Podcasts about Observability

BONUS: The Operating System for Software-Native Organizations - The Five Core Principles In this BONUS episode, the final installment of our Special Xmas 2025 reflection on Software-native businesses, we explore the five fundamental principles that form the operating system for software-native organizations. Building on the previous four episodes, this conversation provides the blueprint for building organizations that can adapt at the speed of modern business demands, where the average company lifespan on the S&P 500 has dropped from 33 years in the 1960s to a projected 12 years by 2027. The Challenge of Adaptation "What we're observing in Ukraine is adaptation happening at a speed that would have been unthinkable in traditional military contexts - new drone capabilities emerge, countermeasures appear within days, and those get countered within weeks." The opening draws a powerful parallel between the rapid adaptation we're witnessing in drone warfare and the existential threats facing modern businesses. While our businesses aren't facing literal warfare, they are confronting dramatic disruption. Clayton Christensen documented this in "The Innovator's Dilemma," but what he observed in the 1970s and 80s is happening exponentially faster now, with software as the accelerant. If we can improve businesses' chances of survival even by 10-15%, we're talking about thousands of companies that could thrive instead of fail, millions of jobs preserved, and enormous value created. The central question becomes: how do you build an organization that can adapt at this speed? Principle 1: Constant Experimentation with Tight Feedback Loops "Everything becomes an experiment. Not in the sense of being reckless or uncommitted, but in being clear about what we're testing and what we expect to learn. I call this: work like a scientist: learning is the goal." Software developers have practiced this for decades through Test-Driven Development, but now this TDD mindset is becoming the ruling metaphor for managing products and entire businesses. The practice involves framing every initiative with three clear elements: the goal (what are we trying to achieve?), the action (what specific thing will we do?), and the learning (what will we measure to know if it worked?). When a client says "we need to improve our retrospectives," software-native organizations don't just implement a new format. Instead, they connect it to business value - improving the NPS score for users of a specific feature by running focused retrospectives that explicitly target user pain points and tracking both the improvements implemented and the actual NPS impact. After two weeks, you know whether it worked. The experiment mindset means you're always learning, never stuck. This is TDD applied to organizational change, and it's powerful because every process change connects directly to customer outcomes. Principle 2: Clear Connection to Business Value "Software-native organizations don't measure success by tasks completed, story points delivered, or features shipped. Or even cycle time or throughput. They measure success by business outcomes achieved." While this seems obvious, most organizations still optimize for output, not outcomes. The practice uses Impact Mapping or similar outcome-focused frameworks where every initiative answers three questions: What business behavior are we trying to change? How will we measure that change? What's the minimum software needed to create that change? A financial services client wanted to "modernize their reporting system" - a 12-month initiative with dozens of features in project terms. Reframed through a business value lens, the goal became reducing time analysts spend preparing monthly reports from 80 hours to 20 hours, measured by tracking actual analyst time, starting with automating just the three most time-consuming report components. The first delivery reduced time to 50 hours - not perfect, but 30 hours saved, with clear learning about which parts of reporting actually mattered. The organization wasn't trying to fulfill requirements; they were laser focused on the business value that actually mattered. When you're connected to business value, you can adapt. When you're committed to a feature list, you're stuck. Principle 3: Software as Value Amplifier "Software isn't just 'something we do' or a support function. Software is an amplifier of your business model. If your business model generates $X of value per customer through manual processes, software should help you generate $10X or more." Before investing in software, ask whether this can amplify your business model by 10x or more - not 10% improvement, but 10x. That's the threshold where software's unique properties (zero marginal cost, infinite scale, instant distribution) actually matter, and where the cost/value curve starts to invert. Remember: software is still the slowest and most expensive way to check if a feature would deliver value, so you better have a 10x or more expectation of return. Stripe exemplifies this principle perfectly. Before Stripe, accepting payments online required a merchant account (weeks to set up), integration with payment gateways (months of development), and PCI compliance (expensive and complex). Stripe reduced that to adding seven lines of code - not 10% easier, but 100x easier. This enabled an entire generation of internet businesses that couldn't have existed otherwise: subscription services, marketplaces, on-demand platforms. That's software as amplifier. It didn't optimize the old model; it made new models possible. If your software initiatives are about 5-10% improvements, ask yourself: is software the right medium for this problem, or should you focus where software can create genuine amplification? Principle 4: Software as Strategic Advantage "Software-native organizations use software for strategic advantage and competitive differentiation, not just optimization, automation, or cost reduction. This means treating software development as part of your very strategy, not a way to implement a strategy that is separate from the software." This concept, discussed with Tom Gilb and Simon Holzapfel on the podcast as "continuous strategy," means that instead of creating a strategy every few years and deploying it like a project, strategy and execution are continuously intertwined when it comes to software delivery. The practice involves organizing around competitive capabilities that software uniquely enables by asking: How can software 10x the value we generate right now? What can we do with software that competitors can't easily replicate? Where does software create a defensible advantage? How does our software create compounding value over time? Amazon Web Services didn't start as a product strategy but emerged from Amazon building internal capabilities to run their e-commerce platform at scale. They realized they'd built infrastructure that was extremely hard to replicate and asked: "What if we offered it to others?" AWS became Amazon's most profitable business - not because they optimized their existing retail business, but because they turned an internal capability into a strategic platform. The software wasn't supporting the strategy - the software became the strategy. Compare this to companies that use software just for cost reduction or process optimization - they're playing defense. Software-native companies use software to play offense, creating capabilities that change the competitive landscape. Continuous strategy means your software capabilities and your business strategy evolve together, in real-time, not in annual planning cycles. Principle 5: Real-Time Observability and Adaptive Systems "Software-native organizations use telemetry and real-time analytics not just to understand their software, but to understand their entire business and adapt dynamically. Observability practices from DevOps are actually ways of managing software delivery itself. We're bootstrapping our own operating system for software businesses." This principle connects back to Principle 1 but takes it to the organizational level. The practice involves building systems that constantly sense what's happening and can adapt in real-time: deploy with feature flags so you can turn capabilities on/off instantly, use A/B testing not just for UI tweaks but for business model experiments, instrument everything so you know how users actually behave, and build feedback loops that let the system respond automatically. Social media companies and algorithmic trading firms already operate this way. Instagram doesn't deploy a new feed algorithm and wait six months to see if it works - they're constantly testing variations, measuring engagement in real-time, adapting the algorithm continuously. The system is sensing and responding every second. High-frequency trading firms make thousands of micro-adjustments per day based on market signals. Imagine applying this to all businesses: a retail company that adjusts pricing, inventory, and promotions in real-time based on demand signals; a healthcare system that dynamically reallocates resources based on patient flow patterns; a logistics company whose routing algorithms adapt to traffic, weather, and delivery success rates continuously. This is the future of software-native organizations - not just fast decision-making, but systems that sense and adapt at software speed, with humans setting goals and constraints but software executing continuous optimization. We're moving from "make a decision, deploy it, wait to see results" to "deploy multiple variants, measure continuously, let the system learn." This closes the loop back to Principle 1 - everything is an experiment, but now the experiments run automatically at scale with near real-time signal collection and decision making. It's Experiments All The Way Down "We established that software has become societal infrastructure. That software is different - it's not a construction project with a fixed endpoint; it's a living capability that evolves with the business." This five-episode series has built a complete picture: Episode 1 established that software is societal infrastructure and fundamentally different from traditional construction. Episode 2 diagnosed the problem - project management thinking treats software like building a bridge, creating cascade failures throughout organizations. Episode 3 showed that solutions already exist, with organizations like Spotify, Amazon, and Etsy practicing software-native development successfully. Episode 4 exposed the organizational immune system - the four barriers preventing transformation: the project mindset, funding models, business/IT separation, and risk management theater. Today's episode provides the blueprint - the five principles forming the operating system for software-native organizations. This isn't theory. This is how software-native organizations already operate. The question isn't whether this works - we know it does. The question is: how do you get started? The Next Step In Building A Software-Native Organization "This is how transformation starts - not with grand pronouncements or massive reorganizations, but with conversations and small experiments that compound over time. Software is too important to society to keep managing it wrong." Start this week by doing two things.  First, start a conversation: pick one of these five principles - whichever resonates most with your current challenges - and share it with your team or leadership. Don't present it as "here's what we should do" but as "here's an interesting idea - what would this mean for us?" That conversation will reveal where you are, what's blocking you, and what might be possible.  Second, run one small experiment: take something you're currently doing and frame it as an experiment with a clear goal, action, and learning measure. Make it small, make it fast - one week maximum, 24 hours if you can - then stop and learn. You now have the blueprint. You understand the barriers. You've seen the alternatives. The transformation is possible, and it starts with you. Recommended Further Reading Tom Gilb and Simon Holzapfel episodes on continuous strategy  The book by Christensen, Clayton: "The Innovator's Dilemma"  The book by Gojko Adzic: Impact Mapping  Ukraine drone warfare Company lifespan statistics: Innosight research on S&P 500 turnover  Stripe's impact on internet businesses Amazon AWS origin story DevOps observability practices About Vasco Duarte Vasco Duarte is a thought leader in the Agile space, co-founder of Agile Finland, and host of the Scrum Master Toolbox Podcast, which has over 10 million downloads. Author of NoEstimates: How To Measure Project Progress Without Estimating, Vasco is a sought-after speaker and consultant helping organizations embrace Agile practices to achieve business success. You can link with Vasco Duarte on LinkedIn.

Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun sit down with Spring Team member Brian Clozel to discuss OpenTelemetry (OTEL) and how to leverage it in your Spring Boot applications. Learn how OTEL provides a vendor-neutral standard for collecting telemetry data including traces, metrics, and logs to gain deeper observability into your applications. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform.Show Notes:OpenTelemtry with Spring BootBrian Clozel GitHubBrian Clozel on Mastodon 

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubCheck out more here:https://gotopia.tech/episodes/402Albert S. Tanure - Cross Solutions Architec at Microsoft & Author of "ASP.NET Core 9 Essentials"Rafael Herik de Carvalho - Platform & DevOps Engineering at DevoteamRESOURCESAlberthttps://x.com/alberttanurehttps://github.com/tanurehttps://www.linkedin.com/in/albert-tanurehttps://www.codefc.io/enRafaelhttps://x.com/rafaelherikhttps://github.com/rafaelherikhttps://www.linkedin.com/in/rafaelh-carvalhohttps://dev.to/rafaelherikDESCRIPTIONMicrosoft Solutions Architect Albert Tanure explores his approach to writing "ASP.NET Core 9 Essentials", a guide designed to take developers from basic .NET concepts to advanced cloud-native application development. Albert emphasizes the intentional structure of starting with foundations before introducing best practices, covering the complete application lifecycle from UI development and APIs to deployment, monitoring, and cloud operations.The conversation highlights how modern development requires understanding not just coding, but also DevOps practices, observability with tools like OpenTelemetry, dynamic configurations, containers, and cloud-native principles. The book serves both beginners seeking solid foundations and experienced developers looking to understand modern deployment strategies, with particular emphasis on chapters 9-11 that cover cloud native mindsets and operational considerations.RECOMMENDED BOOKSAlbert Tanure • ASP.NET Core 9 Essentials • https://amzn.to/43bH73tMark J. Price • Real-World Web Development with .NET 9 • https://amzn.to/46ZKsnwMark J. Price • C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals • https://amzn.to/4o5E5FZFabrizio Romano & Heinrich Kruger • Learning Python Programming • https://amzn.to/4myLBItBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Groundcover CEO Shahar Azulay joins Dash0's Mirko Novakovic for a candid conversation on why modern observability needs a fundamental reset. They dive into the real-world challenges of eBPF-based instrumentation, migration friction from legacy vendors and bold go-to-market strategies. They also debate Groundcover's “Bring Your Own Cloud” model and how it prompts a reassessment of cost, control and business model incentives in observability.

Enterprises are racing to deploy AI services, but the teams responsible for running them in production are seeing familiar problems reemerge—most notably, silos between data scientists and operations teams, reminiscent of the old DevOps divide. In a discussion recorded at AWS re:Invent 2025, IBM's Thanos Matzanas and Martin Fuentes argue that the challenge isn't new technology but repeating organizational patterns. As data teams move from internal projects to revenue-critical, customer-facing applications, they face new pressures around reliability, observability, and accountability.The speakers stress that many existing observability and governance practices still apply. Standard metrics, KPIs, SLOs, access controls, and audit logs remain essential foundations, even as AI introduces non-determinism and a heavier reliance on human feedback to assess quality. Tools like OpenTelemetry provide common ground, but culture matters more than tooling.Both emphasize starting with business value and breaking down silos early by involving data teams in production discussions. Rather than replacing observability professionals, AI should augment human expertise, especially in critical systems where trust, safety, and compliance are paramount.Learn more from The New Stack about enabling AI with silos: Are Your AI Co-Pilots Trapping Data in Isolated Silos?Break the AI Gridlock at the Intersection of Velocity and TrustTaming AI Observability: Control Is the Key to SuccessJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, Steph Hippo, Platform Engineering Director at Honeycomb, joins The Prodcast to discuss AI and SRE.  Steph explains how observability helps us understand complex systems from their outputs, and provides a foundation for SRE to respond to system problems. This episode explains how AI and observability build a self-reinforcing loop.  We also discuss how AI can detect and respond to certain classes of incidents, leading to self-healing systems and allowing SREs to focus on novel and interesting problems. She advises small businesses adopting AI to learn from others' mistakes (post-mortems) and to commit time and budget to experimentation.  

As AI tools and agentic AI become part of how applications are developed, delivered, and managed, application performance monitoring and observability have to adapt. Ned Bellavance sits down with Drew Flowers and Jacob Yackenovich from IBM Instana about where these fields sit today, and the potential impacts of AI. They detail the challenges of application... Read more »

As AI tools and agentic AI become part of how applications are developed, delivered, and managed, application performance monitoring and observability have to adapt. Ned Bellavance sits down with Drew Flowers and Jacob Yackenovich from IBM Instana about where these fields sit today, and the potential impacts of AI. They detail the challenges of application... Read more »

As AI tools and agentic AI become part of how applications are developed, delivered, and managed, application performance monitoring and observability have to adapt. Ned Bellavance sits down with Drew Flowers and Jacob Yackenovich from IBM Instana about where these fields sit today, and the potential impacts of AI. They detail the challenges of application... Read more »

The worlds of IT security and operations are being pulled together and AI is a catalyst that's making it happen. The focus on observability that's been part of the DevOps movement, is playing an important role in improving security effectiveness and Scott Crawford, Mark Ehr and Mike Fratto return to look at how this is happening with host Eric Hanselman. Security teams have always wrestled with making effective use of telemetry data from the infrastructure and applications they are securing. Correlating data from just the security tooling is hard enough, let alone adding operational data to the mix. Security Information and Event Management (SIEM) systems came into existence many years ago specifically to address this problem, but they were complex to configure and operate and needed tending to stay accurate. The volumes of data coming from observability initiatives was promising, but new approaches were required and AI and ML have been key to unlocking that value. Once again, we've hit an opportunity where it's all about the data and getting it to where it can be put to work. The Open Telemetry project simplified data interchange, but the question remained as to where all of this data had to live. It's not practical to get all of the data in one place, but data fabrics and federation can manage access effectively. Better correlation opens the door to many possibilities, including building a single source of truth for IT assets. There's a lot of benefit to bringing security and operations together.   More S&P Global Content: AI for security: Agentic AI will be a focus for security operations in 2025 AI in action: unleashing agentic potential   For S&P Global subscribers: 2026 Trends in Information Security Deal Analysis: Palo Alto Acquires Chronosphere Big Picture Report: 2026 AI Outlook – Unleashing agentic potential   Credits: Host/Author: Eric Hanselman Guests: Scott Crawford, Mark Ehr, Mike Fratto Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith

Neste episódio, destrinchamos como o Kong conversa com a Gateway API no Kubernetes, passamos por GatewayClass, Gateway e HTTPRoute, e mostramos onde os plugins entram para dar aquele boost de segurança e observabilidade.A gente também faz o raio‑X dos componentes, comenta escolhas de arquitetura (do balanceamento de tráfego ao mTLS com cert‑manager) e debate os trade‑offs entre Ingress Controller tradicional e o ecossistema moderno da Gateway API. Sem prometer milagres, mas prometendo menos YAML sofrido.E claro: não faltam comparações sinceras entre OSS e Enterprise, além de dicas de onde cavar documentação que presta.Links Importantes: - Marco Ollivier - https://www.linkedin.com/in/marcopollivier/ - Slides DOD - https://docs.google.com/presentation/d/1GxcpOBaomthc4gDnmNSakEMfMZIkiseB16KMRVdnNkw/edit?usp=sharing - João Brito - https://www.linkedin.com/in/juniorjbn/ - Kong - https://github.com/Kong/kongO Kubicast é uma produção da Getup, empresa especialista em Kubernetes e projetos open source para Kubernetes. Os episódios do podcast estão nas principais plataformas de áudio digital e no YouTube.com/@getupcloud.

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubAlex Ewerlöf - Senior Staff Engineer at Volvo Cars & Author of "Reliability Engineering Mindset"Charity Majors - Co-Founder & CTO of honeycomb.io & Co-Author of "Observability Engineering"RESOURCESAlexhttps://bsky.app/profile/alexewerlof.comhttps://www.linkedin.com/in/alexewerlofhttps://www.alexewerlof.comCharityhttps://twitter.com/mipsytipsyhttps://linkedin.com/in/charity-majorshttps://charity.wtfhttps://www.honeycomb.io/blog/slos-are-the-api-for-your-engineering-teamDESCRIPTIONAlex Ewerlöf shares his journey from product engineering to reliability engineering and discusses the practical challenges of implementing Google's SRE practices in real-world companies.He emphasizes the significant gap between Google's idealized SRE approach — which he links to "a fantastic chef's recipe for Michelin-starred restaurants" — and the reality most companies face with limited resources and infrastructure. The discussion covers key topics including the evolution from traditional operations to where engineers own their code in production, the critical importance of choosing SLIs that align with business impact, and how SLOs help set expectations and help the service consumers prepare non-functional requirements.Alex coined the law of 10x per 9 highlighting that reliability isn't free and requires careful cost-benefit analysis.RECOMMENDED BOOKSAlex Ewerlöf • Reliability Engineering Mindset • https://blog.alexewerlof.com/p/remC. Majors, L. Fong-Jones & G. Miranda • Observability Eng. • https://amzn.to/38scbmaC. Majors & L. Campbell • Database Reliability Eng. • https://amzn.to/3ujybdSAlex Hidalgo • Implementing Service Level Objectives • https://amzn.to/4pbWJxwBrian Klaas • Fluke • https://amzn.to/41V1CkoSimler & Hanson • The Elephant in the BrPsst! The Folium Diary has something it wants to tell you - please come a little closer...YOU can change the world - you do it every day. Let's change it for the better, together.Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Tech leaders are often led to believe that they have “full-stack observability.” The MELT framework—metrics, events, logs, and traces—became the industry standard for visibility. However, Robert Cowart, CEO and Co-Founder of ElastiFlow, believes that this MELT framework leaves a critical gap. In the latest episode of the Tech Transformed podcast, host Dana Gardner, President and Principal Analyst at Interabor Solutions, sits down with Cowart to discuss network observability and its vitality in achieving full-stack observability.The speakers discuss the limitations of legacy observability tools that focus on MELT and how this leaves a significant and dangerous blind spot. Cowart emphasises the need for teams to integrate network data enriched with application context to enhance troubleshooting and security measures. What's Beyond MELT?Cowart explains that when it comes to the MELT framework, meaning “metrics, events, logs, and traces, think about the things that are being monitored or observed with that information. This is alluded to servers and applications.“Organisations need to understand their compute infrastructure and the applications they are running on. All of those servers are connected to networks, and those applications communicate over the networks, and users consume those services again over the network,” he added.“What we see among our growing customer base is that there's a real gap in the full-stack story that has been told in the market for the last 10 years, and that is the network.”The lack of insights results in a constant blind spot that delays problem-solving, hides user-experience issues, and leaves organizations vulnerable to security threats. Cowart notes that while performance monitoring tools can identify when an application call to a database is slow, they often don't explain why.“Was the database slow, or was the network path between them rerouted and causing delays?” he questions. “If you don't see the network, you can't find the root cause.”The outcome is longer troubleshooting cycles, isolated operations teams, and an expensive “blame game” among DevOps, NetOps, and SecOps.Elastiflow's approaches it differently. They focus on observability to network connectivity—understanding who is communicating with whom and how that communication behaves. This data not only speeds up performance insights but also acts as a “motion detector” within the organization. Monitoring east-west, north-south, and cloud VPC flow logs helps organizations spot unusual patterns that indicate internal threats or compromised systems used for launching external attacks.“Security teams are often good at defending the perimeter,” Cowart says. “But once something gets inside, visibility fades. Connectivity data fills that gap.”Isolated Monitoring to Unified Experience Cowart believes that observability can't just be about green lights...

Kennst du das? Neun Klicks sind blitzschnell, der zehnte hängt gefühlt ewig. Genau da frisst die Tail Latency deine User Experience und der Durchschnittswert hilft dir kein bisschen. In dieser Episode tauchen wir in Request Hedging ein, also das bewusste Duplizieren von Requests, um P99 zu drücken und Ausreißer zu entschärfen.Wir starten mit einem kurzen Recap zu Resilience Engineering: Timeouts, Retries, Exponential Backoff, Jitter, Circuit Breaker. Danach gehen wir tief rein ins Hedging: Was ist der Hedge Threshold, warum optimieren wir auf Tail statt Head Latency und wie Perzentile wie P50, P95 und P99 die Sicht auf Performance verändern. Wir zeigen, wie du Hedging sicher umsetzt, ohne dein Backend zu überlasten, wo Idempotenz Pflicht ist und warum Schreibzugriffe besonders heikel sind.In der Praxis klären wir, wie du Requests sauber cancelst: HTTP 1.1 via FIN und Reset, HTTP 2 mit RESET_STREAM, gRPC Support und wie Go mit Context Cancellation nativ hilft. Zum Tooling gibt es echte Beispiele: Envoy als Cloud-native Proxy mit Hedging, gRPC, Open Source Erfahrungen. In der Datenbankwelt sprechen wir über Read Hedging, Quorum Reads und Write-Constraints bei Cassandra und Kafka, über Vitess im MySQL-Universum und Grenzen von PG Bouncer. Auch Caches wie Redis und Memcached sowie DNS Patterns wie Happy Eyeballs sind am Start. Historisch ordnen wir das Ganze mit The Tail at Scale von Jeff Dean ein und schauen, wie Google, Netflix, Uber, LinkedIn oder Cloudflare Hedging verwenden.Am Ende nimmst du klare Best Practices mit: Hedging gezielt auf Tail Latency einsetzen, Requests wirklich canceln, Idempotenz sicherstellen, dynamische Thresholds mit Observability füttern und deine Guardrails definieren.Neugierig, ob Hedging dein P99 rettet, ohne dich selbst zu ddosen? Genau darum geht es.Bonus: Hedgehog hat damit nichts zu tun, auch wenn der Name dazu verführt.Keywords: Resilience Engineering, Request Hedging, Tail Latency, P99, Perzentile, Microservices, HTTP 2, gRPC, Go Context, Observability, Monitoring, Prometheus, Grafana, Envoy, Open Source, Cassandra, Kafka, Vitess, Redis, Memcached, Quorum Reads, Tech Community, Networking.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

A lot of network monitoring tools allow you to say, “It's not the network,” but a more useful tool would not only tell you that it's not the network, but also what the problem actually is. Today our guest is Brandon Hale, CTO at IBM SevOne. He is here to give us an overview of... Read more »

A lot of network monitoring tools allow you to say, “It's not the network,” but a more useful tool would not only tell you that it's not the network, but also what the problem actually is. Today our guest is Brandon Hale, CTO at IBM SevOne. He is here to give us an overview of... Read more »

DevOps practitioners — whether developers, operators, SREs or business stakeholders — increasingly rely on telemetry to guide decisions, yet face growing complexity, siloed teams and rising observability costs. In a conversation at KubeCon + CloudNativeCon North America, IBM's Jacob Yackenovich emphasized the importance of collecting high-granularity, full-capture data to avoid missing critical performance signals across hybrid application stacks that blend legacy and cloud-native components. He argued that observability must evolve to serve both technical and nontechnical users, enabling teams to focus on issues based on real business impact rather than subjective judgment.AI's rapid integration into applications introduces new observability challenges. Yackenovich described two patterns: add-on AI services, such as chatbots, whose failures don't disrupt core workflows, and blocking-style AI components embedded in essential processes like fraud detection, where errors directly affect application function.Rising cloud and ingestion costs further complicate telemetry strategies. Yackenovich cautioned against limiting visibility for budget reasons, advocating instead for predictable, fixed-price observability models that let organizations innovate without financial uncertainty.Learn more from The New Stack about the latest in observability: Introduction to ObservabilityObservability 2.0? Or Just Logs All Over Again?Building an Observability Culture: Getting Everyone OnboardJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Adriana is a CNCF Ambassador, blogger, host of the Geeking Out Podcast, and a maintainer of the OpenTelemetry End User SIG. By day, she's a Principal Developer Advocate at Dynatrace, focusing on Observability and OpenTelemetry. By night, she climbs walls. She also loves capybaras, because they make her happy.You can find Adriana on the following sites:BlueskyBlogLinkedInGitHubMastodonYouTubePLEASE SUBSCRIBE TO THE PODCASTSpotifyApple PodcastsYouTube MusicAmazon MusicRSS FeedYou can check out more episodes of Coffee and Open Source on https://www.coffeeandopensource.comCoffee and Open Source is hosted by Isaac Levin

This interview was recorded for GOTO Unscripted.https://gotopia.techJessica Kerr - Engineering Manager of Developer Relation at Honeycomb.io & SymmathecistDiana Montalion - Systems Architect, Mentrix Founder & Author of "Learning Systems Thinking"RESOURCESJessicahttps://bsky.app/profile/jessitron.bsky.socialhttps://linkedin.com/in/jessicakerrhttps://www.twitch.tv/jessitronicahttps://jessitron.comDianahttps://bsky.app/profile/dianamontalion.comhttps://www.linkedin.com/in/dianamontalionhttps://montalion.comhttps://learningsystemsthinking.comDESCRIPTIONSystems architect Diana Montalion and engineering manager Jessica Kerr cut through the AI coding hype to explore what these tools actually do well - and where they have room for improvement. Moving beyond the "AI will replace developers" narrative, they reveal how AI assistants excel at the tedious work of typing, scaffolding, and error handling while remaining surprisingly bad at the nuanced thinking that experienced developers bring to complex systems.Their discussion illuminates a more mature relationship with AI tools: one where developers maintain agency over design decisions while leveraging AI's strengths in automation, synthesis, and rapid prototyping. The result is a pragmatic roadmap for using AI to amplify human expertise rather than replace it.RECOMMENDED BOOKSDiana Montalion • Learning Systems Thinking • https://amzn.to/3ZpycdJAndrew Harmel-Law • Facilitating Software Architecture • https://amzn.eu/d/5kZKVfUDonella H. Meadows • Thinking in Systems • https://amzn.to/3XtqYCVYu-kai Chou • Actionable Gamification • https://amzn.to/45D8bHAInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Splunk Infrastructure Monitoring is becoming a must-have for teams managing cloud-native and hybrid environments. In this episode, we break down how Splunk delivers real-time observability, AI-powered insights, and seamless cloud integration to help organizations detect issues faster, optimize performance, and support digital transformation.

AI without observability is guesswork.I har a blast chatting with Patrick Lin, SVP and GM of Observability at Splunk on The Ravit Show. We get straight into how teams keep AI reliable and how leaders turn telemetry into business results.What we cover: • .conf25 updates in Splunk Observability • AI Agentic Monitoring and AI Infrastructure Monitoring • How a unified experience with Splunk AppDynamics and Splunk Observability Cloud helps teams ship faster with fewer surprises • Why observability is now a growth lever, not just a safety net • Fresh insights from the State of Observability 2025 reportMy take: • The nervous system of AI is observability • Signal quality beats signal volume • OpenTelemetry works best when tied to business context • When SecOps and Observability work together, incidents become learning momentsIf you care about reliable AI, faster recovery, and clear impact on productivity and revenue, this one will help.#data #ai #conf2025 #splunk #splunkconf25 #SplunkSponsored #theravitshow

For years, observability sat quietly in the background of enterprise technology, an operational tool for engineers, something to keep the lights on and costs down. As systems became more intelligent and automated, observability has stepped into a far more strategic role. It now acts as the connective tissue between business intent and technical execution, helping organizations understand not only what is happening inside their systems, but why it's happening and what it means.This shift forms the core of a recent Tech Transformed podcast episode between host Dana Gardner and Pejman Tabassomi, Field CTO for EMEA at Datadog. Together, they explore how observability has changed into what Tabassomi calls the “nervous system of AI”, a framework that allows enterprises to translate complexity into clarity and automation into measurable outcomes.Building AI LiteracyAI models make decisions that can affect everything from customer experiences to financial forecasting. It's important to understand that without observability, those decisions remain obscure.“Visibility into how models behave is crucial,” Tabassomi notes. True observability allows teams to see beyond outputs and into the reasoning of their systems, even if a model is drifting, automation is adapting effectively, and results align with strategic goals. This transparency builds trust. It also ensures accountability, giving organizations the confidence to scale AI responsibly without losing sight of the outcomes that matter most.Observability Observability is not merely about monitoring; it is about decision-making. It provides the insight required to manage complex systems, optimize outcomes, and act with agility. For organizations relying on AI and automation, observability becomes the differentiator between being merely efficient and achieving a sustainable competitive edge. In short, observability is no longer optional; it is central to translating technology into strategy and strategy into advantage.For more insights follow Datadog:X: @datadoghq Instagram: @datadoghq Facebook: facebook.com/datadoghq facebook.comLinkedIn: linkedin.com/company/datadogTakeawaysObservability has evolved from cost efficiency to a strategic role in...

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview here:https://gotopia.tech/episodes/393Victor Grazi - Oracle Java Champion & Co-Author of "Real-World Java"Jeanne Boyarsky - Oracle Java Champion, Co-Author of "Real-World Java" & "OCP 21 Java Cert Book"Barry Burd - Professor at Drew University, Owner at Burd Brain Consulting & Author of "Java for Dummies"RESOURCESVictorhttps://x.com/vgrazihttps://www.linkedin.com/in/victorgraziJeannehttps://bsky.app/profile/jeanneboyarsky.bsky.socialhttps://www.linkedin.com/in/jeanne-boyarskyBarryhttps://x.com/allmycodehttps://www.linkedin.com/in/barry-burdLinkshttps://projectlombok.orghttps://www.selikoff.net/2014/12/07/why-i-like-regular-expressions-who-says-they-arent-readableDESCRIPTIONBarry interviews Victor and Jeanne  about their book "Real-World Java: Helping You Navigate the Java Ecosystem".Victor emphasizes that knowing how to use your development tools, particularly IDE refactoring features, is a better indicator of developer experience than algorithm tests.Rather than just teaching "hello world" examples, the authors focus on the essential ecosystem components needed to succeed in enterprise Java environments, making it accessible for anyone who knows the Java language but needs to understand the broader technological landscape they'll encounter in professional development roles. RECOMMENDED BOOKSVictor Grazi & Jeanne Boyarsky • Real-World Java • https://amzn.to/4oCEeBRJeanne Boyarsky &Inspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts Spotify Canada NowBold ideas with the people shaping Canada's next chapter.Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Want trustworthy AI? Discover how observability, real-time monitoring, and modern platforms are reshaping how we build accountable, explainable systems.

Honeycomb's VP of Marketing Shabih Syed reveals why traditional observability is dead and how AI-powered tools are transforming the way engineers debug production systems, with real examples.Topics Include:Observability is how you understand and troubleshoot your production systems in real-timeShabih's 18-year journey: developer to product manager to marketing VP shares unique perspectiveAI coding assistants are fundamentally changing how fast engineers ship code to productionCustomer patience is gone - one checkout failure means losing them foreverOver 90% of engineers now "vibe code" with AI, creating new complexityObservability costs are spiraling - engineers forced to limit logging, creating debugging dead-endsHoneycomb reimagines observability: meeting expectations, reducing complexity, breaking the cost curveMajor customers like Booking.com and Intercom already transforming with AI-native observabilityMCP server brings production data directly into your IDE for real-time AI assistanceCanvas enables plain English investigations to find "unknown unknowns" before they become problemsAnomaly detection helps junior engineers spot issues they wouldn't know to look forStatic dashboards are dead - AI-powered workflows are the future of system observationParticipants:Shabih Syed - VP Product Marketing, Honeycomb.io See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

The AWS US-East problems on Oct 27th was a good reminder how depending we are on globally shared services. Built-in Resiliency is not guaranteed if systems have a hard dependency on a single region of a single vendor. Many of us have experienced systems being impacted that we use on a daily basis - some critical - some not so critical as Andi will tell you when he found out that is beloved Leberkas Pepi App didnt work!Besides this outage we discuss lessons learned from Cloud Native Days Austria, Observability and Platform Engineering Meetups in Gdansk and Tallinn as well as giving an outline to the upcoming Cloud and AI-Native US Tour from Henrik Rexed and Andi GrabnerAll the links we discussed are hereLeberkas Pepi: https://www.leberkaspepi.at/Cloud Native Austria: https://www.linkedin.com/company/cndaustria/Observability Meetup: https://www.meetup.com/observability-tech-community-meetup-group/US Tour from Henrik and Andi: https://events.dynatrace.com/noram-all-de-engineering-efficiency-tour-2025-28225/

Ever wondered how source maps actually work? In this episode, Nicolo Ribaudo, Babel maintainer and TC39 delegate, breaks down how source maps connect your JavaScript, TypeScript, and CSS back to the original code — making debugging, stack traces, and observability smoother in Chrome dev tools. We dive into how source maps help in both development and production with minified code, explore tools like Webpack, Rollup, Next.js, and Svelte, and share when you should turn off source maps to avoid confusion. Links Website: https://nicr.dev LinkedIn: https://www.linkedin.com/in/nicol%C3%B2-ribaudo-bb94b4187 BlueSky: https://bsky.app/profile/nicr.dev Github: https://github.com/nicolo-ribaudo Resources Squiggleconf talk: https://squiggleconf.com/2025/sessions#source-maps-how-does-the-magic-work Slide deck: https://docs.google.com/presentation/d/1lyor5xgv821I4kUWJIwrrmXBjzC_qiqIqcZxve1ybw0 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabet.becz@logrocket.com (mailto:elizabeth.becz@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Check out our newsletter (https://blog.logrocket.com/the-replay-newsletter/)! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Chapters 00:00 Intro – Welcome to PodRocket + Introducing Nicolo Ribaudo 00:45 What Are Source Maps and Why They Matter for Debugging 01:20 From Babel to TC39 – Nicolo's Path to Source Maps 02:00 Source Maps Beyond JavaScript: CSS, C, and WebAssembly 03:00 The Core Idea – Mapping Compiled Code Back to Source 04:00 How Source Maps Work Under the Hood (Encoded JSON) 05:10 File Size and Performance – Why It Doesn't Matter in Production 06:00 Why Source Maps Are Useful Even Without Minification 07:00 Sentry and Error Monitoring – How Source Maps Are Used in Production 08:10 Two Worlds: Local Debugging vs. Remote Error Analysis 09:00 You're Probably Using Source Maps Without Realizing It 10:00 Why Standardization Was Needed After 15+ Years of Chaos 11:00 TC39 and the Creation of the Official Source Maps Standard 12:00 Coordinating Browsers, Tools, and Vendors Under One Spec 13:00 How Chrome, Firefox, and WebKit Implement Source Maps Differently 14:00 Why the Source Maps Working Group Moves Faster Than Other Standards 15:00 A Small, Focused Group of DevTools Engineers 16:00 How Build Tools and Bundlers Feed Into the Ecosystem 17:00 Making It Easier for Tool Authors to Generate Source Maps 18:00 How Frameworks Like Next.js and Vite Handle Source Maps for You 19:00 Common Pitfalls When Chaining Build Tools 20:00 Debugging Wrong or Broken Source Maps in Browsers 21:00 Upcoming Feature: Scopes for Variables and Functions 22:00 How Scopes Improve the Live Debugging Experience 23:00 Experimental Implementations and How to Try Them 24:00 Where to Find the TC39 Source Maps Group + Get Involved 25:00 Nicolo's Links – GitHub, BlueSky, and Talks Online 25:30 Closing Thoughts

[We accidentally deleted this episode from audio, so this is a re-upload of the same content. Sorry!]In this episode of Book Overflow, Carter and Nathan discuss the first half of Mastering OptenTelemetry and Observability by Steve Flanders!-- Want to talk with Carter or Nathan? Book a coaching session! ------------------------------------------------------------Carterhttps://www.joinleland.com/coach/carter-m-1Nathanhttps://www.joinleland.com/coach/nathan-t-2-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------Mastering OpenTelemetry and Observatibiltyhttps://amzn.to/4nTzXJ1----------------Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

Coralogix CEO Ariel Assaraf reveals how their observability lake lets companies own their data, reduce costs, and use AI agents to transform monitoring into actionable business intelligence.Topics Include:Coralogix solves observability scaling issues: tool disparity, sprawling costs, limited control.Streama parses data pre-ingestion; DataPrime queries directly on customer's own S3 buckets.AI will generate massive unstructured data, making observability challenges exponentially worse.CTOs should ask: Can observability data drive business decisions beyond just monitoring?Observability lake lets you own data in open format versus vendor lock-in.OLLI designed as research engine, not another natural language database interface.Ask business questions like "What's customer experience today?" instead of technical queries.Trading platform unified tools, reduced resolution time 6x, now uses for business intelligence.Future: Multiple AI personas, automated investigations, hypothesis-driven alerts without human prompting.AWS partnership enables S3 innovation, Bedrock models, and strong co-sell growth motion.Data sovereignty solved: customers control their S3, remove access anytime, own encryption.Business data experience will match consumer AI tools within two years fundamentally.Participants:Ariel Assaraf – Chief Executive Officer, CoralogixBoaz Ziniman – Principal Developer Advocate - EMEA, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In this episode of Careers and the Business of Law, David Cowen sits down with Nathan Reichardt, PwC's Lead Managed Services Director and AI Champion, for a conversation that bridges technology and humanity. They unpack why “observability” isn't just a technical concept, it's the foundation of trust in an age of autonomous agents. From building glass-box systems that make AI accountable to recognizing the invisible pressures on professionals, this discussion explores what it really takes to lead responsibly in the era of AI. Key Topics Covered: Agents aren't magic, you must observe them. Why oversight is essential as AI agents act and learn autonomously. From black box to glass box. Transparency, explainability, and compliance as non-negotiable design principles. Responsible AI in practice. What observability really means for governance, risk, and trust. The rise of new roles. Why “AI Observer” and “Observability Lead” may soon become critical titles inside legal and business ops. The human dimension. How leaders can apply observability to people spotting stress, isolation, and burnout before it's too late. From pilot to practice. PwC's approach to scaling agentic AI safely through iteration, measurement, and feedback.

In this episode of Crazy Wisdom, host Stewart Alsop talks with Jared Zoneraich, CEO and co-founder of PromptLayer, about how AI is reshaping the craft of software building. The conversation covers PromptLayer's role as an AI engineering workbench, the evolving art of prompting and evals, the tension between implicit and explicit knowledge, and how probabilistic systems are changing what it means to “code.” Stewart and Jared also explore vibe coding, AI reasoning, the black-box nature of large models, and what accelerationism means in today's fast-moving AI culture. You can find Jared on X @imjaredz and learn more or sign up for PromptLayer at PromptLayer.com.Check out this GPT we trained on the conversationTimestamps00:00 – Stewart Alsop opens with Jared Zoneraich, who explains PromptLayer as an AI engineering workbench and discusses reasoning, prompting, and Codex.05:00 – They explore implicit vs. explicit knowledge, how subject matter experts shape prompts, and why evals matter for scaling AI workflows.10:00 – Jared explains eval methodologies, backtesting, hallucination checks, and the difference between rigorous testing and iterative sprint-based prompting.15:00 – Discussion turns to observability, debugging, and the shift from deterministic to probabilistic systems, highlighting skill issues in prompting.20:00 – Jared introduces “LM idioms,” vibe coding, and context versus content—how syntax, tone, and vibe shape AI reasoning.25:00 – They dive into vibe coding as a company practice, cloud code automation, and prompt versioning for building scalable AI infrastructure.30:00 – Stewart reflects on coding through meditation, architecture planning, and how tools like Cursor and Claude Code are shaping AGI development.35:00 – Conversation expands into AI's cultural effects, optimism versus doom, and critical thinking in the age of AI companions.40:00 – They discuss philosophy, history, social fragmentation, and the possible decline of social media and liberal democracy.45:00 – Jared predicts a fragmented but resilient future shaped by agents and decentralized media.50:00 – Closing thoughts on AI-driven markets, polytheistic model ecosystems, and where innovation will thrive next.Key InsightsPromptLayer as AI Infrastructure – Jared Zoneraich presents PromptLayer as an AI engineering workbench—a platform designed for builders, not researchers. It provides tools for prompt versioning, evaluation, and observability so that teams can treat AI workflows with the same rigor as traditional software engineering while keeping flexibility for creative, probabilistic systems.Implicit vs. Explicit Knowledge – The conversation highlights a critical divide between what AI can learn (explicit knowledge) and what remains uniquely human (implicit understanding or “taste”). Jared explains that subject matter experts act as the bridge, embedding human nuance into prompts and workflows that LLMs alone can't replicate.Evals and Backtesting – Rigorous evaluation is essential for maintaining AI product quality. Jared explains that evals serve as sanity checks and regression tests, ensuring that new prompts don't degrade performance. He describes two modes of testing: formal, repeatable evals and more experimental sprint-based iterations used to solve specific production issues.Deterministic vs. Probabilistic Thinking – Jared contrasts the old, deterministic world of coding—predictable input-output logic—with the new probabilistic world of LLMs, where results vary and control lies in testing inputs rather than debugging outputs. This shift demands a new mindset: builders must embrace uncertainty instead of trying to eliminate it.The Rise of Vibe Coding – Stewart and Jared explore vibe coding as a cultural and practical movement. It emphasizes creativity, intuition, and context-awareness over strict syntax. Tools like Claude Code, Codex, and Cursor let engineers and non-engineers alike “feel” their way through building, merging programming with design thinking.AI Culture and Human Adaptation – Jared predicts that AI will both empower and endanger human cognition. He warns of overreliance on LLMs for decision-making and the coming wave of “AI psychosis,” yet remains optimistic that humans will adapt, using AI to amplify rather than atrophy critical thinking.A Fragmented but Resilient Future – The episode closes with reflections on the social and political consequences of AI. Jared foresees the decline of centralized social media and the rise of fragmented digital cultures mediated by agents. Despite risks of isolation, he remains confident that optimism, adaptability, and pluralism will define the next AI era.

In this episode of Book Overflow, Carter and Nathan discuss the first half of Mastering OptenTelemetry and Observability by Steve Flanders!-- Want to talk with Carter or Nathan? Book a coaching session! ------------------------------------------------------------Carterhttps://www.joinleland.com/coach/carter-m-1Nathanhttps://www.joinleland.com/coach/nathan-t-2-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------Mastering OpenTelemetry and Observatibiltyhttps://amzn.to/4nTzXJ1----------------Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

“At TeleMate, our job is to make IT and communications troubleshooting easy,” said Reginald Pearson, VP of Sales & Strategy at TeleMate. At WebexOne in San Diego, Doug Green, Publisher of Technology Reseller News, spoke with Pearson about how TeleMate delivers observability across the entire Cisco collaboration suite. Pearson explained that TeleMate provides end-to-end visibility for Webex Calling, Webex Contact Center, and messaging platforms—while also supporting hybrid environments that combine on-premises and cloud technologies such as Cisco Call Manager and CUBEs. With a single-pane-of-glass interface, TeleMate aggregates logs, traces, alarms, and analytics to ensure full service assurance for enterprise IT and communications teams. The platform is vendor-neutral, designed to simplify troubleshooting and performance monitoring across complex UC ecosystems. TeleMate's value proposition: faster troubleshooting, better visibility, and proactive assurance that keeps collaboration platforms running smoothly. Learn more at www.telemate.net.

In this episode of Book Overflow, Carter and Nathan discuss the first half of Mastering OptenTelemetry and Observability by Steve Flanders!-- Want to talk with Carter or Nathan? Book a coaching session! ------------------------------------------------------------Carterhttps://www.joinleland.com/coach/carter-m-1Nathanhttps://www.joinleland.com/coach/nathan-t-2-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------Mastering OpenTelemetry and Observatibiltyhttps://amzn.to/4nTzXJ1----------------Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

Security leaders from CyberArk, Fortra, and Sysdig share actionable strategies for securely implementing generative AI and reveal real-world insights on data protection and agent management.Topics Include:Panel explores practical security approaches for GenAI from prototype to productionThree-phase framework discussed: planning, pre-production, and production security considerationsSecurity must be built-in from start - data foundation is criticalUnderstanding data location, usage, transformation, and regulatory requirements is essentialFortra's security conglomerate approach integrates with AWS native tools and partnersMachine data initially easier for compliance - no PII or HIPAA concernsIdentity paradigm shift: agents can dynamically take human and non-human roles97% of organizations using AI tools lack identity and access policiesSecurity responsibility increases as you move up the customization stackOWASP Top 10 for GenAI addresses prompt injection and data poisoningRigorous model testing including adversarial attacks before deployment is crucialSysdig spent 6-9 months stress testing their agent before production releaseTension exists between moving fast and implementing proper security controlsDifferent security approaches needed based on data sensitivity and model usageZero-standing privilege and intent-based policies critical for agent managementMulti-agent systems create "Internet of Agents" with exponentially multiplying risksDiscovery challenge: finding where GenAI is running across enterprise environmentsAPI security and gateway protection becoming critical with acceptable latencyTop customer need: translating written AI policies into actionable controlsThreat modeling should focus on impact rather than just vulnerability severityParticipants:Prashant Tyagi - Go-To-Market Identity Security Technology Strategy Lead, CyberArkMike Reed – Field CISO, Cloud Security & AI, FortraZaher Hulays – Vice President Strategic Partnerships, SysdigMatthew Girdharry - WW Leader for Observability & Security Partnerships, Amazon Web ServicesFurther Links:CyberArk: Website – LinkedIn – AWS MarketplaceFortra: Website – LinkedIn – AWS MarketplaceSysdig: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Разбираем Thoughtworks Technology Radar Vol.32: где Adopt/Trial/Hold и что реально полезно DevOps-командам в 2025. AI-ассистенты (Cursor, QCLI, Claude), Observability (OpenTelemetry, Alloy/Loki), безопасность (SBOM) и практичные инструменты. О ЧЁМ ВЫПУСК • Как читать Tech Radar и зачем он инженерам/архитекторам. • AI-ассистенты для кодинга: опыт Copilot, Cursor, QCLI (Claude Sonnet), цены и риски. • Observability сейчас: OpenTelemetry, Grafana Alloy, Loki v3, зачем это бизнесу. • Безопасность: почему SBOM в Adopt и как это помогает на проектах. • Архитектурные решения без бюрократии: ADR, ответственность команд. • Инструменты из «Тулов»: UV (Python), Renovate, Vite, D2/JSON Crack, и где они заходят. ССЫЛКИ

Astasia Myers is a GP at Felicis, an iconic VC firm with investments in companies like Shopify, Canva, Adyen, Notion, Mercor, Plaid, Supabase, Flexport, and more. Astasia's favorite books: God's Bankers (Author: Gerald Posner)(00:01) Introduction(00:26) Astasia's Infra Thesis(03:59) Golden Age of Infra & Innovators Network(06:22) RL Environments & AI Agents(08:57) Disruption Opportunities: Data & Observability(11:31) Where to Find Infra Founders(16:31) Early Signals & Thesis-Driven Investing(18:01) Picking & Decision-Making Process(20:11) Red Flags in Infra Investing(22:20) References & Diligence(24:35) Proof of Usage & Production Signals(26:24) Building Edge as an Investor(28:01) How Felicis Helps Founders Post-Investment(30:05) Consensus vs. Contrarian Views in Infra(32:09) Tourist Traps in Infra Investing(34:43) GTM & Sales Motion in Infra(37:25) Pricing Strategies for Infra Startups(40:09) Ecosystem vs. Core Product Focus(42:15) Lessons from Outlier vs. Good Companies(44:30) Infra Wedges to Fund Today(45:23) Commoditized but Promising Categories(47:06) Exciting AI Advancements(48:21) Rapid Fire Round--------Where to find Astasia Myers: LinkedIn: https://www.linkedin.com/in/astasiamyers/--------Where to find Prateek Joshi: Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekvjoshiResearch column: https://infrastartups.com 

Sponsored by Axonius. Visit https://www.axonius.com/idac to learn more.In this sponsored episode of the Identity at the Center Podcast, hosts Jeff and Jim talk with Amir Ofek, the CEO of AxoniusX, about the company's innovative solutions in identity and access management (IAM). The discussion covers Amir's journey into IAM, the unique challenges of managing identities, and how AxoniusX's data-driven approach provides comprehensive visibility and intelligence. The episode breaks down various use cases, the importance of identity hygiene, automation of identity processes, and the newly recognized identity visibility and intelligence platform (IVIP) by Gartner.Timestamps:00:00 Introduction and Episode Overview00:57 Guest Introduction: Amir, CEO of AxoniusX01:12 Amir's Journey into Identity Access Management02:40 Understanding Axonius and AxoniusX08:03 The Importance of Identity Visibility and Intelligence11:48 Challenges in Identity Management22:10 Axonius's Approach to Identity Visibility26:35 Leveraging AI and Machine Learning in Identity Management31:18 Understanding Permission Changes and Their Importance32:10 The Role of Observability in Axonius32:37 Driving Actions with Axonius33:30 Common Use Cases and Workflows35:19 Axonius as a Swiss Army Knife36:16 Ease of Use and AI Integration38:49 Starting with Axonius and Measuring Value43:42 Future Directions for Axonius49:49 The Identity Community and Upcoming Events51:23 Skiing Adventures and Tips57:54 Conclusion and Final ThoughtsConnect with Amir: https://www.linkedin.com/in/amirofek/Learn more about Axonius: https://www.axonius.com/idacConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at idacpodcast.com

The transition from traditional monitoring technologies to modern observability tools can leave teams confused. Satbir Singh joins us to talk about the new goals of observability tooling and how it will help teams conquer challenges in complex systems.

Marcin Grzejszczak, a veteran of observability spaces, discusses the current state of the space, including its evolution and the fine-grained details of how to instrument your system to capture all relevant information at every level - both inside services and between services communication. Read a transcript of this interview: http://bit.ly/4mDTkFW Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter Upcoming Events: InfoQ Dev Summit Munich (October 15-16, 2025) Essential insights on critical software development priorities. https://devsummit.infoq.com/conference/munich2025 QCon San Francisco 2025 (November 17-21, 2025) Get practical inspiration and best practices on emerging software trends directly from senior software developers at early adopter companies. https://qconsf.com/ QCon AI New York 2025 (December 16-17, 2025) https://ai.qconferences.com/ QCon London 2026 (March 16-19, 2026) https://qconlondon.com/ The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - X: https://x.com/InfoQ?from=@ - LinkedIn: https://www.linkedin.com/company/infoq/ - Facebook: https://www.facebook.com/InfoQdotcom# - Instagram: https://www.instagram.com/infoqdotcom/?hl=en - Youtube: https://www.youtube.com/infoq - Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of experts. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq

Most AI projects still fail, are too costly, or don't provide the value they hoped to gain. The root cause is nothing new: it's non-optimized models or code that runs the logic behind your AI Apps. The solution is also not new: tuning the system based on insights from Observability!To learn more about the state of AI Observability, we invited back Nir Gazit, CEO and Co-Founder of traceloop, the company behind OpenLLMetry, the open source observability standard that is seeing exponential adoption growth!Tune in and learn how OpenLLMetry became such a successful open source project, which problems it solves, and what we can learn from other AI project implementations that successfully launched their AI Apps and AgentsLinks we discussedNir's LinkedIn: https://www.linkedin.com/in/nirga/OpenLLMetry: https://github.com/traceloop/openllmetryTraceloop Hub LLM Gateway: https://www.traceloop.com/docs/hub

The discussion centers on two key design principles: observability, which ensures humans can understand what automated systems are doing and why, and direct ability, which allows humans to steer automation rather than simply turning it on or off. Using examples from aviation incidents like Boeing's MCAS system and emerging AI technologies, the episode demonstrates how these 25-year-old principles remain relevant for contemporary automation challenges in safety-critical systems. Discussion Points:(00:00) Background on automation and natural experiments in safety(04:58) Hard vs soft skills debate and limitations of binary thinking(08:12) Two common approaches to automation problems and their flaws(12:20) The substitution myth and why simple replacement doesn't work(17:25) Design principles for coordination, observability, and direct ability(24:33) Observability challenges with AI and machine learning systems(26:25) Direct ability and the problem of binary control options(30:47) Design implications and avoiding simplistic solutions(33:27) Practical takeaways for human automation coordinationLike and follow, send us your comments and suggestions for future show topics! Quotes:Drew Rae: "The moment you divide it up and you just try to analyze the human behavior or analyze the automation, you lose the understanding of where the safety is coming from and what's necessary for it to be safe."David Provan: "We actually don't think about that automation in the context of the overall system and all of the interfaces and everything like that. So we, we look at AI as AI and, you know, deploying. Introducing ai, but we don't do any kind of comprehensive analysis of, you know, what's gonna be all of the flow on implications and interfaces and potentially unintended consequences or the system, not necessarily just the technology or automation itself."Drew Rae: "It's not enough for an expert system to just like constantly tell you all of the underlying rules that it's applying, that that doesn't really give you the right level of visibility as understanding what it thinks the current state is."David Provan: "But I think this paper makes a really good argument, which is actually our automated system should be far more flexible than that. So I might be able to adjust, you know, it's functioning. If I know, if I, if I know enough about how it's functioning and why it's functioning, and I realize that the automation can't understand context and situation, then I should be able to make adjustments."Drew Rae: "There's, there's gotta be ways of allowing all the animation to keep working, but to be able to. Retain control, and that's a really difficult design problem."Resources:Link to the PaperThe Safety of Work PodcastThe Safety of Work on LinkedInFeedback@safetyofwork

The ClickHouse open source project has gained interest in the observability community, thanks to its outstanding performance benchmarks. Now ClickHouse is doubling down on observability with the release of ClickStack, a new open source observability stack that bundles in ClickHouse, OpenTelemetry and  HyperDX frontend. I invited Mike Shi, the co-founder of HyperDX and co-creator of ClickStack, to tell us all about this new project. Mike is Head of Observability at ClickHouse, and brings prior observability experience with Elasticsearch and more.You can read the recap post: https://medium.com/p/73f129a179a3/Show Notes:00:00 episode and guest intro04:38 taking the open source path as an entrepreneur10:51 the HyperDX observability user experience 16:08 challenges in implementing observability directly on ClickHouse20:03 intro to ClickStack and incorporating OpenTelemetry32:35 balancing simplicity and flexibility36:15 SQL vs. Lucene query languages 39:06 performance, cardinality and the new JSON type52:14 use cases in production by OpenAI, Anthropic, Tesla and more55:38 episode outroResources:HyperDX https://github.com/hyperdxio/hyperdx ClickStack https://clickhouse.com/docs/use-cases/observability/clickstack Shopify's Journey to Planet-Scale Observability: https://medium.com/p/9c0b299a04ddClickHouse: Breaking the Speed Limit for Observability and Analytics https://medium.com/p/2004160b2f5e New JSON data type for ClickHouse: https://clickhouse.com/blog/a-new-powerful-json-data-type-for-clickhouseSocials:BlueSky: https://bsky.app/profile/openobservability.bsky.socialTwitter: ⁠https://twitter.com/OpenObserv⁠LinkedIn: https://www.linkedin.com/company/openobservability/YouTube: ⁠https://www.youtube.com/@openobservabilitytalks⁠Dotan Horovits============Twitter: @horovitsLinkedIn: www.linkedin.com/in/horovitsMastodon: @horovits@fosstodonBlueSky: @horovits.bsky.socialMike Shi=======Twitter: https://x.com/MikeShi42LinkedIn: https://www.linkedin.com/in/mikeshi42BlueSky: https://bsky.app/profile/mikeshi42.bsky.socialOpenObservability Talks episodes are released monthly, on the last Thursday of each month and are available for listening on your favorite podcast app and on YouTube.

Today, the Elixir Wizards wrap up Season 14 “Enter the Elixirverse.” Dan, Charles, and Sundi look back at some common themes: Elixir plays well with others, bridges easily to access languages and tools, and remains a powerful technology for data flow, concurrency, and developer experience. We revisit the popular topics of the year, from types and tooling to AI orchestration and reproducible dev environments, and share what we're excited to explore next.   We also invite your questions and takeaways to help shape future seasons and conference conversations. Season 14 doubles as a handy primer for anyone curious about how Elixir integrates across the stack.   Key topics discussed in this episode:   * Lessons from a season of interoperability * Set-theoretic types and what new compiler warnings unlock * AI in practice: LLM orchestration, fallbacks, and real-world use * SDUI and GraphQL patterns for shipping UI across web/iOS/Android * Dataframes in Elixir with Explorer for analytics workflows * Python interoperability (ErlPort, PythonX) and when to reach for it * Reproducible dev environments with Nix and friends * Performance paths: Rustler and Zig for native extensions * Bluetooth & Nerves: Blue Heron and hardware integrations * DevEx upgrades: LiveView, build pipelines, and standard project setup * Observability and ops: Prometheus/Grafana and sensible deployments * Community feedback, conferences, and what's on deck for next season   Links mentioned in this episode: Cars.com S14E06 SDUI at Scale with Elixir https://youtu.be/nloRcgngTk?si=g4Zd4N1s56Ronrtw https://hexdocs.pm/phoenixliveview/Phoenix.LiveView.html https://wordpress.com/ https://elixir-lang.org/ S14E01 Zigler: Zig NIFs for Elixir https://youtu.be/hSAvWxh26TU?si=d55tVuZbNw0KCfT https://ziglang.org/ https://hexdocs.pm/zigler/Zig.html https://github.com/blue-heron/blueheron https://github.com/elixir-explorer/explorer S14E08 Nix for Elixir Apps https://youtu.be/yymUcgy4OAk?si=BRgTlc2VK5bsIhIf https://nixos.org/ https://nix.dev/ S14E07 Set Theoretic Types in Elixir https://youtu.be/qMmEnXcHxL4?si=Ux2lebiwEp3mc0e S14E10 Python in Elixir Apps https://youtu.be/SpVLrrWkRqE?si=ld3oQVXVlWHpo7eV https://www.python.org/ https://hexdocs.pm/pythonx/ https://github.com/Pyrlang/Pyrlang https://github.com/erlport/erlport S14E03 LangChain: LLM Integration for Elixir https://youtu.be/OwFaljL3Ptc?si=A0sDs2dzJ0UoE2PY https://github.com/brainlid/langchain S14E04 Nx & Machine Learning in Elixir https://youtu.be/Ju64kAMLlkw?si=zdVnkBTTLHvIZNBm S14E05 Rustler: Bridging Elixir and Rust https://youtu.be/2RBw7B9OfwE?si=aRVYOyxxW8fTmoRA https://github.com/rusterlium/rustler Season 3: Working with Elixir https://youtube.com/playlist?list=PLTDLmInI9YaDbhMRpGuYpboVNbp1Fl9PD&si=hbe7qt4gRUfrMtpj S14E11 Vibe Coding the LoopedIn Crochet App https://youtu.be/DX0SjmPE92g?si=zCBPjS1huRDIeVeP Season 5: Adopting Elixir  YouTubeLaunchisode and Outlaws Takeover with Chris Keathley, Amos King, and Anna Neyzberg S13E01 Igniter: Elixir Code Generation https://youtu.be/WM9iQlQSF_g?si=e0CAiML2qC2SxmdL Season 8: Elixir in a Polyglot Environment https://youtube.com/playlist?list=PLTDLmInI9YaAPlvMd-RDp6LWFjI67wOGN&si=YCI7WLA8qozD57iw !! We Want to Hear Your Thoughts *!!* Have questions, comments, or topics you'd like us to discuss on the podcast? Share your thoughts with us here: https://forms.gle/Vm7mcYRFDgsqqpDC9

Marty Kagan, co-founder and CEO of Hydrolix, joined me for a detailed conversation on the importance of delivering better end-user QoE with real-time telemetry and the challenges that come with CDN observability. Marty highlights why more hot data is crucial for AIOps and challenges that M&E customers face today, including the need to discard data due to storage costs. Marty presents a compelling argument against the notion that most data is irrelevant and that content owners only need to retain a small percentage as a sample. We also discuss why Hydrolix isn't an AI company and Marty's plans to continue growing its streaming data lake platform in verticals outside of video.Podcast produced by Security Halt Media

Qian Li of DBOS, a durable execution platform born from research by the creators of Postgres and Spark, speaks with host Kanchan Shringi about building durable, observable, and scalable software systems, and why that matters for modern applications. They discuss database-backed program state, workflow orchestration, real-world AI use cases, and comparisons with other workflow technologies. Li explains how DBOS persists not just application data but also program execution state in Postgres to enable automatic recovery and exactly-once execution. She outlines how DBOS uses workflow and step annotations to build deterministic, fault-tolerant flows for everything from e-commerce checkouts to LLM-powered agents. Observability features, including SQL-accessible state tables and a time-travel debugger, allow developers and business users to understand and troubleshoot system behavior. Finally, she compares DBOS with tools like Temporal and AWS Step Functions. Brought to you by IEEE Computer Society and IEEE Software magazine.

AI-driven search (AISEO) is opening a new lane for brands in competitive categories. Joe Davies from FATJOE explains why branded mentions (not just links) are increasingly what LLMs use to decide recommendations—and how teams can systematically earn those mentions. We cover tactics like guest blogging at scale, context-seeding your USP across reviews/listicles, building deep product docs to feed LLMs, and using tier-two links to get your “influencer pages” ranking. Early data shows 2–3× higher conversion rates from AI-referred traffic because buyers arrive pre-educated and ready to act.What You'll LearnWhy AISEO rewards brand mentions and clear USPs more than classic link metrics.How AI-referred traffic converts 2–3× higher than traditional search.A repeatable process to seed your brand in listicles, reviews, and comparisons.How to “context-seed” your USP so LLMs recommend you for the right reason.Why deep help docs / knowledge bases make LLMs more confident recommending you.How to choose targets (DR + real traffic), then lift them with tier-two links.The state of AISEO observability (what to track, what's still immature).Tactical Playbook (Step-by-Step)Define your USP: the specific “best for ___” angle you want LLMs to repeat.Keyword map long-tail, bottom-funnel queries (e.g., “best X for Y,” “X vs Y,” “X alternatives,” “[product] review”).Prospect targets with credible traffic (DR is fine as a filter, but prioritize verified organic traffic).Commission content: secure guest posts/listicles and full reviews on those sites. Mix formats to look natural.Context-seed your USP in every placement (e.g., “Best for small teams,” “Most features,” “Best value”).Include competitors in listicles/reviews so the page is useful (LLMs prefer balanced sources).Boost with tier-two links (niche edits, syndication) to help these pages rank on pages 1–3.Expand surface area: Reddit answers, YouTube/tutorial mentions, and social chatter to reinforce brand salience.On-site foundation: build exhaustive docs—features, integrations, FAQs, facts sections—so LLMs can learn you deeply.Measure pragmatically: track referral traffic from AI surfaces and downstream conversions; current “AI visibility” tools are early.Resources & MentionsChatGPT Path (shows the searches/sources ChatGPT runs under the hood): https://chromewebstore.google.com/detail/chatgpt-path/kiopibcjdnlpamdcdcnphaajccobkbanFATJOE — Brand Mentions Service: http://fatjoe.com/brand-mentionsFATJOE: https://fatjoe.com/Key TakeawaysAISEO is early but growing fast and already drives higher-intent traffic.Focus on being mentioned credibly across the open web; LLMs synthesize those signals.Listicles + reviews on high-trust, real-traffic sites are the current highest-leverage assets.Your docs are marketing now—LLMs read them and recommend accordingly.Don't abandon SEO; it remains the foundation that AI systems lean on.Chapters00:00 Cold open: AISEO's opportunity & why mentions matter03:45 Data: AI referrals converting 2–3× vs. classic SEO07:50 Who should prioritize AISEO (and who can wait)10:30 Tactics: listicles, reviews, and “context-seeding” your USP15:45 Tools & workflows; extension that reveals ChatGPT's queries19:45 Content ops: human vs. AI writing, plans, and clustering22:30 Build deep product docs to feed LLM understanding26:10 Ranking the influencer pages + tier-two links33:00 Observability today: what's useful, what isn't yet36:50 The next 5–10 years: AI + SEO, not AI vs. SEOGuestJoe DaviesX: https://x.com/fatjoedaviesLinkedIn: https://es.linkedin.com/in/joe-davies-seoWebsite: https://fatjoe.com/

Jacob Visovatti and Conner Goodrum of Deepgram speak with host Kanchan Shringi about testing ML models for enterprise use and why it's critical for product reliability and quality. They discuss the challenges of testing machine learning models in enterprise environments, especially in foundational AI contexts. The conversation particularly highlights the differences in testing needs between companies that build ML models from scratch and those that rely on existing infrastructure. Jacob and Conner describe how testing is more complex in ML systems due to unstructured inputs, varied data distribution, and real-time use cases, in contrast to traditional software testing frameworks such as the testing pyramid. To address the difficulty of ensuring LLM quality, they advocate for iterative feedback loops, robust observability, and production-like testing environments. Both guests underscore that testing and quality assurance are interdisciplinary efforts that involve data scientists, ML engineers, software engineers, and product managers. Finally, this episode touches on the importance of synthetic data generation, fuzz testing, automated retraining pipelines, and responsible model deployment—especially when handling sensitive or regulated enterprise data. Brought to you by IEEE Computer Society and IEEE Software magazine.

Samuel Colvin, the CEO and founder of Pydantic, speaks with host Gregory M. Kapfhammer about the ecosystem of Pydantic's Python frameworks, including Pydantic, Pydantic AI, and Pydantic Logfire. Along with discussing the design, implementation, and use of these frameworks, they dive into the refactoring of Pydantic and the follow-on performance improvements. They also explore ways in which Python programmers can use these three frameworks to build, test, evaluate, and monitor their own applications that interact with both local and cloud-based large language models. Brought to you by IEEE Computer Society and IEEE Software magazine.

How do you keep complex digital experiences running smoothly when every layer, from networks to cloud infrastructure to applications, can break in ways that frustrate customers and burn out IT teams? This question is at the heart of my conversation recorded live at Cisco Live in San Diego with Patrick Lin, Senior Vice President and General Manager for Observability at Splunk, now part of Cisco. In this episode, Patrick explains how observability has evolved far beyond simple monitoring and is becoming the nerve centre for digital resilience in a world where reactive alerts no longer cut it. We unpack how Splunk and Cisco ThousandEyes are now deeply integrated, giving teams a single source of truth that connects application behaviour, infrastructure health, and network performance, even across systems they do not directly control. Patrick also shares what these two-way integrations mean in practice: faster incident resolution, fewer blame games, and far less time wasted chasing false alerts. We explore how AI is enhancing this vision by cutting through the noise to detect real anomalies, correlate related events, and suggest root causes at a speed no human team could match. If your business depends on staying online and your teams are drowning in disconnected data, this conversation offers a glimpse into the next phase of unified observability and assurance. It might even help quiet the flood of alerts that keep IT professionals awake at night. How is your organisation tackling alert fatigue and rising complexity? Listen in and tell me what strategies you have found that actually work.