What's Up with Tech?

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AI is rewriting the rules of healthcare software development, but building a secure scalable healthcare platform still comes down to fundamentals: clear requirements, strong ownership, and serious engineering discipline around HIPAA compliance and PHI protection. We sit down with Ghazenfer Mansoor, founder and CEO of TechnologyRivers, to talk through what’s working right now as digital health teams race from prototype to production.

We get practical about how AI fits into the real build process, including rapid POCs, code generation, and stronger testing through AI-assisted unit tests and code coverage. Then we dig into the part most teams learn the hard way: you can’t treat AI like a simple Google search when the data is sensitive and the scale is real. We talk about why “upload your company docs” breaks down, and how patterns like RAG architecture and vector databases can support safer internal search and analysis without losing control of private data.

We also cover the repeat mistakes that sink health tech MVPs, especially unclear requirements and fuzzy decision-making. From there, we talk UX and UI in healthcare, focusing on designing for patients, clinicians, and admins based on what they actually need to do. Finally, we look at low-code and no-code in healthcare, how to turn white-coded prototypes into production-ready HIPAA compliant software, and what interoperability and emerging ideas like MCP could mean for the next wave of EHR-connected apps.

Subscribe for more conversations on digital health, healthcare IT, and AI in clinical workflows, then share this with a builder on your team and leave a review. What’s the hardest part of shipping healthcare software in your world right now?
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Phishing used to be a numbers game. With AI, it’s becoming a personalization game and that’s exactly why “good enough” login security is quietly turning into a liability. We sit down with Dawn Manley, PhD, SVP, Product Management of Yubico to talk about why phishing-resistant authentication is now a must-have for anyone protecting high-value accounts, especially as AI tools become central to daily work and decision-making. 

We get specific about OpenAI’s Advanced Account Security and why YubiKeys are part of the story. AI accounts can hold an incredible amount of sensitive context: drafts, strategies, internal notes, customer data, and the breadcrumbs of how you think. That makes ChatGPT account security and account takeover prevention far more than a technical checkbox. Dawn explains how hardware-backed security keys work in real life, why they’re considered phishing resistant, and how they avoid the weak points of SMS codes, intercepted OTPs, and push-notification tricks. 

Then we zoom out to the next wave: AI agents and agentic commerce. When software can act on your behalf, the question shifts from “who logged in?” to “who approved this action?” We talk about proving human intent for high-risk moments, using strong cryptography as a trust anchor, and what it looks like to deploy hardware-backed passkeys across an entire organization without relying on users to spot every scam. 

If you care about identity and access management, FIDO2 passkeys, enterprise security, or protecting AI workflows, you’ll walk away with a clearer model for what “strong authentication” really means now. Subscribe, share this with someone still relying on codes, and leave a review with your biggest question about phishing-resistant security.
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The leadership playbook a lot of us grew up with is starting to fail in the moments that matter most: high pressure decisions, fast growth, founder stress, and team conflict that can’t be solved with a new script. We sit down with Peter Carnochan , an executive coach who is also a psychologist and trained psychoanalyst, to talk about a simple idea with big consequences: the next frontier of leadership development is within, not without.

We unpack why “good enough” emotional skills stop working when you’re building at the highest levels, and why surface changes can feel impressive until stress hits and the veneer cracks. Peter introduces what he calls “radiant change” and explains how real, durable growth starts at the center of the self and spreads outward into communication, decision-making, and culture. Along the way, we talk about why a therapeutic approach to executive coaching is less generic, why not everyone should try to lead like the loudest celebrity CEO, and why the future belongs to a plurality of leadership styles that fit the person, not the stereotype.

The conversation gets personal through the story behind the documentary “Andre Is An Idiot,” a comedy about dying made after a stage four cancer diagnosis. It becomes a lens for meaning, mortality, and purpose and a reminder that people want leaders who care about the whole organization and community, not only the bottom line at all costs. If you’re a founder, CEO, or operator looking for sustainable high performance, emotional resilience, and deeper leadership presence, this one will challenge you in the best way.

Subscribe, share this with a leader who’s under pressure, and leave a review if it helps you lead with more clarity. What inner pattern do you think most limits great leadership?
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AI isn’t a side project for marketing anymore. It’s becoming the way work gets done, and that shift is happening faster than most teams can measure, govern, or even fully see. We sit down with Ed from Callan Consulting to unpack what he’s hearing directly from CMOs and heads of marketing about real-world AI adoption in tech marketing, from early-stage startups to multi-billion-dollar enterprises. 

We talk about the move from experimental “skunkworks” use to embedded AI across the marketing tech stack, including LLMs like ChatGPT and Claude, AI features inside core MarTech platforms, and a growing wave of AI-native tools designed for specific workflows. Ed shares why so many leaders report major impact while still struggling to quantify ROI, and how “born-in-AI” companies are rethinking org design and productivity from day one, sometimes even putting agents on the org chart. 

Then we get into the tradeoffs: token budgets, tool sprawl, and the rising risk of overreliance. If everyone ships AI-generated content at scale, everything starts to sound the same, mistakes slip through, and the internet fills with “AI slop” that models train on again. We lay out a practical path that protects brand voice: keep the hero content human-led, then use AI for atomization, localization, optimization, and distribution. Finally, we look ahead at generative engine optimization (GEO), the early dip in traditional SEO traffic, and why “machine engine optimization” could matter as buyers use agents to research vendors. 

If you want a grounded, executive-level view of generative AI in marketing, listen now, then subscribe, share with a teammate, and leave a review so more marketers can find it.
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
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AI is everywhere on stage, but production AI is won in the details. From HPE Discover, we sit down with Jason Schradel, Director of Enterprise Platforms at NVIDIA, to unpack what it really takes to build an “AI factory” that an enterprise can deploy, operate, and scale without turning every upgrade into a science project. If you’re trying to move from AI proofs of concept to real business outcomes, this conversation maps the stack in plain terms. 

We start with what’s being showcased on the floor and why it matters: next-generation platforms like Vera Rubin and the new Vera CPU, enterprise-ready GPUs including RTX Pro Blackwell Server Edition options, and the networking layer that keeps modern AI workloads moving, from Spectrum-X Ethernet switching to BlueField DPUs. We also talk about how HPE systems, storage, and the private cloud experience come together with NVIDIA accelerated computing and NVIDIA AI software to form a repeatable blueprint for enterprise AI infrastructure. 

From there, we zoom out to the go-to-market reality: global customers, real deployments across industries like healthcare, manufacturing, financial services, and telecom, plus the growing role of ISVs and partner ecosystems in making AI usable for specific workflows. Jason also shares what he’s watching on the roadmap, especially agentic AI and the importance of confidential computing to protect sensitive data and model weights as hybrid cloud AI becomes the norm. 

If you’re planning an enterprise AI strategy, you’ll leave with a clearer view of the components that matter most and the tradeoffs you can’t ignore. Subscribe for more conversations like this, share this episode with a teammate building your AI platform, and leave a review. What part of the AI factory stack feels hardest to get right right now?
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