The Tech Trek

Matt Lynch, CTO and co-founder of Sage, joins the show to share how his team is using modern technology to transform elderly care. From rethinking outdated nurse call systems to capturing real-time data that improves both patient safety and caregiver retention, Matt breaks down the technical and human challenges of modernizing this critical industry. This is a story of mission-driven innovation, scale, and the surprising role data plays in quality of life.Key Takeaways• The aging population is outpacing caregiver availability, creating an urgent need for tech-driven solutions.• Most facilities still rely on fragmented or low-quality data, making it difficult to improve care.• Real-time documentation and streamlined workflows give caregivers credit for their work and reduce burnout.• New technology can deliver results at a fraction of the cost of outdated systems.• Adoption succeeds when tech is simple, resident-driven, and seamlessly fits into caregiver routines.Timestamped Highlights00:34 — Why Sage was founded: the gaps in elderly care tech that inspired three co-founders04:36 — The looming scale problem: a 40% surge in the aging population with flat caregiver growth06:56 — How poor data practices erode care quality—and what synchronous documentation changes12:05 — Why old systems cost 10x more and how modern tools flip the economics18:06 — Giving caregivers proof of their work and reducing turnover with better data21:54 — Lessons in building: why focusing on workflow software beat hardware reinventionA line worth remembering“Caregivers finally get credit for the work they’re doing—and that completely changes how families and facilities work together.”Pro TipStart simple. In elderly care tech, the real breakthrough wasn’t reinventing hardware but streamlining the caregiver workflow to make adoption natural.Stay ConnectedIf this episode resonated, share it with a friend in tech or healthcare. Don’t forget to follow the show on Apple Podcasts and Spotify so you never miss new conversations with the builders shaping our future.

What does it really mean to build in stealth—and when does it help or hurt? In this episode, Amir sits down with Yoni Michael, co-founder of Typedef, an AI infrastructure startup that recently came out of stealth. Yoni shares why his team chose to stay under the radar early on, how they balanced secrecy with customer discovery, and the lessons they learned about finding product-market fit in a noisy AI landscape. If you’re a founder or tech leader navigating early-stage strategy, this conversation offers practical insights you can apply right away.Key Takeaways• Stealth mode isn’t all or nothing—there’s a spectrum between total secrecy and open visibility• Execution and speed of iteration matter more than protecting “the idea”• Customer discovery should start before you even write code• Messaging is never final—test, refine, and keep adjusting as you learn from design partners• Investors expect shifts at the seed stage, but keeping them in the loop builds trustTimestamped Highlights00:38 — Why Typedef chose to launch in stealth and what they’re building in AI infrastructure04:31 — The double-edged sword of operating in a crowded AI market09:22 — How Yoni approaches customer discovery without giving away too much13:55 — Shaping messaging and narrative before coming out of stealth19:49 — Managing investor expectations when your product vision evolves25:21 — How to connect with Yoni for advice and community in AI infraA line that stuck with us“Your competitive edge isn’t the idea—it’s the ability to execute and course correct fast enough to hit your runway.”Resources mentioned• Typedef: typedef.ai• FENEC open-source framework: [GitHub link from Typedef site]• Yoni Michael on LinkedIn: linkedin.com/in/yonimichaelPro TipsYoni advises founders to test messaging as early as possible—whether through decks, demo sandboxes, or LinkedIn posts. The feedback loop is as valuable as product feedback.Stay connectedIf this episode gave you something to think about, share it with a founder or tech leader who’d benefit. And don’t forget to follow The Tech Trek on Apple Podcasts, Spotify, or YouTube so you never miss future conversations.

Simone Kalmakis, VP of Engineering at Viam, joins the show to share what it takes to lead teams that bridge hardware and software in the robotics space. With a background in software and machine learning, she now manages engineers with expertise ranging from code to mechanical systems. Simone talks about how she fosters collaboration across disciplines, adapts her leadership style, and keeps her teams aligned while building cutting-edge robotics solutions. If you want to understand what it’s really like to lead at the intersection of AI, software, and hardware, this conversation is for you.Key Takeaways• Why managing hardware and software together requires humility, curiosity, and alignment• How integrating testing across both domains changes the development process• The role of “20% projects” in helping engineers learn and innovate faster• Why passion and purpose matter more than hours worked when it comes to work-life balance• Why strong engineers without PhDs can still excel in robotics and AITimestamped Highlights01:05 — How Viam bridges the gap between physical devices and AI04:04 — The challenges of testing hardware versus software, and why small changes have big consequences10:57 — How hobbyist projects and 20% time accelerated Simone’s growth as a leader in robotics14:09 — What one-on-ones look like when leading both software and mechanical engineers16:14 — Attracting and retaining top robotics and AI talent in a competitive market22:54 — How passion and vision drive sustainable work-life balance in high-pressure environmentsA Line That Stuck With Us“Even the slightest change in hardware can have huge ramifications, and it’s humbling to lead people whose expertise far outweighs your own.”Call to ActionIf you enjoyed this episode, share it with a colleague who’s curious about robotics or leadership at the hardware-software frontier. And don’t forget to follow the show so you never miss insights from top tech leaders.

What does it really mean to be data-driven? Mark Gergess, VP of Data and BI at DoubleVerify, joins the show to unpack how data teams can go beyond dashboards to drive meaningful business action. From building an internal consulting lens to evaluating the latest AI tools, Mark shares how his team translates complex data flows into measurable revenue impact. If you’ve ever wrestled with the gap between insights and outcomes, this conversation will hit home.Key Takeaways• Being data-driven is about driving action, not just reporting numbers• Stakeholders don’t care about your data problems—they care about business outcomes• The biggest challenge with AI adoption isn’t the model, it’s the use cases• Efficiency gains from AI should shift focus from ETL tasks to solving real business problems• Data culture health is measured by how naturally teams rely on data day-to-dayTimestamped Highlights01:17 How DoubleVerify helps advertisers build safer, more effective digital campaigns04:55 Why the definition of “data-driven” still varies and why it matters09:25 Measuring whether data efforts are moving the needle on revenue13:15 How to separate hype from value when evaluating AI and GenAI tools17:10 Lessons from the data science boom and why companies must go “all in” with AI25:31 Can AI act as your junior analyst? Where efficiency gains really show up27:01 How freeing up time changes the structure of data teams and boosts business impactA thought worth holding onto“It’s not about dashboards. It’s not about reporting. It’s about doing something with the information.”Pro TipsMark recommends treating AI as a “junior analyst”—let it handle quick, lower-priority questions so your team can focus on bigger business challenges.Call to ActionEnjoyed the conversation? Share this episode with a colleague who talks about being “data-driven.” Subscribe on your favorite podcast platform and connect with me on LinkedIn for more insights from leaders shaping the future of data and technology.

Teddy Solomon, co-founder and CEO of Fizz, joins the show to share his journey from Stanford dropout to leading one of the fastest-growing Gen Z social platforms. In this conversation, Teddy breaks down what it’s like to learn leadership on the fly, the misconceptions around Gen Z founders, and how he’s scaling a product used by 95% of students at some of the top universities in the country. His story is a rare look at what it takes to build, adapt, and lead with composure at a young age.Key Takeaways• How Teddy turned a pandemic side project into the leading college social platform in the U.S.• Why staying “high with the lows and low with the highs” is his guiding principle for leadership and culture• The role of mentorship and humility in navigating early CEO decisions• How to earn respect and lead teams older and more experienced than you• Why real-world interaction still matters in an era dominated by digital connectionsTimestamped Highlights[01:00] Teddy explains how Fizz became the go-to social app for U.S. college campuses[02:45] The bold decision to drop out of Stanford after meeting a key mentor and investor[09:15] Why bringing in an external CEO was the best move for the company’s survival[12:30] Lessons on leadership: performing your best in the worst moments[15:59] The reality of leading teams as a young founder[18:45] Challenging Gen Z stereotypes through action and connectionMemorable Line“You need to stay high with the lows and low with the highs. If you can perform at your best when things are at their worst, you give your company the best chance to survive.”Pro TipsFocus on results and culture. Age, background, and pedigree matter far less than the ability to create a strong team environment and deliver outcomes.Call to ActionIf you enjoyed this episode, share it with someone who’s passionate about leadership and startups. Follow the show for more conversations with founders shaping the future of tech, and connect with us on LinkedIn for behind-the-scenes insights.

Desiree Lee, one of the Business CTOs at Armis, joins Amir to unpack one of the toughest realities in cybersecurity: the biggest risks aren’t always technical, they’re human. From phishing and deepfakes to the way AI is reshaping both attackers’ and defenders’ playbooks, Desiree shares hard-won insights on what companies should actually prioritize. If you’re a tech leader navigating the expanding attack surface, this episode will sharpen how you think about security in the AI era.Key Takeaways• Most breaches stem from human behavior, not lack of technology.• Attackers adopt AI faster because there’s no downside for them—defenders must catch up.• Fundamentals like patching and asset inventory still make or break resilience.• AI can reduce noise for security teams by spotting patterns in overwhelming data.• Small and midsize businesses will benefit from AI-driven tools that lower the barrier to effective security.Timestamped Highlights00:34 — How Armis evolved from asset inventory to full security solutions03:30 — Why security failures are more about psychology than technology07:32 — The deepfake CFO story and why training alone can’t solve phishing risks09:18 — Why most enterprises struggle with basics like patching and automation11:41 — Where AI gives defenders an edge in processing massive data sets18:33 — Practical ways AI can ease alert fatigue and vulnerability management21:03 — The overlooked need to label assets by business criticalityA Moment Worth Remembering“There is no penalty on the attacking side for embracing AI. It’s only good things for them. So they will adopt it quickly.” — Desiree LeeResources MentionedArmis: armis.comDesiree Lee on LinkedIn: linkedin.com/in/desireedleePro TipsTagging assets with their business criticality is one of the simplest, highest-impact steps companies can take. It turns asset inventories from static lists into real decision-making tools for AI-driven defense.Call to ActionIf you found this episode valuable, share it with a colleague who’s thinking about security and AI. Subscribe on your favorite podcast platform so you never miss future conversations with tech leaders pushing the edge of what’s possible.

Chris Ghanbarzadeh, Senior Director of Engineering at Game Changer, joins the show to unpack the shifting landscape of mobile development. From balancing quality with speed to navigating the rise of AI-assisted engineering, Chris shares how teams can adapt to new tools, methodologies, and user expectations. If you’re building mobile products—or leading teams that do—this conversation will sharpen how you think about stability, velocity, and the future of software development.Key Takeaways• Why stability is the true driver of long-term speed in mobile development• How shifts away from native development are reshaping efficiency and delivery• The role of AI toolchains in accelerating experimentation and engineering workflows• Why leaders should avoid top-down mandates when introducing AI and instead create space for experimentation• How AI may reshape software methodologies, from Scrum to requirements gathering and beyondTimestamped Highlights01:45 – The eternal challenge of balancing quality with velocity in mobile engineering06:42 – Why “stability is speed” and how tech debt slows teams down09:15 – The move away from native development and how it impacts engineering teams13:27 – Experimenting with AI toolchains and finding the right balance of autonomy and structure18:05 – Standardization vs freedom: the future of AI tools in the enterprise22:23 – Will AI change the very way we build and support software?A standout moment“Stability is speed. If you’re constantly chasing velocity without shoring up quality, you end up slower in the long run.”Resources Mentioned• Game Changer app – scorekeeping and live streaming for youth sportsPro TipsGive your teams space to experiment with AI tools. Celebrate learning—even when experiments fail—so engineers build confidence and marketable skills for the future.Call to ActionEnjoyed this conversation? Follow the show for more insights from engineering and tech leaders, and share this episode with a colleague who’s navigating mobile development or exploring AI in their workflows.

Moinul Khan, co-founder and CEO of Aurascape, joins the show to unpack what it takes to build a cybersecurity startup in the age of AI. With decades of experience at companies like Zscaler, Palo Alto Networks, and FireEye, Moinul shares why AI demands an entirely new security stack, how agentic AI is changing the game, and why prevention—not dashboards—must be at the heart of real solutions. If you’re a tech leader navigating the future of AI and security, this is a conversation you won’t want to miss.Key Takeaways• Traditional security stacks can’t keep up with dynamic, evolving AI tools• Prevention-focused solutions matter more than dashboards or API visibility• Agentic AI is both an opportunity and a security challenge that startups must address• CISOs are rethinking consolidation and becoming more open to best-of-breed solutions in AI security• Building with a long-term prevention mindset creates stronger, more resilient startupsTimestamped Highlights00:37 — Aurascape’s mission to deliver an all-encompassing AI security solution02:27 — The “aha” moment: why legacy firewalls and proxies can’t secure AI08:23 — How Aurascape’s vision has evolved from public AI tools to securing private and third-party applications13:17 — Agentic AI, MCP protocols, and why startups need to secure the next wave of AI agents16:44 — Best-of-breed vs consolidation: where the security market is really heading20:37 — Advice for founders: why prevention-first is the only real path to solving security problemsA standout moment“If you try to patch what you have built in the last 20 years, you will fail. If you want to secure AI, you have to build your entire stack from the ground up.” — Moinul KhanResources MentionedAurascape.aiPro TipDon’t build for a quick exit. Focus on prevention, even if it’s the harder road—it’s what truly solves customer problems in cybersecurity.Call to ActionIf you enjoyed this episode, share it with someone exploring AI security. Subscribe or follow the show for more conversations with the builders shaping the future of tech.

Cat Miller, CTPO of Talkiatry, shares her unconventional path from coding to leadership, including a detour into acting before rising into executive roles. She talks candidly about the realities of building a tech career you actually want, navigating transitions from engineering to management, and what it takes to succeed at the VP and C-level. This conversation is packed with lessons for anyone in tech who’s asking themselves, “What’s next for me?”Key Takeaways• Career paths in tech don’t have to be linear. Detours can provide perspective that makes you a stronger leader.• Early dissatisfaction with day-to-day coding doesn’t mean you don’t belong in technology—it often evolves into broader roles.• Doing your current job really well is often the fastest way to position yourself for growth when opportunity knocks.• Building a network of peers and mentors is essential when stepping into senior leadership.• Self-awareness and documenting your wins helps you stay grounded and measure progress at the executive level.Timestamped Highlights01:29 — Why coding wasn’t fulfilling long-term and how Cat thought about her next move04:54 — Leaving a stable job to explore acting, and how planning made the risk manageable10:35 — Returning to tech and finding the “perfect fit” role that shifted her career trajectory16:35 — Rethinking work-life balance and how mission-driven work changes the equation19:17 — Lessons from moving from VP to C-suite and the role luck and preparation both play24:40 — Why building a strong peer network is critical once you reach the CTO level29:14 — Tracking wins and staying accountable for your own performance as a leaderMemorable Line“It just feels really gross to be bad at your job. So why wouldn’t you always do your best to be good at it?”Pro TipsWrite down your team and personal wins each quarter. It helps you see progress that isn’t always obvious in the day-to-day grind.Stay ConnectedIf this episode resonated with you, share it with someone who’s navigating their own tech career path. Follow the show for more conversations with leaders who’ve carved their own way forward.

What does it take to reimagine how hardware products are built in a world moving at the speed of AI? Michael Corr, founder and CEO of Duro, shares how he turned two decades of experience in engineering and manufacturing into a modern platform that helps hardware teams move faster and smarter. From journaling early product ideas to navigating the relentless pace of innovation, Michael reveals what it really means to be a founder when the path is anything but straight.Key takeaways• Why traditional hardware manufacturing processes create hidden risks—and how software can solve them• The journaling habit that helped shape Duro’s first product features• How to balance investor demands with long-term product vision• The danger of chasing every shiny object as a CEO and how to filter noise for your team• Why adaptability matters more than rigid 5-year plans in today’s tech landscapeTimestamped highlights00:36 — How Duro is reinventing product lifecycle management for hardware teams05:39 — “If I were king for a day…” the origin story of Duro06:52 — The role of note-taking and journaling in building a company from scratch09:31 — Staying true to a mission while adapting to market and investor pressures14:54 — The trap of chasing every customer request and how to avoid burning out your team19:03 — Why looking beyond 18 months is mostly speculation in a fast-changing industryMemorable insight“All we can really focus on is the next 12 to 18 months—everything beyond that is just speculation.”Resources mentionedDuro website: getduro.comPro tipWhen you’re leading a fast-moving company, not every customer request deserves a green light. The best founders know when to say no, even to a big check, to protect long-term focus.If you enjoyed this episode, follow the show for more conversations with tech leaders shaping the future of software, hardware, and everything in between.