge graphs with guest Youssef Tharwat, the founder of NoodlBox who is building dot get for context. Their conversation spans from the philosophical nature of context and its crucial role in AI development, to the technical challenges of creating deterministic tools for software development. Tharwat explains how his product creates portable, versionable knowledge graphs from code repositories, leveraging the semantic relationships already present in programming languages to provide agents with better contextual understanding. They discuss the limitations of large context windows, the advantages of Rust for AI-assisted development, the recent Claude/Bun acquisition, and the broader geopolitical implications of the AI race between big tech companies and open-source alternatives. The conversation also touches on the sustainability of current AI business models and the potential for more efficient, locally-run solutions to challenge the dominance of compute-heavy approaches.For more information about NoodlBox and to join the beta, visit NoodlBox.io.Timestamps00:00 Stewart introduces Youssef Tharwat, founder of NoodlBox, building context management tools for programming05:00 Context as relevant information for reasoning; importance when hitting coding barriers10:00 Knowledge graphs enable semantic traversal through meaning vs keywords/files15:00 Deterministic vs probabilistic systems; why critical applications need 100% reliability20:00 CLI tool makes knowledge graphs portable, versionable artifacts with code repos25:00 Compiler front-ends, syntax trees, and Rust's superior feedback for AI-assisted coding30:00 Claude's Bun acquisition signals potential shift toward runtime compilation and graph-based context35:00 Open source vs proprietary models; user frustration with rate limits and subscription tactics40:00 Singularity path vs distributed sovereignty of developers building alternative architectures45:00 Global economics and why brute force compute isn't sustainable worldwide50:00 Corporate inefficiencies vs independent engineering; changing workplace dynamics55:00 February open beta for NoodlBox.io; vision for new development tool standardsKey Insights1. Context is semantic information that enables proper reasoning, and traditional LLM approaches miss the mark. Youssef defines context as the information you need to reason correctly about something. He argues that larger context windows don't scale because quality degrades with more input, similar to human cognitive limitations. This insight challenges the Silicon Valley approach of throwing more compute at the problem and suggests that semantic separation of information is more optimal than brute force methods.2. Code naturally contains semantic boundaries that can be modeled into knowledge graphs without LLM intervention. Unlike other domains where knowledge graphs require complex labeling, code already has inherent relationships like function calls, imports, and dependencies. Youssef leverages these existing semantic structures to automatically build knowledge graphs, making his approach deterministic rather than probabilistic. This provides the reliability that software development has historically required.3. Knowledge graphs can be made portable, versionable, and shareable as artifacts alongside code repositories. Youssef's vision treats context as a first-class citizen in version control, similar to how Git manages code. Each commit gets a knowledge graph snapshot, allowing developers to see conceptual changes over time and share semantic understanding with collaborators. This transforms context from an ephemeral concept into a concrete, manageable asset.4. The dependency problem in modern development can be solved through pre-indexed knowledge graphs of popular packages. Rather than agents struggling with outdated API documentation, Youssef pre-indexes popular npm packages into knowledge graphs that automatically integrate with developers' projects. This federated approach ensures agents understand exact APIs and current versions, eliminating common frustrations with deprecated methods and unclear documentation.5. Rust provides superior feedback loops for AI-assisted programming due to its explicit compiler constraints. Youssef rebuilt his tool multiple times in different languages, ultimately settling on Rust because its picky compiler provides constant feedback to LLMs about subtle issues. This creates a natural quality control mechanism that helps AI generate more reliable code, making Rust an ideal candidate for AI-assisted development workflows.6. The current AI landscape faces a fundamental tension between expensive centralized models and the need for global accessibility. The conversation reveals growing frustration with rate limiting and subscription costs from major providers like Claude and Google. Youssef believes something must fundamentally change because $200-300 monthly plans only serve a fraction of the world's developers, creating pressure for more efficient architectures and open alternatives.7. Deterministic tooling built on semantic understanding may provide a competitive advantage against probabilistic AI monopolies. While big tech companies pursue brute force scaling with massive data centers, Youssef's approach suggests that clever architecture using existing semantic structures could level the playing field. This represents a broader philosophical divide between the "singularity" path of infinite compute and the "disagreeably autistic engineer" path of elegant solutions that work locally and affordably.

Favour Obasi-ike, MBA, MS delves into the intricacies of social media marketing, with a special focus on hacking the Instagram and TikTok algorithms. Favour shares valuable insights on how to gain maximum visibility and grow your business by understanding the underlying mechanics of these platforms. The episode covers the importance of creating engaging content, the power of a strong call to action, and the strategic use of social media analytics. Favour also introduces a powerful tool called "Sort Feed" for analyzing content performance and provides a live demonstration of how to leverage it for your own business. This episode is packed with actionable tips and strategies for anyone looking to up their social media game in 2026.Book SEO Services | Quick Links for Social Business>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book SEO Services with Favour Obasi-ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats Online>> Favour Obasi-ike Quick LinksLearning TopicsUnderstanding Social Media Algorithms: Learn the difference between social media platforms and search engines, and how to leverage their APIs for growth.Content Strategy: Discover how to create content that resonates with your audience and encourages engagement.The Power of Call to Action (CTA): Understand the importance of a clear and compelling CTA in driving user action.Leveraging Social Media Analytics: Learn how to use tools like "Sort Feed" to analyze content performance and gain a competitive edge.The Psychology of Social Media: Explore the psychological principles behind effective social media marketing, including the use of color and emotional triggers.Cross-Platform Promotion: Discover how to increase the visibility of your social media content by embedding it on your website.Episode Timestamps[00:00 - 02:00] Introduction to the topic: Social Media Marketing, Instagram and TikTok algorithm hacks.[02:00 - 04:10] Introduction to the "Sort Feed" tool for analyzing Instagram and TikTok content.[08:02 - 10:13] The difference between social media platforms and search engines.[20:05 - 25:15] Analysis of a viral post and the importance of a strong CTA.[40:08 - 46:22] The power of comments and engagement in boosting visibility.[53:01 - 58:24] How to embed social media posts on your website to increase reach.[58:08 - 58:24] The psychology of color in marketing.[01:15:11 - 01:16:52] Recap and key takeaways.Frequently Asked Questions (FAQs)Q: What is "Sort Feed" and how can it help my business?A: Sort Feed is a Google Chrome Extension tool that allows you to sort and analyze Instagram and TikTok content by various metrics such as likes, comments, and views. It can help you understand what content is performing well in your industry, identify trends, and gain insights to inform your own content strategy.Q: Should I focus on creating content for the algorithm or for my audience?A: While it's important to understand the algorithm, the primary focus should always be on creating valuable and engaging content for your audience. By building a strong connection with your followers, you will naturally see better results in the long run.Q: How can I increase the visibility of my social media posts?A: One effective strategy is to embed your social media posts on your website or blog. This can help you reach a wider audience and drive more traffic to your social media profiles.Q: What is the most important element of a social media post?A: A clear and compelling call to action (CTA) is one of the most important elements of a social media post. It tells your audience what you want them to do next, whether it's to like, comment, share, or visit your website.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most orgs have a major blind spot: the browser.This week on Defender Fridays, we're joined by Cody Pierce, Co-Founder and CEO at Neon Cyber, to discuss why browser security remains a critical gap, from sophisticated phishing campaigns that bypass traditional controls to shadow AI tools operating outside your security perimeter.Cody began his career in the computer security industry twenty-five years ago. The first half of his journey was rooted in deep R&D for offensive security, and he had the privilege of leading great teams working on elite problems. Over the last decade, Cody have moved into product and leadership roles that allowed him to focus on developing and delivering innovative and differentiated capabilities through product incubation, development, and GTM activities. Cody says he gets the most joy from building and delivering products that bring order to the chaos of cyber security while giving defenders the upper hand.About This SessionThis office hours format brings together the LimaCharlie team to share practical experiences with AI-powered security operations. Rather than theoretical discussions, we demonstrate working tools and invite the community to share their own AI security experiments. The session highlights the rapid evolution of AI capabilities in cybersecurity and explores the changing relationship between security practitioners and automation.Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you – our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, a cloud-native SecOps platform where AI agents operate security infrastructure directly. Founded in 2018, LimaCharlie provides complete API coverage across detection, response, automation, and telemetry, with multi-tenant architecture designed for MSSPs and MDR providers managing thousands of unique client environments.Why LimaCharlie?Transparency: Complete visibility into every action and decision. No black boxes, no vendor lock-in.Scalability: Security operations that scale like infrastructure, not like procurement cycles. Move at cloud speed.Unopinionated Design: Integrate the tools you need, not just those contracts allow. Build security on your terms.Agentic SecOps Workspace (ASW): AI agents that operate alongside your team with observable, auditable actions through the same APIs human analysts use.Security Primitives: Composable building blocks that endure as tools come and go. Build once, evolve continuously.Try the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieio X: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - CEO / Co-founder at LimaCharlie

On the podcast we talk with Tanmay and Jack about how earned media can drive paid performance, building features that make for good tweets, and why stripping out your onboarding quiz might beat optimizing it.Top Takeaways:

Scott and Wes run through their wishlist for the web platform, digging into the UI primitives, DOM APIs, and browser features they wish existed (or didn't suck). From better form controls and drag-and-drop to native reactivity, CSS ideas, and future-facing APIs, it's a big-picture chat on what the web could be. Show Notes 00:00 Welcome to Syntax! Wes Tweet 00:39 Exploring What's Missing from the Web Platform 02:26 Enhancing DOM Primitives for Better User Experience 03:59 Multi-select + Combobox. Open-UI 04:49 Date Picker. Thibault Denis Tweet 07:18 Tabs. 08:01 Image + File Upload. 09:08 Toggles. 10:23 Native Drag and Drop that doesn't suck. 12:03 Syntax wishlist. 12:06 Type Annotations. 15:07 Pipe Operator. 16:33 APIs We Wish to See on the Web 18:31 Brought to you by Sentry.io 19:51 Identity. 21:33 getElementByText() 24:09 Native Reactive DOM. Templating in JavaScript. 24:48 Sync Protocol. 25:52 Virtualization that doesn't suck. 27:40 Put, Patch, and Delete on forms. Ollie Williams Tweet SnorklTV Tweet 28:55 Text metrics: get bounding box of individual characters. 29:42 Lower Level Connections. 29:50 Bluetooth API. 30:47 Sockets. 31:29 NFC + RFID. 34:34 Things we want in CSS. 34:40 Specify transition speed. 35:24 CSS Strict Mode. 36:25 Safari moving to Chromium. 36:37 The Need for Diverse Browser Engines 37:48 AI Access. 44:49 Other APIs 46:59 Qwen TTS 48:07 Sick Picks + Shameless Plugs Sick Picks Scott: Monarch Wes: Slonik Headlamp Shameless Plugs Scott: Syntax on YouTube 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

In this episode of HomeKit Insider, Andrew teams up with shortcut expert Matthew Cassinelli to delve into the world of smart home automation. They explore the evolution of shortcuts from Workflow to Apple Shortcuts, offering insights into personal and home automations within Apple HomeKit. Listeners will learn how to enhance their home setups with advanced logic, integrate APIs for custom solutions, and troubleshoot common issues. The episode also highlights future smart home interfaces, real-world automation examples, and the potential of AI in home automation. Perfect for tech enthusiasts eager to elevate their smart home experience.Send us your HomeKit questions and recommendations with the hashtag homekitinsider. Tweet and follow our hosts at:@andrew_osu on Twitter@andrewohara941 on ThreadsEmail me hereSponsored by:Shopify: Sign up for a one-dollar-per-month trial period at: shopify.com/homekitIncogni: Take your personal data back with Incogni! Get 60% off an annual plan at https://incogni.com/homekit and use code HOMEKIT at checkout.HomeKit Insider YouTube ChannelSubscribe to the HomeKit Insider YouTube Channel and watch our episodes every week! Click here to subscribe.Links from the showMatthew Cassinelli on TwitterMatthew Cassinelli consultingSonos Amp MultiAirTag 2 detailsAqara U400 Deluxe Kit at AppleThose interested in sponsoring the show can reach out to us at: andrew@appleinsider.com

If you're struggling to integrate legacy systems without killing visibility or choose the right TMS partner that actually delivers long-term value, tune in to this episode with Petra Nenickova from Legacy Supply Chain to know what real-world technology integration and digital transformation look like in today's freight environment across the US and Canada! From managing complex legacy systems and selecting flexible, integration-ready TMS platforms to driving user adoption through frontline involvement, practical training, and ongoing post-launch support, we get into how to evaluate vendors beyond polished demos, why system integrations like EDI and APIs are non-negotiable, how to prioritize core operations at go-live, and why clear communication and change management are the real drivers of ROI, visibility, and scalable freight technology success!   About Petra Nenickova Petra currently manages the transportation tech stack, including their TMS, and she led several TMS projects from early discovery all the way through implementation. These experiences have taught her a lot about what truly works, what doesn't, and how to guide teams through change with confidence. She's genuinely passionate about both technology and transportation, and she loves the challenge of making systems more effective in supporting real business processes while creating a positive user experience. With a background in both front‑line operations and management, she understands the everyday challenges people face and what makes technology genuinely useful. She thrives on connecting the dots, solving problems, and helping people work smarter through better processes and better technology.   Connect with Petra Website: https://legacyscs.com/  LinkedIn: https://www.linkedin.com/in/petranenickova/  

Join us for a special Defender Fridays Office Hours session where the LimaCharlie team demonstrates the new Agentic SecOps Workspace (ASW) and explores what's possible when AI agents operate security infrastructure directly.At Defender Fridays, we delve into the dynamic world of information security, exploring its defensive side with seasoned professionals from across the industry. Our aim is simple yet ambitious: to foster a collaborative space where ideas flow freely, experiences are shared, and knowledge expands.What We'll DiscussIn this hands-on session, we showcase real working implementations of AI in cybersecurity operations. From reverse engineering malware to automated rule tuning and infrastructure management, we demonstrate how AI agents are transforming security workflows from concept to production-ready tools in hours instead of days.Key TopicsAutomated malware analysis and decompilation without traditional manual reverse engineering workflowsRule tuning at scale: Investigating noisy detections, writing false positive rules, and deploying them autonomouslyInfrastructure automation: Setting up data sources, configuring tenants, and managing security operations through AI agentsThe permission model: Balancing AI capability with human oversight and approval workflowsReal-world applications: Custom reporting, detection coverage analysis, and operational time savingsAbout This SessionThis office hours format brings together the LimaCharlie team to share practical experiences with AI-powered security operations. Rather than theoretical discussions, we demonstrate working tools and invite the community to share their own AI security experiments. The session highlights the rapid evolution of AI capabilities in cybersecurity and explores the changing relationship between security practitioners and automation.Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you – our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, a cloud-native SecOps platform where AI agents operate security infrastructure directly. Founded in 2018, LimaCharlie provides complete API coverage across detection, response, automation, and telemetry, with multi-tenant architecture designed for MSSPs and MDR providers managing thousands of unique client environments.Why LimaCharlie?Transparency: Complete visibility into every action and decision. No black boxes, no vendor lock-in.Scalability: Security operations that scale like infrastructure, not like procurement cycles. Move at cloud speed.Unopinionated Design: Integrate the tools you need, not just those contracts allow. Build security on your terms.Agentic SecOps Workspace (ASW): AI agents that operate alongside your team with observable, auditable actions through the same APIs human analysts use.Security Primitives: Composable building blocks that endure as tools come and go. Build once, evolve continuously.Try the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieio X: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - CEO / Co-founder at LimaCharlie

Patrick McKenzie (patio11) walks through a coding session with Claude Code to demonstrate what the fuss is about. The business problem: recovering failed subscription payments that required coordinating APIs across Stripe, Ghost, and email providers, and the surprising experience of watching Claude read documentation, resolve dependency conflicts, and make sensible security choices. The episode offers a pedantic level of detail on why the sharpest technologists use words like “fundamentally transformed” to describe the impact of LLMs on coding.–Full annotated transcript available here: www.complexsystemspodcast.com/claude-code/–Sponsor: FramerBuilding and maintaining marketing websites shouldn't slow down your engineers. Framer gives design and marketing teams an all-in-one platform to ship landing pages, microsites, or full site redesigns instantly—without engineering bottlenecks. Get 30% off Framer Pro at framer.com/complexsystems.–Links:Odd Lots episode with Noah Brier: https://open.spotify.com/episode/2fd3hvYmplEnQzxYZaxPg3?si=ylFxFe3HQ4uivH29uqC_rABits about Money: https://www.bitsaboutmoney.com/ –Timestamps:(00:00) Intro(02:21) All engineering work happens in a business context(03:47) Payment failures briefly taxonomized(08:25) Now follows a conversation with Claude Code(20:37) Sponsor: Framer(21:53) Conversation with Claude Code (continued)(39:07) My final thoughts on this(41:15) Wrap

How are leading founders actually using AI day to day?In this episode of the Digital Irish Podcast, we're sharing highlights from the first session in our Practical Artificial Intelligence series — focused on real, hands-on experiences with AI from people building and using it right now.Our guest is Sean Blanchfield — Irish entrepreneur behind Demonware and Phorest, and founder of his latest venture, Jentic. Sean shares his perspective on how AI is reshaping enterprise software, where the real opportunities lie with APIs and AI agents, and how he personally uses AI in his own workflows.This episode features selected snippets from the live webinar. You can watch the full conversation and Q&A on Digital Irish Connect.

Find out if EDI is still the backbone of scalable freight operations and what happens when you stop penalizing brokers for growth in this episode with our returning guest, Brad Perling of Bitfreighter! Brad shares why their EDI-first freight technology strategy is quietly reshaping shipper integration, automated quoting, and brokerage scalability. Brad and I talk through why EDI remains the most reliable foundation for freight data integrity, how unlimited EDI messaging pricing removes one of the biggest cost barriers for growing brokerages, seamless integration through APIs and RPA across TMS platforms and load boards, and how real-time quoting analytics are driving millions in new revenue for customers. If you're a freight broker or shipper looking to scale without breaking your tech stack or your budget, this conversation lays out exactly why EDI (if done right) is still a competitive advantage in modern freight tech!   About Brad Perling Brad Perling is the CEO and co-founder of Bitfreighter. With over 15 years of experience in the industry and growing 2 successful brokerages, Brad's deep understanding of logistics challenges has fueled his passion for finding better software solutions. He knew there was a need for a disruptive new model in the EDI space and was determined to create it. He has a passion for aviation and enjoys playing hockey and golf while spending time with his wife and 2 kids.   Connect with Brad Website: https://www.bitfreighter.com/  LinkedIn: https://www.linkedin.com/in/brad-perling-5a101655/  

In this episode of Torsion Talk, Ryan Lucia breaks down one of the most viral and controversial AI tools to hit the market: Clawdbot. At the time of recording, this technology was exploding across the internet as a potential game changer for home service businesses. Since then, things have taken a sharp turn, prompting serious conversations around security, permissions, and safety. Ryan opens the episode with an important warning about downloading imitation software and explains why rushing into new AI tools without understanding the risks can expose your business and personal data.Ryan explains what makes Clawdbot fundamentally different from traditional AI automations. Unlike tools that rely on APIs and backend integrations, Clawdbot operates like a human by controlling a computer directly, opening browsers, reading emails, responding to messages, booking appointments, and completing tasks exactly as a person would. This shift unlocks massive potential, but it also introduces new risks that most businesses are not prepared for.The episode explores real-world use cases for home service companies, including instant lead intake and follow-up, automated estimating and proposal creation, faster commercial quoting, project coordination, change order management, inbox triage, and accounts receivable support. Ryan shares why estimating and project coordination may be the biggest opportunities of all, especially for commercial work where speed and accuracy can determine who wins the job.At the same time, Ryan doesn't shy away from the darker side of this technology. He discusses authentication risks, financial access concerns, two-factor security challenges, and why giving AI control over a computer without proper guardrails can lead to unintended consequences. Through real examples and humor, he highlights just how quickly things could go sideways if safeguards aren't in place.Ryan zooms out to explain why tools like Clawdbot represent the first real step toward a future where humans work less, delegate more, and manage AI assistants instead of grinding nonstop. He reflects on hustle culture, personal sacrifice, and how the next three to five years may completely redefine work, productivity, and balance.This episode is both a glimpse into the future and a cautionary tale. Clawdbot may be one of the most powerful AI shifts we've seen yet, but understanding when to slow down, test carefully, and protect yourself matters just as much as innovation.Find Ryan at:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://garagedooru.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aaronoverheaddoors.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://markinuity.com/⁠Check out our sponsors!Sommer USA - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://sommer-usa.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Surewinder - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://surewinder.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stealth Hardware - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://quietmydoor.com/⁠

In this episode of The Dish on Health IT, host Tony Schueth, CEO of Point-of-Care Partners (POCP), is joined by colleagues Mary Griskewicz, Regulatory Resource Center Lead, and Janice Reese, Senior Consultant and Program Manager of FHIR at Scale Taskforce (FAST), for a wide-ranging discussion on two major proposed rules released in mid-December 2025: the HTI-5 proposed rule from the Assistant Secretary for Technology Policy (ASTP) and CMS's latest proposal on healthcare price transparency.Rather than treating these rules as abstract policy exercises, the conversation focuses on what the government is trying to accomplish, how these proposals may reshape the interoperability and data access landscape, and why stakeholder participation during the comment period is not optional if the industry wants workable outcomes.Setting the Stage: How Proposed Rules Become RealityThe episode opens with a level set for listeners who do not spend their days in the Federal Register. Mary walks through how proposed rules originate, typically from legislation or executive policy, and how they move from proposal to public comment to either a final rule, an interim final rule, or, in some cases, a complete pause or reset.She emphasizes a point that often gets overlooked: every public comment is read and reviewed. The agencies group and analyze the comments section by section and respond to themes and concerns in the final rule text. Janice builds on this by explaining that the comment period is where high-level policy intent meets operational reality. The most effective comments are not lengthy manifestos, but specific, experience-based feedback that highlights feasibility issues, sequencing challenges, and unintended consequences.HTI-5: From Experimentation to ExecutionThe discussion then turns to HTI-5, with Mary outlining the core problem the rule is trying to address. Prior certification requirements placed a significant burden on vendors, often locking innovation into long development cycles while the market waited for updates. HTI-5 seeks to modernize this approach by reducing prescriptive certification requirements and relying more on modern, open architecture, particularly FHIR-based APIs, to enable faster, more scalable data exchange.Janice frames HTI-5 as a clear signal that the industry is moving out of the experimentation phase and into execution. By reinforcing a “FHIR-first” direction while pulling back on some certification detail, the rule implicitly raises expectations for real-world performance. As FHIR becomes the default, security, identity, consent, and trust cannot be treated as optional or inconsistently implemented components.From a FAST perspective, this shift is critical. HTI-5 creates the regulatory space, but the infrastructure and implementation guidance needed to make trusted interoperability work at scale must come from industry-led collaboration. Janice explains that FAST's work on security, identity, consent, and national directory services is about operationalizing trust so organizations are not reinventing these foundations on their own.Information Blocking, Automation, and Trust at ScaleA pivotal moment in the conversation centers on HTI-5's clarification that information blocking explicitly includes automated and AI-driven access. Mary underscores that automation is now central to how data moves across the healthcare ecosystem. When access decisions are embedded in APIs, workflows, and algorithms, trust becomes the defining requirement.Janice expands on this by noting that the issue is not just whether data can be accessed, but whether access is appropriate, provable, and governed. As automation increases, expectations shift toward accountability, auditability, and consistent enforcement of identity and consent. FHIR APIs, once viewed as certification checkboxes, are becoming the primary channel for data exchange across networks, including consumer-facing applications.Stakeholder Impacts: Vendors, Providers, and PayersThe episode then walks through how HTI-5 affects different stakeholder groups. For health IT vendors and digital health companies, Janice describes a trade-off: fewer certification guardrails provide flexibility but also remove a layer of protection. Vendors will be judged less on formal compliance artifacts and more on how their systems perform across networks at scale, including security, identity management, and reliability.Mary cautions that vendors should not interpret HTI-5 as traditional deregulation. With HTI-6 already on the horizon, organizations that underinvest now risk facing more stringent outcome-based expectations later. Tony reinforces this point, arguing that the real risk is collective. A single high-profile failure due to weak security or identity practices could undermine trust across the ecosystem and invite a regulatory response that affects everyone.For providers and health systems, the shift means becoming more informed consumers of technology. Certification alone will no longer guarantee interoperability or trustworthiness. Providers will increasingly need to ask vendors how solutions perform in environments beyond a single one and how identity, consent, and security are handled across organizational boundaries.From a payer perspective, Mary explains that while HTI-5 does not directly change prior authorization requirements, it fundamentally reshapes the data access environment. As FHIR APIs become the default, plans will be expected to exchange data more dynamically and through automated workflows. This raises expectations around timeliness, quality, and trust, and accelerates a shift from managing transactions to managing trust at scale.Price Transparency: Compliance Without ClarityThe conversation then transitions to CMS's proposed price transparency rule, with Tony noting the absence of POCP's usual price transparency expert and setting expectations for a higher-level discussion. Mary explains that this tri-agency proposal builds on earlier rules by clarifying standards, easing some reporting burdens, and refining requirements around machine-readable files, metadata, and reporting timelines.While these changes offer some relief to plans, Janice highlights a deeper challenge. Making pricing data available does not make it meaningful. Without consistent ways to connect clinical concepts to billing codes and pricing structures, patients and employers are left with technically accurate but practically unusable information. True transparency will require better integration of pricing data into real-time workflows, supported by APIs, governance, and trust frameworks.Mary also reminds listeners that employers are a critical stakeholder often overlooked in these discussions. As purchasers of coverage, they rely on usable pricing data to understand utilization and manage costs, making their perspective essential during the comment period.The Closing Message: Comment, Participate, Get InvolvedThe episode closes with a strong call to action. Mary urges listeners to “get off the bench” and engage, regardless of which rule is at issue. Comment periods directly affect compliance programs, product roadmaps, and competitive positioning. Janice reinforces that policy alone cannot solve interoperability challenges. Progress depends on shared implementation guidance, testing, governance, and sustained participation in standards organizations and multi-stakeholder initiatives, including FAST.The final takeaway is clear: HTI-5 and the price transparency proposal are not just regulatory events. They are inflection points. Organizations that participate now can help shape outcomes that are achievable, scalable, and trusted. Those that sit out will be left reacting to decisions made without their operational realities at the table.Listeners are reminded that both proposed rules have comment deadlines in late February, and that POCP is available to support organizations in understanding the implications and crafting effective comments. The episode closes, as always, with the reminder that Health IT is a dish best served hot. 

Today we sit down with John Lack, Global Head of Sales Development at Airtable, to demystify the world of software sales as a profession. John breaks down the immense rewards of the industry—from earning six figures right out of college as a successful BDR to mastering the "autonomy, mastery, and purpose" of high-level tech sales. We explore why the BDR role is the most critical time in a career for building foundational grit and why 90% of AE struggles stem from poor front-end pipeline generation. John also shares his own unconventional journey, starting as a BDR at age 30 and scaling teams through massive growth phases at Oracle and MongoDB.

What happens when AI stops talking and starts working, and who really owns the value it creates? In this episode of Tech Talks Daily, I'm joined by Sina Yamani, founder and CEO of Action Model, for a conversation that cuts straight to one of the biggest questions hanging over the future of artificial intelligence.  As AI systems learn to see screens, click buttons, and complete tasks the way humans do, power and wealth are concentrating fast. Sina argues that this shift is happening far quicker than most people realize, and that the current ownership model leaves everyday users with little say and even less upside. Sina shares the thinking behind Action Model, a community-owned approach to autonomous AI that challenges the idea that automation must sit in the hands of a few giant firms. We unpack the concept of  Large Action Models, AI systems trained to perform real online workflows rather than generate text, and why this next phase of AI demands a very different kind of training data. Instead of scraping the internet in the background, Action Model invites users to contribute actively, rewarding them for helping train systems that can navigate software, dashboards, and tools just as a human worker would. We also explore ActionFi, the platform's outcome-based reward layer, and why Sina believes attention-based incentives have quietly broken trust across Web3. Rather than paying for likes or impressions, ActionFi focuses on verifying real actions across the open web, even when no APIs or integrations exist. That raises obvious questions around security and privacy.  This conversation does not shy away from the uncomfortable parts. We talk openly about job displacement, the economic reality facing businesses, and why automation is unlikely to slow down. Sina argues that resisting change is futile, but shaping who benefits from it remains possible. He also reflects on lessons from his earlier fintech exit and how movements grow when people feel they are pushing back against an unfair system. By the end of the episode, we look ahead to a future where much of today's computer-based work disappears and ask what success and failure might look like for a community-owned AI model operating at scale. If AI is going to run more of the internet on our behalf, should the people training it have a stake in what it becomes, and would you trust an AI ecosystem owned by its users rather than a handful of billionaires? Useful Links Connect with Sina Yamani on LinkedIn or X Learn more about the Action Model Follow on X Learn more about the Action Model browser extension Check out the whitelabel integration docs Join their Waitlist Join their Discord community Thanks to our sponsors, Alcor, for supporting the show.

An airhacks.fm conversation with Holly Cummins (@holly_cummins) about: first computer experience with her dad's Kaypro CPM machine and ASCII platform games, learning Basic programming on an IBM PC clone to build a recipe management system, studying physics at university with a doctorate in quantum computing, self-teaching Java to create 3D visualizations of error correction on spheres during PhD research, joining IBM as a self-taught programmer without formal computer science education, working on Business Event Infrastructure (BDI) at IBM, brief unhappy experience porting JMS to .net with Linux and VNC, moving to IBM's JVM performance team working on garbage collection analysis, creating Health Center visualization tooling for J9 as an alternative to JDK Mission Control, innovative low-overhead always-on profiling by leveraging JIT compiler's existing method hotness data, transitioning to WebSphere Liberty team during its early development, Liberty's architectural advantage of OSGi-based modular core enabling small fast startup while maintaining application compatibility, working on Apache Aries enterprise OSGi project and writing a book about it, discussion of OSGi's strengths in protecting internal APIs versus complexity costs for application developers, the famous OSGi saying about making the impossible possible and the possible hard, microservices solving modularity problems through network barriers versus class loader barriers, five years as IBM consultant helping customers adopt cloud-native technologies, critique of cloud-native terminology becoming meaningless when everything required the native suffix, detailed analysis of 12-factor app principles and how most were already standard Java practices, stateless processes as the main paradigm shift from JavaServer Faces session-based applications, joining Red Hat's quarkus team three and a half years ago through Erin Schnabel's recommendation, working on Quarkiverse community aspects and ecosystem development, leading energy efficiency measurements confirming Quarkus's sustainability advantages, current role as cross-portfolio sustainability architect for Red Hat middleware, writing Pact contract testing extension for Quarkiverse to understand extension author experience, re-architecting Quarkus test framework class loading to enable deeper extension integration, recent work on Dev Services lazy initialization to prevent eager startup of multiple database instances across test profiles, fixing LGTM Dev Services port configuration bugs for multi-microservice observability setups, upcoming JPMS integration work by colleague David Lloyd requiring class loader simplification, the double win of saving money while also reducing environmental impact, comparison of sustainability benefits to accessibility benefits for power users, mystery solved about the blue-haired speaker at European Java User Groups years ago Holly Cummins on twitter: @holly_cummins

Principal Security Consultant and community favorite Jake Hildreth returns to The PowerShell Podcast to talk about building smarter automation, leveling up through community, and creating tools that solve real problems. Andrew shares his “stop trying so hard” theme for the year, how working smarter applies directly to scripting and security, and why getting involved with others is one of the fastest ways to grow in your career. The conversation dives into Jake's recent projects including Deck, a Markdown-to-terminal presentation tool built on Spectre.Console, and Stepper, a resumable scripting framework designed for long-running workflows that can't be fully automated end-to-end. They also explore presentation skills, avoiding “death by PowerPoint,” and why security work requires constantly re-checking assumptions as threats evolve.   Key Takeaways: • Work smarter, not harder — Whether you're scripting or building a career, small sustainable improvements beat grinding yourself into a corner. • Resumable automation is a game changer — Stepper helps scripts safely pause and resume, making real-world workflows more reliable when humans or flaky APIs are part of the loop. • Community turns into real momentum — Contributing, asking questions, and sharing feedback builds skills, friendships, and opportunities faster than trying to learn alone.   Guest Bio: Jake Hildreth is a Principal Security Consultant at Semperis, Microsoft MVP, and longtime builder of tools that make identity security suck a little less. With nearly 25 years in IT (and the battle scars to prove it), he specializes in helping orgs secure Active Directory and survive the baroque disaster that is Active Directory Certificate Services. He's the creator of Locksmith, Stepper, Deck, BlueTuxedo, and PowerPUG!, open-source tools built to make life easier for overworked identity admins. When he's not untangling Kerberos or wrangling DNS, he's usually hanging out with his favorite people and most grounding reality check: his wife and daughter.   Resource Links: • Jake Hildreth's Website – https://jakehildreth.com • Jake's GitHub - https://github.com/jakehildreth Andrew's Links - https://andrewpla.tech/links • PowerShell Spectre Console – https://pwshspectreconsole.com/ • PDQ Discord – https://discord.gg/PDQ • PowerShell Conference Europe – https://psconf.eu • PowerShell + DevOps Global Summit – https://powershellsummit.org • Jake's PowerShell Wednesday – https://www.youtube.com/watch?v=YdV6Qecn9v0 The PowerShell Podcast on YouTube: https://youtu.be/rFeoTKLerkA  

It's an annual tradition, the AVNation "Best of" Awards, and Chi Hang Lo is up for AV Professional of the Year, along with UCLA's Classroom Modernization Pilot, and HETMA for Best Technical Support! Take a listen as Joe Way sits down with Chi to discuss this honor and why you should #VoteForChi.Joe Way drops a special Friday episode to spotlight the AV Nation Awards (Readers' Choice “Best of 2025”) and rally the higher ed community around three finalists: Chi Hang Lo for AV Professional of the Year, UCLA's Classroom Modernization Pilot for Project of the Year, and HETMA for Best Technical Support. Chi joins to share what the nomination represents, why the UCLA pilot is different, and how the higher ed community lifts each other up through collaboration, shared evaluation, and real-world support. The episode closes with a clear call: go vote, support the people and projects pushing the industry forward, and keep building a better future together.Vote now: https://www.avnation.tv/avnation-best-of-2025-awards/Featured GuestChi Hang Lo — Manager, AV/IT Solutions (UCLA)Leads a team designing and delivering scalable AV + IT solutions that support UCLA's learning environments and broader smart campus vision.What You'll Hear in This Episode1) Why this episode, and why nowA bonus Friday release to interrupt the usual schedule and highlight the AV Nation Awards as a uniquely people-driven recognition.Joe frames Readers' Choice as a rare moment for the industry to advocate for the people, projects, and platforms that matter most to the community.2) The three higher ed finalistsChi Hang Lo — AV Professional of the Year finalistUCLA — Project of the Year finalist for the Classroom Modernization PilotHETMA — Technical Support finalistJoe emphasizes how significant it is to see higher ed represented across multiple major categories in the finals.3) The UCLA Classroom Modernization Pilot: what makes it specialChi explains why the pilot stands out as more than a refresh—it's a different way of thinking:Moving from traditional room-by-room AV to a cloud-first, scalable control approach designed for enterprise scale (think: up to 1,000 spaces).Leveraging web technologies, REST APIs, and integrations to enable flexibility, interoperability, and future growth.Building for adaptability so the system isn't locked to one manufacturer ecosystem—prioritizing integration-first design and long-term scalability.Aiming toward a platform approach: “AV as a platform” that can support more than AV control.4) The “why” behind going cloud-firstJoe asks the question everyone asks: why not just keep doing “simple” AV? Chi's answer points to:Preparing the team—and the campus—for the future skill sets needed in modern learning environments.Meeting expanding demands: conferencing, capture, collaboration, active learning, and rapid shifts in pedagogy.Treating AV as part of a broader AV/IT solutions ecosystem, not a standalone technical island.5) Smart campus, not just AVThe conversation expands into the broader vision:AV systems already contain meaningful data (occupancy, environmental signals, usage patterns)—the opportunity is connecting it to the rest of campus.Collaboration across departments (facilities, security, events, transportation, IT, and more) becomes possible when you build a platform that can integrate.Chi shares work toward data aggregation and dashboards, including collaboration with a Data Lake approach to create better operational insight and decision-making.6) The team behind the pilotChi introduces the core members of his team and their contributions:Project coordination and process leadership (including agile/scrum-style development support)Technical design and 2D/3D modeling workflows, standards-based design language for facilities alignmentSoftware/automation engineering, signal distribution/recording, and architecture to connect devices to the cloudPartnerships with manufacturers to improve firmware/APIs and enable deeper integration at scaleJoe underscores how innovation required close collaboration between UCLA, solution providers, and manufacturers—engineering alongside engineering.7) Career growth: from technical expert to leaderJoe shifts the conversation to professional development: what changes when you move from “doing” to “leading.”Chi shares leadership themes that have guided him:Staying humble, collaborative, and relationship-drivenBalancing strong technical conviction with empathy and communicationCreating opportunities for the next generation by helping people navigate common roadblocks (communication, attitude, relationship dynamics)Treating the industry like a community—because you'll keep working with the same people for years8) The HETMA community impactChi shares how community support—especially collaborative technology evaluation and shared learning—helps smaller institutions gain access, influence, and manufacturer attention they might not get alone. Joe reinforces the higher ed ethos: we're collaborators, not competitors.Memorable Moments / Quotes (paraphrased)The awards matter because the people choose—it's advocacy, not just adjudication.The pilot isn't just “AV”—it's building infrastructure for a smart campus platform.The real work is turning AV data into insight and integration that improves the campus experience.Calls to ActionVote for Chi Hang Lo — AV Professional of the YearVote for UCLA's Classroom Modernization Pilot — Project of the YearVote for HETMA — Technical SupportAnd vote for the products, people, and projects you believe represent the best of 2025.Vote now: https://www.avnation.tv/avnation-best-of-2025-awards/Connect with Chi Hang Lo: https://www.linkedin.com/in/chihanglo/

Mews just raised $300 million in a Series D, valuing the company at $2.5 billion — one of the largest hotel tech raises ever. But this conversation isn't about hype. In this GMH exclusive, Wil Slickers sits down for a third time with Richard Valtr, Founder of Mews, to unpack what this funding actually unlocks and why Richard believes much of hospitality technology has been built on the wrong assumptions for decades. They dig into why hotels still struggle with data ownership, how PMS platforms became gatekeepers instead of enablers, and why AI will only work if the industry fixes its foundations first. Richard also explains why RevPAR may be the industry's “original sin,” why guest experience should be a measurable output, and why fully autonomous hotels are the wrong goal. This is a wide-ranging, philosophical, and practical conversation about: • What Mews' $300M raise really changes • Why hotel tech copied the wrong SaaS playbook • Data standards, open APIs, and industry gatekeeping • AI agents, automation, and what should (and shouldn't) be automated • Why hospitality is more human than ever, even in an AI world Extra Links Related or Mentioned in This Episode: My first episode with Richard in 2021 My second episode with Richard in 2023 Skift Article around the $300M fund raise Connect with Airline Weekly LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/airline-weekly/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ X: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://x.com/Airline_Weekly/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/airlineweekly/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/skiftnews/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ WhatsApp: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://whatsapp.com/channel/0029VaAL375LikgIXmNPYQ0L/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Subscribe to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@SkiftNews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and never miss an update from the airline and travel industries.

Read the Enterprise 2030 study → https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/enterprise-2030 Is Claude Code having its ChatGPT moment? This week on Mixture of Experts, host Tim Hwang is joined by Chris Hay, Gabe Goodhart and Francesco Brenna to unpack the shifts happening in AI as 2026 kicks off. First, OpenAI confirms ads are coming to ChatGPT, raising questions about trust, economics and the future of AI product models. Next, Claude Code is exploding in popularity! Developers are discovering what agentic coding can really do, and it's transforming how software gets built. Then, we analyze a new report from IBM's Institute for Business Value— “The enterprise in 2030”, which reveals how executives are planning to shift from AI-driven efficiency to AI-powered innovation. Finally, Hugging Face launches Open Responses, a new standard for agent APIs that could reshape AI development while raising questions about transparency and control. All that and more on this week's Mixture of Experts to learn more. 00:00 – Introduction 01:30 – OpenAI brings ads to ChatGPT 12:25 – Claude Code's breakout moment 22:57 – IBV's Enterprise 2030 report 36:09 – Open Responses: The future of agent APIs The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Explore IBM Enterprise Advantage Visit Mixture of Experts podcast page to get more AI content

Performance marketers struggle with direct mail attribution and speed. Ryan Ferrier is CEO of Lob, the direct mail automation platform serving over 12,000 businesses with API-driven personalized campaigns. The discussion covers AI-powered delivery optimization that automatically selects standard vs. first-class postage based on speed requirements, real-time address verification APIs that prevent undeliverable mail and save millions in wasted sends, and QR code attribution systems with personalized URLs achieving 5% average conversion rates and up to 30% for compliance-ready campaigns.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Como a inteligência artificial pode ajudar a identificar COMPORTAMENTOS em VÍDEO? No episódio 19 do Hipsters.Talks, PAULO SILVEIRA , CVO do Grupo Alura, conversa com RUBENS RODRIGUES , CTO da School Guardian sobre VISÃO COMPUTACIONAL, detecção de objetos com YOLO e PYTHON e como treinar MODELOS DE IA PARA IDENTIFICAR COMPORTAMENTOS específicos em vídeos. Descubra a diferença entre usar bibliotecas prontas e treinar seus próprios modelos, APIs de cloud vs processamento local e o futuro dos desenvolvedores na era da IA! Sinta-se à vontade para compartilhar suas perguntas e comentários. Vamos adorar conversar com você!

Performance marketers struggle with direct mail attribution and speed. Ryan Ferrier is CEO of Lob, the direct mail automation platform serving over 12,000 businesses with API-driven personalized campaigns. The discussion covers AI-powered delivery optimization that automatically selects standard vs. first-class postage based on speed requirements, real-time address verification APIs that prevent undeliverable mail and save millions in wasted sends, and QR code attribution systems with personalized URLs achieving 5% average conversion rates and up to 30% for compliance-ready campaigns.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 In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the practical application of AI agents to automate mundane marketing tasks. You will define what an AI agent is and discover how this technology performs complex, multi-step marketing operations. You will learn a simple process for creating knowledge blocks and structured recipes that guide your agents to perform repetitive work. You will identify which tools, like your content scheduler or website platform, are necessary for successful, end-to-end automation. You will understand crucial data privacy measures and essential guardrails to protect your sensitive company information when deploying new automated systems. Tune in now to see how you can permanently eliminate hours of boring work from your weekly schedule! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-agentic-ai-practical-applications-claude-cowork.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, one of the things that people have said, me especially, is that 2026 is the year of the agent. The way I define an agent is it’s like a real estate agent or a travel agent or a tax agent. It’s something that just goes and does, then comes back to you and says, “Hey, boss, I’m done.” Katie, you and I were talking before the show about there’s a bunch of mundane tasks, like, let’s write some evergreen social posts, let’s get some images together, let’s update a landing page. Let me ask you this: when you look at those tasks, do they feel repetitive to you? Katie Robbert: Oh, 100%. I’ve automated a little bit of it. And by that, what I mean is I have the background information about Trust Insights. I have the tone and brand guidelines for Trust Insights. So if I didn’t have those things, those would probably be the biggest lift. And so all I’m doing is taking all of the known information and saying, okay, let’s create some content—social posts, landing pages—out of all of the requirements that I’ve already gathered, and I’m just reusing over and over again. So it’s completely repetitive. I just don’t have that more automated repeatability where I can just push a button and say, “Go.” I still have to do the work of loading everything up into a single system, going through it piece by piece. What do I want? Am I looking at the newsletter? Am I looking at the live stream? Am I looking at this podcast? So there’s still a lot of manual that I know could be automated, and quite frankly, it’s not the best use of my time. But it’s got to get done. Christopher S. Penn: And so my question to you is, what would it look like? We’ll leave the technology aside for the moment, but what would it look like to automate that? Would that be something where you would say, “Hey, I want to log into something, push a button, and have it spit out some stuff. I approve it, and then it just…” Katie Robbert: Goes, yeah, that would be amazing. I would love to, let’s say on a Monday morning, because I’m always online early. I would love to, when I get up and I’m going through everything in the background, have something running, and I can just say, “Hey, I want two evergreen posts per asset that I can schedule for this week.” You already have all of the information. Let’s go ahead and just draft those so I can take a look. Having that stuff ready to go would be so helpful versus me having to figure out where does. It’s not all in one place right now. So that’s part of the manual process is getting the Trust Insights knowledge block, finding the right gem that has the Trust Insights tone, giving the background information on the newsletter and the background information on the podcast and so on so forth, making sure that data is up to date. As I was working through it this morning and drafting the post and the landing pages, the numbers of subscribers were wrong. That’s an easy fix, but it’s something that somebody has to know. And that’s the critical thinking part in order to update it appropriately. Those kinds of things, it all exists. It’s just a matter of getting into one place. And so when I think about automation, there’s so much within our business that gets neglected because of these—I’m not going to call them barriers—it’s just bandwidth that if I had a more automated way, I feel like I would be able to do that much more. Christopher S. Penn: So let’s think about this. There’s obviously a lot of systems, Claude Code, for example, and QWEN Code and stuff, the big heavy coding systems. But could you put all those requirements, all those basics into a folder on your desktop? Katie Robbert: Oh, absolutely. Christopher S. Penn: Okay. And if you had some help from a machine to say, “Hey, looks like you’re using our social media scheduling software, AgoraPulse. AgoraPulse has an API?” Katie Robbert: Yep. Christopher S. Penn: Would you feel comfortable saying to a machine, “AgoraPulse has an API. Here’s the URL for it. I ain’t going to read the documentation. You’re going to read the documentation and you’re going to come up with a way to talk to it.” Would you then feel comfortable just logging into, say, Claude Cowork, which came out recently and is iterating rapidly? It is becoming Claude Code for non-technical people. Katie Robbert: Yep. Christopher S. Penn: And Monday morning, say, “Hey, Claude, good morning, it’s Monday. You know what to do.” Invoke the Monday morning skill. It goes and it reads all the stuff in those folders because you’ve written out a recipe, a process, and then it says, “Here’s this week’s social posts. What do you think?” And you say, “That looks good.” And by the way, all of the images and stuff are already stored in the folders so you don’t need to go and download them every single time. This is great. “I will go push those to the AgoraPulse system.” Would that be something that you would feel comfortable using that would not involve writing Python code after the first setup? Katie Robbert: Oh, 100%. Because what I’m talking about is when we talk about evergreen content—and I’m not a social media manager, but we’re a small company and we all kind of do everything—this is content that’s not timely. It’s not to a specific. It only works for this quarter or it only works for this specific topic. Our newsletter is evergreen in the sense that we always want people subscribing to it. We always want people to go to TrustInsights.ai/Newsletter and get the newsletter every Wednesday. The topic within the newsletter changes. But posting about the fact that it’s available for people to subscribe to is the evergreen part. The same is true of the podcast, we want people to go to TrustInsights.ai/TIpodcast, or we want people to join us on our live stream every Thursday at 1:00 PM Eastern, and they can go to TrustInsights.ai/YouTube. What changes is the topic that we go through each week, but the assets themselves are available either live or on demand at those URLs at all times. I just wanted to give that clarification in case I was dating myself and people don’t still use the term evergreen content. Christopher S. Penn: Well, that makes total sense. I mean, those are the places that we want people to go. What I’m thinking about, and maybe this is something for a live stream at some point, is now that we have agentic frameworks for non-technical people, it might be worth trying to wire that up. If we think about it, of course, we’re going to use the 5Ps. What is the purpose? The purpose is to save you time and to have more things automated that really should be automated. And obviously, the performance measure of it is stop doing that thing. It’s 2 seconds on a Monday morning, or maybe 2 seconds on the first of the month. Because an agentic framework can crank out as much stuff as you have capacity for. If you buy the Claude Max plan, you can basically create 2 years worth of content all in one shot. And so it becomes People, Process, Platform. So you’re the people. The process is writing down what you want the agent to do, knowing that it can code, knowing that it can find stuff in your inbox, in your folder that you put on your desktop, knowing that it can reference knowledge blocks. And you could even turn those into skills to say, “Trust Insights Brand Voice is now a skill.” You’ll just use that skill when you’re writing. And the platform is obviously a system, like Cowork. And given how fast it’s been adopted and how many people are using it, every provider is going to have a version of this in the next quarter. They’d be stupid if they didn’t. That’s how I think you would approach this problem. But I think this is a solvable problem today, without buying anything new—because you’re already paying for it. Without creating anything new, because we’ve already got the brand voice, the style guide, the assets, the images. What would be the barrier other than free time to making this happen? Katie Robbert: I think that’s really it. It’s the free time to not only set it up, but also to do a couple of rounds of QA—quality assurance. Because, as I’ve been using the Trust Insights Brand Voice gem this morning, I’m already looking at places where I could improve upon it, places where I could inject a little more personality into it, but that takes more time, that’s more maintenance, and that just makes my list longer. And so for me, it really is time. Are the knowledge blocks where I want them to be? Do I need to? This is my own personal process. And this is why I get inundated in the weeds: I start using these tools, I see where there could be improvements or there needs to be updates. So I stop what I’m doing and I start to walk backwards and start to update all of the other things, which just becomes this monster that builds on itself. And my to-do list has suddenly gotten exponentially larger. I do feel like, again, there’s probably ways to automate that. For example, send out a skill that says, “Hey, here’s the latest information on what Trust Insights does. Update all the places that exist.” That’s a very broad stroke, but that’s the kind of stuff that if I had more automation, more support to do that, I could get myself out of the weeds. Because right now, to be completely honest, if I’m not doing it, that stuff’s not getting done. So nobody else is saying, our ideal customer profile should probably be updated for 2026. We all know it needs to be done, but guess who’s doing it? This guy with whatever limited time I have, I’m trying to carve out time to do that maintenance. And so it is 100% something I would feel comfortable handing off to automation with the caveat that I could still oversee it and make sure that things are coming out correctly so it doesn’t just black box itself and be like, “Okay, I did these 20 steps that you can no longer see, and it’s done.” And I’m like, “Well, where did it go wrong?” That’s the human intervention part that I want to make sure we don’t lose. Christopher S. Penn: Exactly. The number 1 question that people need to ask for any of these agentic tools for figuring out, “Can I do this?” is really simple: Is there an API? If there is an API, a machine can talk to a machine, which means AgoraPulse, our social media scheduling software, has an API. Our WordPress website—our WordPress itself has an API. Gravity Forms, the form management system that we have, has an API, YouTube has an API, etc. For example, in what you were just talking about, if you set up your API key in WordPress and gave it to Claude in Cowork and said, “Hey, Claude, you’re going to need to talk to my website. Here’s my API key. You write the code to talk to the website, but I want you to use your Explore agents to search the Trust Insights website for references to—I will call it dark data. Make me a list, make me a spreadsheet of all the references to dark data on a website, with column 1 being the URL and column 2 being the paragraph of text.” Then you could look at it and go, “Hey, Claude, every time we’ve said dark data prior to 2023, we meant something different. Go.” And using the WordPress API, change those posts or change those pages. This is the—I hate this term because it’s such a tech bro term, but it actually works. That is the unlock for a web, for any system: to say, is there an API that I can literally open up a system? And then as long as you trust your knowledge blocks, as long as you trust your recipe, your process, the system can go and do that very manual work. Katie Robbert: That would be amazing because you know a little bit more about my process. This morning, I was on those two systems. I was on our WordPress site, and I was on our YouTube channel. As I was drafting posts for our podcast, I went to our YouTube channel and took a screenshot of our playlist to get the topics that we’ve covered so that I could use those to update the knowledge block about the podcast, which I realized was outdated and still very focused on things like Google Analytics 4. It wasn’t really thinking about the topics we’ve been talking about in the past 6 to 12 months. I did that, and I also gave it the content from the landing page from our website about the podcast, realizing that was super out of date, but it gave enough information of, “And here’s all the places where the podcast lives that you can access it.” It was all valuable information, but it was in a few different places that I first had to bring together. And you’re saying there’s APIs for these things so that I don’t have to sit here with every other screenshot of Snagit crashing, pulling out my hair and going, “I just want to write some evergreen posts so that more people subscribe?” Christopher S. Penn: That’s exactly what I’m saying. Katie Robbert: Oh, my goodness. Christopher S. Penn: And I would say, now that I think about this, what you’re describing, you wouldn’t even need to use the API for that. Katie Robbert: Great. Christopher S. Penn: Because a lot of today’s agentic tools have the ability to say, “I can just go search the web. I can go look at your YouTube channel and see what’s on it.” And it can just browse. It will literally fire up a browser. So you can say, “I want you to go browse our YouTube channel for the last 6 months. Or, here’s the link to our podcast on Libsyn. I want you to go browse the last 25 episodes. And here’s the knowledge block in my folder on my desktop. Update it based on what you browse and call it version 2 so that we don’t overwrite the original one.” Katie Robbert: Oh, my goodness. Christopher S. Penn: Yeah, that. So this is the thing that again, when we think about AI agents and agentic AI, this is where there’s so much value. Everyone’s focused on, “I’m going to make the biggest flashes.” No. You can do the boring crap with it and save yourself so much sanity, but you have to know where to get started. And the system today that I would recommend to people as of January 2026 is Claude Cowork. Because you already installed Claude on your desktop, you tell it which folder it can work in so it’s not randomly wandering all over your computer and say, “Do these things.” And it’s no different than building an SOP. It’s just building an SOP for the junior most person on your team. Katie Robbert: Well, good news, that is my bailiwick: SOPs and process. And so, shocker, I tend to do things the exact same way every single time. That part of it: great, it needs a process done. It’s going to take me 2 seconds to write out exactly what I’m doing, how I want it done. That’s the part that I have nailed. The question I have for you, because I’ll bet this question is going up from a lot of people, is what kind of data privacy do we need to be thinking about? Because it sounds like we’re installing this third-party application on our work machines, on our laptops, and many of us keep sensitive information on our laptops—not in the cloud, not in Google Drive or SharePoint, wherever people have that shared information. Obviously, we’re saying you can only look at these things, but what is it? What do we need to be aware of? Is there a chance that these third-party systems could go rogue and be like, “Effort? I’m going to go look at everything. I’m going to look at your financials, I’m going to get your social. That photo that you have of your driver’s license that you have to upload every 3 months to keep your insurance? I’m going to grab that too.” What kind of things do we need to be aware of, and how do we protect ourselves? Christopher S. Penn: It comes down to permissions. The Anthropic’s app—I should be very clear about this—Anthropic’s app is very good about respecting permissions. It will work within the folder you tell it and it will ask you if it needs to reference a different folder: “Can I look at this folder?” It does not do it on its own. Claude Code. There is a special mode called Live Dangerously which basically says, “Claude, you can do whatever you want on my system.” It is not on by default. It cannot be turned on by default. You have to invoke it specifically. QWEN’s version is called YOLO. Cowork doesn’t even have that capability because they recognize just how stupidly dangerous that is. If you are working on very sensitive data, obviously the recommendation there would be to use it in a different profile on your computer. If your Windows machine or your Mac can have different profiles, you might have an AI only profile that will have completely different directories. You won’t even be able to see your main user’s. And then if you’re really, really concerned about privacy, then I would not use a cloud-based provider at all. I would use a system like QWEN Code, which does not have telemetry to relay back to anybody what you’re doing other than actions you take, like you turned it on, you turned it off, etc. And you can download QWEN Code source and modify it to turn all the telemetry off if you want to, or just delete it out of the code base and then use a local model that has no connection to the Internet if you’re working on the most sensitive data. Katie Robbert: Got it. I think that’s incredibly helpful because you and I, we’re very aware of data privacy and what sensitive data and protected data entails. But when I think about the average marketer—and it’s not to say that they don’t care, they do care—but it’s not top of mind because they’re just underwater trying to find any life raft to get out of the weeds and be like, “Okay, great, this is a great solution, I’m going to go ahead and stand it up.” And data privacy tends to be an afterthought after these systems have already accessed all of your stuff. Again, it’s not that people using them don’t care, it’s just not something that they’re thinking about because we make big assumptions that these tech companies are building things to only do what they’re saying they do. And we’ve been around long enough to know that they’re trying to get all. Christopher S. Penn: Our data exactly. The where the biggest leak for the casual user is going to be is in the web search capabilities. Because we’ve done demos on our live streams and things in the past of watching the tools do web search. If you do not provide it a secure form of web search, it will just use regular web search, and then all that stuff can be tracked back to your IP, etc. So there are ways to protect against that, and that’s a topic for another time. Katie Robbert: All right, go ahead. Christopher S. Penn: I think the next steps we should be doing is let’s get Claude Cowork set up maybe on a live stream and get the knowledge blocks without them being updated and say, “Let’s do this as a first test. Let’s try to update these knowledge blocks using web search tools and see what Claude Cowork can do for you.” Katie Robbert: I was going to suggest the exact same thing because if you’re not aware, every week, every Thursday at 1:00 PM Eastern, we have our live stream, which you can catch at TrustInsights.ai/YouTube. And we walk through these very practical things, very much a how-to. And so I love the idea of using our live stream to set up Claude Cowork. Is that what it’s called? Christopher S. Penn: That’s what it’s called, yes. Katie Robbert: Because I feel like it’s easy for you and I to talk about theoretically, “Here’s all the stuff you should do,” but people are craving the, “Can you just show me?” And that’s what we can do on the live stream, which is what I was trying to write for social posts, full circle. “Here’s the podcast, it introduces the idea. Here’s the live stream, it’s the how-to. Here’s the newsletter. It’s the big overarching theme.” I was trying to write social posts to do all of those things, and my gosh, if I just had an agent to do it for me, I could have done other things this morning because I’ve been working on that for about 2 hours. Christopher S. Penn: Yep. So the good news is once we do this, and once you start using this, you never do that again. That’s always the goal of automation. You solve the problem algorithmically and then you never solve it again. So that’ll be this week’s live stream. Katie Robbert: Yes. Christopher S. Penn: If you’ve got some thoughts about how you’re using AI agents to take care of mundane tasks, pop on by our free Slack. Go to TrustInsights.ai/analyticsformarketers, where you and over 4,500 other marketers are asking and answering each other’s questions every single week. And wherever it is that you watch or listen to the show, if there’s a channel you’d rather have it on, go to TrustInsights.ai/TIpodcast. You can find us at all the places where podcasts are served. Thanks for tuning in and we’ll talk to you on the next one. Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable Insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting. This encompasses emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the *In-Ear Insights* podcast, the *Inbox Insights* newsletter, the *So What?* live stream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations: Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of Generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

In this episode, we welcome Joe Eskenazi, CRO at Kong, to discuss the critical transition from an elite salesperson to a top-tier business leader. Joe shares how he bypassed the typical "salesperson" label by treating every interaction as a business consultancy, fueled by a concurrent MBA and an early career in sports broadcasting. We dive deep into the reality of the CRO role—orchestrating cross-functional ecosystems rather than just closing deals—and the personal journey of managing high-intensity burnout. Joe also offers powerful advice on finding an authentic leadership voice and why organizations must prioritize leadership training to protect their talent.

What does sales leadership actually look like once the AI experimentation phase is over and real results are the only thing that matters? In this episode of Tech Talks Daily, I sit down with Jason Ambrose, CEO of the Iconiq backed AI data platform People.ai, to unpack why the era of pilots, proofs of concept, and AI theater is fading fast. Jason brings a grounded view from the front lines of enterprise sales, where leaders are no longer impressed by clever demos. They want measurable outcomes, better forecasts, and fewer hours lost to CRM busywork. This conversation goes straight to the tension many organizations are feeling right now, the gap between AI potential and AI performance. We talk openly about why sales teams are drowning in activity data yet still starved of answers. Emails, meetings, call transcripts, dashboards, and dashboards about dashboards have created fatigue rather than clarity. Jason explains how turning raw activity into crisp, trusted answers changes how sellers operate day to day, pulling them back into customer conversations instead of internal reporting loops. The discussion challenges the long held assumption that better selling comes from more fields, more workflows, and more dashboards, arguing instead that AI should absorb the complexity so humans can focus on judgment, timing, and relationships. The conversation also explores how tools like ChatGPT and Claude are quietly dismantling the walls enterprise software spent years building. Sales leaders increasingly want answers delivered in natural language rather than another system to log into, and Jason shares why this shift is creating tension for legacy platforms built around walled gardens and locked down APIs.  We look at what this means for architecture decisions, why openness is becoming a strategic advantage, and how customers are rethinking who they trust to sit at the center of their agentic strategies. Drawing on work with companies such as AMD, Verizon, NVIDIA, and Okta, Jason shares what top performing revenue organizations have in common. Rather than chasing sameness, scripts, and averages, they lean into curiosity, variation, and context. They look for where growth behaves differently by market, segment, or product, and they use AI to surface those differences instead of flattening them away. It is a subtle shift, but one with big implications for how sales teams compete. We also look ahead to 2026 and beyond, including how pricing models may evolve as token consumption becomes a unit of value rather than seats or licenses. Jason explains why this shift could catch enterprises off guard, what governance will matter, and why AI costs may soon feel as visible as cloud spend did a decade ago. The episode closes with a thoughtful challenge to one of the biggest myths in the industry, the belief that selling itself can be fully automated, and why the last mile of persuasion, trust, and judgment remains deeply human. If you are responsible for revenue, sales operations, or AI strategy, this episode offers a clear-eyed look at what changes when AI stops being an experiment and starts being held accountable, so what assumptions about sales and AI are you still holding onto, and are they helping or quietly holding you back? Useful Links Follow Jason Ambrose on LinkedIn Learn more about people.ai Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.

Recorded live at MBA Annual25 in Las Vegas, this special edition of the Equifax Market Pulse explores how data, workflow automation, and AI are reshaping mortgage lending. Tanja Cleve, SVP of Solution Sales at Equifax sits down with Craig Rebmann, Product Evangelist at Dark Matter Technologies, to discuss capturing data earlier in the process, automating complex borrower scenarios, managing costs in tight margin environments, and preparing lenders for the next market turn through smarter technology investments.In this episode:What is the biggest operational challenge mortgage lenders are facing right now?Beyond rates and affordability, lenders are grappling with process inefficiencies, higher fallout rates, and rising costs. This makes automation and better data workflows essential.How does capturing data earlier in the loan process help lenders?Early data capture allows lenders to assess risk sooner, automate pre-approvals, reduce downstream surprises, and create more productive borrower conversations upfront.How can automation support complex borrower profiles like self-employed income?Automation helps identify complexity early and uses tax and income data to streamline calculations, reducing manual review and improving underwriting readiness.How are lenders balancing innovation with cost control in a tight market?Many are focusing on capacity management, which used technology to increase efficiency with existing staff while remaining scalable as volumes return.What role does AI play in today's mortgage technology stack?AI is increasingly used to gather and prepare information, while humans remain essential for judgment, decision-making, and borrower communication.What is “agentic AI” and why does it matter for lenders?Agentic AI refers to systems that can take action—not just provide insights—while still operating within defined workflows and human oversight.How do integrations and APIs improve borrower experience?Connected systems allow data to flow in real time, trigger automations instantly, reduce back-and-forth, and give borrowers greater transparency throughout the process.

In this episode of Tech Talks Daily, I sit down with Imran Nino Eškić and Boštjan Kirm from HyperBUNKER to unpack a problem many organisations only discover in their darkest hour. Backups are supposed to be the safety net, yet in real ransomware incidents, they are often the first thing attackers dismantle. Speaking with two people who cut their teeth in data recovery labs across 50,000 real cases gave me a very different perspective on what resilience actually looks like. They explain why so many so-called "air-gapped" or "immutable" backups still depend on identities, APIs, and network pathways that can be abused. We talk through how modern attackers patiently map environments for weeks before neutralising recovery systems, and why that shift makes true physical isolation more relevant than ever. What struck me most was how calmly they described failure scenarios that would keep most leaders awake at night. The heart of the conversation centres on HyperBUNKER's offline vault and its spaceship-style double airlock design. Data enters through a one-way hardware channel, the network door closes, and only then is information moved into a completely cold vault with no address, no credentials, and no remote access. I also reflect on seeing the black box in person at the IT Press Tour in Athens and why it feels less like a gadget and more like a last-resort lifeline. We finish by talking about how businesses should decide what truly belongs in that protected 10 percent of data, and why this is as much a leadership decision as an IT one. If everything vanished tomorrow, what would your company need to breathe again, and would it actually survive?   Useful LInks Connect with Imran Nino Eškić Connect With Boštjan Kirm Learn More about HyperBUNKER Lear more about the IT Press Tour Thanks to our sponsors, Alcor, for supporting the show.

Are you an AI skeptic or an enthusiast? Ethan and Drew sit down with Igor Tarasenko, Senior Director of Product Software Architecture and Engineering at Equinix, to break down the reality of AI in the network. In this sponsored episode, Tarasenko discusses why APIs are the new CLI, the critical need for observability in AI,... Read more »

Are you an AI skeptic or an enthusiast? Ethan and Drew sit down with Igor Tarasenko, Senior Director of Product Software Architecture and Engineering at Equinix, to break down the reality of AI in the network. In this sponsored episode, Tarasenko discusses why APIs are the new CLI, the critical need for observability in AI,... Read more »

Are you an AI skeptic or an enthusiast? Ethan and Drew sit down with Igor Tarasenko, Senior Director of Product Software Architecture and Engineering at Equinix, to break down the reality of AI in the network. In this sponsored episode, Tarasenko discusses why APIs are the new CLI, the critical need for observability in AI,... Read more »

Podcast SEO and monetization strategies tailored for local businesses is today's episode discussion. Favour Obasi-ike emphasizes the importance of metadata, noting that elements like podcast titles, descriptions, and author names serve as critical search signals for discovery.By treats these fields as structured data, creators can establish local authority and ensure their content surfaces in specific user queries across platforms like Spotify and Apple Podcasts.The source further highlights the compounding value of backlinking, explaining how consistent episode releases create a vast network of searchable links that drive traffic back to a brand's website. Ultimately, the text argues that a well-optimized podcast acts as a long-term intellectual property asset that builds credibility and solves audience problems through searchable, evergreen audio content.In the 2026 search ecosystem, local visibility is no longer a matter of chance; it is a matter of engineering. This episode serves as a strategic blueprint for local businesses to command "page dominance" by transforming audio content into a high-authority digital asset. By deploying a "spread map" strategy—scaling influence from local roots to international authority—business owners can ensure their brand is the definitive answer to specific consumer queries.The objective is to move beyond the "hobbyist" mindset and treat podcasting as a capital-efficient SEO machine. We explore how to build an "engine" that runs independently via technical metadata and RSS syndication, allowing your brand to reside permanently in the search database.Key Takeaways for Local Business Owners1. Metadata is Your Search ID: Your title, author field, and description must match the exact phrases your customers use. If your "ID" doesn't match the search query, the algorithm cannot process your "legal documents," and your business remains invisible.2. Exploit the 50x50 Rule: Syndication is a volume game. By appearing on 50 platforms, you create thousands of high-authority backlinks. This sheer volume of structured data makes your brand unavoidable in local searches.3. Implementation over Information: ROI is the result of action, not note-taking. Podcasting is a long-term index fund for your brand; the earlier you start the "audio documentation," the more interest your digital legacy accrues. Move from "doer" to "architect" today.Need to Book An Appointment?>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book a Complimentary SEO Discovery Call with Favour Obasi-Ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats OnlinePodcast Timestamps[00:00:00] – The Spread Map: Establishing the strategic journey from local business to international brand authority.[00:03:00] – Statistical Authority: Reviewing personal benchmarks (600 episodes, 156 countries) as a model for growth.[00:06:00] – The Harry Potter Paradox: Why naming your show for the "benefit" is the only way to be found before you are famous.[00:10:00] – The Psychology of Blue Links: Why "Blue Links" signify trust and confidence in the search results.[00:14:00] – Spotify Signal Case Study: Using the phrase "workout habits for men over 40" to identify exact-match search signals.[00:22:00] – Compounding Link Math: The 50x50 breakdown of how to generate 2,500 links across platforms like SiriusXM and iHeart.[00:31:00] – The Celese Interaction: Overcoming ADHD and task-paralysis by choosing documentation over perfection.[00:45:00] – The Legacy Challenge: Transitioning from a task-based worker to a legacy-based brand architect.The Mathematics of Syndication & The "Compounding Effect"Strategic dominance is a function of Depth and Cadence. While frequency is important, "Depth" is determined by your average episode length. A 60-minute episode provides sixty times more data points for an algorithm to index than a one-minute clip.The true ROI of podcasting is found in the Compounding Link Formula:50 Episodes (One year of weekly audio documentation) x 50 Distribution Platforms (Apple, Spotify, SiriusXM, Podchaser, Castbox, iHeart, etc.) = 2,500 High-Authority BacklinksThis volume creates a "digital balloon that never pops." As you add more helium (content), the structure becomes stiffer and more secure. To maximize this, maintain a Cadence (release cycle) closer to "1" (daily). A faster cadence spins the RSS feed more frequently, signaling to search engines that your brand is an active, relevant authority.The following 15 monetization levers are the tactical parameters required to convert conversational documentation into long-term ROI and a lasting digital legacy.Episode Breakdown on the 15 Monetization StrategiesPART 1: CORE DISCOVERY METADATA (Your Digital ID Card)1. Podcast TitleExecution: Match the show name to the specific topic or core benefit your audience seeks.So What? Listeners search for solutions and interests, not your name. A descriptive title ensures discoverability in search before you have a famous brand.2. Podcast DescriptionExecution: Exploit the full ~4,000-character limit as a "Search Bank." Use refined keywords, clear value propositions, and a strong call-to-action.So What? This is your show's primary Search ID. If it doesn't match user queries, algorithms can't "read" or rank your content effectively.3. Author/Host FieldExecution: Strategically expand your name with professional identifiers (e.g., "Alex Chen | Venture Capital Analyst").So What? This data feeds APIs and LLMs, establishing your niche authority within recommendation systems and digital assistants.4. Genre & Category SelectionExecution: Use platform hierarchies (e.g., ListenNotes, Apple) to select precise Primary, Secondary, and Tertiary categories.So What? Correct categorization moves you from competing with millions of general shows to dominating a specific, interested listener ecosystem.5. Episode TitleExecution: Adopt a clear, "Guest-First" or "Topic-First" naming convention (e.g., "Dr. Sarah Lee: The Neuroscience of Sleep").So What? It maximizes clarity for listeners and SEO. A guest's name at the front captures their audience and amplifies "link juice" to that episode URL.6. Episode DescriptionExecution: Implement web-style formatting: use H2/H3 headers, bullet points, timestamps, and hyperlinks to key resources.So What? Structured data helps both listeners scan and bots "dissect" your content, boosting engagement metrics and canonical linking power.PART 2: VISUAL & TECHNICAL EXECUTION7. Podcast Cover ArtExecution: Command professionalism with compliant, 3000 x 3000 pixels, visually simple art that is legible at thumbnail size.So What? High-quality, optimized art provides an immediate competitive edge against the significant portion of shows using amateur visuals.8. Episode Cover Art (Optional but Powerful)Execution: For key interviews, create guest-centric visuals that differ from your main show art.So What? Visual differentiation in a subscriber's feed signals unique, fresh value, increasing click-through rates for specific high-interest topics.9. Ad Roll PlacementsExecution: Strategically engineer ad breaks: pre-roll (for direct response), mid-roll (for highest attention), post-roll (for brand storytelling).So What? These are primary monetization vehicles. Placement affects listener retention and ad performance by capturing attention at different psychological stages.10. RSS Feed ManagementExecution: Balance your public RSS feed with private, gated feeds (via platforms like Hello Audio or Supercast) for bonus or premium content.So What? Private feeds enable direct community monetization and foster loyalty by delivering exclusive, "trust-based" content to high-value subscribers.PART 3: DISTRIBUTION & AMPLIFICATION11. Email & Affiliate LeverageExecution: Use automated tools to turn podcast transcripts into newsletter content that drives traffic to affiliate offers or key resources.So What? This captures high-intent listeners where they live (their inbox), converting passive listening into measurable action.12. Social Media DistributionExecution: Systematically cross-post short, thematic audio clips (with captions and video) to platforms like LinkedIn and Instagram.So What? It transforms one hour of recording into weeks of "top-of-funnel" awareness, building connection volume and attracting new audiences.13. Backlink GenerationExecution: Understand that every major hosting platform (Spotify, Apple) creates a backlink to your website from your show profile.So What? This generates vital "link juice" from high-authority domains, strengthening your primary website's search engine ranking.14. Website Integration & AnalyticsExecution: Host a dedicated podcast page on your site and connect it to Google Search Console.So What? This allows you to track how people find and interact with your podcast via search, providing data to refine your topic and keyword strategy.15. Sonic Branding (Musical Intelligence)Execution: Deploy a distinct instrumental theme for each season or series.So What? A fresh sonic identity signals a new "era" or focus for your show, boosting production value and maintaining listener retention through auditory novelty.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI, AI, and more AI. Do you even live in Silicon Valley if you're not talking about it every episode? This week, we go deep on how open-source vibe-coding tools are starting to replace the need for traditional SaaS contracts. Dave shows (and tells) how he used the open-source “Claude bot” to reverse-engineer his Mural photo frames and spin up a better web UI in under 30 minutes. Brit test-drives Anthropic's new Cowork, auto-mapping the entire seed VC market while it runs her browser, and celebrates how much these agents are boosting household productivity. Sam loves the power but calls local agents a massive security backdoor, argues trust will consolidate with Apple and Google, declares that “software is not a business,” and announces we've officially entered the fart-app era of AI toys. Jessica flags rising panic among SaaS vendors. Don't miss Sam's hot-chick analogy and Brit's Pop Corner to close it out

Okay. This show today is part of our Relentless Health Value "The Inches Are All Around Us" series. This Inches Talk is a metaphor for finding all those little places where there is healthcare waste as a first step in an effort to excise all these little pockets of waste. For a full transcript of this episode, click here. If you enjoy this podcast, be sure to subscribe to the free weekly newsletter to be a member of the Relentless Tribe. Shane Cerone said this phrase during episode 492, and I loved it because there are inches all around us for sure. And the thing with all these inches that we're gonna talk about today and last week and next week and the week after that, yeah, these are inches that actually you could cut them. And there are millions and billions of dollars, and you actually improve patient care. You improve clinical team experience. Also, you're cutting out friction and making it easier to do the right thing to care for patients. These are no-brainer kinds of stuff if your North Star is better and more affordable patient care, but they are also somebody else's bread and butter in a "one person's cost is another person's revenue" kind of way. So, yeah … what makes perfect common sense might not be as easy as it might look on paper, as we all know so well. So, last week we dug into all of the inches of expensive friction that develop when stakeholders interact—like, a clinical organization and a payer and a plan sponsor, self-insured employer. They try to get paid or pay. They try to direct contract because what will be found fast enough is that the data is not the data is not the data, as Mark Newman talked about last week (EP496); and a dollar is not a dollar is not a dollar. Again, you'll find this out fast enough. All of you know when you talk to entities up and down the patient journey or across the life of a claim, otherwise known as a healthcare transaction. It's mayhem to get a claim paid often enough. Each stakeholder comes in with their own priorities and views and accounting methods and various rollups. I like how Stephanie Hartline put it. She wrote, "Healthcare … moves through many hands without a rail that preserves truth along the way. Attribution breaks, and truth gets reassembled later. The difference isn't capability—it's infrastructure. Line-item billing ≠ line-item settlement." Or I also like how Chris Erwin put it. He wrote, "When the blueprint isn't standardized, you aren't scaling. You're just compounding chaos." And yeah, then all of a sudden when there's no through line, there's no rail that connects all the data to the data to the data, or all the dollars to the dollars to the dollars. Suddenly 30% of any given healthcare transaction goes to trying to straighten it all back out again—to reassemble it, as Stephanie said. It's like unleashing 100 chaos monkeys and then having to pay to recapture them all. Listen to the show with David Scheinker, PhD (EP363) from last year about "Hey, how about we all just use the same template and avoid a lot of this." Or read Zeke Emanuel's book about how the USA should potentially consider copying the Netherlands model because they have private insurance. But they cut admin costs 75% or something like that. Oh, right … through standardization. Jesse Hendon summarized this the other day. He wrote, "Providers don't need armies of coders to fight 50 different insurance rule books [when you have some standardization here]." I say all this to say after recording the episode with Mark Newman from last week, I have become intently fascinated by what goes on in this non-standardized or otherwise friction points between stakeholders. There are a lot of inches in this gray area land of confusion.   This show today digs into one of them, which is what does it take to process a claim? Just technically. What are the pipes involved to submit a claim and, again, get paid for it, which is a healthcare transaction—just simply the technology moving the data around—even if everything in the pipes is a non-standardized hot mess. Because just fixing up the processing and the pipes here—again, while this doesn't solve the entire data isn't a data isn't a data or a dollar isn't a dollar isn't a dollar problem—if we can just cut out some of the processing and the moving the data around costs, just this all by itself is $6 billion a year worth of inches. Plus, as an added bonus, fix up the pipes for better data flow and now patient care can be faster if, for example, the prior auth or etc. processes transpire faster. And clearinghouses have entered the chat. But you know, when clearinghouses come up, at least in my world, when the clearinghouse word gets dropped, it's usually accompanied by like a puff of smoke because no one is quite sure what those guys do all day. So, we all sort of look at each other in the conversation and move on. Lucky for me and possibly you if I've managed to suck you into my web of intrigue, I ran into Zack Kanter from Stedi, a new clearinghouse, who agreed to come on the pod here and aid my exploration into this demarcation zone between stakeholders. So, let's start here. What is a clearinghouse? Well, a clearinghouse is the same thing as a switch when we're talking about pharmacy data transfers, if you're familiar with that terminology and that's helpful. But either way, in the conversation with Zack Kanter that follows, Zack will explain this better; but clearinghouses are like a hub, maybe, that connects all the payers with all the providers. So, if you want an eligibility check or you wanna submit a claim or do a prior auth of the payer, whatever you're trying to do, get paid, you as an EHR system or a doctor's office or an RCM (revenue cycle management) company, you don't have to set up your own personal data connection with every single payer out there. You don't have to go through all the authentications and the BAAs (Business Associate Agreements) and map all the fields and set up the 100 SOC 2–compliant APIs (application programming interfaces). Instead, you can hook up to one clearinghouse, and then that clearinghouse connects with everybody else. So, most medical claims transactions have a clearinghouse in the middle, like an old-timey telephone operator routing your claim or denial or approval of that claim or eligibility check or whatever to the right place. And unfortunately, old-timey telephone operator is a pretty apt metaphor, depending on which clearinghouse you're using. Anyway, Zack Kanter told me that the price to just send and receive an electronic little piece of data in healthcare through a clearinghouse costs about 1,000 times more than any other industry would pay. Like, if you do an eligibility check, that's gonna cost 10 to 15 cents per. The trucking industry pays that much for 1,000 such data transfers. They would riot if someone asked them to spend a dollar for 10 data transfers. That'd be ridiculous in their eyes. But in healthcare, all these dimes add up to, again, $6 billion a year—them's some inches there—which also equal delays in payment and patient care. Now you might be thinking, "Oh, well, maybe it costs this much because healthcare is so much more complicated than trucking or whatever." Well, turns out the opposite is true: Because of HIPAA, ironically enough, healthcare is, in fact, much more standardized (we were talking about standardization before); but healthcare is actually much more standardized than many other industries due to HIPAA's administrative simplification rules, which mandate a universal language for transactions—the pipes I'm talking about now. So, actually, for as much as I was just kvetching about chaos monkeys, compared to other industries, the baseline construct here is actually much more orderly than, for example, the trucking industry or whatever, like Amazon or Walmart has to deal with with their millions of vendors. Now—and here's a really big point, especially for self-insured employers—you know who the main customer is for a lot of the more programmatic, the newer kinds of clearinghouses? I'll tell you: newer digital entities who do RCM (revenue cycle management) for provider organizations, and that can be great if you're a practice just trying to keep up with payer denials and expedite patient care. But look, all you plan sponsors and self-assured employers and maybe unions out there, the more RCM purveyors start working with programmatic clearinghouses, the more you not doing programmatic prepayment integrity programs with unconflicted third-party prepayment integrity vendors who are as hooked into the data streams and the clearinghouses as the RCM vendors are, the more, as I said last week, increasingly you're bringing an ever more rusty knife to a gunfight. So, that is certainly something to consider. There's a whole episode next week about this with Mark Noel from ClaimInsight. Or if you just can't wait, go back and listen to the show with Kimberly Carleson (EP480) just for the gist of it, or the one with Dawn Cornelis (EP285) from a few years ago. They're talking post-payment integrity programs, but a lot of the same rules apply. The show today is sponsored by Aventria Health Group, as usual. But I do want to say that we got some very appreciated financial support from Stedi, the only programmable healthcare clearinghouse. And here is my conversation about all of the inches that are all around us, specifically in the healthcare data pipes, with Zack Kanter, who is the CEO and founder over at Stedi. Also mentioned in this episode are Stedi; Shane Cerone; Mark Newman; Stephanie Hartline; Chris Erwin; David Scheinker, PhD; Zeke Emanuel, MD, PhD; Jesse Hendon; Mark Noel; ClaimInsight; Kimberly Carleson; Dawn Cornelis; Aventria Health Group; Preston Alexander; Eric Bricker, MD; and Kada Health. For a list of healthcare industry acronyms and terms that may be unfamiliar to you, click here. You can learn more at stedi.com. You can also follow Zack and Stedi on LinkedIn.   Zack Kanter is the founder and CEO of Stedi, the only programmable healthcare clearinghouse. Stedi has raised $92 million from Stripe, Addition, First Round, USV, Bloomberg Beta, and other top investors. He has previously appeared on podcasts, including In Depth by First Round Capital, Invest Like the Best, Village Global, and Rule Breaker Investing.   09:47 What things are being paid for that we might not be aware we're paying for in healthcare? 12:09 Why HIPAA actually makes healthcare more standardized than other industries. 15:35 How healthcare is ahead in some ways and behind in others. 18:03 Where do the 4 to 5 days come from in healthcare transaction processing? 20:39 Why these transaction delays affect care delay. 23:14 EP482 with Preston Alexander. 23:18 EP472 with Eric Bricker, MD. 27:10 How should the process work from the time a provider clicks "validate"? 30:19 Why is the clearinghouse the right place to solve all these issues? 31:41 Why are we where we are in terms of these issues? 35:28 Why people should be looking at their clearinghouse costs. 36:59 What to know about Stedi.   You can learn more at stedi.com. You can also follow Zack and Stedi on LinkedIn.   @zackkanter discusses #healthcaretransactions and #clearinghouses on our #healthcarepodcast. #healthcare #podcast #financialhealth #patientoutcomes #primarycare #digitalhealth #healthcareleadership #healthcaretransformation #healthcareinnovation   Recent past interviews: Click a guest's name for their latest RHV episode! Mark Newman, Stacey Richter (INBW45), Stacey Richter (INBW44), Marilyn Bartlett (Encore! EP450), Dr Mick Connors, Sarah Emond (EP494), Sarah Emond (Bonus Episode), Stacey Richter (INBW43), Olivia Ross (Take Two: EP240)

Change how you look at unsold inventory in this episode with Amrita Bhasin of Sotira, joining the show to break down how poor inventory forecasting is crushing CPG brands, why nearly a quarter of all retail and e-commerce inventory never sells, and how excess inventory liquidation has become one of the biggest supply chain challenges today! We dive deeper into how Sotira is using AI to power a tech-driven reverse logistics marketplace that connects sellers, buyers, and donation partners while protecting brand equity, enforcing expiration and regional compliance laws, and improving recovery rates, how integrated freight optimization APIs help control transportation costs, why mismanaged forecasting leads to millions in deadstock, and how smarter liquidation strategies can reduce waste, unlock tax benefits, and keep inventory moving.   About Amrita Bhasin Amrita Bhasin is the co-founder and CEO of Sotira, an award winning reverse logistics company that enables retailers, manufacturers and brands to discreetly monetize and donate unsold inventory.  Amrita was named to the 2026 Forbes 30 under 30 list and the 2025 Mayfield AI List. Amrita has been invited to speak on national and international broadcast networks including CBS, Fox, ABC, Scripps, and CGTN and has been profiled in Forbes, TechCrunch, and Business Insider. She is regularly quoted as an expert by leading publications such as Reuters, Bloomberg, Wired, Fortune, CNBC, Glossy, Huffington Post, Sourcing Journal, Reader's Digest, Modern Retail, AP, Yahoo Finance, and FreightWaves. Amrita has spoken about reverse logistics at leading conferences and trade shows such as TechCrunch Disrupt 2024, Home Delivery World 2025, HumanX 2025, ReTHINK Retail 2025 and Groceryshop 2025. Amrita was a delegate speaker at the 2025 One Young World Summit in Munich, Germany. She is an upcoming speaker at Manifest 2026 and Food Waste Summit 2026.  Amrita was a 1st place winner at Shoptalk 2025 and 1st place winner at Reverse Logistics Conference and Expo 2025. Amrita has been recognized by the State of California and Stop Waste for contributions to reducing enterprise waste via reverse logistics automation.   Connect with Amrita LinkedIn: https://www.linkedin.com/in/amrita-bhasin/  Website: https://www.sotira.co/  Email: amrita@sotira.co  

After three decades working to deliver easy, fast and cost-effective patient experiences through technology, Ryan Howells is more optimistic about the future than he's ever been before.At a time when healthcare has been at the center of polarizing and partisan politics, Ryan is focused on an area foundational to digital health that he says draws consensus across party lines: data exchange and interoperability. Freely moving data can unlock innovation in technology, payment models, and regulation to make healthcare work better for everyone, and Ryan is extremely encouraged by the openness to ideas and volume of activity he's seeing from the second Trump Administration in these areas.As Principal at Leavitt Partners since 2015, Ryan collaborates with the private sector, the White House, Congress, HHS, and the VHA to improve health care nationwide. For the past ten years, he has also led the CARIN Alliance, a bi-partisan, multi-sector alliance uniting industry leaders to advance the adoption of consumer-directed exchange across the U.S.In January 2023, Ryan joined Keith Figlioli on the podcast to discuss the myriad of new possibilities emerging in healthcare as a result of better access to data. In this episode, he recounts the progress and obstacles since that conversation, but more importantly, helps unpack the flurry of new activity.Topics Ryan and Keith covered include:ACCESS & TEMPO. These are the latest examples of two new government programs that Ryan believes will remove barriers to innovation. ACCESS is a CMS initiative that now makes it possible for technology companies to bill Medicare directly for digital health services – and get paid only when patients achieve specific, measurable clinical outcomes. Ryan explains how ACCESS is a breakthrough for transparency and has the potential to change contracting for digital health vendors as health system may now ask to share risk. TEMPO is a program from the FDA that complements ACCESS by allowing participating companies to bypass traditional device clearance processes through “enforcement discretion,” provided they share real time data with the FDA. Ryan explains how this oversight lowers cost and complexity for startups and accelerates the path to market for new digital health solutions.Removing administrative roadblocks. In early 2025, Ryan's team at Leavitt Partners published a paper titled, “Kill the Clipboard” that offered recommendations to cut administrative costs, lower the burden on consumers and providers, and modernize the health care data exchange ecosystem. Ryan discussed recommendations like the need for stronger enforcement of information blocking rules and suggestions for the government to change its certification program to focus on APIs, versus functionality of EHRs. He explained how these things would allow health systems to control their own data, build cloud-based workflows, and integrate with payers and innovative companies more easily.Linchpins for data liquidity. Ryan believes that achieving true data liquidity in healthcare requires three foundational elements: a cloud-based data store, an API endpoint, and robust digital identity credentials. With these in place, he says organizations can exchange data securely and efficiently, supporting everything from public health to quality measurement and pharmacy exchange. He says these are the linchpins to finally achieve the data liquidity needed for innovation, interoperability, and improved patient outcomes.To hear Ryan and Keith discuss these topics and more, listen to this episode of Healthcare is Hard: A Podcast for

#395 - Sponsor Spotlight - RedblockThis episode is sponsored by Redblock. Visit redblock.ai/idac to learn more.Jeff and Jim come to you live from the Gartner IAM Summit in Grapevine, Texas, for a special Sponsor Spotlight with Redblock. They sit down with CEO Indus Khaitan to discuss how Redblock uses AI and computer vision to solve the "last mile" problem in identity management: disconnected applications.Indus explains how Redblock acts as an "agentic" layer, using screen recordings to learn administrative tasks for apps that lack APIs. The conversation covers the origin of the company name, the urgency of securing the "long tail" of applications, and how they build trust and guardrails around AI execution. They also discuss the "DoorDash" analogy for identity fulfillment and wrap up with a fun chat about Indus's passion for flying planes.Connect with Indus: https://www.linkedin.com/in/khaitan/Learn more: redblock.ai/idacConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at [idacpodcast.com](http://idacpodcast.com)Timestamps00:00 Introduction from Gartner IAM Summit00:46 Guest Introduction: Indus Khaitan of Redblock01:40 Indus's Journey into Identity02:41 The Origin of the Name "Redblock"04:20 The Underserved Market: Services vs. Software07:34 The Urgency of Securing Disconnected Apps09:19 Why Traditional IGA and PAM Aren't Enough11:35 The DoorDash Analogy: Where Redblock Fits14:30 What Makes Redblock Unique? (Agentic Process Automation)16:15 Trusting AI with Security Tasks18:50 Onboarding Apps via Video Recording21:23 Deployment: Running Air-Gapped on Customer Cloud22:17 Handling UI Changes and "Full Self-Driving" Analogy25:40 Integration with SailPoint and Governance Tools27:13 Speed of Integration: Days vs. Years32:00 How the "Headless Browser" Works33:35 Limitations: Web Apps vs. Thick Clients36:58 Redblock's 2025 Milestones and Future Outlook39:48 Call to Action: Solving Disconnected Apps40:27 Impressions of the Gartner IAM Summit44:26 Are We in an AI Bubble?46:46 Indus's Hobby: Flying PlanesKeywordsIDAC, Identity at the Center, Jeff Steadman, Jim McDonald, Redblock, Indus Khaitan, AI, Artificial Intelligence, IAM, Identity and Access Management, Disconnected Apps, Agentic AI, Computer Vision, Gartner IAM Summit, RPA, IGA, Cybersecurity

In this episode of The Digital Executive, host Brian Thomas speaks with Andrew Harrison-Chinn, Chief Marketing Officer at Dragonpass, about how technology is reshaping the modern travel experience. Drawing on his end-to-end leadership journey as CEO, Global Managing Director, and now CMO, Andrew shares insights on building brand trust, scaling globally, and listening deeply to customers as a catalyst for innovation. The conversation explores key friction points in travel—such as fragmentation and lack of transparency—and how digital platforms, APIs, and data can simplify complexity and improve access to benefits. Andrew also discusses the evolving economics of loyalty, where personalization, comfort, and reliable customer support now matter more than points or discounts, and outlines how data, digital identity, and seamless access will define the future of passenger experiences.If you liked what you heard today, please leave us a review - Apple or Spotify. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Cash flow used to mean waiting and worrying. Today, it can mean deciding and doing. We sit down with Ana Garcia of JPMorgan Payments and James Chi of Block to unpack how real-time payments are reshaping the small business playbook - turning every sale into instantly usable working capital and replacing uncertainty with visibility you can act on.Ana pulls back the curtain on how instant payments actually work: APIs or portals trigger transactions, banks authenticate and screen for fraud and compliance, networks like The Clearing House RTP clear and settle, and funds land in seconds with instant confirmation. James maps those capabilities to real merchant needs - Square's instant transfers to linked accounts, immediate spend via Square Checking, and faster marketplace payouts for merchants - showing how speed enables on-time payroll, proactive inventory management, and smoother refunds.We also get real about adoption. Many owners still don't know they can move money this fast, so education in context is key - surfacing instant options exactly when cash is tight. On safety, both leaders emphasize layered defenses: identity checks, behavioral analytics, transaction limits, and step-up authentication, proving you don't need to trade security for speed. Looking forward, we explore request for payment for cleaner collections, the march toward near-universal bank coverage, and the promise of cross-border instant payments that could redefine supplier and marketplace flows. If you care about liquidity, predictability, and customer trust, this conversation shows how real-time payments turn pressure into momentum.This episode is part of our special series on The Future of Modern Payments sponsored by The Clearing House.

In this episode, I sit down with Daniel Marin, co‑founder of Nexus.xyz, the next‑generation Layer‑1 blockchain built for financial applications. We dig into why the future of blockchains may not be general purpose, but specialized and verifiable. Daniel breaks down how Nexus uses CK proofs, dual‑core architecture, and native APIs to bring Web‑2 finance experiences on‑chain. We talk about algorithmic trading, prediction markets, sustainable revenue models, ecosystem incentives, and what the market needs to scale in 2026 and beyond. If you're curious about where blockchain infrastructure and financial products are headed, this is a must‑listen.00:01:30 – Daniel's path into crypto and Nexus's origin.00:02:45 – What verifiable finance really means for a Layer‑1.00:04:00 – Why traditional Web3 chains fail at Web‑2‑like financial UX.00:06:30 – The case for specialization over general purpose chains.00:08:00 – Nexus's dual‑core architecture: benefits & trade‑offs.00:11:45 – Best‑suited applications: algorithmic trading & native APIs.00:14:30 – How CK proofs enable scalability & verifiability.00:16:30 – Revenue capture: why Nexus prioritizes business sustainability.00:18:30 – Balancing developer incentives and protocol economics.00:21:45 – Exciting innovations: tokenized prediction markets & composability.00:23:30 – Other projects worth watching (Hyperliquid, Lighter, Tempo, stablecoin builders).00:26:00 – Nexus's 2026 roadmap: mainnet + perpetual exchange launch.00:27:45 – Lessons learned: move fast, stay adaptive.00:30:00 – Community ask: engage with the Nexus ecosystem.Connect with Nexus and Daniel hereDisclaimer:- Nothing mentioned in this podcast is investment advice and please do your own research.It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.Be a guest on the podcast or contact us – https://www.web3pod.xyz/

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

In this episode, Jeff Mains sits down with Luv Kapur, a technology leader at Bit who's reshaping how enterprises build software. Luv shares his journey from leading platform engineering at one of Canada's largest pension funds to joining a startup on a mission to help organizations scale development through composability and AI-powered tools.The conversation explores how AI is fundamentally changing software development—not by writing more code, but by enabling teams to compose better solutions with less custom code. Luv challenges the hype around code generation, arguing that the real bottleneck isn't writing code but translating business requirements into sound architecture and reusing battle-tested components.Luv also offers a grounded perspective on AI's impact on jobs, the importance of discoverability in component libraries, and practical advice for CTOs building composable organizations.Key Takeaways[0:00] - Episode introduction: AI-powered, cloud-native enterprise development tools[1:00] - The hidden cost of poor discoverability in internal libraries and how it silently slows high-performing teams[4:26] - Luv's background: From leading platform engineering at Healthcare of Ontario Pension Plan to joining Bit[4:47] - The spark for the leap: Believing in the mission of helping enterprises scale development globally[5:19] - The consistency problem: When products span multiple teams but feel disjointed to users[6:37] - Building a platform team whose customers are developers themselves[7:23] - Discoverability as the key problem: Developers couldn't find what already existed[9:24] - Why inner source software transforms development artifacts into invaluable organizational assets[11:37] - Viewing your org chart as a dependency graph, not a hierarchy[15:51] - The AI hype is justified, but code generation isn't the real bottleneck[17:01] - The bottleneck is translating business requirements into software architecture, not writing code[18:41] - AI should help us do less work, not more work[19:27] - Why developers won't lose jobs: There's infinite work, not finite work[20:19] - Reusing battle-tested components increases quality and reduces surface area for errors[21:59] - Reducing AI context to dependency graphs and APIs prevents hallucinations[23:05] - Private enterprise data is the gold mine for AI value[24:35] - The rise of citizen developers: Non-technical people building with natural language[26:40] - Empowering citizen developers with internal component marketplaces[27:19] - How AI changes the build vs. buy equation through faster prototyping[30:09] - Internal tools will be hit hardest by AI disruption[34:41] - SaaS companies must align with core business value to stay sticky[36:19] - The biggest mistake: Equating vibe-engineered solutions with production-ready software[39:01] - Building AI muscle: Start with clear scoped goals, not vague initiatives[40:45] - The future: Higher skill ceiling, elimination of junior developer roles, but more opportunities overall[43:45] - Junior developers must contribute to open source and build visible impact[44:31] - The one capability every software leader needs: Willingness to adopt AI and keep learningTweetable Quotes"For an internal team, if it doesn't get adopted, it's useless. Adoption is key." - Luv...

Radar processes billions of location events daily, powering geofencing and location APIs for companies like Uber, Lyft, and thousands of other apps. When their existing infrastructure started hitting performance and cost limits, they built HorizonDB, a specialized database which replaced both Elasticsearch and MongoDB with a custom single binary written in Rust and backed by RocksDB.In this episode, we dive deep into the technical journey from prototype to production. We talk about RocksDB internals, finite-state transducers, the intricacies of geospatial indexing with Hilbert curves, and why Rust's type system and performance characteristics made it the perfect choice for rewriting critical infrastructure that processes location data at massive scale.

Derick Schaefer, author of CLI: A Practical Guide to Creating Modern Command-Line Interfaces, talks with host Robert Blumen about command-line interfaces old and new. Starting with a short review of the origin of commands in the early unix systems, they trace the evolution of commands into modern CLIs. Following the historic rise, fall, and re-emergence of CLIs, they consider innovative examples such as git, github, WordPress, and warp. Schaefer clarifies whether commands are the same as CLIs and then discusses a range of topics, including implementation languages, packages in the golang ecosystem for CLI development, CLIs and APIs, CLIs and AIs, AI tooling versus MCP, the object-command pattern, command flags, API authentication, whether CLIs should be stateless, and output formats - json, rich text. Brought to you by IEEE Computer Society and IEEE Software magazine.

First show of 2026: we talk Garmin Autoland in a King Air and why internet speculation is the fastest way to sound like a jabroni. We also hit the chaos of international ops (Mexico permits/APIS pain) and tease the Chicago Layover Guide dropping soon. In the Mailbag: Coeur d'Alene layover intel, a legendary lav story, Pilot Pete confusion gets cleaned up, airline-switching "sunk cost" drama, and surviving an unhinged sim instructor. Flight Advice is a big one: a 2,000-hr pilot with a baby inbound weighs staying in a single-pilot piston twin 135 gig vs taking a King Air 200 EMS job (and whether a regional/fractional move makes more sense). Luggage Review Series Show Notes 0:00 Intro 4:02 Musings: Training & Self-Landing Planes 28:03 Other Incidents 50:55 Caribbean Airspace Shutdown 56:07 Reviews 1:01:15 Mailbag 1:24:21 Flight Advice Our Sponsors Tim Pope, CFP® — Tim is both a CERTIFIED FINANCIAL PLANNER™ and a pilot. His practice specializes in aviation professionals and aviation 401k plans, helping clients pursue their financial goals by defining them, optimizing resources, and monitoring progress. Click here to learn more. Also check out The Pilot's Portfolio Podcast. Advanced Aircrew Academy — Enables flight operations to fulfill their training needs in the most efficient and affordable way—anywhere, at any time. They provide high-quality training for professional pilots, flight attendants, flight coordinators, maintenance, and line service teams, all delivered via a world-class online system. Click here to learn more. Raven Careers — Helping your career take flight. Raven Careers supports professional pilots with resume prep, interview strategy, and long-term career planning. Whether you're a CFI eyeing your first regional, a captain debating your upgrade path, or a legacy hopeful refining your application, their one-on-one coaching and insider knowledge give you a real advantage. Click here to learn more. The AirComp Calculator™ is business aviation's only online compensation analysis system. It can provide precise compensation ranges for 14 business aviation positions in six aircraft classes at over 50 locations throughout the United States in seconds. Click here to learn more. Vaerus Jet Sales — Vaerus means right, true, and real. Buy or sell an aircraft the right way, with a true partner to make your dream of flight real. Connect with Brooks at Vaerus Jet Sales or learn more about their DC-3 Referral Program. Harvey Watt — Offers the only true Loss of Medical License Insurance available to individuals and small groups. Because Harvey Watt manages most airlines' plans, they can assist you in identifying the right coverage to supplement your airline's plan. Many buy coverage to supplement the loss of retirement benefits while grounded. Click here to learn more. VSL ACE Guide — Your all-in-one pilot training resource. Includes the most up-to-date Airman Certification Standards (ACS) and Practical Test Standards (PTS) for Private, Instrument, Commercial, ATP, CFI, and CFII. 21.Five listeners get a discount on the guide—click here to learn more. ProPilotWorld.com — The premier information and networking resource for professional pilots. Click here to learn more.   Feedback & Contact Have feedback, suggestions, or a great aviation story to share? Email us at info@21fivepodcast.com. Check out our Instagram feed @21FivePodcast for more great content (and our collection of aviation license plates). The statements made in this show are our own opinions and do not reflect, nor were they under any direction of any of our employers.

In this episode, we dig into how modern SaaS platforms turn payments into a core product, a revenue engine, and a defensible moat without breaking customer trust or slowing product velocity. With NMI's CMO Peter Galvin and Product Director Luis Peña, we unpack the real path from “just accept cards” to a fully integrated commerce stack that handles fraud, chargebacks, compliance, and omnichannel experiences at scale.We start with the SaaS payments maturity curve: ship fast with basic acceptance, then refine UX with tokenization and branded flows, and finally operate payments as a true business line with pricing strategy and revenue share. From there, we explore the tough stuff most teams underestimate - risk management, underwriting discipline, and the operational muscle needed to keep approval rates high while keeping losses and support tickets low. If you're wondering when you've outgrown your current processor, we outline the telltale signs and how to plan a migration that is modular, phased, and invisible to your merchants.Omnichannel is also a major focus. We break down card-present choices like Tap to Pay, offline-capable devices for field service, and cloud APIs for always-connected point of sale - all while tying in text-to-pay, QR codes, wallets, and ACH via open banking. Beyond lending, we also highlight high-impact add-ons: instant payouts, network tokenization, invoicing, and loyalty programs that raise approval rates, reduce churn, and boost margin. And we look ahead at what's next: stablecoins for cross-border efficiency, open banking data for smoother experiences, and agent-driven discovery that transforms how buyers find and pay for products.

Introducing Rob Ruiz Meet Rob Ruiz, a seasoned Senior Full Stack Developer with nearly two decades of expertise in WordPress innovation and open-source magic. As the Lead Maintainer of WP Rig since 2020, Rob has been the driving force behind this groundbreaking open-source framework that empowers developers to craft high-performance, accessible, and progressively enhanced WordPress themes with ease. WP Rig isn’t just a starter theme—it’s a turbocharged toolkit that bundles modern build processes, linting, optimization, and testing to deliver lightning-fast, standards-compliant sites that shine on any device. Show Notes For more on Rob and WP Rig, check out these links: LinkedIn Profile: https://www.linkedin.com/in/robcruiz WP Rig Official Site: https://wprig.io GitHub Repository: https://github.com/wprig/wprig Latest Releases: https://github.com/wprig/wprig/releases WP Rig 3.1 Announcement: https://wprig.io/wp-rig-3-1/ Transcript: Topher DeRosia: Hey everybody. Welcome to Hallway Chats. I’m your host Topher DeRosia, and with me today I have- Rob Ruiz: Rob Ruiz. Topher: Rob. You and I have talked a couple of times, once recently, and I learned about a project you’re working on, but not a whole lot about you. Where do you live? What do you do for a living? Rob: Yeah, for sure. Good question. Although I’m originally from Orlando, Florida, I’ve been living in Omaha, Nebraska for a couple of decades now. So I’m pretty much a native. I know a lot of people around here and I’ve been fairly involved in various local communities over the years. I’m a web developer. Started off as a graphic designer kind of out of college, and then got interested in web stuff. And so as a graphic designer turned future web developer, I guess, I was very interested in content management systems because it made the creating and managing of websites very, very easy. My first couple of sites were Flash websites, sites with macro media Flash. Then once I found content management systems, I was like, “Wow, this is way easier than coding the whole thing from scratch with Flash.” And then all the other obvious benefits that come from that. So I originally started with Joomla, interestingly enough, and used Joomla for about two or three years, then found WordPress and never looked back. And so I’ve been using WordPress ever since. As the years have gone on, WordPress has enabled me to slowly transition from a more kind of web designer, I guess, to a very full-blown web developer and software engineer, and even software architect to some degree. So here we are many years later. Topher: There’s a big step from designer to developer. How did that go for you? I’m assuming you went to PHP. Although if you were doing Flash sites, you probably learned ActionScript. Rob: Yeah. Yeah. That was very convenient when I started learning JavaScript. It made it very easy to learn JavaScript faster because I already had a familiarity with ActionScript. So there’s a lot of similarities there. But yeah. Even before I started doing PHP, I started learning more HTML and CSS. I did do a couple of static websites between there that were just like no content management system at all. So I was able to kind of sharpen my sword there with the CSS and HTML, which wasn’t particularly hard. But yeah, definitely, the PHP… that was a big step was PHP because it’s a proper logical programming language. There was a lot there I needed to unpack, and so it took me a while. I had to stick to it and really rinse and repeat before I finally got my feet under me. Topher: I can imagine. All right. So then you work for yourself or you freelance or do you have a real job, as it were? Rob: Currently, I do have a real job. Currently, I’m working at a company called Bold Orange out of Minneapolis. They’re a web agency. But I kind of bounce around from a lot of different jobs. And then, yes, I do freelance on the side, and I also develop my own products as well for myself and my company. Topher: Cool. Bold Orange sounds familiar. Who owns that? Rob: To be honest, I don’t know who the owners are. It’s just a pretty big web agency out of Minneapolis. They are a big company. You could just look them up at boldorange.com. They work for some pretty big companies. Topher: Cool. All right. You and I talked last about WP Rig. Give me a little background on where that came from and how you got it. Rob: Yeah, for sure. Well, there was a period of time where I was working at a company called Proxy Bid that is in the auction industry, and they had a product or a service — I don’t know how you want to look at that —called Auction Services. That product is basically just building WordPress sites for auction companies. They tasked us with a way to kind of standardize those websites essentially. And what we realized is that picking a different theme for every single site made things difficult to manage and increase tech debt by a lot. So what we were tasked with was, okay, if we’re going to build our own theme that we’re just going to make highly dynamic so we can make it look different from site to site. So we want to build it, but we want to build it smart and we want to make it reusable and maintainable. So let’s find a good framework to build this on so that we can maintain coding standards and end up with as little tech debt as possible, essentially. That’s when I first discovered WP Rig. In my research, I came across it and others. We came across Roots Sage and some of the other big names, I guess. It was actually a team exercise. We all went out and looked for different ones and studied different ones and mine that I found was WP Rig. And I was extremely interested in that one over the other ones. Interestingly enough- Topher: Can you tell me why over the other ones? Rob: That’s a great question. Yeah. I really liked the design patterns. I really liked the focus on WordPress coding standards. So having a system built in that checked all the code against WordPress coding standards was cool. I loved the compiling transpiling, whatever, for CSS and JavaScript kind of built in. That sounded really, really interesting. The fact that there was PHP unit testing built into it. So there’s like a starter testing framework built in that’s easy to extend so that you can add additional unit tests as your theme grows. We really wanted to make sure… because we were very into CICD pipelines. So we wanted to make sure that as developers were adding or contributing to any themes that we built with this, that we could have automated tests run and automated builds run, and just automate as much as possible. So WP rig just seemed like something that gave us those capabilities right out of the box. So that was a big thing. And I loved the way that they did it. Roots Sage does something similar, but they use their blade templating engine built in there. We really wanted to stick to something that was a bit more standard WordPress so that there wasn’t like a large knowledge overhead so that we didn’t have to say like, okay, if we’re bringing on other developers, like junior developers work on it, oh, it would be nice if you use Laravel too because we use this templating engine in all of our themes. We didn’t want to have to worry about that essentially. It was all object-oriented and all that stuff too. That’s what looked interesting to me. We ended up building a theme with WP Rig. I don’t know what they ended up doing with it after that, because I ended up getting let go shortly thereafter because the company had recently been acquired. Also, this was right after COVID too. So there was just a lot of moving parts and changing things at the time. So I ended up getting let go. But literally a week after I got let go, I came across a post on WP Tavern about how this framework was looking for new maintainers. Basically, this was a call put out by Morton, the original author of WP Rig. He reached out to WP Tavern and said, “Look, we’re not interested in maintaining this thing anymore, but it’s pretty cool. We like what we’ve built. And so we’re looking for other people to come in and adopt it essentially.” So I joined a Zoom meeting with a handful of other individuals that were also interested in this whole endeavor, and Morton reached out to me after the call and basically just said, “I looked you up. I liked some of the input that you had during the meeting. Let’s talk a little bit more.” And then that eventually led to conversations about me essentially taking the whole project over entirely. So, the branding, the hosting of the website, being lead maintainer on the project. Basically, gave me the keys to the kingdom in terms of GitHub and everything. So that’s how it ended up going in terms of the handoff between Morton and I. And I’m very grateful to him. They really created something super cool and I was honored to take it over and kind of, I don’t know, keep it going, I guess. Topher: I would be really curious. I don’t think either of us have the answer. I’d be curious to know how similar that path is to other project handoffs. It’s different from like an acquisition. You didn’t buy a plugin from somebody. It was kind of like vibes, I guess. Rob: It was like vibes. It was very vibey. I guess that’s probably the case in an open source situation. It’s very much an open source project. It’s a community-driven thing. It’s for everybody by everybody. I don’t know if all open source community projects roll like that, but that’s how this one worked out. There was some amount of ownership on Morton’s behalf. He did hire somebody to do the branding for WP Rig and the logo. And then obviously he was paying for stuff like the WPrig.io domain and the hosting through SiteGround and so on and so forth. So, we did have to transfer some of that and I’ve taken over those, I guess, financial burdens, if you want to think of it like that. But I’m totally okay with it. Topher: All right. You sort of mentioned some of the things Rig does, compiling and all that kind of stuff. Can you tell me… we didn’t discuss this before. I’m sitting at my desk and I think I want a website. How long does it take to go from that to looking at WordPress and logging into the admin with Rig? Rob: Okay. Rig is not an environment management system like local- Topher: I’m realizing my mistake. Somebody sends me a design in Figma. How long does it take me to go from that to, I’m not going to say complete because I mean, that’s CSS, but you know, how long does it take me to get to the point where I’m looking at a theme that is mine for the client that I’m going to start converting? Rob: Well, if you’re just looking for a starting point, if you’re just like, okay, how long does it take to get to like, okay, here’s my blank slate and I’m ready to start adopting all of these rules that are set up in Figma or whatever, I mean, you’re looking at maybe 5 minutes, 10 minutes, something like that. It’s pretty automated. You just need some simple knowledge of Git. And then there are some prerequisites to using WP Rig. You do have to have composer installed because we do leverage some Composer packages to some of it, although to be honest, you could probably get away with not using Composer. You just have to be okay with sacrificing some of the tools the WP Rig assumes you’re going to have. And then obviously Node. You have to have Node installed. A lot of our documentation assumes that you have NPM, that you’re using NPM for all your Nodes or your package management. But we did recently introduce support for Bun. And so you can use Bun instead of NPM, which is actually a lot faster and better in many ways. Topher: Okay. A lot of my audience are not developers, users, or light developers, like they’ll download a theme, hack a template, whatever. Is this for them? Am I boring those people right now? Rob: That’s a great question. I mean, and I think this is an interesting dichotomy and paradigm in the WordPress ecosystem, because you’ve got kind of this great divide. At least this is something I’ve noticed in my years in the WordPress community is you have many people that are not coders or developers that are very interested in expanding their knowledge of WordPress, but it’s strictly from a more of a marketing perspective where it’s like, I just want to know how to build websites with WordPress and how to use it to achieve my goals online from a marketing standpoint. You have that group of people, and then you have this other group of people that are very developer centric that want to know how to extend WordPress and how to empower those other people that we just discussed. Right? Topher: Right. Rob: So, yeah, that’s a very good question. I would say that WP Rig is very much designed for the developers, not for the marketers. The assumption there is that you’re going to be doing some amount of coding. Now, can you get away with doing a very light amount of coding? Yes. Yes, you can. I mean, if you compare what you’re going to get out of that assumed workflow to something that you would get off like Theme Forest or whatever, it’s going to be a night and day difference because those theme, Forest Themes, have hours, hundreds, sometimes hundreds of hours of development put into them. So, you’re not going to just out of the box immediately get something that is comparable to that. Topher: You need to put in those hundreds of hours of development to make a theme. Rob: As of today, yes. That may change soon though. Topher: Watch this space. Rob: That’s all I’ll say. Topher: Okay. So now we know who it’s for. I’m assuming there’s a website for it. What is it? Rob: Yeah. If you go to WPrig.io, we have a homepage that shows you all the features that are there in WP Rig. And then there’s a whole documentation area that helps people get up and running with WP Rig because there is a small learning curve there that’s pretty palatable for anybody who’s familiar with modern development workflows. So that is a thing. So the type of person that this is designed for anybody that wants to make a theme for anything. Let’s say you’re a big agency and you pull in a big client and that client wants something extremely custom and they come to you with Figma designs. Sure, you could go out there and find some premium theme and try to like child theme and overhaul that if you want. But in many situations, I would say in most situations, if you’re working from a Figma design that’s not based off of another theme already that’s just kind of somebody else’s brainchild, then you’re probably going to want to start from scratch. And so the idea here is that this is something to replace an approach, like underscores an approach. Actually, WP Pig was based off of underscores. The whole concept of it, as Morton explained it to me, was that he wanted to build an underscores that was more modern and full-featured from a development standpoint. Topher: Does it have any opinions about Gutenberg? Rob: It does now, but it did not when I took it over because Gutenberg did not exist yet when I took over WP Rig. Topher: Okay. What are its opinions? Rob: Yeah, sure. The opinion right out of the gate is that you can use Gutenberg as an editor and it has support like CSS rules in it for the standard blocks. So you should be able to use regular Gutenberg blocks in your theme and they should look just fine. There’s no resets in there. It doesn’t start from scratch. There’s not a bunch of styling you have to do for the blocks necessarily. Now, if you go to the full site editing or block-based mentality here, there are some things you need to do in WP Rig to convert the out-of-the-box WP Rig into another paradigm essentially. Right when you pull WP Rig, the assumption is you’re building what most people would refer to as a hybrid theme. The theme supports API or whatever, and the assumption is that you’re not going to be using the site editor. You’re just going to kind of do traditional WordPress, but you might be using Gutenberg for your content. So you’re just using Gutenberg kind of to author your pages and your posts and stuff like that, but not necessarily the whole site. WP Rig has the ability to kind of transform itself into other paradigms. So the first paradigm we built out was the universal theme approach. And the idea there is that you get a combination of the full site editing capabilities. But then you also have the traditional menu manager and the settings customizer framework or whatever is still there, right? These are things that don’t exist in a standard block-based theme. So I guess an easy example would be like the 2025 WordPress theme that comes right out of the box. It comes installed in WordPress. That is a true block-based theme, not a universal theme. So it doesn’t have those features because the assumption there is that it doesn’t need those features. You can kind of transform WP Rig into a universal theme that’s kind of a hybrid between a block-based and a classic theme. And then it can also transform into a strictly block-based theme as well. So following the same architecture as like the WordPress 2025 theme or Ollie or something like that is also a true block-based theme as well. So you can easily convert or transform the starting point of WP Rig into either of those paradigms if that’s the type of theme you’re setting out to build. Topher: Okay. That sounds super flexible. How much work is it to do that? Rob: It’s like one command line. Previously we had some tutorials on the website that showed you step-by-step, like what you needed to change about the theme to do that. You would have to add some files, delete some files, edit some code, add some theme supports into the base support class and some other stuff. I have recently, as of like a year and a half ago or a year ago, created a command line or a command that you can type into the command line that basically does that entire conversion process for you in like the blink of an eye. It takes probably a second to a second and a half to perform those changes to the code and then you’re good to go. It is best to do that conversion before you start building out your whole theme. It’s not impossible to do it after. But you’re more likely to run into problems or conflicts if you’ve already set out building your whole theme under one paradigm, and then you decide how the project you want to switch over to block-based or whatever. You’re likely to run into the need to refactor a bunch of stuff in that situation. So it is ideal to make that choice extremely early on in the process of developing your theme. But either way it’ll still work. That’s just one of the many tools that exist in WP Rig to transform it or convert it in several ways. That’s just one example. There are other examples of ways that Rig kind of converts itself to other paradigms as well. Topher: Yeah. All right. In my development life, I’ve had two parts to it. And one is the weekend hobbyist, or I download cadence and I whip something up in 20 minutes because I just want to experiment and the other is agency life where everything’s in Git, things are compiled, there are versions, blah, blah, blah. This sounds very friendly to that more professional pathway. Rob: Absolutely. Yes. Or, I mean, there’s another situation here too. If you’re a company who develops themes and publishes them to a platform like ThemeForest or any other platform, perhaps you’re selling themes on your own website, whatever, if you’re making things for sale, there’s no reason you couldn’t use WP Rig to build your themes. We have a bundle process that bundles your theme for publication or publishing. Whether you’re an agency or whether you’re putting your theme out for sale, it doesn’t matter, during that bundle process, it does actually white label the entire code base to where there’s no mention of WP Rig in the code whatsoever. Let’s say you were to build a theme that you wanted to put up for sale because you have some cool ideas. Say, page transitions now are completely supported in all modern or in most modern browsers. And when I say print page transitions, for those that are in the know, I am talking about not single page app page transitions, but through website page transitions. You can now do that. Let’s say you were like, “Hey, I’m feeling ambitious and I want to put out some new theme that comes with these page transitions built in,” and that’s going to be fancy on ThemeForest when people look at my demo, people might want to buy that. You could totally use WP Rig to build that out into a theme and the bundle process will white label all of the code. And then when people buy your theme and download that code, if they’re starting to go through and look through your code, they’re not going to have any way of knowing that it was built with WP Rig unless they’re familiar with the base WP Rig architecture, like how it does its object-oriented programming. It might be familiar with the patterns that it’s using and be able to kind of discern like, okay, well, this is the same pattern WP Rig uses, so high likelihood it was built with WP Rig. But they’re not going to be able to know by reading through the code. It’s not going to say WP Rig everywhere. It’s going to have the theme all over the place in the code. Topher: Okay. So then is that still WP Rig code? It just changed its labels? Rob: Yeah. Topher: So, it’s not like you’re exporting HTML, CSS and JavaScript? The underlying Rig framework is still there. Rob: Yeah. During the bundle process, it is bundling CSS and HTML. Well, HTML in the case of a block-based theme. But, yeah, it is bundling your PHP, your CSS, your JavaScript into the theme that you’re going to let people download when they buy it, or that you’re going to ship to your whatever client’s website. But all that code is going to be transpiled. In the case of CSS and JavaScript, there’s only going to be minified versions of that code in that theme. The source code is not actually going to be in there. Topher: This sounds pretty cool. You mentioned some stuff might be coming. You don’t have to tell me what it is, but do you have a timeline? When should we be watching for the next cool thing from Rig? Rob: Okay, cool. Well, I’m going to keep iterating on Rig forever. Regardless of any future products that might be built on WP Rig, WP Rig will always and forever remain an open source product for anybody to use for free and we, I, and possibly others in the future will continue to update it and support it over time. We just recently put out 3.1. You could expect the 3.2 anytime in the next six months to a year, probably closer to six months. One feature I’m looking at particularly closely right now is the new stuff coming out in version 6.9 of WordPress around the various APIs that are there. I think one of them is called the form… There’s a field API and a form API or view API or something like that. So WP Rig comes with a React-based settings framework in it. So if you want your theme to have a bunch of settings in it to make it flexible for whoever buys your theme, you can use this settings framework to easily create a bunch of fields, and then that framework will automatically manage all your fields and store all the data from those fields and make it easy to retrieve the values of the input on those fields, without knowing any React at all. Now, if you know React, you can go in there and, you know, embellish what’s already there, but it takes a JSON approach. So if you just understand JSON, you can go in and change the JSON for the framework, and that will automatically add fields into the settings framework. So you don’t even have to know React to extend the settings page if you want. That will likely get an overhaul using these new APIs being introduced into Rig. Topher: All right. How often have you run into something where, “Oh, look, WordPress has a new feature, I need to rebuild my system”? Rob: Over the last four or five years, it’s happened a lot because, yeah, I mean, like I said, when I first took this thing over, Gutenberg had not even been introduced yet. So, you had the introduction of Gutenberg and blocks. That was one thing. Then this whole full site editing became a thing, which later became the site editor. So that became a whole thing. Then all these various APIs. I mean, it happens quite frequently. So I’ve been working to keep it modern and up to date over the past four years and it’s been an incredible learning experience. It not only keeps my WordPress knowledge extremely sharp, but I’ve also learned how various other toolkits are built. That’s been the interesting thing. From a development standpoint, there’s two challenges here. One of the challenges is staying modern on the WordPress side of things. For instance, WordPress coding standards came out with a version 3 and then a version 3.1 about two years ago. I had to update WP Rig to leverage those modern coding standards. So that’s one example is as WordPress changes, the code in WP Rig also needs to change. Or for instance, if new CSS standards change, right, new CSS properties come out, it is ideal for the base CSS in WP Rig, meaning the CSS that you get right out of the box with it, comes with some of these, for instance, CSS grid, Flexbox, stuff like that. If I was adopting a theme framework to build a theme on, I would expect some of that stuff to be in there. And those things were extremely new when I first took over WP Rig and were not all baked in there essentially. So I’ve had to add a lot of that over time. Now there’s another side to this, which is not just keeping up with WordPress and CSS and PHP, 8. whatever, yada yada yada. You’ve also got the toolkit. There are various node packages and composer packages of power WP Rig and the process in which it does the transpiling, the bundling, the automated manipulation of your code during various aspects of the usage of WP Rig is a whole nother set of challenges because now you have to learn concepts like, well, how do I write custom node scripts? Right? Like there were no WP CLI commands built into WP Rig when I first took it over. Now there’s a whole list. There’s a whole library of WP CLI commands that come in Rig right out of the gate. And so I’ve had to learn about that. So just various things that come with knowing how do you automate the process of converting code, that’s something that was completely foreign to me when I first took over WP Rig. That’s been another incredible learning experience is understanding like what’s the difference between Webpack and Gulp. I didn’t know, right? I would tell people I’m using Gulp and WP Rig and they would be like, “Well, why don’t you just use Webpack?” and I would say, “I don’t know. I don’t know what the difference is.” So over time I could figure out what are the differences? Why aren’t we using Webpack? And I’m glad I spent some time on that because it turns out Webpack is not the hottest thing anymore, so I just skipped right over all that. When I overhauled for version 3, we’re now not using Gulp anymore as of 3.1. We’re now using more of a Vite-like process, far more modern than Webpack and far better and faster and sleeker and lighter. I had to learn a bunch about what powers Vite. What is Vite doing under the hood that we might be able to also do in WP Rig, but do it in a WordPress way. Because Vite is a SaaS tool. If you’re building a SaaS, like React with a… we’re not a SaaS. I guess a spa is a better term to use here. If you’re building a single page application with React or view or belt or whatever, right, then knowing what Vite is and just using Vite right out of the box is perfect. But it doesn’t translate perfectly to WordPress land because WordPress has its own opinions. And so I did have to do some dissecting there and figure out what to keep and what to not keep to what to kind of set aside so that WordPress can keep doing what WordPress does the way WordPress likes to do it, but also improve on how we’re doing some of the compiling and transpiling and the manipulation of the code during these various. Topher: All right. I want to pivot a little bit to some personal-ish questions. Rob: Okay. Topher: This is a big project. I’m sure it takes up plenty of your time. How scalable is that in your life? Do you want to do this for the rest of your life? Rob: That’s a fantastic question. I don’t know about the rest of my life. I mean, I definitely want to do web development for the rest of my life because the web has, let’s be honest, it’s transformed everyone’s way of life, whether you’re a web developer or not. You know, the fact that we have the internet in our pocket now, you know, it has changed everything. Apps, everything. It’s all built on the web. So I certainly want to be involved in the web the rest of my life. Do I want to keep doing WordPress the rest of my life? I don’t know. Do I want to keep doing WP Rig the rest of my life? I don’t know. But I will say that you bring up a very interesting point, which is it does take up a lot of time and also trust in open source over the past four or five years I would argue has diminished a little bit as a result of various events that have occurred over the past two or three years. I mean, we could cite the whole WP Engine Matt Mullerwig thing. We can also cite what’s going on with Oracle and JavaScript. Well, I mean, there’s many examples of this. I mean, we can cite the whole thing that happened… I mean, there’s various packages out there that are used and developed and open source to anybody, and some of them are going on maintained and it’s causing security vulnerabilities and degradation and all this stuff. So it’s a very important point. One thing I started thinking about after considering that in relation to WP Rig was I noticed that there’s usually a for-profit arm of any of these frameworks that seems to extend the lifespan of it. Let’s just talk about React, for example, React is an open source JavaScript framework, but it’s used by Facebook and Facebook is extremely for-profit. So companies that are making infrastructural or architectural decisions, they will base their choice on whether or not to use a framework largely on how long they think this framework is going to remain relevant or valid or maintained, right? A large part of that is, well, is there a company making money off of this thing? Because if there is, the chances- Topher: They’re going to keep doing that. Rob: They’re going to keep doing it. It’s going to stay around. That’s good. I think that’s healthy. A lot of people that like open source and want everything to be free, they might look at something like that and say like, well, I don’t want you to make a paid version of it or there shouldn’t be a pro version. I think that’s a very short-sighted way of looking at that software and these innovations. I think a more experienced way of looking at it is if you want something to remain relevant and maintained for a long period of time, having a for-profit way in which it’s leveraged is a very good thing. I mean, let’s be real. Would WordPress still be what it is today if there wasn’t a wordpress.com or if WooCommerce wasn’t owned by Automattic or whatever, right? They’ll be on top. I mean, it’s obviously impossible to say, but my argument would be, probably not. I mean, look at what’s happened to the other content management systems out there. You know, Joomla Drupal. They don’t really have a flourishing, you know, paid pro service that goes with their thing that’s very popular, at least definitely not as popular as WordPress.com or WordPress VIP or some of these other things that exist out there. And so having something that’s making and generating money that can then contribute back into it the way Automattic has been doing with WordPress over these years has, in my opinion, been instrumental. I mean, people can talk smack about Gutenberg all they want, but let’s be real, it’s 2025, would you still feel that WordPress is an elegant solution if we were still working from the WYSIWYG and using the classic editor? And I know a lot of people are still using the classic editor and there’s classic for us, the fork and all that stuff. But I mean, that only makes sense in a very specific implementation of WordPress, a very specific paradigm. If you want to explore any of these other paradigms out there, that way of thinking about WordPress kind of falls apart pretty quickly. I, for one, am happy that Gutenberg exists. I’m very happy that Automattic continues. And I’m grateful, actually, that Automattic continues to contribute back into WordPress. And not just them, obviously there’s other companies, XWP, 10Up, all these other companies are also contributing as well. But I’m very grateful that this ecosystem exists and that there’s contribution going back in and it’s happening from companies that are making money with this. And I think that’s vital. All that to say that WP Rig may and likely will have paid products in the future that leverage WP Rig. So that’s not to say that WP Rig will eventually cost money. That’s just to say that eventually people can expect other products to come out in the future that will be built on WP Rig and incentivize the continued contributions back into WP Rig. The open source version of WP Rig. Topher: That’s cool. I think that’s wise. If you want anything to stay alive, you have to feed it. Rob: That’s right. Topher: I had some more questions but I had forgotten them because I got caught up in your answer. Rob: Oh, thank you. I’ll take that as a compliment. I mean, my answer was eloquent. But I’m happy to expand on anything, know you, WordPress related, me related, you know, whether it comes to the ecosystem in WordPress, the whole WordCamp meetup thing is very interesting. I led the WP Omaha meetup for many years here in Omaha, Nebraska and I also led the WordCamp, the organizing of WordCamp here in Omaha for several years as well. That whole community, the whole ecosystem, at least in America seems to have largely fallen apart. I don’t know if you want to talk about that at all. But yeah, I’m ready to dive into any topics. Topher: I’m going to have one more question and then we’re going to wrap up. And it was that you were talking about all the things you had to learn. I’m sure there were nights where you were looking at your computer thinking, “Oh man, I had it working, now I gotta go learn a new thing.” I would love for you to go back in time and blog all of that if you would. But given that you can’t, I would be interested in a blog moving forward, documenting what you’re learning, how you’re learning it and starting maybe with a post that’s summarizes all of that. Obviously, that’s up to you and how you want to spend your time, but I think it’d be really valuable to other people starting a project, picking up somebody else’s project to see what the roadmap might look like. You know what I mean? Rob: For sure. Well, I can briefly summarize what I’ve learned over the years and where I’m at today with how I do this kind of stuff. I will say that a lot of the improvements to WP Rig that have happened over the last year or two would not be possible without the advent of AI. Topher: Interesting. Rob: That’s a fancy way of saying that I have been by coding a lot of WP Rig lately. If you know how to use AI, it is extremely powerful and it can help you do many things very quickly that previously would have taken much longer or more manpower. So, yeah, perhaps if there was like five, six, seven people actively, excuse me, actively contributing to WP Rig, then this type of stuff would have been possible previously, but that’s not the case. There is one person, well, one main contributor to WP Rig today and you’re talking to them. There are a handful of other people that have been likely contributing to WP Rig over the versions and you can find their contributions in the change log file in WP Rig. But those contributions have been extremely light compared to what I’ve been doing. I wouldn’t be able to do any of it without AI. I have learned my ability to learn things extremely rapidly has ramped up tenfold since I started learning how to properly leverage LLMs and AI. So that’s not to say that like, you know, WP Rig, all the code is just being completely written by AI and I’m just like. make it better, enter, and then like WP Rig is better. I wish it was that easy. It’s certainly not that. But when I needed to start asking some of these vital questions that I really didn’t have anyone to turn to to help answer them, I was able to turn to AI. For instance, let’s go back to the Webpack versus Gulp situation. Although Gulp is no longer used in WP Rig, you know, it was used in WP Rig until very recently. So I had to understand like, what is this system, how does it work, how do I extend it and how do I update it and all these things, right? And why aren’t we using WebPack and you know, is there validity to this criticism behind you should use webpack instead of Gulp or whatever, right? I was able to use AI to ask these questions and be able to get extremely good answers out of it and give me the direction I needed to make some of these kind of higher level decisions on like architecturally where should WP Rig go? It was through these virtual conversations with LLMs that I was able to refine the direction of WP Rig in a direction that is both modern and forward-thinking and architecturally sound. I learned a tremendous amount from AI about the architecture, about the code, about all of it. My advice to anybody that wants to extend their skill set a little bit in the development side of things is to leverage this new thing that we have in a way that is as productive as possible for you. So that’s going to vary from person to person. But for me, if I’m on a flight or if I’m stuck somewhere for a while, like, let’s say I got to take my kid to practice or something and I’m stuck there for an hour and I got to find some way to kill my time 9 times out of 10, I’m on my laptop or on my phone having conversations with Grok or ChatGPT or Gemini or whatever. I am literally refining… I’m just sitting there asking it questions that are on my mind that I wish I could ask somebody who’s like 10 times more capable than me. It has been instrumental. WP Rig wouldn’t be where it is today if it wasn’t for that. I would just say to anybody, especially now that it’s all on apps and you don’t have to be on a browser anymore, adopt that way of thinking. You know, if you’re on your lunch break or whatever and you have an hour lunch break and you only take 15 minutes to eat, what could you be doing with those other 45 minutes? You could just jump on this magical thing that we have now and start probing it for questions. Like, Hey, here’s what I know. Here’s what I don’t know. Fill these knowledge gaps for me.” And it is extremely good at doing that. Topher: So my question was, can you blog this and your answer told me that there’s more there that I want to hear. That’s the stuff that should be in your book when you write your book. Rob: I’m flattered that you would be interested in reading anything that I write. So thank you. I’ve written stuff in the past and it hasn’t gotten a lot of attention. But I also don’t have any platforms to market it either. But yeah, no, I made some… I’m sorry. Topher: I think your experience is valuable far beyond Rig or WordPress. If you abstract it out of a particular project to say, you know, I did this with a project, I learned this this way, I think that would be super valuable. Rob: Well, I will say that recently at my current job, I was challenged to create an end to end testing framework with Playwright that would speed up how long it takes to test things and also prevent, you know, to make things fail earlier, essentially, to prevent broken things from ending up in the wild, right, and having to catch them the hard way. I didn’t know a lot about Playwright, but I do know how toolkits work now because of WP Rig. And I was able to successfully in a matter of, I don’t know, three days, put together a starter kit for a test framework that we’re already using at work to test any website that we create for any client. It can be extended and it can be hooked into any CI CD pipeline and it generates reports for you and it does a whole bunch of stuff. I was able to do this relatively quickly. This knowledge, yes, does come in handy in other situations. Will I end up developing other toolkits like WP Rig in the future for other things? I guess if I can give any advice to anybody listening out there, another piece of advice I would give people is, you know, especially if you’re a junior developer and you’re still learning or whatever, or you’re just a marketing person and just want to have more control over the functionality side of what you’re creating or more insight into that so you could better, you know, manage projects or whatever. My advice would be to take on a small little project that is scoped relatively small that’s not too much for you to chew and go build something and do it with… Just doing that will be good. But if you can do it with the intent to then present it in some fashion, whether it be a blog article or creating a YouTube video or going to a meetup and giving a talk on it or even a lunch and learn at work or whatever, right, that will, in my experience, it will dramatically amplify how much you learn from that little pet project that’s kind of like a mini learning experience. And I highly encourage anybody out there to do that on the regular. Actually, no matter what your experience level is in development, I think you should do these things on a regular basis. Topher: All right. I’m going to wrap this up. I got to get back to work. You probably have to get back to work. Rob: Yeah. Topher: Thanks for talking. Rob: Thanks for having me, Topher. Really appreciate it. Topher: Where could people find you? WPrig.io?  Rob: Yeah, WPrig.io. WP rig has accounts on all of the major platforms and, even on Bluesky and Mastodon. You can look me up, Rob Ruiz. You can find me on LinkedIn. You can find me on all of those same platforms as well. You can add me on Facebook if you want, whatever. And I’m also in the WordPress Slack as well as Rob Ruiz. You can find me in the WordPress Slack. And then I’m on the WordPress Reddit and all that stuff. So yeah, reach out. If anybody wants to have any questions about Rig or anything else, I’m happy to engage.  Topher: Sounds good. All right, I’ll see you. Rob: All right, thanks, Topher. Have a good day. Topher: This has been an episode of the Hallway Chats podcast. I’m your host Topher DeRosia. Many thanks to our sponsor Nexcess. If you’d like to hear more Hallway Chats, please let us know on hallwaychats.com.

How is artificial intelligence transforming the way businesses operate? Can cutting-edge technology be the key to scaling success? In this episode, Ephraim Ebstein, Founder and CEO of Fit Solutions, sits down to share his insights… Fit Solutions is a $30 million IT and cybersecurity firm that helps thousands of businesses increase efficiency, reduce IT costs, and protect against cyber threats. Ephraim is also the Co-Founder of AI Integrators, a venture focused on leveraging AI to streamline business operations and optimize performance. With over 15 years in the tech industry, Ephraim has a background in managed IT services, network engineering, and cybersecurity consulting. Before founding Fit Solutions, he served as Senior Systems Engineering Team Lead at All Covered, a division of Konica Minolta. He holds a Bachelor's degree in Management Information Systems and has a proven track record in scaling tech businesses while fostering a strong company culture. In this discussion, we cover: The difference between an enterprise and a medium-sized business.  How AI "employees" are transforming customer service and operational efficiency. Why company culture and leadership systems are essential to business growth. How AI and automation are reducing costs while driving revenue. Find out more about Fit Solutions and their AI initiatives by visiting their website!

In this episode, Anderson Business Advisors host Clint Coons, Esq., sits down with Brian Hanson, co-founder of Real Advisors and AI for Business, to explore how artificial intelligence is revolutionizing real estate investing. Brian, who has been teaching business owners and investors about AI and marketing for several years, shares how investors can use AI to crunch massive amounts of data in seconds to identify the most predictable houses likely to sell — something that used to cost $20,000+ from data scientists. They discuss using humanized chatbots and voice bots that can have thousands of personalized conversations simultaneously without sounding robotic, automating follow-up sequences that never miss opportunities, and building custom apps in under five minutes without any coding knowledge. Brian reveals specific tools like Rest Bag for analyzing repair costs at 10 cents per photo, Yellow Pages Scraper for building 20,000-person cash buyer lists for just $80, and browser-use.com for creating custom APIs by simply showing the system what you do manually. As Brian explains, "I just don't think that most people really realize what's possible out there." The conversation covers everything from data mining and lead generation to creating high-converting marketing campaigns using competitive intelligence, virtual staging, and automation tools like Lovable, Google's AI Studio, Air DNA, House Canary, and Semrush. Tune in to discover how AI is the ultimate force multiplier for real estate investors looking to scale their businesses efficiently! Brian Hanson is the co-founder of Real Advisors and AI for Business. He got his start in real estate in his early 20s working with renowned real estate educator Ron LeGrand, where he developed a passion for marketing. Over the years, Brian has become obsessed with finding smarter, faster ways to grow businesses, and when AI emerged, he immediately recognized its transformative potential. Brian now teaches business owners and investors how to leverage AI to dramatically scale their operations, reduce costs, and increase output. He hosts the AI for Business podcast and regularly conducts three-day intensive training events where he shares cutting-edge AI strategies and tools. Brian's approach focuses on practical implementation—helping entrepreneurs automate processes, eliminate roadblocks, and achieve results they never thought possible. Highlights/Topics: (00:00) - Brian Hanson and the AI Opportunity (05:23) - Finding Off-Market Deals: Data Crunching and Lead Generation (11:35) - Automating Follow-Up and Conversations with AI (17:24) - Property Analysis, Contracts, and What AI Can't Replace (25:19) - Building Custom Apps in Minutes Without Coding (30:13) - AI-Powered Marketing and Competitive Intelligence (33:17) - Where to Learn More and Final Thoughts   Resources: https://podcasts.apple.com/ke/podcast/ai-for-business-podcast/id1821570230 https://www.linkedin.com/in/brian-hanson-1548797 https://www.facebook.com/brian.hanson1?mibextid=LQQJ4d https://events.aiforbusiness.com/ Schedule Your FREE Consultation https://andersonadvisors.com/strategy-session/?utm_source=ai-for-real-estate-investing&utm_medium=podcast Tax and Asset Protection Events https://andersonadvisors.com/real-estate-asset-protection-workshop-training/?utm_source=ai-for-real-estate-investing&utm_medium=podcast  Anderson Advisors https://andersonadvisors.com/ Anderson Advisors Podcast https://andersonadvisors.com/podcast/ Clint Coons YouTube https://www.youtube.com/channel/UC5GX-U6VbvMkhSM1ONBiW8w Anderson Advisors Tax Planning Appointment https://andersonadvisors.com/ss/  

Professor Toby Wilkinson. After their defeat, Antony died in Cleopatra's arms. Cleopatra committed suicide to avoid Roman humiliation, ending the Ptolemaic dynasty. Octavian annexed Egypt, dismissing its religious traditions regarding the Apis Bull and exploiting the land solely as a grain source for the Roman Empire. 1900