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Why does healthcare keep investing in new technology while so many clinicians feel buried under paperwork and admin work that has nothing to do with patient care? In this episode of Tech Talks Daily, I'm joined by Dr. Rihan Javid, psychiatrist, former attorney, and co-founder and president of Edge. Our conversation cuts straight into an issue that rarely gets the attention it deserves, the quiet toll that administrative overload takes on doctors, care teams, and ultimately patients. Nearly half of physicians now link burnout to paperwork rather than clinical work, and Rihan explains why this problem keeps slipping past leadership discussions, even as budgets for digital tools continue to rise. Drawing on his experience inside hospitals and clinics, Rihan shares how operational design shapes outcomes in ways many healthcare leaders underestimate. We talk about why short-term staffing fixes often create new problems down the line, and how practices that invest in stable, well-trained remote administrative teams see real improvements. That includes faster billing cycles, fewer errors, and more time back for clinicians who want to focus on care rather than forms. What stood out for me was his framing of workforce infrastructure as a performance driver rather than a compliance box to tick. We also dig into how hybrid operations are becoming the default model. Local clinicians working alongside remote admin teams, supported by AI-assisted workflows, are now common across healthcare. Rihan is clear that while automation and AI can remove friction and cost, human oversight still matters deeply in high-compliance environments. Trust, accuracy, and patient confidence depend on knowing where automation fits and where human judgment must stay firmly in place. Another part of the discussion that stuck with me was Rihan's idea that stability is emerging as a better success signal than raw cost savings. High turnover may look efficient on paper, but it quietly limits a clinic's ability to grow, retain knowledge, and improve patient outcomes. We unpack why consistent administrative support can influence revenue cycles, satisfaction, and long-term resilience in ways traditional metrics often miss. If you're a healthcare leader, operator, or technologist trying to understand how AI, remote teams, and smarter operations can work together without losing trust or care quality, this conversation offers plenty to reflect on. As healthcare systems rethink how work gets done behind the scenes, what would it look like if stability and clinician well-being were treated as core performance measures rather than afterthoughts, and how might that change the future of care? Useful Links Connect with Dr. Rihan Javid Edge Health Rinova AI Thanks to our sponsors, Alcor, for supporting the show.
Dmytro Ovcharenko lives in Palo Alto, CA. He graduated from Berkeley in 2015 - not as an engineer, but as a lawyer. His first connection to tech was in his first role, as an attorney at a tech company. But outside of technology, he loves good sushi and burgers. In addition, he does a bit of hiking - some for fun, but also some for business. He's been known to take a meeting or two on the hiking trail.Dmytro very much enjoyed working at his prior company. But he noticed the large gap between what his business was charging, and what the engineers themselves received. He thought he could close this gap, to provide a better wage for the workers while saving businesses money.This is the creation story of Alcor.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiAlcorEquitybeeTerms and conditions: Equitybee executes private financing contracts (PFCs) allowing investors a certain claim to ESO upon liquidation event; Could limit your profits. Funding in not guaranteed. PFCs brokered by EquityBee Securities, member FINRA.Linkshttp://alcor.com/https://www.linkedin.com/in/dmitryovcharenkoSupport this podcast at — https://redcircle.com/code-story-insights-from-startup-tech-leaders/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
What happens when artificial intelligence starts accelerating cyberattacks faster than most organizations can test, fix, and respond? In this fast-tracked episode of Tech Talks Daily, I sat down with Sonali Shah, CEO of Cobalt, to unpack what real-world penetration testing data is revealing about the current state of enterprise security. With more than two decades in cybersecurity and a background that spans finance, engineering, product, and strategy, Sonali brings a grounded, operator-level view of where security teams are keeping up and where they are quietly falling behind. Our conversation centers on what happens when AI moves from an experiment to an attack surface. Sonali explains how threat actors are already using the same AI-enabled tools as defenders to automate reconnaissance, identify vulnerabilities, and speed up exploitation. We discuss why this is no longer theoretical, referencing findings from companies like Anthropic, including examples where models such as Claude have demonstrated both power and unpredictability. The takeaway is sobering but balanced. AI can automate a large share of the work, but human expertise still plays a defining role, both for attackers and defenders. We also dig into Cobalt's latest State of Pentesting data, including why median remediation times for serious vulnerabilities have improved while overall closure rates remain stubbornly low. Sonali breaks down why large enterprises struggle more than smaller organizations, how legacy systems slow progress, and why generative AI applications currently show some of the highest risk with some of the lowest fix rates. As more companies rush to deploy AI agents into production, this gap becomes harder to ignore. One of the strongest themes in this episode is the shift from point-in-time testing to continuous, programmatic risk reduction. Sonali explains what effective continuous pentesting looks like in practice, why automation alone creates noise and friction, and how human-led testing helps teams move from assumptions to evidence. We also address a persistent confidence gap, where leaders believe their security posture is strong, even when testing shows otherwise. We close by tackling one of the biggest myths in cybersecurity. Security is never finished. It is a constant process of preparation, testing, learning, and improvement. The organizations that perform best accept this reality and build security into daily operations rather than treating it as a one-off task. So as AI continues to accelerate both innovation and attacks, how confident are you that your security program is keeping pace, and what would continuous testing change inside your organization? I would love to hear your thoughts. Useful Links Connect with Sonali Shah Learn more about Cobalt Check out the Cobalt Learning Center State of Pentesting Report Thanks to our sponsors, Alcor, for supporting the show.
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.
What does it really take to move AI from proof-of-concept to something that delivers value at scale? In this episode of Tech Talks Daily, I'm joined by Simon Pettit, Area Vice President for the UK and Ireland at UiPath, for a grounded conversation about what is actually happening inside enterprises as AI and automation move beyond experimentation. Simon brings a refreshingly practical perspective shaped by an unconventional career path that spans the Royal Navy, nearly two decades at NetApp, and more than seven years at UiPath. We talk about why the UK and Ireland remain a strategic region for global technology adoption, how London continues to play a central role for companies expanding into Europe, and why AI momentum in the region is very real despite the broader economic noise. A big part of our discussion focuses on why so many organizations are stuck in pilot mode. Simon explains how hype, fragmented experimentation, and poor qualification of use cases often slow progress, while successful teams take a very different approach. He shares real examples of automation already delivering measurable outcomes, from long-running public sector programs to newer agent-driven workflows that are now moving into production after clear ROI validation. We also explore where the next wave of challenges is emerging. As agentic AI becomes easier for anyone to create, Simon draws a direct parallel to the early days of cloud computing and VM sprawl. Visibility, orchestration, and cost control are becoming just as important as innovation itself. Without them, organizations risk losing control of workflows, spend, and accountability as agents multiply across the business. Looking ahead, Simon outlines why AI success will depend on ecosystems rather than single platforms. Partnerships, vertical solutions, and the ability to swap technologies as the market evolves will shape how enterprises scale responsibly. From automation in software testing to cross-functional demand coming from HR, finance, and operations, this conversation captures where AI is delivering today and where the real work still lies. If you're trying to separate AI momentum from AI noise, this episode offers a clear, experience-led view of what it takes to turn potential into progress. What would need to change inside your organization to move from pilots to production with confidence? Useful Links Learn more about Simon Pettit Connect with UiPath Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
What happens when speed, scale, and convenience start to erode trust in the images brands rely on to tell their story? In this episode of Tech Talks Daily, I spoke with Dr. Rebecca Swift, Senior Vice President of Creative at Getty Images, about a growing problem hiding in plain sight, the rise of low-quality, generic, AI-generated visuals and the quiet damage they are doing to brand credibility. Rebecca brings a rare perspective to this conversation, leading a global creative team responsible for shaping how visual culture is produced, analyzed, and trusted at scale. We explore the idea of AI "sloppification," a term that captures what happens when generative tools are used because they are cheap, fast, and available, rather than because they serve a clear creative purpose. Rebecca explains how the flood of mass-produced AI imagery is making brands look interchangeable, stripping visuals of meaning, craft, and originality. When everything starts to look the same, audiences stop looking altogether, or worse, stop trusting what they see. A central theme in our discussion is transparency. Research shows that the majority of consumers want to know whether an image has been altered or created using AI, and Rebecca explains why this shift matters. For the first time, audiences are actively judging content based on how it was made, not just how it looks. We talk about why some brands misread this moment, mistaking AI usage for innovation, only to face backlash when consumers feel misled or talked down to. Rebecca also unpacks the legal and ethical risks many companies overlook in the rush to adopt generative tools. From copyright exposure to the use of non-consented training data, she outlines why commercially safe AI matters, especially for enterprises that trade on trust. We discuss how Getty Images approaches AI differently, with consented datasets, creator compensation, and strict controls designed to protect both brands and the creative community. The conversation goes beyond risk and into opportunity. Rebecca makes a strong case for why authenticity, real people, and human-made imagery are becoming more valuable, not less, in an AI-saturated world. We explore why video, photography, and behind-the-scenes storytelling are regaining importance, and why audiences are drawn to evidence of craft, effort, and intent. As generative AI becomes impossible to ignore, this episode asks a harder question. Are brands using AI as a thoughtful tool to support creativity, or are they trading long-term trust for short-term convenience, and will audiences continue to forgive that choice? Useful Links Connect with Dr. Rebecca Swift on LinkedIn VisualGSP Creative Trends Follow on Instagram and LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
What does it actually take to build trust with developers when your product sits quietly inside thousands of other products, often invisible to the people using it every day? In this episode of Tech Talks Daily, I sat down with Ondřej Chrastina, Developer Relations at CKEditor, to unpack a career shaped by hands-on experience, curiosity, and a deep respect for developer time. Ondřej's story starts in QA and software testing, moves through development and platform work, and eventually lands in developer relations. What makes his perspective compelling is that none of these roles felt disconnected. Each one sharpened his understanding of real developer friction, the kind you only notice when you have lived with a product day in and day out. We talked about what changes when you move from monolithic platforms to API-first services, and why developer relations looks very different depending on whether your audience is an application developer, a data engineer, or an integrator working under tight delivery pressure. Ondřej shared how his time at Kentico, Kontent.ai, and Ataccama shaped his approach to tooling, documentation, and examples. For him, theory rarely lands. Showing something that works, even in a small or imperfect way, tends to earn attention and respect far faster. At CKEditor, that thinking becomes even more interesting. The editor is everywhere, yet rarely recognized. It lives inside SaaS platforms, internal tools, CRMs, and content systems, quietly doing its job. We explored how developer experience matters even more when the product itself fades into the background, and why long-term maintenance, support, and predictability often outweigh short-term feature excitement. Ondřej also explained why building instead of buying an editor is rarely as simple as teams expect, especially when standards, security, and future updates enter the picture. We also got into the human side of developer relations. Balancing credibility with business goals, staying useful rather than loud, and acting as a bridge between engineering, product, marketing, and the outside world. Ondřej was refreshingly honest about the role ego can play, and why staying close to real usage is the fastest way to keep yourself grounded. If you care about developer experience, internal tooling, or how invisible infrastructure shapes modern software, this conversation offers plenty to reflect on. What have you seen work, or fail, when it comes to earning developer trust, and where do you think developer relations still get misunderstood? Useful Links Connect with Ondrej Chrastina Learn more about CK Editor Thanks to our sponsors, Alcor, for supporting the show.
What if your AI systems could explain why something will happen before it does, rather than simply reacting after the damage is done? In this episode of Tech Talks Daily, I sat down with Zubair Magrey, co-founder and CEO of Ergodic AI, to unpack a different way of thinking about artificial intelligence, one that focuses on understanding how complex systems actually behave. Zubair's journey begins in aerospace engineering at Rolls-Royce, moves through a decade of large-scale enterprise AI programs at Accenture, and ultimately leads to building Ergodic, a company developing what he describes as world models for enterprise decision making. World models are often mentioned in research circles, but rarely explained in a way that business leaders can connect to real operational decisions. In our conversation, Zubair breaks that gap down clearly. Instead of training AI to spot patterns in past data and assume the future will look the same, world-model AI focuses on cause and effect. It builds a structured representation of how an organization works, how different parts interact, and how actions ripple through the system over time. The result is an AI approach that can simulate outcomes, test scenarios, and help teams understand the consequences of decisions before they commit to them. We explored why this matters so much as organizations move toward agentic AI, where systems are expected to recommend or even execute actions autonomously. Without an understanding of constraints, dependencies, and system dynamics, those agents can easily produce confident but unrealistic recommendations. Zubair explains how Ergodic uses ideas from physics and system theory to respect real-world limits like capacity, time, inventory, and causality, and why ignoring those principles leads to fragile AI deployments that struggle under pressure. The conversation also gets practical. Zubair shares how world-model simulations are being used in supply chain, manufacturing, automotive, and CPG environments to detect early risks, anticipate disruptions, and evaluate trade-offs before problems cascade across customers and regions. We discuss why waiting for perfect data often stalls AI adoption, how Ergodic's data-agnostic approach works alongside existing systems, and what it takes to deliver ROI that teams actually trust and use. Finally, we step back and look at the organizational side of AI adoption. As AI becomes embedded into daily workflows, cultural change, experimentation, and trust become just as important as models and metrics. Zubair offers a grounded view on how leaders can prepare their teams for faster cycles of change without losing confidence or control. As enterprises look ahead to a future shaped by autonomous systems and real-time decision making, are we building AI that truly understands how our organizations work, or are we still guessing based on the past, and what would it take to change that? Useful Links Connect with Zubair Magrey Learn more about Ergodic AI Thanks to our sponsors, Alcor, for supporting the show.
What really happens after the startup advice runs out and founders are left facing decisions no pitch deck ever prepared them for? In this episode of Tech Talks Daily, I sit down with Vijay Rajendran, a founder, venture capitalist, UC Berkeley instructor, and author of The Funding Framework, to discuss the realities of company building that rarely appear on social feeds or investor blogs. Vijay has spent years working alongside founders at the sharpest end of growth, from early fundraising conversations through to the personal and leadership shifts that scaling demands. That experience shapes a conversation that feels refreshingly honest, thoughtful, and grounded in lived reality. We explore why building something people actually want sounds simple in theory yet proves brutally difficult in practice. Vijay explains how timing, learning velocity, and the willingness to adapt often matter more than stubborn vision, and why many founders misunderstand what momentum really looks like. From there, the discussion moves into investor relationships, not as transactional events, but as long-term partnerships that require founders to shift their mindset from defense to evaluation. The emotional and psychological dynamics of fundraising come into focus, especially the moments when founders underestimate how much power they actually have in shaping those relationships. A big part of this conversation centers on leadership identity. Vijay breaks down the messy transition from being the "chief everything officer" to becoming a true chief executive, and why the most overlooked stage in that journey is learning how to enable others. We talk about the point where founders become the bottleneck, often without realizing it, and why this tends to surface as teams grow and decisions start happening outside the founder's direct line of sight. The plateau many companies hit around scale becomes less mysterious when viewed through this lens. We also challenge some of the most popular startup advice circulating online today, particularly around fundraising volume, pitching styles, and the idea that persistence alone guarantees outcomes. Vijay shares why treating fundraising like enterprise sales, focusing on alignment over volume, and listening more than pitching often leads to better results. The conversation closes with practical reflections on personal growth, co-founder dynamics, and how leaders can regain clarity during periods of pressure without stepping away from responsibility. If you are building a company, leading a team, or questioning whether you are evolving as fast as your business demands, this episode will likely hit closer to home than you expect. And once you've listened, I'd love to hear what resonated most with you and the leadership questions you're still sitting with after the conversation. Useful Links Connect with Vijay Rajendran The Funding Framework Startup Pitch Deck Thanks to our sponsors, Alcor, for supporting the show.
What happens when decades of clinical research experience collide with a regulatory environment that is changing faster than ever? In this episode of Tech Talks Daily, I sat down with Dr Werner Engelbrecht, Senior Director of Strategy at Veeva Systems, for a wide-ranging conversation that explores how life sciences organizations across Europe are responding to mounting regulatory pressure, rapid advances in AI, and growing expectations around transparency and patient trust. Werner brings a rare perspective to this discussion. His career spans clinical research, pharmaceutical development, health authorities, and technology strategy, shaped by firsthand experience as an investigator and later as a senior industry leader. That background gives him a grounded, practical view of what is actually changing inside pharma and biotech organizations, beyond the headlines around AI Acts, data rules, and compliance frameworks. We talk openly about why regulations such as GDPR, the EU AI Act, and ACT-EU are creating real pressure for organizations that are already operating in highly controlled environments. But rather than framing compliance as a blocker, Werner explains why this moment presents an opening for better collaboration, stronger data foundations, and more consistent ways of working across internal teams. According to him, the real challenge is less about technology and more about how companies manage data quality, align processes, and break down silos that slow everything from trial setup to regulatory response times. Our conversation also digs into where AI is genuinely making progress today in life sciences and where caution still matters. Werner shares why drug discovery and non-patient-facing use cases are moving faster, while areas like trial execution and real-world patient data still demand stronger evidence, cleaner datasets, and clearer governance. His perspective cuts through hype and focuses on what is realistic in an industry where patient safety remains the defining responsibility. We also explore patient recruitment, decentralized trials, and the growing complexity of diseases themselves. Advances in genomics and diagnostics are reshaping how trials are designed, which in turn raises questions about access to electronic health records, data harmonization across Europe, and the safeguards regulators care about most. Werner connects these dots in a way that highlights both the operational strain and the long-term upside. Toward the end, we look ahead at emerging technologies such as blockchain and connected devices, and how they could strengthen data integrity, monitoring, and regulatory confidence over time. It is a thoughtful discussion that reflects both optimism and realism, rooted in lived experience rather than theory. If you are working anywhere near clinical research, regulatory affairs, or digital transformation in life sciences, this episode offers a clear-eyed view of where the industry stands today and where it may be heading next. How should organizations turn regulation into momentum instead of resistance, and what will it take to earn lasting trust from patients, partners, and regulators alike? Useful Links Connect with Dr Werner Engelbrecht Learn more about Veeva Systems Viva Summit Europe and Viva Summit USA Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
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.
In this episode of Tech Talks Daily, I sat down with Keith Zubchevich, CEO of Conviva, to unpack one of the most honest analogies I have heard about today's AI rollout. Keith compares modern AI agents to toddlers being sent out to get a job, full of promise, curious, and energetic, yet still lacking the judgment and context required to operate safely in the real world. It is a simple metaphor, but it captures a tension many leaders are feeling as generative AI matures in theory while so many deployments stumble in practice. As ChatGPT approaches its third birthday, the narrative suggests that GenAI has grown up. Yet Keith argues that this sense of maturity is misleading, especially inside enterprises chasing measurable returns. He explains why so many pilots stall or quietly disappoint, not because the models lack intelligence, but because organizations often release agents without clear outcomes, real-time oversight, or an understanding of how customers actually experience those interactions. The result is AI that appears to function well internally while quietly frustrating users or failing to complete the job it was meant to do. We also dig into the now infamous Chevrolet chatbot incident that sold a $76,000 vehicle for one dollar, using it as a lens to examine what happens when agents are left without boundaries or supervision. Keith makes a strong case that the next chapter of enterprise AI will not be defined by ever-larger models, but by visibility. He shares why observing behavior, patterns, sentiment, and efficiency in real time matters more than chasing raw accuracy, especially once AI moves from internal workflows into customer-facing roles. This conversation will resonate with anyone under pressure to scale AI quickly while worrying about brand risk, accountability, and trust. Keith offers a grounded view of what effective AI "parenting" looks like inside modern organizations, and why measuring the customer experience remains the most reliable signal of whether an AI system is actually growing up or simply creating new problems at speed. As leaders rush to put agents into production, are we truly ready to guide them, or are we sending toddlers into the workforce and hoping for the best? Useful Links Connect with Keith Zubchevich Learn more about Conviva Chevrolet Dealer Chatbot Agrees to Sell Tahoe for $1 Thanks to our sponsors, Alcor, for supporting the show.
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.
What happens when the AI race stops being about size and starts being about sense? In this episode of Tech Talks Daily, I sit down with Wade Myers from MythWorx, a company operating quietly while questioning some of the loudest assumptions in artificial intelligence right now. We recorded this conversation during the noise of CES week, when headlines were full of bigger models, more parameters, and ever-growing GPU demand. But instead of chasing scale, this discussion goes in the opposite direction and asks whether brute force intelligence is already running out of road. Wade brings a perspective shaped by years as both a founder and investor, and he explains why today's large language models are starting to collide with real-world limits around power, cost, latency, and sustainability. We talk openly about the hidden tax of GPUs, how adding more compute often feels like piling complexity onto already fragile systems, and why that approach looks increasingly shaky for enterprises dealing with technical debt, energy constraints, and long deployment cycles. What makes this conversation especially interesting is MythWorx's belief that the next phase of AI will look less like prediction engines and more like reasoning systems. Wade walks through how their architecture is modeled closer to human learning, where intelligence is learned once and applied many times, rather than dragging around the full weight of the internet to answer every question. We explore why deterministic answers, audit trails, and explainability matter far more in areas like finance, law, medicine, and defense than clever-sounding responses. There is also a grounded enterprise angle here. We talk about why so many organizations feel uneasy about sending proprietary data into public AI clouds, how private AI deployments are becoming a board-level concern, and why most companies cannot justify building GPU-heavy data centers just to experiment. Wade draws parallels to the early internet and smartphone app eras, reminding us that the playful phase often comes before the practical one, and that disappointment is often a signal of maturation, not failure. We finish by looking ahead. Edge AI, small-footprint models, and architectures that reward efficiency over excess are all on the horizon, and Wade shares what MythWorx is building next, from faster model training to offline AI that can run on devices without constant connectivity. It is a conversation about restraint, reasoning, and realism at a time when hype often crowds out reflection. So if bigger models are no longer the finish line, what should business and technology leaders actually be paying attention to next, and are we ready to rethink what intelligence really means? Useful Links Connect with Wade Myers Learn More About MythWorx Thanks to our sponsors, Alcor, for supporting the show.
What happens when we finally admit that stopping every cyberattack was never realistic in the first place? That is the thread running through this conversation, recorded at the start of the year when reflection tends to be more honest and the noise dial is turned down a little. I was joined by returning guest Raghu Nandakumara from Illumio, nearly three years after our last discussion, to pick up a question that has aged far too well. How do organizations talk about cybersecurity value when breaches keep happening anyway? This episode is less about shiny tools and more about uncomfortable truths. We spend time unpacking why security teams still struggle to show value, why prevention-only thinking keeps setting leaders up for disappointment, and why the conversation is slowly shifting toward resilience and containment. Raghu is refreshingly direct on why reducing cyber risk, rather than chasing impossible guarantees, is the only metric that really holds up under boardroom scrutiny. We also talk about the strange contradiction playing out across industries. Attackers are often using familiar paths like misconfigurations, excessive permissions, and missing patches, yet many organizations still fail to close those gaps. The issue, as Raghu explains, is rarely a lack of tools. It is usually fragmented coverage, outdated processes, and a talent pipeline that blocks capable people from entering the field while claiming there is a skills shortage. One of the most practical parts of this conversation centers on mindset. Instead of asking whether an attacker got in, Raghu argues that leaders should be asking how far they were able to go once inside. That shift alone changes how success is measured, how teams prepare for incidents, and how pressure-filled P1 moments are handled when boards want answers every fifteen minutes. We also touch on how legal action, public claims campaigns, and customer lawsuits are changing the stakes after a breach, forcing executives to rethink how they frame cyber investment. From there, Raghu shares how Illumio has been working with Microsoft to strengthen internal resilience at massive scale, and why visibility and segmentation are becoming harder to ignore. This is a conversation about realism, responsibility, and growing up as an industry. If cybersecurity is really about safety and not slogans, what would you want your organization to stop saying, and what would you rather hear instead? Please feel free to upload the podcast. Here are also the links we discussed on the call: Useful Links Connect with Raghu Nandakumara on LinkedIn and Twitter Learn more about Illumio Lateral Movement in Cyberattacks Illumio Podcast Follow on Facebook, Twitter, LinkedIn, and YouTube Thanks to our sponsors, Alcor, for supporting the show.
What really happens inside an organization when a cyber incident hits and the neat incident response plan starts to fall apart? That question sat at the heart of my return conversation with Max Vetter, VP of Cyber at Immersive. It has been a big year for breaches, public fallout, and eye-watering financial losses, and this episode goes beyond headlines to examine what cyber crisis management actually looks like when pressure, uncertainty, and human behavior collide. Max brings a rare perspective shaped by years in law enforcement, intelligence work, and hands-on cyber defense, and he is refreshingly honest about where most organizations are still unprepared. We talked about why written incident response plans tend to fail at the exact moment they are needed most. Cyber incidents are chaotic, emotional, and non-linear, yet many plans assume calm decision-making and perfect coordination. Max explains why success or failure is often defined by the response rather than the initial breach itself, and why leadership, communication, and judgment matter just as much as technical skill. Real-world examples from major incidents highlight how competing pressures quickly emerge, whether to contain or keep systems running, whether to pay a ransom or risk prolonged downtime, and how every option comes with consequences. One idea that really stood out is Max's belief that resilience is revealed, not documented. Compliance and audits may tick boxes, but they rarely expose how teams behave under stress. We explored why organizations that rely on annual tabletop exercises often develop a false sense of confidence, and how that confidence can become dangerous when decisions are made quickly and publicly. Max shared why the best-performing teams are often the ones that feel less certain in the moment, because they question assumptions and adapt faster. We also dug into the growing role of crisis simulations and micro-drills. Rather than rehearsing a single scenario once a year, Immersive focuses on repeated, realistic practice that builds muscle memory across technical teams, executives, legal, and communications. The goal is not to predict the exact attack, but to train people to think clearly, collaborate across functions, and make defensible decisions when there are no good options. That preparation becomes even more important as cyber incidents increasingly spill into supply chains, manufacturing, and the physical world. As public scrutiny rises and consumer-led legal action becomes more common after breaches, reputation and response speed now sit alongside forensics and recovery as business-critical concerns. This episode is a candid look at why cyber crisis readiness is a discipline, not a document, and why assuming you will cope when the moment arrives is a risky bet. So if resilience only truly shows itself when everything is on the line, how confident are you that your organization would perform when the pressure is real and the clock is ticking? Useful Links Connect with Max Vetter on Linkedin Learn more about Immersive Labs Follow on LinkedIn, Instagram, Twitter and Facebook Thanks to our sponsors, Alcor, for supporting the show.
What happens when the web browser stops being a passive window to information and starts acting like an intelligent coworker, and why does that suddenly make security everyone's problem? At the start of 2026, I sat down with Michael Shieh from Mammoth Cyber to unpack a shift that is quietly redefining how work gets done. AI browsers are moving fast from consumer curiosity to enterprise reality, embedding agentic AI directly into the place where most work already happens, the browser. Search, research, comparison, analysis, and decision support are no longer separate steps. They are becoming one continuous workflow. In this conversation, we talk openly about why consumer adoption has surged while enterprise teams remain hesitant. Many employees already rely on AI-powered browsing at home because it removes ads, personalizes results, and saves time. Inside organizations, however, the same tools raise difficult questions around data exposure, credential safety, and indirect prompt injection. Once an AI agent starts reading untrusted external content, the browser itself becomes a new attack surface. Michael explains why this risk is often misunderstood and why the real danger is not internal documents, but external websites designed to manipulate AI behavior. We dig into how Mammoth Cyber approaches this challenge differently, starting with a secure-first architecture that isolates trusted internal data from untrusted external sources. Every AI action, from memory to model connections to data access, is monitored and governed by policy. It is a practical response to a problem many security teams know is coming but feel unprepared to manage. We also explore how AI browsers change day-to-day work. A task like competitive analysis, which once took days of manual research and document comparison, can now be completed in minutes when an AI browser securely connects internal knowledge with external intelligence. That productivity gain is real, but only if enterprises trust the environment it runs in. We touch on Zero Trust principles, including work influenced by Chase Cunningham, and why 2026 looks like a tipping point for enterprise AI browsing. The technology is maturing, security controls are catching up, and businesses are starting to accept that blocking AI outright is no longer realistic. If you are curious to see how this works in practice, Mammoth Cyber offers a free Enterprise AI Browser that lets you experience what secure AI-powered browsing actually looks like, without putting your organization at risk. I have included the link so you can explore it yourself and decide whether this is where work is heading next. So, as AI browsers become the new workflow hub for knowledge workers everywhere, is your organization ready to secure the browser before it becomes your most exposed endpoint, and what would adopting one safely change about how your teams work? If you want to see what an enterprise-grade AI browser looks like when security is built in from day one, Mammoth Cyber is offering free access to its Enterprise AI Browser. It gives you a hands-on way to experience how agentic AI can automate real work inside the browser while keeping internal data isolated from untrusted external sources. You can explore it yourself and decide whether this is how your organization should be approaching AI-powered browsing in 2026. Useful Links Learn more about the Mammoth Enterprise Browser and try it for free Connect with Michael Shieh on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
What happens when engineering teams can finally see the business impact of every technical decision they make? In this episode of Tech Talks Daily, I sat down with Chris Cooney, Director of Advocacy at Coralogix, to unpack why observability is no longer just an engineering concern, but a strategic lever for the entire business. Chris joined me fresh from AWS re:Invent, where he had been challenging a long-standing assumption that technical signals like CPU usage, error rates, and logs belong only in engineering silos. Instead, he argues that these signals, when enriched and interpreted correctly, can tell a much more powerful story about revenue loss, customer experience, and competitive advantage. We explored Coralogix's Observability Maturity Model, a four-stage framework that takes organizations from basic telemetry collection through to business-level decision making. Chris shared how many teams stall at measuring engineering health, without ever connecting that data to customer impact or financial outcomes. The conversation became especially tangible when he explained how a single failed checkout log can be enriched with product and pricing data to reveal a bug costing thousands of dollars per day. That shift, from "fix this tech debt" to "fix this issue draining revenue," fundamentally changes how priorities are set across teams. Chris also introduced Oli, Coralogix's AI observability agent, and explained why it is designed as an agent rather than a simple assistant. We talked about how Oli can autonomously investigate issues across logs, metrics, traces, alerts, and dashboards, allowing anyone in the organization to ask questions in plain English and receive actionable insights. From diagnosing a complex SQL injection attempt to surfacing downstream customer impact, Oli represents a move toward democratizing observability data far beyond engineering teams. Throughout our discussion, a clear theme emerged. When technical health is directly tied to business health, observability stops being seen as a cost center and starts becoming a competitive advantage. By giving autonomous engineering teams visibility into real-world impact, organizations can make faster, better decisions, foster innovation, and avoid the blind spots that have cost even well-known brands millions. So if observability still feels like a necessary expense rather than a growth driver in your organization, what would change if every technical signal could be translated into clear business impact, and who would make better decisions if they could finally see that connection? Useful LInks Connect with Chris Cooney Learn more about Coralogix Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
What does real AI transformation look like when leaders stop chasing prototypes and start demanding outcomes they can actually measure? That question sat at the center of my conversation with Alex Cross, Chief Technology Officer for EMEA at CI&T, alongside Melissa Smith, as we unpacked why so many organizations feel stuck between AI ambition and business reality. There is no shortage of excitement around AI, but there is growing skepticism too, especially from leadership teams who have seen pilots come and go without clear return. This episode focuses on how CI&T is addressing that gap head on. Alex shared how CI&T frames its work as AI-enabled transformation rather than simply layering AI tools onto existing processes. The distinction matters. Instead of using AI to speed up broken workflows, CI&T reshapes how work gets done so AI becomes part of value creation itself. We explored a standout example from ITAU, the largest bank in Latin America, where deep modernization work helped deliver gains that most executives only ever see in strategy decks. Productivity rose sharply, digital launch cycles collapsed from years to months, customer satisfaction jumped, and the commercial impact reached hundreds of millions in uplift. These are the kinds of results that change boardroom conversations. A big part of how CI&T gets there is its proprietary Flow platform. Alex explained how Flow gives clients a day-one AI environment, removing the heavy upfront cost and complexity that often slows momentum. Instead of spending months building platforms before any value appears, teams can move from proof of concept to production in as little as six to eight weeks. Flow also plays a second role that many AI programs miss, acting as a measurement layer so performance, efficiency, and ROI are visible rather than assumed. We also talked about why partnerships matter when execution is the goal. CI&T works closely with hyperscalers like AWS and Databricks, combining native tools with its own codified expertise. That combination has helped the company achieve an unusually high success rate in bringing AI initiatives to production, a challenge many organizations still struggle with. For Alex, the difference comes down to a relentless focus on production readiness and collaboration between business and technology teams from day one. Looking ahead, the conversation turned to CI&T's expansion across EMEA and what the company's 30th year represents. Rather than chasing every new trend, the focus is on productizing services around real client problems, whether that is legacy modernization, efficiency, or growth. The goal is to bridge strategy and execution in a way that feels practical, fast, and accountable. If you are leading AI initiatives and wondering why progress feels slower than the hype suggests, this episode offers a grounded perspective from the front lines. So, as organizations head into another year of bold AI plans, the real question becomes this. Are you building faster caterpillars, or are you ready to do the harder work required to turn ambition into something that can truly scale? Useful Links Connect with Alex Cross Connect With Melissa Smith Learn more about CI&T Follow CI&T on LinkedIn and YouTube Thanks to our sponsors, Alcor, for supporting the show.
What does AI-led transformation actually look like when it moves beyond pilots, hype, and slide decks and starts changing how work gets done every day? That question framed my conversation with Venk Korla, CEO of HGS, at a time when many organizations feel both excited and exhausted by AI. Boards want results, teams are buried in proofs of concept, and leaders are under pressure to show progress without breaking trust, budgets, or operations. This episode cuts through that tension and focuses on what it takes to turn ambition into outcomes. Venk shared how HGS thinks about what he calls intelligent experiences, where customer interactions are directly connected to operational follow-through. Instead of treating AI as a front-end layer or a chatbot add-on, HGS links context, data, and fulfillment so the experience continues after the conversation ends. We talked through practical examples, from airlines proactively rebooking stranded passengers before they queue at a desk, to healthcare providers guiding patients step by step before and after surgery with timely, relevant messages. In each case, the value comes from anticipation and execution, not novelty. A big part of our discussion centered on why so many AI initiatives stall. Venk described how organizations often chase technology first, launching pilots without redesigning the underlying process. HGS takes a different route through what they call Realized AI, embedding AI into specific workflows with clear ownership and measurable goals. The focus is on outcomes such as faster processing, higher compliance, and improved customer satisfaction, all proven within a ninety day proof of value. It is a disciplined approach that favors repeatability over experimentation theater. We also spent time on cloud strategy, an area where expectations and reality often collide. Venk was candid about why simple lift-and-shift migrations fail to deliver value. Without re-architecting applications to take advantage of elasticity and serverless compute, cloud spend can grow while performance stalls. He shared how a FinOps mindset, combined with application redesign, helped one client dramatically improve load speeds while reducing costs, reinforcing the idea that transformation requires structural change, not surface movement. Ethics and trust were another thread running through the conversation. Venk emphasized that AI systems are only as reliable as the data, governance, and oversight behind them. Human-in-the-loop design remains central at HGS, ensuring accountability, empathy, and confidence for both customers and employees working alongside AI. This balance between automation and human judgment came up again when we discussed their software-as-a-surface model, where AI and people work together in a carefully orchestrated way, with pricing tied to resolved outcomes rather than activity alone. As the pace of change continues to accelerate, this episode offers a grounded perspective on how to move forward without getting lost in noise. If you are leading transformation and feeling pressure to show progress, the real challenge may not be choosing the right tool, but deciding which outcomes truly matter and redesigning work around them. As AI, cloud, and customer experience continue to converge, are you building systems that look impressive in demos or that deliver predictable results when it counts? Useful Links Connect with Venk Korla Learn more about HGS Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
What if your website could spot its own problems, fix them, and quietly make more money while you focus on building your business? That question sat at the heart of my conversation with Aviv Frenkel, co-founder and CEO of Moonshot AI, and it speaks to a frustration almost every founder and digital leader recognizes. Traffic is expensive, attention is fragile, and even small issues in design or flow can quietly drain revenue for months before anyone notices. Traditional optimization often means long cycles, internal debates, and teams juggling analytics, design tools, and testing platforms while hoping the next experiment moves the needle. Aviv's perspective is shaped by lived experience. Before building Moonshot AI, he ran an e-commerce company that had plenty of visitors but disappointing conversion. Like many founders, he watched teams guess at fixes, wait weeks for tests to run, then struggle to link effort to outcome. Moonshot AI was born from that frustration, with a simple ambition. Let the website diagnose what is broken, generate solutions, test them, and deploy the winner automatically, without the need for a dedicated growth team. In our discussion, Aviv explained how Moonshot focuses on front-end experience and site performance, spotting issues such as unclear value propositions, poorly placed calls to action, or confusing mobile navigation. The platform generates its own design, copy, and code variants, runs live tests, and then rolls out what actually works. The results are hard to ignore. Brands across beauty, fashion, jewelry, and consumer electronics are seeing revenue per visitor lift by thirty to fifty percent within months. One small change to a mobile navigation menu at Hugh Jewelry led to a fifty seven percent increase in revenue per visitor, which is the kind of outcome that gets leadership teams paying attention. We also talked about momentum behind the company itself. A recently announced ten million dollar seed round has given Moonshot AI the resources to scale engineering and go-to-market teams at a time when demand is accelerating fast. But beyond funding and growth charts, what stood out most was Aviv's longer-term view. As more people turn to AI assistants and agents instead of traditional search, websites need to be structured so machines can understand them as clearly as humans. Moonshot is already optimizing for that future, preparing sites for an agent-driven web where the customer might be an algorithm as much as a person. Aviv also shared his personal journey, moving from a successful career as a tech journalist and TV host into the far more humbling world of building companies. Rejection, uncertainty, and hard lessons came with the territory, but so did clarity. His guiding idea, inspired by Jeff Bezos, is a minimum regret mindset, choosing the harder path now to avoid looking back later and wondering what might have been. So as AI moves from tools that assist to systems that act, and as websites become active participants in growth rather than static assets, the big question becomes this. Are you still relying on slow, manual optimization cycles, or are you ready to let your website start improving itself, and what does that shift mean for how you build and scale in the years ahead? Useful Links Connect with Aviv Frenkel Learn More About Moonshot AI Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
What happens when decades of supply chain planning collide with AI, volatility, and a world that no longer moves at a predictable pace? That question sat at the heart of my conversation with Piet Buyck, a serial entrepreneur whose career spans early optimization engines, cloud-era planning systems, and now AI-driven decision environments. Speaking from Antwerp just days before the holidays, Piet brought a calm, grounded perspective shaped by years inside organizations operating under real commercial pressure. His journey includes building Garvis, an AI-native planning platform later acquired by Logility, which itself became part of Aptean. That arc alone tells a story about consolidation, scale, and where modern planning is heading. We spent time unpacking ideas from Piet's book, AI Compass for Supply Chain Leaders, particularly his view that planning drifted too far into abstract numbers and away from real-world context. Long before AI became a boardroom obsession, he saw how centralized models created distance between decisions and reality. When disruption arrives, whether through pandemics, tariffs, or geopolitical tension, that distance becomes costly. Piet shared vivid examples of how slow, spreadsheet-heavy processes fail precisely when speed and clarity matter most. One thread that kept resurfacing was data. Many leaders believe their data is "good enough" until volatility exposes blind spots. Piet pushed the conversation further, explaining that AI's value goes beyond crunching clean datasets. It can move understanding across silos, surface the reasons behind decisions, and make context visible without endless meetings. That idea of explainable, collaborative AI came up repeatedly, especially as a counterpoint to opaque automation that creates confidence without understanding. We also tackled the human side. There is anxiety around skills erosion and entry-level roles disappearing, but Piet's view was more nuanced. AI shifts where time and energy go, away from gathering information and toward judgment, fairness, and accountability. In his eyes, the real challenge for leaders is choosing the right scope. Projects that are too small fade into irrelevance, while those that are too big stall under their own weight. As we looked ahead, Piet reflected on how leadership itself may change as data becomes accessible to everyone. Authority based on instinct alone becomes harder to defend when assumptions are visible. The leaders who thrive will be those who can explain direction clearly, connect data to purpose, and bring people with them. So after hearing how planning, AI, and leadership are converging in real organizations today, how do you see the balance between human judgment and machine intelligence playing out in your own world, and are we truly ready for what that shift demands? Useful Links Connect with Piet Buyck The AI Compass for Supply Chain Leaders Book Logility Website Follow on LinkedIn Thanks to our sponsors, Alcor, for supporting the show.
What happens when the systems we rely on every day start producing more signals than humans can realistically process, and how do IT leaders decide what actually matters anymore? In this episode of Tech Talks Daily, I sit down with Garth Fort, Chief Product Officer at LogicMonitor, to unpack why traditional monitoring models are reaching their limits and why AI native observability is starting to feel less like a future idea and more like a present day requirement. Modern enterprise IT now spans legacy data centers, multiple public clouds, and thousands of services layered on top. That complexity has quietly broken many of the tools teams still depend on, leaving operators buried under alerts rather than empowered by insight. Garth brings a rare perspective shaped by senior roles at Microsoft, AWS, and Splunk, along with firsthand experience running observability at hyperscale. We talk about how alert fatigue has become one of the biggest hidden drains on IT teams, including real world examples where organizations were dealing with tens of thousands of alerts every week and still missing the root cause. This is where LogicMonitor's AI agent, Edwin AI, enters the picture, not as a replacement for human judgment, but as a way to correlate noise into something usable and give operators their time and confidence back. A big part of our conversation centers on trust. AI agents behave very differently from deterministic automation, and that difference matters when systems are responsible for critical services like healthcare supply chains, airline operations, or global hospitality platforms. Garth explains why governance, auditability, and role based controls will decide how quickly enterprises allow AI agents to move from advisory roles into more autonomous ones. We also explore why experimentation with AI has become one of the lowest risk moves leaders can make right now, and why the teams who treat learning as a daily habit tend to outperform the rest. We finish by zooming out to the bigger picture, where observability stops being a technical function and starts becoming a way to understand business health itself. From mapping infrastructure to real customer experiences, to reshaping how IT budgets are justified in boardrooms, this conversation offers a grounded look at where enterprise operations are heading next. So, as AI agents become more embedded in the systems that run our businesses, how comfortable are you with handing them the keys, and what would it take for you to truly trust them? Useful Links Connect with Garth Fort Learn more about LogicMonitor Check out the Logic Monitor blog Follow on LinkedIn, X, Facebook, and YouTube. Alcor is the Sponsor of Tech Talks Network
Are we asking ourselves an honest question about who really owns automation inside a business anymore? In my conversation with Darin Patterson, Vice President of Market Strategy at Make, we explore what happens when speed becomes the default requirement, but visibility and structure fail to keep up. Make has become one of the breakout platforms for teams that want to build automated workflows without writing code, and now, with AI agents joining the mix, the stakes feel even higher. Darin talks candidly about the tension between empowerment and chaos, especially in organizations that embraced no-code tools fast and early, only to discover that automation can quietly turn into sprawl if left unchecked. What struck me most is how strongly Darin challenges the idea that documentation alone can save modern IT teams. He argues that traditional monitoring tools and workflow documentation are breaking down under the weight of constant iteration. That's where Make Grid comes in. Make Grid creates an auto-generated, real-time visual map of a company's automation ecosystem, something Darin describes as a turning point for governance. He explains why this matters now, not later. As companies deploy AI into processes that used to be owned by specialists, Grid provides a shared lens for understanding what is running, who built it, and where dependencies exist. It's an answer to a problem many IT leaders are reluctant to admit publicly, that automation systems often grow faster than oversight systems ever could. Darin also offers a refreshingly grounded take on the psychology of ambitious teams. He talks about the need to prevent "no-code anarchy," a phrase I've heard whispered at conferences, but rarely unpacked with clarity. His view is simple, trust teams to build, but give them shared maps, guardrails, and governance that don't slow them down. That balance between autonomy and oversight becomes even more meaningful when AI is introduced into workflows that touch security, IT performance, and cross-team accountability. Make Grid attempts to solve that balance by showing the automation architecture visually, even when internal documentation has gone stale. So here's the question I want to leave you with, if AI agents can now design, connect, and deploy workflows across an organization, what role will visual governance play in keeping businesses both fast and accountable? And what does good oversight look like when humans are no longer the only builders in the system? Useful Links Learn more about Make Connect with Darin Patterson Thanks to our sponsors, Alcor, for supporting the show.
Was 2025 the year the games industry finally stopped talking about direct-to-consumer and started treating it as the default way to do business? In this episode of Tech Talks Daily, I'm joined by Chris Hewish, President at Xsolla, for a wide-ranging conversation about how regulation, platform pressure, and shifting player expectations have pushed D2C from the margins into the mainstream. As court rulings, the Digital Markets Act, and high-profile battles like Epic versus Apple continue to reshape the industry, developers are gaining more leverage, but also more responsibility, over how they distribute, monetize, and support their games. Chris breaks down why D2C is no longer just about avoiding app store fees. It is about owning player relationships, controlling data, and building sustainable businesses in a more consolidated market. We explore how tools like Xsolla's Unity SDK are lowering the barrier for studios to sell directly across mobile, PC, and the web, while handling the operational complexity that often scares teams away from global payments, compliance, and fraud management. We also dig into what is changing inside live service games. From offer walls that help monetize the vast majority of players who never spend, to LiveOps tools that simplify campaigns and retention strategies, Chris shares real examples of how studios are seeing meaningful lifts in revenue and engagement. The conversation moves beyond technology into mindset, especially for indie and mid-sized teams learning that treating a game as a long-term business needs to start far earlier than launch day. Here in 2026, we talk about account-centric economies, hybrid monetization models running in parallel, and the growing role of community-driven commerce inspired by platforms like Roblox and Fortnite. There is optimism in these shifts, but also understandable anxiety as studios adjust to managing more of the stack themselves. Chris offers a grounded perspective on how that balance is likely to play out. So if games are becoming hobbies, platforms are opening up, and developers finally have the tools to meet players wherever they are, what does the next phase of direct-to-consumer really look like, and are studios ready to fully own that relationship? Useful Links Connect with Chris Hewish on LinkedIn Learn more about Xsolla Follow on LinkedIn, Twitter, and Facebook Thanks to our sponsors, Alcor, for supporting the show.
Chegou o tão especial último OVERTIME do ano e contamos com a presença de Cristino Melo e João Alcorão para falar sobre o ano de CS2 e VALORANT, se juntando a nossa já conhecida bancada de especialistas Lucas Benvegnu e apresentação de Pietro Santiago.
Esse mês eu vou trazer 9 livros que vão levar vcs a um passeio por aspectos de algumas das religiões com mais praticantes no mundo e no Brasil: catolicismo, protestantismo (evangélicos), judaísmo, islamismo, hinduísmo, budismo, espiritismo, além das religiões afro-brasileiras candomblé e umbanda. Atualmente, mais do que nunca, o mundo precisa de tolerância, empatia e respeito a diversidade religiosa. Continuando o especial desse mês de dezembro, hoje nosso passeio pelas religiões do mundo mergulha no islamismo, a segunda maior religião do mundo, com o livro "Salat in Secret", ou "Salá em segredo", escrito por Jamilah Thompkins-Bigelow, ilustrado por Hatem Aly e ainda não publicado no Brasil, por isso eu traduzi e adaptei especialmente pra esse episodio. O Salá refere-se às cinco orações públicas que cada muçulmano deve realizar diariamente, voltado para Meca, e é um dos Cinco Pilares do Islamismo. Os salás devem ser efetuadas em árabe, mesmo que o crente não conheça a língua, embora as súplicas (dua) possam ser feitas em outro idioma. As orações devem ser feitas em momentos concretos do dia, que não correspondem a horas, mas sim a etapas do curso do Sol. Consistem na recitação de um conjunto de versículos do Alcorão, num ciclo de posições (em pé, curvado, de joelhos, prostrado e sentado) a que se chama de rakca (ou genuflexão); o número de genuflexões varia de acordo com a oração do dia. Nesta bela história sobre comunidade, família e aceitação, um menino chamado Muhammad recebe um tapete especial para o salá no seu sétimo aniversário. Sete é a idade em que as crianças muçulmanas são incentivadas a rezar, e Muhammad está determinado a fazer todas as cinco orações diárias na hora certa. Mas uma das orações ocorre durante o horário escolar — e ele está preocupado em ser visto rezando na escola. Seu pai estaciona sua caminhonete para rezar em locais públicos, e as pessoas ficam olhando e zombando dele. Será que o mesmo acontecerá com Muhammad? No final, com a ajuda de sua professora, ele encontra o lugar perfeito para rezar. "Salat in Secret", de dois criadores muçulmanos altamente aclamados, é um olhar comovente e empoderador sobre uma faceta importante do Islã que muitas crianças praticantes apreciam, mas podem ter medo de compartilhar. Para acompanhar a história juntamente com as ilustrações do livro, compre o livro aqui: https://amzn.to/3LSYmAYEsse livro trouxe um aspecto do islamismo, seguido pelos muçulmanos, que é a segunda maior religião do mundo. O islamismo, assim como o judaísmo e o cristianismo, é uma religião monoteísta, ou seja, os muçulmanos acreditam na existência de apenas um Deus que é chamado por eles de Allah. Seu livro sagrado é o Alcorão e os muçulmanos acreditam que três cidades são sagradas: Medina, Meca e Jerusalém. Fiquem ligados que daqui a 3 dias sai mais um episodio, dessa vez sobre a umbanda, não percam! Se vc gostou, compartilhe com seus amigos e me siga nas redes sociais! https://www.instagram.com/bookswelove_livrosqueamamos/
Desmascarando o Islã | Os relatos sobre Jesus no Alcorão
Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Meditación predicada a Numerarias en Alcor, con motivo del Aniversario de la Fundación del Opus Dei. Meditamos en la necesidad de hacer la Obra "de rodillas", rezando, como hizo San Josemaría desde el comienzo. Ilusión por entrar en esa sinfonía de entrega para sacar la Obra adelante.Escucha este episodio completo y accede a todo el contenido exclusivo de Meditaciones diarias. Descubre antes que nadie los nuevos episodios, y participa en la comunidad exclusiva de oyentes en https://go.ivoox.com/sq/874295
Quem Abraão realmente representa? Judeus, cristãos e muçulmanos o chamam de pai da fé. Mas será que todos creem no mesmo Deus? Este episódio expõe a origem histórica e teológica do Islã, a verdadeira identidade de Allah, e as diferenças cruciais entre o Deus da Bíblia e o deus do Alcorão. Com base em Romanos 4 e Gálatas 3, vemos que Abraão foi justificado pela fé no Redentor prometido—Jesus Cristo. Essa promessa não aponta para uma religião construída sobre tradições misturadas, mas para a verdade revelada por Deus. O apóstolo Paulo nos lembra que os verdadeiros filhos de Abraão são aqueles que creem. Neste estudo esclarecedor, descubra por que o Cristianismo e o Islã não são variações do mesmo caminho, mas destinos completamente diferentes. Para mais ensinamentos bíblicos, visite nosso site: https://www.wisdomonline.org/?lang=Portuguese
Miércoles primaveral en Sevilla, con máximas que van a alcanzar los 28 grados en la capital. Unas temperaturas que van a ir subiendo en lo que resta de semana hasta superar los treinta grados. Hoy abre la capilla ardiente en la Basílica de San Pedro, el lunes será la misa funeral en la Catedral de Sevilla por la muerte del Papa Francisco. Un fallecimiento que por el momento no suspendería el traslado a Roma del Cachorro, aunque podría haber cambios en la agenda de actos.Miércoles 23 de abril en que se conmemora el día del libro, vamos a repasar algunas de las actividades previstas, y en el que comienza la primera feria de Andalucía, la feria de Mairena del Alcor.Las noticias de Sevilla con Marta Sánchez y Ángel MontanerEscuchar audio
If you have sharp eyes or good binoculars, you can sometimes see double in the night sky – two stars that are quite close together. In some cases, the stars really are close – they can be bound to each other by gravity, forming a binary. In other cases, though, it’s just a coincidence. The stars are unrelated, but they just happen to line up in the same direction. These stars are known as optical doubles. And some of them are beautiful sights – especially when viewed through a telescope. The two members can show different colors, providing a nice contrast. One of the best-known examples is Albireo. It marks the head of Cygnus, the swan, and it’s high overhead at nightfall. A small telescope reveals one blue star and one gold star. The best measurements to date indicate that the stars are about 30 light-years apart. An example that’s visible to the eye alone is Algedi, in Capricornus. It’s due south at nightfall. The eye can just make it out as two separate points of light. The stars are hundreds of light-years apart. Sometimes, it’s hard to tell if a double star is a binary or not. The best example is Mizar and Alcor, in the handle of the Big Dipper. It’s fairly easy to see the stars as two points of light. They actually move through space together, so they probably were born together. But astronomers aren’t sure if they’re a true binary – or just two related stars moving through space on their own. Script by Damond Benningfield
SERIES 2 EPISODE 210: COUNTDOWN WITH KEITH OLBERMANN A-Block (1:44) SPECIAL COMMENT: It's over. If President Biden and his campaign team and his party discipline team can handle Trump and the electorate and the news conferences and the debates as efficiently as they have turned around the effort to push him off the ticket, they are going to win the election in a landslide. You never say it's over, especially not with Biden headed for a news conference tomorrow as the NATO summit in Washington concludes in which literally two bad answers or one REALLY bad answer could end his candidacy before noon on Friday. But for now, and skipping for a moment the obviously larger question over whether this signals the start of democracy's going out of business sale… it… is… over. Final score: Number of Democrats in the House who called for him to step down, on the record… SEVEN. Number of Democrats in the Senate who called for him to step down, on the record… NONE. Number of ex-presidents who called for him to step down… NONE. Number of historians he loves who called for him to step down…NONE. Number of former Speakers of the House who reportedly told friends in private she's deeply uneasy with his continued candidacy and of whom a Biden confidante said "He would listen reluctantly, but he'd listen to her”who called for him to step down… NONE. Semafor News asked an attendee, off the record, if the mood was comparable to that at a funeral. The answer? “That is an insult to funerals.” Now there's just one problem in all this and I think you already know what it is. The only problem it solves is the one Joe Biden had, or has. Out here on the going-out-of-business sale floor, the President is suddenly losing Wisconsin by five, and the Cook Political report just moved Arizona and Georgia and Nevada from Toss-Up to Lean Trump; and just moved Minnesota, Nebraska-Second, and New Hampshire from Likely Biden to Lean Biden. And the Biden Campaign seems to think this is over, rather than permanent. They will find out otherwise tomorrow night when he gets parsed at that press conference like he was a newly-found Dead Sea Scroll. ALSO: Two Senators actually call for a Special Prosecutor to investigate Clarence Thomas! Project 2025 is trending. And we'll play another edition of "What Crime Did Trump Commit TODAY?" B-Block (27:00) THE WORST PERSONS IN THE WORLD: There are people preparing to be cryogenically frozen at the facility where they still keep Ted Williams in a jar who are setting up thousand-year investment funds for when they get thawed. Brian Stelter is so desperate to be rehired by The New York Times he's pretending his old paper also wrote an editorial calling for Trump to bow out. And Dana Bash? Jake Tapper? Trump praised your work on the debate. Your careers are thus over. C-Block (38:10) THINGS I PROMISED NOT TO TELL: I watched an Atlanta Braves game the other night and was thus flashed back to the World Series I covered there where the hotel put me in a room next to all-night choir practice.See omnystudio.com/listener for privacy information.
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