Podcasts about dependencies

Erstes Thema: SearchLeak (CVE-2026-42824). Varonis Threat Labs hat eine dreistufige Angriffskette in Microsoft 365 Copilot Enterprise Search entdeckt: Ein präparierter Microsoft-Link, dessen URL-Parameter Copilot als Prompt interpretiert, kombiniert mit einer HTML-Rendering-Race-Condition und Bings Search-by-Image-Endpunkt als unfreiwilligem Exfiltrationsproxy. Ein Klick reicht – E-Mails, MFA-Codes, Kalendereinträge, alles was der User sehen darf, fließt ab. Microsoft hat server-seitig gepatcht. Das Muster – KI-Assistent wird durch Prompt Injection zur Datenwaffe – ist strukturell: EchoLeak, Reprompt, jetzt SearchLeak, drei Angriffe derselben Klasse.Max bringt einen Blogpost von Google Cloud CISO Chris Betz, der beschreibt, wie Google seine eigene KI intern wie einen Insider behandelt: mit Least Privilege, Monitoring, Auditing und Segmentierung. Was mich interessiert: auch Google musste dafür erst mal sein Asset Management nachziehen und konsolidieren. Die Kernbotschaft bleibt trotzdem richtig – wenn Angreifer mit Machine Speed arbeiten, muss die Verteidigung das auch. Für CISOs bedeutet das: Model Security, Agent Security, Data Governance werden zur Pflicht, nicht zur Kür.Dann Robert über NPM 12: Install Scripts von Dependencies laufen nicht mehr automatisch, bestimmte Remote-Dependencies werden blockiert. Überfällig und sinnvoll – aber 30-40% der NPM-Malware läuft erst beim Import, nicht bei der Installation. Wer einen Maintainer kompromittiert, kommt weiterhin durch. Gute Iteration, kein Allheilmittel.Zum Abschluss: Apple erweitert Private Cloud Compute auf die Google Cloud. Dasselbe Zero-Trust-Prinzip wie bisher – selbst Google soll keinen Zugriff auf verarbeitete Nutzerdaten bekommen. Clevere Partnerschaft statt Frontier-Rennen.SearchLeak / CVE-2026-42824 (Varonis)https://www.varonis.com/blog/searchleakGoogle Cloud CISO Chris Betz: AI Threat Defensehttps://cloud.google.com/blog/products/identity-security/how-google-cloud-is-applying-ai-to-threat-defenseNPM 12 / Risky Business Soapbox mit Paul McCartyhttps://risky.biz/soapbox_npm12Apple Private Cloud Compute auf Google Cloudhttps://security.apple.com/blog/private-cloud-compute-google-cloud

A pronounced infrastructure dependence on third-party AI models has emerged across the MSP ecosystem, largely due to the rapid adoption and integration of AI-powered features within vendor products. This structural shift is increasingly opaque, as providers are sold features rather than transparent access to underlying models, leaving MSPs exposed to changes in technologies and policies enacted upstream by vendors or regulators. The episode highlights how this dependency extends to delivery teams and end clients, with operational continuity tightly linked to decisions and actions outside the MSP's direct control. The most consequential development referenced is Anthropic's release and rapid withdrawal of its Fable 5 AI model following a directive from the U.S. Commerce Department, which ordered a cutoff of model access to foreign nationals within 72 hours of public launch. According to published benchmarks, Fable 5 surpassed GPT 5.5 in performance, but the government-mandated suspension exposed how quickly model access can be rescinded. The policy move immediately impacted any MSP or client with offshore or nearshore staff relying on AI features invisibly powered by that model. Further supporting the central theme, companies such as PAX8, Enforcer, and CloudRadio are embedding AI capabilities into platforms used by MSPs to manage Microsoft 365 environments, automate ticketing, and support scalable client operations. In parallel, vendors like Proofpoint are integrating compliance solutions directly with AI model APIs, further entwining risk management tools with the same core AI infrastructures. A Netrio survey cited in the episode found that while 82% of mid-market IT leaders have AI in production, only 26% report organization-wide governance, highlighting an accountability and visibility gap. Operationally, MSPs face heightened contract and vendor risk. Most lack an accurate inventory of which AI models underpin their services and how rapidly these dependencies can be affected by regulatory directives or vendor shifts. The discussion underscores the need for explicit procurement protocols, delivery mapping, and outage runbooks that account for opaque model dependencies. As clients seek greater transparency and contractual assurances regarding model use and continuity, MSPs who anticipate and document these dependencies may be positioned to reduce exposure and establish clearer accountability. 00:00 Switched Off  03:19 Painted Over 05:20 Govern or Absorb 08:41 Why Do We Care?  Supported by: Pax8 Sign up for the SMB Online Conference: www.smbonlineconference.com

In this episode of the Finovate podcast, host Greg speaks with Oren Buskila, CEO and co-founder of Cobalt, a FinovateSpring 2026 Best of Show winner. Cobalt has developed enterprise architectural intelligence specifically designed for financial institutions, addressing a critical challenge in modern banking: the lack of understanding of complex system dependencies.Banks operate on enormously intricate systems comprising their own code and dozens of third-party vendor applications, yet most institutions don't fully comprehend how these systems interconnect and depend on one another. This knowledge gap leads to significant problems, including development slowness—with banks spending 70% of their IT budgets on maintenance rather than innovation—and costly production failures that can result in millions of dollars in direct and indirect costs when changes are deployed without full visibility into system dependencies.Cobalt's solution automatically maps both IT systems and business processes in real-time by scanning existing data sources including event logs, API logs, code, and database tables. The platform creates a comprehensive topology map that aligns business processes with their underlying technical infrastructure, allowing banks to see exactly which systems support which business functions and understand the full impact of any proposed changes. This "bank on a page" architecture view enables technical teams to anticipate the consequences of modifications before implementation, preventing failures and ensuring safer deployments. The platform maintains a live, continuously updated view of the system architecture by taking frequent snapshots that can be compared to detect changes and investigate issues, a capability that was previously impossible with manual architecture mapping methods.The demo at FinovateSpring resonated strongly with attendees, particularly technical leaders like CIOs and CTOs from medium and large banks who recognized the transformative potential of having complete visibility into their systems. The presentation also attracted significant interest from venture capitalists and leaders from smaller banks and credit unions, the latter group seeing opportunities to introduce agentic AI into their operations by first mapping existing business processes. Looking ahead, Cobalt positions itself as the essential architectural layer for AI-driven development in banking, as the industry moves toward having AI agents generate, maintain, and modify code at unprecedented velocities—a shift that will require the contextual understanding and change management capabilities that Cobalt provides.More info:Cobalt AI: https://www.getcobalt.ai/; https://www.linkedin.com/company/getcobalt/Oren Buskila: https://www.linkedin.com/in/oren-buskila/Greg Palmer: https://www.linkedin.com/in/gregbpalmer/Finovate: https://www.finovate.com; https://www.linkedin.com/company/finovate-conference-series/FinovateSpring: https://informaconnect.com/finovatespring/#Finovate #FinovateSpring #Banking #banks #creditunions #personalization #data #communitybanks #AI #backoffice #corebanking #digitaladoption #podcast #fintechpodcast #financialservices #innovation #digitraltransformation #fintech #finserv #modernization

Christian Thordal: Managing Cross-Team Dependencies in Scaled Agile, From Planning to Real-Time Coordination Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "When one team's plan failed, the rest collapsed — deliveries and outcomes were delayed across the entire domain." - Christian Thordal   In this episode, Christian Thordal shares the biggest challenge he faced as an Agile Coach working within a large Danish broadcast company's technology division, where 32 teams operate across multiple domains. Within his domain of 10 teams, they plan in three-month cycles using OKRs, but a critical blind spot kept undermining their results: nobody had a clear grasp of the dependencies between teams and sister domains. When one team's delivery slipped in a previous cycle, it triggered a cascade of failures across the organization. Christian and the agile coaching community escalated the issue to the portfolio and delivery department, pushing to synchronize cycle timing across domains. He introduced a "big room planning" approach within his domain to map out which teams they impact and who impacts them, structured around a three-week cadence: define OKRs, align, then commit. A key coaching insight reshaped his thinking: dependencies are not facts — they are decisions. By naming the specific people involved (the person who needs resolution and the person who provides it), teams can manage dependencies in real-time rather than waiting for a program management layer that only addresses problems after escalation. Christian now plans to establish dedicated coordination days during each cycle where teams actively collaborate and resolve dependency issues together.   Self-reflection Question: When dependencies between your teams cause delivery failures, do you treat them as coordination problems to solve in real-time, or do you wait for escalation through a management layer?   [The Scrum Master Toolbox Podcast Recommends]

Many of us working with databases know the problems of a single point of failure. We build HA/DR technologies into a lot of systems precisely because many of us know if the database goes down, a lot of stuff goes down. Broken software is easier to fix and rollback, but a broken database can be a much bigger problems. We also know an overloaded server doesn't handle a workload well, hence our quest for well-written SQL code, but we often lose that battle with developers. Read the rest of The Dangers of Dependencies

In this Week's Talking Travel, Rory takes a trip to the self-governing islands around the UK – the Isle of Man, Jersey and Guernsey, delving into their relationship with the UK and how they're considered independent, self-governing states. 

The central structural shift examined is the widening disconnect between the vendor-driven narrative of rapid AI monetization and the operational reality faced by MSPs, as exposed by recent research from GTIA and CompTIA. Despite pervasive messaging from technology vendors that AI features are ready for seamless integration and immediate profitability, survey data indicates that most MSPs remain in early adoption stages, lack tangible processes to operationalize AI, and are stymied by workforce and workflow constraints. Supporting evidence is drawn from CompTIA's data showing that 70% of businesses are still in early AI adoption stages, and only 55% of MSPs expect to turn a profit on AI initiatives in the near term—up from 34%, but well below vendor promises. The majority of current AI activity remains at the individual user level rather than embedded in business-wide workflows, restricting quantifiable ROI and limiting the visibility of productivity gains. Both Speaker B and Speaker C emphasized that most MSPs do not yet have the organizational capability or maturity to move beyond experimentation to operational deployment and monetization. Related developments further illustrate this operational gap. Research cited by Speaker B highlights that only a subset of larger MSPs with more resources have been able to achieve early success with AI, while most are still grappling with process integration, pricing strategies, and talent acquisition. Both GTIA and CompTIA reports suggest that optimism among firms about AI's potential is running ahead of genuine structural change, with workforce shortages, undefined internal governance, and difficulties in business model adaptation acting as durable barriers. Market sentiment remains positive, but actual organizational transition lags significantly, especially among smaller MSPs. Operationally, this environment introduces heightened risk for MSPs who overcommit on vendor promises without aligning internal processes, workforce strategy, and governance. Dependencies on vendor-supplied AI tools expose firms to pricing uncertainty and potential margin compression, especially as clients begin questioning the value proposition when human roles are replaced by automation. Without formalized internal AI governance and skill development, most MSPs face mounting challenges in demonstrating measurable ROI, adapting delivery models, and sustaining service margins. The implication for decision-makers is the need for prudent, phased adoption—prioritizing internal process maturity and realistic expectations over rapid adoption in response to vendor pressure. Supported by: CometBackUpTimeZest

16. China Navigates Trade Dependencies and Energy Security During War GUEST: Gordon Chang, Andrew Collier SUMMARY: Andrew Collier analyzes China's economic situation, highlighting its 15% industrial profit increase and massive strategic oil reserves. Despite these strengths, China remains vulnerable to global trade downturns and the ongoing property market collapse.,, China saw a 15% rise in industrial profits in early 2026, which may be attributed to global stocking of goods before the war,. The country is prepared for energy shocks with the world's largest strategic oil reserve, estimated to cover 140 days of imports,. However, Andrew Collier warns that China is extremely trade-dependent and faces growing international opposition to its tech exports, while its domestic property market is not expected to hit a bottom until at least 2027,,. (16)1922 ARABIA

On February 28th, the United States and Israel launched airstrikes on multiple sites in Iran, marking the beginning of a wider military conflict with Iran. Tehran responded with attacks on Israel, US military bases, and US allies across the Middle East and closed the Strait of Hormuz. These events have caused a major disruption in the global supply of oil and gas. China, as the world's largest energy importer, is exposed to these disruptions, but its long-term energy security strategy has left it better prepared than most.  How has China approached energy security, and how might the current conflict reshape this strategy? To discuss these issues, we are joined today by Dr. Erica Downs. Erica is a Senior Research Scholar at the Center on Global Energy Policy at Columbia University's School of International and Public Affairs. Her research focuses on Chinese energy markets and geopolitics, and she has published extensively on the subject. Timestamps: [00:00] Introduction  [01:38] China's Energy Security Strategy [03:54] Divergent Approaches to Energy Security in the US and China [06:03] Beijing's Response to Supply Chain Shocks  [09:55] Dependencies on Russian Oil & Gas [12:33] New Lessons for Chinese Policymakers? [15:30] Impact on Teapot Refineries and Responses [18:37] Percentage of Chinese Oil and Gas Impacted [22:26] Could China Buy Gas from the US? [25:15] Potential Wins and Losses for Chinese Industries

A PyPI software supply chain attack hit LiteLLM — a library pulled into developer environments 97 million times a month — and if you use it, you may already be compromised. This wasn't a fake package or a typo-squatting trick. Attackers stole real credentials, published malicious code as the real thing, and walked out with SSH keys, cloud credentials, Kubernetes tokens, API keys, and more — all encrypted and sent home before anyone knew what happened.I'm doing something I've never done before: an emergency episode, recorded and published immediately because this is that serious. I brought in Dr. Mike Saylor, co-author of our book Learning Ransomware Response and Recovery, and my co-host Prasanna Malaiyandi to break down exactly what happened, how to find out if you were hit, and what you need to do to protect yourself going forward.We open with a story from 1982 that perfectly captures what this attack really is — getting poisoned by something you trusted completely. That framing matters. This wasn't a failure of the library. It was a failure of the supply chain. And it can happen again.Chapters:00:00:00 - Intro: Why this is an emergency episode00:01:35 - Meet the guests: Dr. Mike Saylor and Prasanna Malaiyandi00:02:31 - The Tylenol poisoning analogy and what it means for software supply chains00:05:51 - What LiteLLM is and what the malware actually did to your environment00:09:04 - Dependencies explained: why you're affected even if you didn't install LiteLLM directly00:12:24 - How to find out if you were hit: the first things to check right now00:14:23 - IOCs and TTPs: what to look for in your logs and on your systems00:19:07 - Network indicators: unusual traffic and what it tells you00:22:12 - How security teams can find out if developers installed it without telling anyone00:30:38 - Action items for the future: inventory, pinning, and hash verification00:36:55 - Sandboxing new downloads before they touch your environment00:37:59 - Immutable backups: why this attack makes the case for them00:40:33 - Modern authentication: MFA, its limits, and why passkeys matter00:46:53 - Where to get threat intel so you hear about attacks like this faster00:53:23 - Wrap-upIf you installed or upgraded LiteLLM on or after March 24, 2026 without a pinned version, stop what you're doing and listen to this episode first.The story:https://futuresearch.ai/blog/litellm-pypi-supply-chain-attack/ https://securitylabs.datadoghq.com/articles/litellm-compromised-pypi-teampcp-supply-chain-campaign/ https://snyk.io/articles/poisoned-security-scanner-backdooring-litellm/ https://www.wiz.io/blog/threes-a-crowd-teampcp-trojanizes-litellm-in-continuation-of-campaignhttps://checkmarx.com/zero-post/python-pypi-supply-chain-attack-colorama/ https://www.upwind.io/feed/litellm-pypi-supply-chain-attack-malicious-release https://docs.litellm.ai/blog/security-update-march-2026 https://www.helpnetsecurity.com/2026/03/25/teampcp-supply-chain-attacks/ https://www.darktrace.com/resources/the-cisos-guide-to-cyber-aihttps://securitylabs.datadoghq.com/articles/litellm-compromised-pypi-teampcp-supply-chain-campaign/Resources:https://www.stopransomware.comhttps://www.cisa.govhttps://www.cve.org/

Scrum The Toyota WayScrum is everywhere.Most Product Owners understand the framework well. We manage backlogs, prioritize items, attend ceremonies, and track progress sprint after sprint.And still, many products struggle.Value takes too long to reach customers. Teams deliver features that are rarely used. Defects appear late. Dependencies and waiting time quietly erode delivery speed.This is not because Scrum is broken.It's because Scrum alone does not address flow and waste deeply enough. That's where Scrum the Toyota Way (STW) fundamentally changes how Product Ownership works.How to connect with AgileDad:- [website] ⁠https://www.agiledad.com/⁠- [instagram] ⁠https://www.instagram.com/agile_coach/⁠- [facebook] ⁠https://www.facebook.com/RealAgileDad/⁠- [Linkedin] ⁠https://www.linkedin.com/in/leehenson/

AI companionship is a captivating and evolving landscape, especially when we consider the emotional connections being formed. Recent findings show that a staggering 72% of U.S. teens are engaging with AI companions. While many find solace in these digital friends, hoping to enhance their social skills and emotional expression, there's a deeper concern that we need to highlight today—the risks involved in these emotional attachments.A survey conducted in 2025 by Common Sense Media revealed that over half of the teens are interacting with AI companions at least a few times each month. Imagine having a confidant in your pocket who listens without judgment. For some, these AI friends are serving as a bridge to better emotional expression. However, paradoxically, 34% of these teens reported feeling uncomfortable with comments or behaviors from their bots. This discomfort hints at the ethical and psychological complexities of these interactions.Become a supporter of this podcast: https://www.spreaker.com/podcast/lets-talk-sex-and-dating--5052038/support.

Episode 111 - Dependencies at Scale by Dan Vacanti & Prateek Singh

Episode 112 - Forecasting with Dependencies by Dan Vacanti & Prateek Singh

Have you ever felt exhausted — not because you don't care, but because you're carrying more than you can name? Dr. Alison is joined by therapist and New York Times bestselling author Nedra Glover Tawwab to reflect on why caring deeply can sometimes leave us depleted — and why the answer isn't trying harder or pulling away. This conversation doesn't offer quick fixes. Instead, it invites you to pause and consider a different question—one that has less to do with effort and more to do with mutuality. If you've been feeling tired, responsible, or overwhelmed while trying to do the right thing, this episode offers perspective and hope—without pressure or oversimplification.

Building vs. Inheriting: Davy and PJ discuss the performance cost of design system dependencies. Learn how to choose the right framework without bloating your system or losing control of your architecture.

SEGMENT 5: CANADA-CHINA TRADE RELATIONS Guest: Charles Burton Burton examines Canada's complex trade relationship with China amid growing geopolitical tensions. Discussion covers economic dependencies, security concerns over Chinese investment, and how Ottawa balances commercial interests against pressure from Washington to reduce reliance on Beijing for critical goods and strategic resources.1884 OTTAWA RIVER

Across 152 conversations this year, a set of recurring patterns kept surfacing, regardless of whether the discussion focused on application security, software supply chain risk, AI systems, or creative work. The industries varied. The roles varied. The challenges did not.One theme rises above the rest: visibility remains the foundation of everything else, yet organizations continue to accept blind spots as normal. Asset inventories are incomplete. Build systems are poorly understood. Dependencies change faster than teams can track them. The issue is not a lack of tools. It is a willingness to tolerate uncertainty because discovery feels hard or disruptive.Another pattern is equally consistent. Integration matters more than novelty. New features, including AI-driven ones, sound compelling until they fail to connect with what teams already rely on. Security programs fracture when tools operate in isolation. Coverage looks strong on paper while gaps quietly expand in practice. When tools fail to integrate into existing environments, they create complexity instead of reducing risk.Security also continues to struggle with how it shows up in daily work. Programs succeed when security is embedded into workflows, automated where possible, and invisible until it matters. They fail when security acts as a gate that arrives after decisions are already made. Teams either adopt security naturally or route around it entirely. There is no neutral middle ground.Context repeatedly separates effective leadership from noise. Risk only becomes meaningful when it is framed in terms of business operations, delivery speed, and real tradeoffs. Leaders who understand how the business actually functions communicate risk clearly and make better decisions under pressure.Finally, creativity remains undervalued in security conversations. Automation should remove repetitive tasks so people can focus on judgment, problem solving, and design. The same mindset that produces elegant guitars, photographs, or products applies directly to building resilient security programs.These five patterns are not independent ideas. Together, they describe a shift toward security that is visible, integrated, contextual, workflow-driven, and human-centered.Read the full article: https://www.linkedin.com/pulse/five-patterns-from-152-podcast-episodes-2025-changed-i-martin-cissp-st1ge________This story represents the results of an interactive collaboration between Human Cognition and Artificial Intelligence.Enjoy, think, share with others, and subscribe to "The Future of Cybersecurity" newsletter on LinkedIn: https://itspm.ag/future-of-cybersecuritySincerely, Sean Martin and TAPE9________Sean Martin is a life-long musician and the host of the Music Evolves Podcast; a career technologist, cybersecurity professional, and host of the Redefining CyberSecurity Podcast; and is also the co-host of the On Location Event Coverage Podcast. These shows are all part of ITSPmagazine—which he co-founded with his good friend Marco Ciappelli, to explore and discuss topics at The Intersection of Technology, Cybersecurity, and Society.™️Would you like Sean to work with you on a topic/series to help you tell your story? Visit his services page to learn more: https://www.seanmartin.com/servicesWant to connect with Sean and Marco On Location at an event or conference near you? See where they will be next: https://www.itspmagazine.com/on-locationTo learn more about Sean, visit his personal website. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Across 152 conversations this year, a set of recurring patterns kept surfacing, regardless of whether the discussion focused on application security, software supply chain risk, AI systems, or creative work. The industries varied. The roles varied. The challenges did not.One theme rises above the rest: visibility remains the foundation of everything else, yet organizations continue to accept blind spots as normal. Asset inventories are incomplete. Build systems are poorly understood. Dependencies change faster than teams can track them. The issue is not a lack of tools. It is a willingness to tolerate uncertainty because discovery feels hard or disruptive.Another pattern is equally consistent. Integration matters more than novelty. New features, including AI-driven ones, sound compelling until they fail to connect with what teams already rely on. Security programs fracture when tools operate in isolation. Coverage looks strong on paper while gaps quietly expand in practice. When tools fail to integrate into existing environments, they create complexity instead of reducing risk.Security also continues to struggle with how it shows up in daily work. Programs succeed when security is embedded into workflows, automated where possible, and invisible until it matters. They fail when security acts as a gate that arrives after decisions are already made. Teams either adopt security naturally or route around it entirely. There is no neutral middle ground.Context repeatedly separates effective leadership from noise. Risk only becomes meaningful when it is framed in terms of business operations, delivery speed, and real tradeoffs. Leaders who understand how the business actually functions communicate risk clearly and make better decisions under pressure.Finally, creativity remains undervalued in security conversations. Automation should remove repetitive tasks so people can focus on judgment, problem solving, and design. The same mindset that produces elegant guitars, photographs, or products applies directly to building resilient security programs.These five patterns are not independent ideas. Together, they describe a shift toward security that is visible, integrated, contextual, workflow-driven, and human-centered.Read the full article: https://www.linkedin.com/pulse/five-patterns-from-152-podcast-episodes-2025-changed-i-martin-cissp-st1ge________This story represents the results of an interactive collaboration between Human Cognition and Artificial Intelligence.Enjoy, think, share with others, and subscribe to "The Future of Cybersecurity" newsletter on LinkedIn: https://itspm.ag/future-of-cybersecuritySincerely, Sean Martin and TAPE9________Sean Martin is a life-long musician and the host of the Music Evolves Podcast; a career technologist, cybersecurity professional, and host of the Redefining CyberSecurity Podcast; and is also the co-host of the On Location Event Coverage Podcast. These shows are all part of ITSPmagazine—which he co-founded with his good friend Marco Ciappelli, to explore and discuss topics at The Intersection of Technology, Cybersecurity, and Society.™️Would you like Sean to work with you on a topic/series to help you tell your story? Visit his services page to learn more: https://www.seanmartin.com/servicesWant to connect with Sean and Marco On Location at an event or conference near you? See where they will be next: https://www.itspmagazine.com/on-locationTo learn more about Sean, visit his personal website. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Join us for the final episode of 2025 as Mark Tinderholt (Principal Software Engineer at Microsoft Azure, HashiCorp Ambassador, and author of "Mastering Terraform") teaches us Infrastructure as Code through Minecraft! If you've ever wanted to learn Terraform in a fun, visual way, this is the episode for you. Mark demonstrates how to use the Minecraft Terraform provider to build infrastructure in-game, making complex IaC concepts tangible and engaging. You'll see live demos of provisioning Minecraft resources, managing dependencies, handling state, and even importing existing structures into Terraform. This unique approach transforms abstract infrastructure concepts into something you can literally see and interact with—perfect for visual learners, educators, or anyone looking to make IaC training more engaging. Whether you're teaching your team Terraform or just want a creative way to understand infrastructure patterns, this episode shows you how gaming and cloud engineering can come together. Subscribe to vBrownBag for weekly tech education! ⸻ Timestamps 0:00 Welcome & Technical Difficulties 1:27 Last Episode of 2025! 4:41 Planning for 2026 5:37 Mark Tinderholt Joins 6:14 Introduction to Minecraft + Terraform 8:52 Why Use Minecraft for Teaching IaC? 12:35 Getting Started: Requirements & Setup 16:47 The Minecraft Terraform Provider 20:18 First Demo: Provisioning Basic Blocks 28:32 Managing State in Minecraft 35:41 Working with Dependencies 42:16 Advanced Patterns: For_each & Count 48:55 Importing Existing Structures 55:23 Real-World Applications & Teaching 1:00:17 Q&A: Provider Limitations & Features 1:05:24 Minecraft Level Building Tools Discussion 1:09:05 Final Giveaway & Wrap-Up How to find Mark: https://www.linkedin.com/in/marktinderholt/ Links from the show: Marks repos: https://github.com/markti?tab=repositories Marks book: https://amzn.to/3N1rnuJ Mark's Ignite talk: https://ignite.microsoft.com/en-US/sessions/7fa5095f-9f65-46e3-9f82-9af6603ea903

TLDR: It was Claude :-)When I set out to compare ChatGPT, Claude, Gemini, Grok, and ChatPRD for writing Product Requirement Documents, I figured they'd all be roughly equivalent. Maybe some subtle variations in tone or structure, but nothing earth-shattering. They're all built on similar transformer architectures, trained on massive datasets, and marketed as capable of handling complex business writing.What I discovered over 45 minutes of hands-on testing revealed not just which tools are better for PRD creation, but why they're better, and more importantly, how you should actually be using AI to accelerate your product work without sacrificing quality or strategic thinking.If you're an early or mid-career PM in Silicon Valley, this matters to you. Because here's the uncomfortable truth: your peers are already using AI to write PRDs, analyze features, and generate documentation. The question isn't whether to use these tools. The question is whether you're using the right ones most effectively.So let me walk you through exactly what I did, what I learned, and what you should do differently.The Setup: A Real-World Test CaseHere's how I structured the experiment. As I said at the beginning of my recording, “We are back in the Fireside PM podcast and I did that review of the ChatGPT browser and people seemed to like it and then I asked, uh, in a poll, I think it was a LinkedIn poll maybe, what should my next PM product review be? And, people asked for ChatPRD.”So I had my marching orders from the audience. But I wanted to make this more comprehensive than just testing ChatPRD in isolation. I opened up five tabs: ChatGPT, Claude, Gemini, Grok, and ChatPRD.For the test case, I chose something realistic and relevant: an AI-powered tutor for high school students. Think KhanAmigo or similar edtech platforms. This gave me a concrete product scenario that's complex enough to stress-test these tools but straightforward enough that I could iterate quickly.But here's the critical part that too many PMs get wrong when they start using AI for product work: I didn't just throw a single sentence at these tools and expect magic.The “Back of the Napkin” Approach: Why You Still Need to Think“I presume everybody agrees that you should have some formulated thinking before you dump it into the chatbot for your PRD,” I noted early in my experiment. “I suppose in the future maybe you could just do, like, a one-sentence prompt and come out with the perfect PRD because it would just know everything about you and your company in the context, but for now we're gonna do this more, a little old-school AI approach where we're gonna do some original human thinking.”This is crucial. I see so many PMs, especially those newer to the field, treat AI like a magic oracle. They type in “Write me a PRD for a social feature” and then wonder why the output is generic, unfocused, and useless.Your job as a PM isn't to become obsolete. It's to become more effective. And that means doing the strategic thinking work that AI cannot do for you.So I started in Google Docs with what I call a “back of the napkin” PRD structure. Here's what I included:Why: The strategic rationale. In this case: “Want to complement our existing edtech business with a personalized AI tutor, uh, want to maintain position industry, and grow through innovation. on mission for learners.”Target User: Who are we building for? “High school students interested in improving their grades and fundamentals. Fundamental knowledge topics. Specifically science and math. Students who are not in the top ten percent, nor in the bottom ten percent.”This is key—I got specific. Not just “students,” but students in the middle 80%. Not just “any subject,” but science and math. This specificity is what separates useful AI output from garbage.Problem to Solve: What's broken? “Students want better grades. Students are impatient. Students currently use AI just for finding the answers and less to, uh, understand concepts and practice using them.”Key Elements: The feature set and approach.Success Metrics: How we'd measure success.Now, was this a perfectly polished PRD outline? Hell no. As you can see from my transcript, I was literally thinking out loud, making typos, restructuring on the fly. But that's exactly the point. I put in maybe 10-15 minutes of human strategic thinking. That's all it took to create a foundation that would dramatically improve what came out of the AI tools.Round One: Generating the Full PRDWith my back-of-the-napkin outline ready, I copied it into each tool with a simple prompt asking them to expand it into a more complete PRD.ChatGPT: The Reliable GeneralistChatGPT gave me something that was... fine. Competent. Professional. But also deeply uninspiring.The document it produced checked all the boxes. It had the sections you'd expect. The writing was clear. But when I read it, I couldn't shake the feeling that I was reading something that could have been written for literally any product in any company. It felt like “an average of everything out there,” as I noted in my evaluation.Here's what ChatGPT did well: It understood the basic structure of a PRD. It generated appropriate sections. The grammar and formatting were clean. If you needed to hand something in by EOD and had literally no time for refinement, ChatGPT would save you from complete embarrassment.But here's what it lacked: Depth. Nuance. Strategic thinking that felt connected to real product decisions. When it described the target user, it used phrases that could apply to any edtech product. When it outlined success metrics, they were the obvious ones (engagement, retention, test scores) without any interesting thinking about leading indicators or proxy metrics.The problem with generic output isn't that it's wrong, it's that it's invisible. When you're trying to get buy-in from leadership or alignment from engineering, you need your PRD to feel specific, considered, and connected to your company's actual strategy. ChatGPT's output felt like it was written by someone who'd read a lot of PRDs but never actually shipped a product.One specific example: When I asked for success metrics, ChatGPT gave me “Student engagement rate, Time spent on platform, Test score improvement.” These aren't wrong, but they're lazy. They don't show any thinking about what specifically matters for an AI tutor versus any other educational product. Compare that to Claude's output, which got more specific about things like “concept mastery rate” and “question-to-understanding ratio.”Actionable Insight: Use ChatGPT when you need fast, serviceable documentation that doesn't need to be exceptional. Think: internal updates, status reports, routine communications. Don't rely on it for strategic documents where differentiation matters. If you do use ChatGPT for important documents, treat its output as a starting point that needs significant human refinement to add strategic depth and company-specific context.Gemini: Better Than ExpectedGoogle's Gemini actually impressed me more than I anticipated. The structure was solid, and it had a nice balance of detail without being overwhelming.What Gemini got right: The writing had a nice flow to it. The document felt organized and logical. It did a better job than ChatGPT at providing specific examples and thinking through edge cases. For instance, when describing the target user, it went beyond demographics to consider behavioral characteristics and motivations.Gemini also showed some interesting strategic thinking. It considered competitive positioning more thoughtfully than ChatGPT and proposed some differentiation angles that weren't in my original outline. Good AI tools should add insight, not just regurgitate your input with better formatting.But here's where it fell short: the visual elements. When I asked for mockups, Gemini produced images that looked more like stock photos than actual product designs. They weren't terrible, but they weren't compelling either. They had that AI-generated sheen that makes it obvious they came from an image model rather than a designer's brain.For a PRD that you're going to use internally with a team that already understands the context, Gemini's output would work well. The text quality is strong enough, and if you're in the Google ecosystem (Docs, Sheets, Meet, etc.), the integration is seamless. You can paste Gemini's output directly into Google Docs and continue iterating there.But if you need to create something compelling enough to win over skeptics or secure budget, Gemini falls just short. It's good, but not great. It's the solid B+ student: reliably competent but rarely exceptional.Actionable Insight: Gemini is a strong choice if you're working in the Google ecosystem and need good integration with Docs, Sheets, and other Google Workspace tools. The quality is sufficient for most internal documentation needs. It's particularly good if you're working with cross-functional partners who are already in Google Workspace. You can share and collaborate on AI-generated drafts without friction. But don't expect visual mockups that will wow anyone, and plan to add your own strategic polish for high-stakes documents.Grok: Not Ready for Prime TimeLet's just say my expectations were low, and Grok still managed to underdeliver. The PRD felt thin, generic, and lacked the depth you need for real product work.“I don't have high expectations for grok, unfortunately,” I said before testing it. Spoiler alert: my low expectations were validated.Actionable Insight: Skip Grok for product documentation work right now. Maybe it'll improve, but as of my testing, it's simply not competitive with the other options. It felt like 1-2 years behind the others.ChatPRD: The Specialized ToolNow this was interesting. ChatPRD is purpose-built for PRDs, using foundational models underneath but with specific tuning and structure for product documentation.The result? The structure was logical, the depth was appropriate, and it included elements that showed understanding of what actually matters in a PRD. As I reflected: “Cause this one feels like, A human wrote this PRD.”The interface guides you through the process more deliberately than just dumping text into a general chat interface. It asks clarifying questions. It structures the output more thoughtfully.Actionable Insight: If you're a technical lead without a dedicated PM, or you're a PM who wants a more structured approach to using AI for PRDs, ChatPRD is worth the specialized focus. It's particularly good when you need something that feels authentic enough to share with stakeholders without heavy editing.Claude: The Clear WinnerBut the standout performer, and I'm ranking these, was Claude.“I think we know that for now, I'm gonna say Claude did the best job,” I concluded after all the testing. Claude produced the most comprehensive, thoughtful, and strategically sound PRD. But what really set it apart were the concept mocks.When I asked each tool to generate visual mockups of the product, Claude produced HTML prototypes that, while not fully functional, looked genuinely compelling. They had thoughtful UI design, clear information architecture, and felt like something that could actually guide development.“They were, like, closer to, like, what a Lovable would produce or something like that,” I noted, referring to the quality of low-fidelity prototypes that good designers create.The text quality was also superior: more nuanced, better structured, and with more strategic depth. It felt like Claude understood not just what a PRD should contain, but why it should contain those elements.Actionable Insight: For any PRD that matters, meaning anything you'll share with leadership, use to get buy-in, or guide actual product development, you might as well start with Claude. The quality difference is significant enough that it's worth using Claude even if you primarily use another tool for other tasks.Final Rankings: The Definitive HierarchyAfter testing all five tools on multiple dimensions: initial PRD generation, visual mockups, and even crafting a pitch paragraph for a skeptical VP of Engineering, here's my final ranking:* Claude - Best overall quality, most compelling mockups, strongest strategic thinking* ChatPRD - Best for structured PRD creation, feels most “human”* Gemini - Solid all-around performance, good Google integration* ChatGPT - Reliable but generic, lacks differentiation* Grok - Not competitive for this use case“I'd probably say Claude, then chat PRD, then Gemini, then chat GPT, and then Grock,” I concluded.The Deeper Lesson: Garbage In, Garbage Out (Still Applies)But here's what matters more than which tool wins: the realization that hit me partway through this experiment.“I think it really does come down to, like, you know, the quality of the prompt,” I observed. “So if our prompt were a little more detailed, all that were more thought-through, then I'm sure the output would have been better. But as you can see we didn't really put in brain trust prompting here. Just a little bit of, kind of hand-wavy prompting, but a little better than just one or two sentences.”And we still got pretty good results.This is the meta-insight that should change how you approach AI tools in your product work: The quality of your input determines the quality of your output, but the baseline quality of the tool determines the ceiling of what's possible.No amount of great prompting will make Grok produce Claude-level output. But even mediocre prompting with Claude will beat great prompting with lesser tools.So the dual strategy is:* Use the best tool available (currently Claude for PRDs)* Invest in improving your prompting skills ideally with as much original and insightful human, company aware, and context aware thinking as possible.Real-World Workflows: How to Actually Use This in Your Day-to-Day PM WorkTheory is great. Here's how to incorporate these insights into your actual product management workflows.The Weekly Sprint Planning WorkflowEvery PM I know spends hours each week preparing for sprint planning. You need to refine user stories, clarify acceptance criteria, anticipate engineering questions, and align with design and data science. AI can compress this work significantly.Here's an example workflow:Monday morning (30 minutes):* Review upcoming priorities and open your rough notes/outline in Google Docs* Open Claude and paste your outline with this prompt:“I'm preparing for sprint planning. Based on these priorities [paste notes], generate detailed user stories with acceptance criteria. Format each as: User story, Business context, Technical considerations, Acceptance criteria, Dependencies, Open questions.”Monday afternoon (20 minutes):* Review Claude's output critically* Identify gaps, unclear requirements, or missing context* Follow up with targeted prompts:“The user story about authentication is too vague. Break it down into separate stories for: social login, email/password, session management, and password reset. For each, specify security requirements and edge cases.”Tuesday morning (15 minutes):* Generate mockups for any UI-heavy stories:“Create an HTML mockup for the login flow showing: landing page, social login options, email/password form, error states, and success redirect.”* Even if the HTML doesn't work perfectly, it gives your designers a starting pointBefore sprint planning (10 minutes):* Ask Claude to anticipate engineering questions:“Review these user stories as if you're a senior engineer. What questions would you ask? What concerns would you raise about technical feasibility, dependencies, or edge cases?”* This preparation makes you look thoughtful and helps the meeting run smoothlyTotal time investment: ~75 minutes. Typical time saved: 3-4 hours compared to doing this manually.The Stakeholder Alignment WorkflowGetting alignment from multiple stakeholders (product leadership, engineering, design, data science, legal, marketing) is one of the hardest parts of PM work. AI can help you think through different stakeholder perspectives and craft compelling communications for each.Here's how:Step 1: Map your stakeholders (10 minutes)Create a quick table in a doc:Stakeholder | Primary Concern | Decision Criteria | Likely Objections VP Product | Strategic fit, ROI | Company OKRs, market opportunity | Resource allocation vs other priorities VP Eng | Technical risk, capacity | Engineering capacity, tech debt | Complexity, unclear requirements Design Lead | User experience | User research, design principles | Timeline doesn't allow proper design process Legal | Compliance, risk | Regulatory requirements | Data privacy, user consent flowsStep 2: Generate stakeholder-specific communications (20 minutes)For each key stakeholder, ask Claude:“I need to pitch this product idea to [Stakeholder]. Based on this PRD, create a 1-page brief addressing their primary concern of [concern from your table]. Open with the specific value for them, address their likely objection of [objection], and close with a clear ask. Tone should be [professional/technical/strategic] based on their role.”Then you'll have customized one-pagers for your pre-meetings with each stakeholder, dramatically increasing your alignment rate.Step 3: Synthesize feedback (15 minutes)After gathering stakeholder input, ask Claude to help you synthesize:“I got the following feedback from stakeholders: [paste feedback]. Identify: (1) Common themes, (2) Conflicting requirements, (3) Legitimate concerns vs organizational politics, (4) Recommended compromises that might satisfy multiple parties.”This pattern-matching across stakeholder feedback is something AI does really well and saves you hours of mental processing.The Quarterly Planning WorkflowQuarterly or annual planning is where product strategy gets real. You need to synthesize market trends, customer feedback, technical capabilities, and business objectives into a coherent roadmap. AI can accelerate this dramatically.Six weeks before planning:* Start collecting input (customer interviews, market research, competitive analysis, engineering feedback)* Don't wait until the last minuteFour weeks before planning:Dump everything into Claude with this structure:“I'm creating our Q2 roadmap. Context:* Business objectives: [paste from leadership]* Customer feedback themes: [paste synthesis]* Technical capabilities/constraints: [paste from engineering]* Competitive landscape: [paste analysis]* Current product gaps: [paste from your analysis]Generate 5 strategic themes that could anchor our Q2 roadmap. For each theme:* Strategic rationale (how it connects to business objectives)* Key initiatives (2-3 major features/projects)* Success metrics* Resource requirements (rough estimate)* Risks and mitigations* Customer segments addressed”This gives you a strategic framework to react to rather than starting from a blank page.Three weeks before planning:Iterate on the most promising themes:“Deep dive on Theme 3. Generate:* Detailed initiative breakdown* Dependencies on platform/infrastructure* Phasing options (MVP vs full build)* Go-to-market considerations* Data requirements* Open questions requiring research”Two weeks before planning:Pressure-test your thinking:“Play devil's advocate on this roadmap. What are the strongest arguments against each initiative? What am I likely missing? What failure modes should I plan for?”This adversarial prompting forces you to strengthen weak points before your leadership reviews it.One week before planning:Generate your presentation:“Create an executive presentation for this roadmap. Structure: (1) Market context and strategic imperative, (2) Q2 themes and initiatives, (3) Expected outcomes and metrics, (4) Resource requirements, (5) Key risks and mitigations, (6) Success criteria for decision. Make it compelling but data-driven. Tone: confident but not overselling.”Then add your company-specific context, visual brand, and personal voice.The Customer Research WorkflowAI can't replace talking to customers, but it can help you prepare better questions, analyze feedback more systematically, and identify patterns faster.Before customer interviews:“I'm interviewing customers about [topic]. Generate:* 10 open-ended questions that avoid leading the witness* 5 follow-up questions for each main question* Common cognitive biases I should watch for* A framework for categorizing responses”This prep work helps you conduct better interviews.After interviews:“I conducted 15 customer interviews. Here are the key quotes: [paste anonymized quotes]. Identify:* Recurring themes and patterns* Surprising insights that contradict our assumptions* Segments with different needs* Implied needs customers didn't articulate directly* Recommended next steps for validation”AI is excellent at pattern-matching across qualitative data at scale.The Crisis Management WorkflowSomething broke. The site is down. Data was lost. A feature shipped with a critical bug. You need to move fast.Immediate response (5 minutes):“Critical incident. Details: [brief description]. Generate:* Incident classification (Sev 1-4)* Immediate stakeholders to notify* Draft customer communication (honest, apologetic, specific about what happened and what we're doing)* Draft internal communication for leadership* Key questions to ask engineering during investigation”Having these drafted in 5 minutes lets you focus on coordination and decision-making rather than wordsmithing.Post-incident (30 minutes):“Write a post-mortem based on this incident timeline: [paste timeline]. Include:* What happened (technical details)* Root cause analysis* Impact quantification (users affected, revenue impact, time to resolution)* What went well in our response* What could have been better* Specific action items with owners and deadlines* Process changes to prevent recurrence Tone: Blameless, focused on learning and improvement.”This gives you a strong first draft to refine with your team.Common Pitfalls: What Not to Do with AI in Product ManagementNow let's talk about the mistakes I see PMs making with AI tools. Pitfall #1: Treating AI Output as FinalThe biggest mistake is copy-pasting AI output directly into your PRD, roadmap presentation, or stakeholder email without critical review.The result? Documents that are grammatically perfect but strategically shallow. Presentations that sound impressive but don't hold up under questioning. Emails that are professionally worded but miss the subtext of organizational politics.The fix: Always ask yourself:* Does this reflect my actual strategic thinking, or generic best practices?* Would my CEO/engineering lead/biggest customer find this compelling and specific?* Are there company-specific details, customer insights, or technical constraints that only I know?* Does this sound like me, or like a robot?Add those elements. That's where your value as a PM comes through.Pitfall #2: Using AI as a Crutch Instead of a ToolSome PMs use AI because they don't want to think deeply about the product. They're looking for AI to do the hard work of strategy, prioritization, and trade-off analysis.This never works. AI can help you think more systematically, but it can't replace thinking.If you find yourself using AI to avoid wrestling with hard questions (”Should we build X or Y?” “What's our actual competitive advantage?” “Why would customers switch from the incumbent?”), you're using it wrong.The fix: Use AI to explore options, not to make decisions. Generate three alternatives, pressure-test each one, then use your judgment to decide. The AI can help you think through implications, but you're still the one choosing.Pitfall #3: Not IteratingGetting mediocre AI output and just accepting it is a waste of the technology's potential.The PMs who get exceptional results from AI are the ones who iterate. They generate an initial response, identify what's weak or missing, and ask follow-up questions. They might go through 5-10 iterations on a key section of a PRD.Each iteration is quick (30 seconds to type a follow-up prompt, 30 seconds to read the response), but the cumulative effect is dramatically better output.The fix: Budget time for iteration. Don't try to generate a complete, polished PRD in one prompt. Instead, generate a rough draft, then spend 30 minutes iterating on specific sections that matter most.Pitfall #4: Ignoring the Political and Human ContextAI tools have no understanding of organizational politics, interpersonal relationships, or the specific humans you're working with.They don't know that your VP of Engineering is burned out and skeptical of any new initiatives. They don't know that your CEO has a personal obsession with a specific competitor. They don't know that your lead designer is sensitive about not being included early enough in the process.If you use AI-generated communications without layering in this human context, you'll create perfectly worded documents that land badly because they miss the subtext.The fix: After generating AI content, explicitly ask yourself: “What human context am I missing? What relationships do I need to consider? What political dynamics are in play?” Then modify the AI output accordingly.Pitfall #5: Over-Relying on a Single ToolDifferent AI tools have different strengths. Claude is great for strategic depth, ChatPRD is great for structure, Gemini integrates well with Google Workspace.If you only ever use one tool, you're missing opportunities to leverage different strengths for different tasks.The fix: Keep 2-3 tools in your toolkit. Use Claude for important PRDs and strategic documents. Use Gemini for quick internal documentation that needs to integrate with Google Docs. Use ChatPRD when you want more guided structure. Match the tool to the task.Pitfall #6: Not Fact-Checking AI OutputAI tools hallucinate. They make up statistics, misrepresent competitors, and confidently state things that aren't true. If you include those hallucinations in a PRD that goes to leadership, you look incompetent.The fix: Fact-check everything, especially:* Statistics and market data* Competitive feature claims* Technical capabilities and limitations* Regulatory and compliance requirementsIf the AI cites a number or makes a factual claim, verify it independently before including it in your document.The Meta-Skill: Prompt Engineering for PMsLet's zoom out and talk about the underlying skill that makes all of this work: prompt engineering.This is a real skill. The difference between a mediocre prompt and a great prompt can be 10x difference in output quality. And unlike coding or design, where there's a steep learning curve, prompt engineering is something you can get good at quickly.Principle 1: Provide Context Before InstructionsBad prompt:“Write a PRD for an AI tutor”Good prompt:“I'm a PM at an edtech company with 2M users, primarily high school students. We're exploring an AI tutor feature to complement our existing video content library and practice problems. Our main competitors are Khan Academy and Course Hero. Our differentiation is personalized learning paths based on student performance data.Write a PRD for an AI tutor feature targeting students in the middle 80% academically who struggle with science and math.”The second prompt gives Claude the context it needs to generate something specific and strategic rather than generic.Principle 2: Specify Format and ConstraintsBad prompt:“Generate success metrics”Good prompt:“Generate 5-7 success metrics for this feature. Include a mix of:* Leading indicators (early signals of success)* Lagging indicators (definitive success measures)* User behavior metrics* Business impact metricsFor each metric, specify: name, definition, target value, measurement method, and why it matters.”The structure you provide shapes the structure you get back.Principle 3: Ask for Multiple OptionsBad prompt:“What should our Q2 priorities be?”Good prompt:“Generate 3 different strategic approaches for Q2:* Option A: Focus on user acquisition* Option B: Focus on engagement and retention* Option C: Focus on monetizationFor each option, detail: key initiatives, expected outcomes, resource requirements, risks, and recommendation for or against.”Asking for multiple options forces the AI (and forces you) to think through trade-offs systematically.Principle 4: Specify Audience and ToneBad prompt:“Summarize this PRD”Good prompt:“Create a 1-paragraph summary of this PRD for our skeptical VP of Engineering. Tone: Technical, concise, addresses engineering concerns upfront. Focus on: technical architecture, resource requirements, risks, and expected engineering effort. Avoid marketing language.”The audience and tone specification ensures the output will actually work for your intended use.Principle 5: Use Iterative RefinementDon't try to get perfect output in one prompt. Instead:First prompt: Generate rough draft Second prompt: “This is too generic. Add specific examples from [our company context].” Third prompt: “The technical section is weak. Expand with architecture details and dependencies.” Fourth prompt: “Good. Now make it 30% more concise while keeping the key details.”Each iteration improves the output incrementally.Let me break down the prompting approach that worked in this experiment, because this is immediately actionable for your work tomorrow.Strategy 1: The Structured Outline ApproachDon't go from zero to full PRD in one prompt. Instead:* Start with strategic thinking - Spend 10-15 minutes outlining why you're building this, who it's for, and what problem it solves* Get specific - Don't say “users,” say “high school students in the middle 80% of academic performance”* Include constraints - Budget, timeline, technical limitations, competitive landscape* Dump your outline into the AI - Now ask it to expand into a full PRD* Iterate section by section - Don't try to perfect everything at onceThis is exactly what I did in my experiment, and even with my somewhat sloppy outline, the results were dramatically better than they would have been with a single-sentence prompt.Strategy 2: The Comparative Analysis PatternOne technique I used that worked particularly well: asking each tool to do the same specific task and comparing results.For example, I asked all five tools: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This forced each tool to synthesize the entire PRD into a compelling pitch while accounting for a specific, challenging audience. The variation in quality was revealing—and it gave me multiple options to choose from or blend together.Actionable tip: When you need something critical (a pitch, an executive summary, a key decision framework), generate it with 2-3 different AI tools and take the best elements from each. This “ensemble approach” often produces better results than any single tool.Strategy 3: The Iterative Refinement LoopDon't treat the AI output as final. Use it as a first draft that you then refine through conversation with the AI.After getting the initial PRD, I could have asked follow-up questions like:* “What's missing from this PRD?”* “How would you strengthen the success metrics section?”* “Generate 3 alternative approaches to the core feature set”Each iteration improves the output and, more importantly, forces me to think more deeply about the product.What This Means for Your CareerIf you're an early or mid-career PM reading this, you might be thinking: “Great, so AI can write PRDs now. Am I becoming obsolete?”Absolutely not. But your role is evolving, and understanding that evolution is critical.The PMs who will thrive in the AI era are those who:* Excel at strategic thinking - AI can generate options, but you need to know which options align with company strategy, customer needs, and technical feasibility* Master the art of prompting - This is a genuine skill that separates mediocre AI users from exceptional ones* Know when to use AI and when not to - Some aspects of product work benefit enormously from AI. Others (user interviews, stakeholder negotiation, cross-functional relationship building) require human judgment and empathy* Can evaluate AI output critically - You need to spot the hallucinations, the generic fluff, and the strategic misalignments that AI inevitably producesThink of AI tools as incredibly capable interns. They can produce impressive work quickly, but they need direction, oversight, and strategic guidance. Your job is to provide that guidance while leveraging their speed and breadth.The Real-World Application: What to Do Monday MorningLet's get tactical. Here's exactly how to apply these insights to your actual product work:For Your Next PRD:* Block 30 minutes for strategic thinking - Write your back-of-the-napkin outline in Google Docs or your tool of choice* Open Claude (or ChatPRD if you want more structure)* Copy your outline with this prompt:“I'm a product manager at [company] working on [product area]. I need to create a comprehensive PRD based on this outline. Please expand this into a complete PRD with the following sections: [list your preferred sections]. Make it detailed enough for engineering to start breaking down into user stories, but concise enough for leadership to read in 15 minutes. [Paste your outline]”* Review the output critically - Look for generic statements, missing details, or strategic misalignments* Iterate on specific sections:“The success metrics section is too vague. Please provide 3-5 specific, measurable KPIs with target values and explanation of why these metrics matter.”* Generate supporting materials:“Create a visual mockup of the core user flow showing the key interaction points.”* Synthesize the best elements - Don't just copy-paste the AI output. Use it as raw material that you shape into your final documentFor Stakeholder Communication:When you need to pitch something to leadership or engineering:* Generate 3 versions of your pitch using different tools (Claude, ChatPRD, and one other)* Compare them for:* Clarity and conciseness* Strategic framing* Compelling value proposition* Addressing likely objections* Blend the best elements into your final version* Add your personal voice - This is crucial. AI output often lacks personality and specific company context. Add that yourself.For Feature Prioritization:AI tools can help you think through trade-offs more systematically:“I'm deciding between three features for our next release: [Feature A], [Feature B], and [Feature C]. For each feature, analyze: (1) Estimated engineering effort, (2) Expected user impact, (3) Strategic alignment with making our platform the go-to solution for [your market], (4) Risk factors. Then recommend a prioritization with rationale.”This doesn't replace your judgment, but it forces you to think through each dimension systematically and often surfaces considerations you hadn't thought of.The Uncomfortable Truth About AI and Product ManagementLet me be direct about something that makes many PMs uncomfortable: AI will make some PM skills less valuable while making others more valuable.Less valuable:* Writing boilerplate documentation* Creating standard frameworks and templates* Generating routine status updates* Synthesizing information from existing sourcesMore valuable:* Strategic product vision and roadmapping* Deep customer empathy and insight generation* Cross-functional leadership and influence* Critical evaluation of options and trade-offs* Creative problem-solving for novel situationsIf your PM role primarily involves the first category of tasks, you should be concerned. But if you're focused on the second category while leveraging AI for the first, you're going to be exponentially more effective than your peers who resist these tools.The PMs I see succeeding aren't those who can write the best PRD manually. They're those who can write the best PRD with AI assistance in one-tenth the time, then use the saved time to talk to more customers, think more deeply about strategy, and build stronger cross-functional relationships.Advanced Techniques: Beyond Basic PRD GenerationOnce you've mastered the basics, here are some advanced applications I've found valuable:Competitive Analysis at Scale“Research our top 5 competitors in [market]. For each one, analyze: their core value proposition, key features, pricing strategy, target customer, and likely product roadmap based on recent releases and job postings. Create a comparison matrix showing where we have advantages and gaps.”Then use web search tools in Claude or Perplexity to fact-check and expand the analysis.Scenario Planning“We're considering three strategic directions for our product: [Direction A], [Direction B], [Direction C]. For each direction, map out: likely customer adoption curve, required technical investments, competitive positioning in 12 months, and potential pivots if the hypothesis proves wrong. Then identify the highest-risk assumptions we should test first for each direction.”This kind of structured scenario thinking is exactly what AI excels at—generating multiple well-reasoned perspectives quickly.User Story GenerationAfter your PRD is solid:“Based on this PRD, generate a complete set of user stories following the format ‘As a [user type], I want to [action] so that [benefit].' Include acceptance criteria for each story. Organize them into epics by functional area.”This can save your engineering team hours of grooming meetings.The Tools Will Keep Evolving. Your Process Shouldn'tHere's something important to remember: by the time you read this, the specific rankings might have shifted. Maybe ChatGPT-5 has leapfrogged Claude. Maybe a new specialized tool has emerged.But the core principles won't change:* Do strategic thinking before touching AI* Use the best tool available for your specific task* Iterate and refine rather than accepting first outputs* Blend AI capabilities with human judgment* Focus your time on the uniquely human aspects of product managementThe specific tools matter less than your process for using them effectively.A Final Experiment: The Skeptical VP TestI want to share one more insight from my testing that I think is particularly relevant for early and mid-career PMs.Toward the end of my experiment, I gave each tool this prompt: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This is such a realistic scenario. How many times have you needed to pitch an idea to a skeptical technical leader via Slack or email? Someone who's brilliant, who's seen a thousand product ideas fail, and who can spot b******t from a mile away?The quality variation in the responses was fascinating. ChatGPT gave me something that felt generic and safe. Gemini was better but still a bit too enthusiastic. Grok was... well, Grok.But Claude and ChatPRD both produced messages that felt authentic, technically credible, and appropriately confident without being overselling. They acknowledged the engineering challenges while framing the opportunity compellingly.The lesson: When the stakes are high and the audience is sophisticated, the quality of your AI tool matters even more. That skeptical VP can tell the difference between a carefully crafted message and AI-generated fluff. So can your CEO. So can your biggest customers.Use the best tools available, but more importantly, always add your own strategic thinking and authentic voice on top.Questions to Consider: A Framework for Your Own ExperimentsAs I wrapped up my Loom, I posed some questions to the audience that I'll pose to you:“Let me know in the comments, if you do your PRDs using AI differently, do you start with back of the envelope? Do you say, oh no, I just start with one sentence, and then I let the chatbot refine it with me? Or do you go way more detailed and then use the chatbot to kind of pressure test it?”These aren't rhetorical questions. Your answer reveals your approach to AI-augmented product work, and different approaches work for different people and contexts.For early-career PMs: I'd recommend starting with more detailed outlines. The discipline of thinking through your product strategy before touching AI will make you a stronger PM. You can always compress that process later as you get more experienced.For mid-career PMs: Experiment with different approaches for different types of documents. Maybe you do detailed outlines for major feature PRDs but use more iterative AI-assisted refinement for smaller features or updates. Find what optimizes your personal productivity while maintaining quality.For senior PMs and product leaders: Consider how AI changes what you should expect from your PM team. Should you be reviewing more AI-generated first drafts and spending more time on strategic guidance? Should you be training your team on effective AI usage? These are leadership questions worth grappling with.The Path Forward: Continuous ExperimentationMy experiment with these five AI tools took 45 minutes. But I'm not done experimenting.The field of AI-assisted product management is evolving rapidly. New tools launch monthly. Existing tools get smarter weekly. Prompting techniques that work today might be obsolete in three months.Your job, if you want to stay at the forefront of product management, is to continuously experiment. Try new tools. Share what works with your peers. Build a personal knowledge base of effective prompts and workflows. And be generous with what you learn. The PM community gets stronger when we share insights rather than hoarding them.That's why I created this Loom and why I'm writing this post. Not because I have all the answers, but because I'm figuring it out in real-time and want to share the journey.A Personal Note on Coaching and ConsultingIf this kind of practical advice resonates with you, I'm happy to work with you directly.Through my pm coaching practice, I offer 1:1 executive, career, and product coaching for PMs and product leaders. We can dig into your specific challenges: whether that's leveling up your AI workflows, navigating a career transition, or developing your strategic product thinking.I also work with companies (usually startups or incubation teams) on product strategy, helping teams figure out PMF for new explorations and improving their product management function.The format is flexible. Some clients want ongoing coaching, others prefer project-based consulting, and some just want a strategic sounding board for a specific decision. Whatever works for you.Reach out through tomleungcoaching.com if you're interested in working together.OK. Enough pontificating. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Josh discusses updating open source dependencies with Jamie Tanna. Jamie works on Renovate which gives them a lot of insight into the challenges of keeping your open source updated. We discuss the challenges of semantic versioning, supply chain security, and AI-generated code. If you're new or old to the world of open source dependencies, there's something to learn from this chat. The show notes and blog post for this episode can be found at https://opensourcesecurity.io/2025/2025-12-renovate-jamie

This interview was recorded for GOTO Unscripted.http://gotopia.techRead the full transcription of this interview here:https://gotopia.tech/articles/384Liz Fong-Jones - Field CTO at Honeycomb.ioLesley Cordero - Staff Software Engineer, Tech Lead at The New York TimesRESOURCESLizhttps://bsky.app/profile/lizthegrey.comhttps://github.com/lizthegreyhttps://linkedin.com/in/efonghttps://www.lizthegrey.comLesleyhttps://www.lesleycordero.comhttps://twitter.com/clesleycodehttps://github.com/clesleycodehttps://www.linkedin.com/in/lesleycorderoVideoshttps://www.alex-hidalgo.comhttps://www.honeycomb.io/blog/most-important-developer-productivity-metric-build-timesDESCRIPTIONLiz and Lesley explore the evolution of platform engineering from its DevOps and SRE roots. They discuss the challenges of building effective developer platforms, the importance of psychological safety and evidence-based prioritization, the complexities of open source sustainability, and the delicate balance between centralized platform teams and developer autonomy.The conversation covers practical insights on documentation automation, onboarding strategies, the manager-engineer career pendulum, and why treating platform work as a service rather than a mandate is crucial for organizational success.RECOMMENDED BOOKSAdkins, Beyer, Blankinship, Lewandowski, Oprea & Stubblefield • Building Secure and Reliable Systems • https://amzn.to/4n0bjaeCharity Majors, Liz Fong-Jones & George Miranda • Observability Engineering • https://amzn.to/38scbmaBeyer, Murphy, Rensin, Kawahara & Thorne • The Site Reliability Workbook • https://amzn.to/3IwsiOlKelly Shortridge & Aaron Rinehart • Security Chaos Engineering • https://www.verica.io/sce-bookNoInspiring 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!

Renee Troughton: Managing Dependencies and Downstream Bottlenecks in Scrum Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes. "For the actual product teams, it's not a problem for them... It's more the downstream teams that aren't the product teams, that are still dependencies... They just don't see that work until, hey, we urgently need this." Renee brings a dual-edged challenge from her current work with dozens of teams across multiple business lines. While quarterly planning happens at a high level, small downstream teams—middleware, AI, data, and even non-technical teams like legal—are not considered in the planning process. These teams experience unexpected work floods with dramatic peaks and troughs throughout the quarter. The product teams are comfortable with ambiguity and incremental delivery, but downstream service teams don't see work coming until it arrives urgently. Through a coaching conversation, Renee and Vasco explore multiple experimental approaches: top-to-bottom stack ranking of initiatives, holding excess capacity based on historical patterns, shared code ownership where downstream teams advise rather than execute changes, and using Theory of Constraints to manage flow into bottleneck teams. They discuss how lack of discovery work compounds the problem, as teams "just start working" without identifying all players who need involvement. The solution requires balancing multiple strategies while maintaining an experimentation mindset, recognizing that complex systems require sensing our way toward solutions rather than predicting them. Self-reflection Question: Are you actively managing the flow of work to prevent downstream bottlenecks, or are you allowing your "downstream teams" to be repeatedly overwhelmed by last-minute urgent requests? [The Scrum Master Toolbox Podcast Recommends]

In this episode, hosts Chandra and Paul are joined by Senior Principal Software Engineer Jason Rice to dive into the complexities of dependencies within JD Edwards (JDE). The conversation covers the shift from major release upgrades to continuous integration and how this impacts dependency management for customers. Jason explains the types of dependencies—including logical and blind dependencies—and reveals how enhancements and bug fixes are intertwined in the current update model. The team discusses practical strategies for staying current with updates, minimizing technical debt, and handling customizations, all while emphasizing the importance of regular maintenance to avoid overwhelming upgrade projects. 06:00 Introducing Jason Rice 07:15 What are Dependencies? 08:28 Evolution of Continuous Delivery 13:17 Types of Dependencies 18:21 Why Dependencies Have Grown Over Time 22:17 Managing Dependencies in Real Life 26:18 Why Customizations Complicate Things 30:35 Staying Current Without Burning Out 37:58 Midwesternism of the Day Resources: If you have concerns or feedback on this episode or ideas for future episodes, please contact us at thejdeconnection@questoraclecommunity.org

Scrum Masters, You're Not Just the Meeting PersonYou set up the standup, run a clean retro, update the board — and yet, nothing really changes. Dependencies still hit late. Stakeholders still ask for fixed timelines. The team's doing its best, but it always feels like someone outside is pulling the rug. Sound familiar?That's because agility doesn't stop at the team boundary. And if you're only coaching inside the circle, you're missing half the job. The real magic? It happens when you start coaching around the team too.How to connect with AgileDad:- [website] https://www.agiledad.com/- [instagram] https://www.instagram.com/agile_coach/- [facebook] https://www.facebook.com/RealAgileDad/- [Linkedin] https://www.linkedin.com/in/leehenson/

An airhacks.fm conversation with Ronald Dehuysser (@rdehuyss) about: JobRunner evolution from open source to processing 1 billion jobs daily, carbon-aware job processing using European energy grid data ( ENTSO-E ) for scheduling jobs during renewable energy peaks, correlation between CO2 emissions and energy prices for cost optimization, JobRunner Pro vs Open Source features including workflows and multi-tenancy support, bytecode analysis using ASM for lambda serialization, JSON serialization for job state persistence, support for relational databases and MongoDB with potential S3 and DynamoDB integration, distributed processing with master node coordination using heartbeat mechanism, scale-to-zero architecture possibilities using AWS EventBridge Scheduler, Java performance advantages showing 35x faster than python in benchmarks, cloud migration patterns from on-premise to serverless architectures, criticism of kubernetes complexity and lift-and-shift cloud migrations, cost-driven architecture approach using AWS Lambda and S3, quarkus as fastest Java runtime for cloud deployments, infrastructure as code using AWS CDK with Java, potential WebAssembly compilation for Edge Computing, automatic retry mechanisms with exponential backoff, dashboard and monitoring capabilities, medical industry use case with critical cancer result processing, professional liability insurance for software errors, comparison with executor service for non-critical tasks, scheduled and recurring job support, carbon footprint reduction through intelligent scheduling, spot instance integration for cost optimization, simplified developer experience with single JAR deployment, automatic table creation and data source detection in Quarkus, backwards compatibility requirements for distributed nodes, future serverless edition possibilities Ronald Dehuysser on twitter: @rdehuyss

This month we are delighted to be joined by Markus Kröger, professor of Global Development Studies at University of Helsinki. Markus has joined the show before as a founder of the EXALT Initiative and as a PI in the Trees for Development project. This time Markus is here to talk to us about his new book from University of Cambridge Press, Clearcut: Political Economies of Deforestation. This open access book will be published open access in October 2025. Markus gives us a sneak peek into the key topics of the book, primarily the mechanisms of regionally dominant political economies (RDPE) and the role they play in driving extractive sectors. To make this novel theorization, Markus looked at cattle capitalism in Brazil, narco-gold in the Amazon, and paper pulping in Finland. Markus shows how these three different RDPEs drive deforestation in their respective locations.  Interested in reading Clearcut? https://www.cambridge.org/fi/universitypress/subjects/earth-and-environmental-science/environmental-policy-economics-and-law/clearcut-political-economies-deforestation?format=HB&isbn=9781009389549  Interested in learning more about Markus' work? https://researchportal.helsinki.fi/en/persons/markus-kr%C3%B6ger  Interest in the Trees For Development Project? https://www.helsinki.fi/en/researchgroups/trees-for-development   

The dynamics of the transatlantic relationship are changing. As Europe faces the changing policies of the United States toward its alliances, Germany is investing in its armed forces at a …

Thank you to the folks at Sustain (https://sustainoss.org/) for providing the hosting account for CHAOSSCast! CHAOSScast – Episode 119 In this episode of CHAOSScast, we have a special episode from our friends at Sustain. Host Richard Littauer from Sustain is joined by guests Ben Nickolls and Andrew Nesbitt to discuss the ecosyste.ms project. They explore how ecosyste.ms collects and analyzes metadata from various open-source projects to create a comprehensive database that can help improve funding allocation. The discussion covers the importance of funding the most critical open-source projects, the existing gaps in funding, and the partnership between ecosyste.ms and Open Source Collective to create funding algorithms that support entire ecosystems. They also talk about the challenges of maintaining data, reaching out to project maintainers, and the broader implications for the open-source community. Hit the download button now! [00:03:16] Andrew and Ben explain ecosyste.ms, what it does, and how it compares to Libraries.io. [00:06:17] Ecosyste.ms tracks metadata, not the packages themselves, and enriches data via dependency graphs, committers, issues, SBOMs, and more. [00:08:12] Andrew talks about finding 1,890 Git hosts and how many critical projects live outside GitHub. [00:09:55] There's a conversation on metadata uses and SBOM parsing. [00:14:07] Richard inquires about the ecosystem.ms funds on their website which Andrew explains it's a collaboration between Open Collective and ecosyste.ms. that algorithmically distributes funds to the most used, not most popular packages. [00:17:03] Ben shares how this is different from previous projects and brings up a past project, “Back Your Stack” and explains how ecosyste.ms is doing two things differently. [00:20:17] Ben explains how it supports payouts to other platforms and encourages maintainers to adopt funding YAML files for automation. Andrew touches on efficient outreach, payout management, and API usage (GraphQL). [00:26:54] Ben elaborates on how companies can fund ecosyste.ms (like Django) instead of curating their own lists and being inspired by Sentry's work with the Open Source Pledge. [00:30:50] Andrew speaks about scaling and developer engagement and emphasizes their focus is on high-impact sustainability. [00:34:06] Richard asks, “Why does it matter?” Ben explains that most current funding goes to popular, not most used projects and ecosyste.ms aims to fix the gap with data backed funding, and he suggests use of open standards like 360Giving and Open Contracting Data. [00:37:04] Andrew shares his thoughts on funding the right projects by improving 1% of OSS, you uplift the quality of millions of dependent projects with healthier infrastructure, faster security updates, and more resilient software. [00:39:53] Find out where you can follow ecosyste.ms and the blog on the web. Quotes: [00:12:36] “I call them interesting forks. If a fork is referenced by a package, it'll get indexed.” [00:23:25] We've built a service that now moves like $25 million a year between OSS maintainers on OSC.” [00:34:41] “We don't have enough information to make collective decisions about which projects, communities, maintainers, should receive more funding.” [00:35:41] “The NSF POSE Program has distributed hundreds of millions of dollars of funding to open source communities alone.” [00:37:05] “If you have ten, twenty thousand really critical open source projects, that actually isn't unachievable to make those projects sustainable.” Spotlight: [00:40:53] Ben's spotlight is Jellyfin. [00:41:38]** **Andrew's spotlight is zizmor. [00:43:39] Richard's spotlight is The LaTeX Project. Panelist: Richard Littauer Guests: Ben Nickolls Andrew Nesbitt Links: CHAOSS (https://chaoss.community/) CHAOSS Project Twitter (https://twitter.com/chaossproj?lang=en) CHAOSScast Podcast (https://podcast.chaoss.community/) podcast@chaoss.community (mailto:podcast@chaoss.community) Alice Sowerby LinkedIn (https://www.linkedin.com/in/alice-sowerby-ba692a13/?originalSubdomain=uk) SustainOSS (https://sustainoss.org/) podcast@sustainoss.org (mailto:podcast@sustainoss.org) richard@sustainoss.org (mailto:richard@sustainoss.org) SustainOSS Discourse (https://discourse.sustainoss.org/) SustainOSS Mastodon (https://mastodon.social/tags/sustainoss) SustainOSS Bluesky (https://bsky.app/profile/sustainoss.bsky.social) SustainOSS LinkedIn (https://www.linkedin.com/company/sustainoss/) Open Collective-SustainOSS (Contribute) (https://opencollective.com/sustainoss) Richard Littauer Socials (https://www.burntfen.com/2023-05-30/socials) Ben Nickolls LinkedIn (https://www.linkedin.com/in/benjamuk/) Andrew Nesbitt Website (https://nesbitt.io/) Andrew Nesbitt Mastodon (https://mastodon.social/@andrewnez) Octobox (https://github.com/octobox) ecosyste.ms (https://ecosyste.ms/) ecosyste.ms Blog (https://blog.ecosyste.ms/) Open Source Collective (https://oscollective.org/) Open Source Collective Updates (https://opencollective.com/opensource/updates) Open Source Collective Contributions (https://opencollective.com/opensource) Open Source Collective Contributors (https://opencollective.com/open-source) Open Collective (https://opencollective.com/) 24 Pull Requests (https://24pullrequests.com/) Libraries.io (https://libraries.io/) The penumbra of open source (EPJ Data Science) (https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-022-00345-7) FOSDEM '25- Open source funding: you're doing it wrong (Andrew and Ben) (https://fosdem.org/2025/schedule/event/fosdem-2025-5576-open-source-funding-you-re-doing-it-wrong/) Vue.js (https://vuejs.org/) thanks.dev (https://thanks.dev/home) StackAid (https://www.stackaid.us/) Back Your Stack (https://backyourstack.com/) NSF POSE (https://www.nsf.gov/funding/initiatives/pathways-enable-open-source-ecosystems) Django (https://www.djangoproject.com/) GitHub Sponsors (https://github.com/sponsors) Sustain Podcast-Episode 80: Emma Irwin and the Foss Fund Program (https://podcast.sustainoss.org/80) Sustain Podcast- 3 Episodes featuring Chad Whitacre (https://podcast.sustainoss.org/guests/chad-whitacre) Sustain Podcast- Episode 218: Karthik Ram & James Howison on Research Software Visibility Infrastructure Priorities (https://podcast.sustainoss.org/218) Sustain Podcast-Episode 247: Chad Whitacre on the Open Source Pledge (https://podcast.sustainoss.org/247) Invest in Open Infrastructure (https://investinopen.org/) 360Giving (https://www.360giving.org/) Open Contracting Data Standard (https://standard.open-contracting.org/latest/en/) Jellyfin (https://opencollective.com/jellyfin) zizmor (https://github.com/zizmorcore/zizmor) The LaTeX Project (https://www.latex-project.org/) Special Guests: Andrew Nesbitt, Benjamin Nickolls, and Richard Littauer.

Irene Castagnotto: Timing Is Everything - Learning When Agile Teams Are Ready for Change Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes. Irene shares a powerful story about discovering team dependencies and proposing solutions that management initially rejected. When her team identified that Epics weren't organized to avoid dependencies between teams, they proposed using a single unified backlog to manage these challenges. Despite the logical solution, management wasn't ready to accept it. A month later, the same management team returned with the identical proposal. This experience taught Irene that timing is crucial in change management—you don't decide when the right time is; the people involved determine their own readiness. She emphasizes the importance of socializing changes early and often, collecting feedback before proposing major transformations, especially when those changes affect management structures. Self-reflection Question: How do you balance persistence with patience when you know a change is needed but the organization isn't ready to embrace it? [The Scrum Master Toolbox Podcast Recommends]

When we looked at outages from the first half of 2025, we saw distinct patterns in how distributed systems failed. Consistent with our ongoing outage analysis, our data revealed a rise in subtle functional failures and service degradations where symptoms often seem disconnected from their root causes. Tune in to hear more about our findings from 2025 outages so far, or use the chapters below to jump to the sections that most interest you. CHAPTERS: 00:00 Intro 00:46 The Impact of Agile Development 04:40 Hidden Functional Failures 08:04 Outages With Cascading Effects 11:52 Configuration-related Outages 12:39 Backend Issues 16:55 By the Numbers: 2025 Outage Trends 18:11 Get in Touch ——— For additional insights, check out the links below: - The Internet Report's latest blog post: https://www.thousandeyes.com/blog/internet-report-outage-patterns-in-2025?utm_source=soundcloud&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast - The Guide to Next-generation Assurance: https://www.thousandeyes.com/resources/guide-to-next-generation-assurance-ebook?utm_source=soundcloud&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast - Internet Outage Map: https://www.thousandeyes.com/outages?utm_source=youtube&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast - Internet Outages Timeline: https://www.thousandeyes.com/resources/internet-outages-timeline?utm_source=youtube&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast - Internet Outage Survival Kit: https://www.thousandeyes.com/resources/the-internet-outage-survival-kit?utm_source=youtube&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast ——— Want to get in touch? If you have questions, feedback, or guests you would like to see featured on the show, send us a note at InternetReport@thousandeyes.com. Or follow us on LinkedIn or X. ——— ABOUT THE INTERNET REPORT This is The Internet Report, a podcast uncovering what's working and what's breaking on the Internet—and why. Tune in to hear ThousandEyes' Internet experts dig into some of the most interesting outage events from the past couple weeks, discussing what went awry—was it the Internet, or an application issue? Plus, learn about the latest trends in ISP outages, cloud network outages, collaboration network outages, and more. Catch all the episodes on YouTube or your favorite podcast platform: - Apple Podcasts: https://podcasts.apple.com/us/podcast/the-internet-report/id1506984526 - Spotify: https://open.spotify.com/show/5ADFvqAtgsbYwk4JiZFqHQ?si=00e9c4b53aff4d08&nd=1&dlsi=eab65c9ea39d4773 - SoundCloud: https://soundcloud.com/ciscopodcastnetwork/sets/the-internet-report

When we looked at outages from the first half of 2025, we saw distinct patterns in how distributed systems failed. Consistent with our ongoing outage analysis, our data revealed a rise in subtle functional failures and service degradations where symptoms often seem disconnected from their root causes.Tune in to hear more about our findings from 2025 outages so far, or use the chapters below to jump to the sections that most interest you.CHAPTERS00:00 Intro00:46 The Impact of Agile Development04:40 Hidden Functional Failures08:04 Outages With Cascading Effects11:52 Configuration-related Outages12:39 Backend Issues16:55 By the Numbers: 2025 Outage Trends18:11 Get in Touch———For additional insights, check out the links below:- The Internet Report's latest blog post: https://www.thousandeyes.com/blog/internet-report-outage-patterns-in-2025?utm_source=wistia&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast- The Guide to Next-generation Assurance: https://www.thousandeyes.com/resources/guide-to-next-generation-assurance-ebook?utm_source=wistia&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast- Internet Outage Map: https://www.thousandeyes.com/outages?utm_source=wistia&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast- Internet Outages Timeline: https://www.thousandeyes.com/resources/internet-outages-timeline?utm_source=wistia&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast- Internet Outage Survival Kit: https://www.thousandeyes.com/resources/the-internet-outage-survival-kit?utm_source=wistia&utm_medium=referral&utm_campaign=fy26q1_internetreport_q1fy26ep2_podcast———Want to get in touch?If you have questions, feedback, or guests you would like to see featured on the show, send us a note at InternetReport@thousandeyes.com. Or follow us on LinkedIn and X at @thousandeyes.———ABOUT THE INTERNET REPORTThis is The Internet Report, a podcast uncovering what's working and what's breaking on the Internet—and why. Tune in to hear ThousandEyes' Internet experts dig into some of the most interesting outage events from the past couple weeks, discussing what went awry—was it the Internet, or an application issue?Plus, learn about the latest trends in ISP outages, cloud network outages, collaboration network outages, and more.Catch all the episodes on YouTube or your favorite podcast platform:- Apple Podcasts: https://podcasts.apple.com/us/podcast/the-internet-report/id1506984526- Spotify: https://open.spotify.com/show/5ADFvqAtgsbYwk4JiZFqHQ?si=00e9c4b53aff4d08&nd=1&dlsi=eab65c9ea39d4773- SoundCloud: https://soundcloud.com/ciscopodcastnetwork/sets/the-internet-report

Anamaria Ungureanu: The Tech Lead Who Nearly Destroyed the Team Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes. Anamaria describes a seven-member software team that initially seemed engaged but began self-destructing when a senior tech lead refused to embrace transparency and knowledge sharing principles.  The situation escalated when this key team member's four-day absence completely blocked the team's ability to deliver, creating a dangerous single point of failure. Through careful retrospective facilitation and strategic motivation techniques, including offering the specialist new learning opportunities while gradually transferring their legacy knowledge to teammates, Anamaria helped the team overcome knowledge silos and establish sustainable collaboration patterns. Featured Book of the Week: Never Split the Difference by Chris Voss Anamaria recommends “Never Split the Difference” by Chris Voss, a negotiation masterpiece because it taught her essential communication strategies for establishing trust and navigating tense situations. She emphasizes that negotiation is a critical Scrum Master skill, and Voss's techniques help build rapport with stakeholders while managing difficult conversations that arise during team transformations and organizational change initiatives. Self-reflection Question: What knowledge silos exist in your teams, and how might you motivate specialists to share their expertise while providing them with new growth opportunities? [The Scrum Master Toolbox Podcast Recommends]

This week we're joined by Maxwell Brown, a co-founder of Effectful Technologies, the company behind Effect.ts, a library for building robust, production-grade applications with TypeScript. Effect.ts is a library that bends Typescript in many ways, like putting Errors and Dependencies into the type system. Join as as we see what they've been up to and what's next for Effect.ts.Effect Website: https://effect.websiteEffect GitHub: https://github.com/Effect-TS/effectMaxwell's GitHub: https://github.com/IMax153Maxwell's Twitter/X: https://x.com/imax153Effectful Technologies: https://www.effectful.coEffect Discord: https://discord.gg/effect-tsEffect Days Conference: https://effect.website/events/effect-daysAdvanced Effect Workshop Materials: https://github.com/IMax153/advanced-effect-workshop

Today on Valentine In The Morning: Listeners confess the everyday tasks they've never learned to do because their partner always handles them. Plus, some of the weirdest, wackiest, and surprisingly genius ways people have made money.Listen live every weekday from 5–10am Pacific: https://www.iheart.com/live/1043-myfm-173/Website: 1043myfm.com/valentineInstagram: @ValentineInTheMorningFacebook: https://www.facebook.com/valentineinthemorningTikTok: @ValentineInTheMorning

Choice of Rebekah; Virgin?; Pardons; Corruption; Civil government; "Ur"; Nimrod and Terah; Melchizedek - righteous king of peace; Tithing; Rebekah's entourage; Organizing the people; Providers; Lot's place in the gates of Sodom; Judging enemies; Learning to be Israel; Separation; "City"; Unrighteous sacrifice; Meekness of sheep; Willing sacrifices; Understanding Abraham, Isaac and Jacob - Israel; Entrusting power to the people; Covering beauty; Walking in faith; Possession; Becoming merchandise; Welfare snares; Tribute; Protection; Power corrupts; Exercising authority; Freewill contributions; "Tithes"; Love = Charity; Genesis 25 - Abraham's death; Katurah; "Leummim" ; Seeking Holy Spirit; Loving light; Incense?; Driving out evil; Pilate's incense; Tiberius; Living in bondage; Are you Israel?; Abraham's inheritance; "live" = chet-yod; Isaac's half-brothers; Temptation; Abraham's blessing; Importance of wives; Pure Religion; Nahor; Doers of His word; Well Lahairoi - revelation of Holy Spirit; Helping with unbelief; Thinking differently than the world; Effectual prayer; God's blessing to Isaac; Gen 17:20; "Before Egypt"; Rulers; Deceitful meats; The Christian way; Repentance; Covetous practices; Modern doctrines; Barren Rebekah; Twins!; Man/Woman differences; Ministry?; Gen 25:23; Lamad-aleph-mem-yod-mem = people(s); Esau and Jacob; Esau cunning "hunter" (provider); Considering the society; Allowing choice; Letting children grow; Roman and Pharisaical tribute; Forced offerings; vs Charity; "Manners" of people; Cain and Abel; Esau lacking provisions; Selling your birthright; Dependencies of Esau and Jacob; Pure Religion; Which manner of people are you?; Golden calf?; Temple of Ephesus?; Seeing the light of truth; Come to serve.

This week Bart takes a look at some close turn spots dealing with barreling. He explains his approach to betting the turn as a double barrel leading to tripling off and also discusses when its best to some time pull back the reigns.

In this eye-opening episode of The P.A.S. Report, Professor Nick Giordano welcomes back Chris Fenton, author of Feeding the Dragon: Inside the Trillion Dollar Dilemma Facing Hollywood, the NBA, and American Business. They explore how China uses economic leverage, propaganda, and soft power to infiltrate American institutions, from corporate boardrooms to TikTok's algorithm. Fenton breaks down why U.S. companies and even government officials refuse to challenge the CCP, and how American culture has been co-opted to serve Chinese interests. With tensions escalating globally, this is a conversation every American needs to hear. Episode Highlights Why Hollywood and major U.S. businesses bend over backwards to appease the Chinese Communist Party How TikTok became China's most powerful tool to influence and divide America's youth Why the U.S. must prioritize national security over profits and end our dangerous dependency on Beijing

In this episode, Amy dives into the concept of Disguised Dependencies: the subtle emotional and behavioral patterns that can quietly undermine our recovery, self-worth, and relationships. Through candid reflection and humor, Amy explores how unnoticed dependencies—from rituals like infrared saunas to the comfort of Lifesavers Orange Mints—can shape our sense of safety, identity, and connection, even after achieving sobriety.Key Points:Understanding Disguised Dependencies: Amy defines disguised dependencies as the often unseen emotional and physical habits that we rely on for comfort and stability, highlighting how they can persist beneath the surface even in recovery.The Role of Self-Awareness: She emphasizes the importance—and challenge—of developing self-awareness, sharing her own journey of digging deep and facing uncomfortable truths about motives, coping mechanisms, and the need for approval.The False Self & Emotional Dependency: Drawing on Alan Berger's insights and her own experience, Amy unpacks the concept of the “false self”—the persona we create to manipulate others' responses—and how it feeds emotional reliance on external validation.Vulnerability & Acceptance: Amy encourages embracing vulnerability and releasing the need for perfection, recounting her personal struggles with insecurity, loneliness, and the urge to control, especially as a mom and woman in recovery.Tools for Growth: Through practical exercises (like sentence completion to uncover disguised dependencies), Amy provides listeners with actionable ways to identify, understand, and begin to heal from emotional dependencies.Faith, Motherhood, & ADHD: The episode weaves in Amy's reflections on her evolving spirituality, the complexities of parenting in a tech-driven era, and the impact of ADHD on habits and routines.Humor & Humanity: With her signature blend of lightheartedness and depth, Amy shares relatable anecdotes about everything from Botox to love languages, reminding listeners that growth comes from awareness, not perfection.Practicing Emotional Sobriety: The path to emotional freedom, Amy says, is one of ongoing curiosity, resilience, and self-compassion—letting go of blame, embracing imperfection, and making room for the true self to emerge.Hosted by Amy Liz HarrisonBuy Amy's Books: https://amzn.to/3ys8nuvhttp://amylizharrison.com/Subscribe on Apple Podcasts: https://apple.co/3Lgxy8FSubscribe on Spotify: https://spoti.fi/3vHHHoi#EternallyAmy #DisguisedDependencies #RecoveryJourney #EmotionalSobriety #SelfAwareness #PersonalGrowth #Spirituality #Motherhood #ADHD #Vulnerability #Resilience #Authenticity #EternallyAmy #Recovery

News this week has been dominated by dependency issues and attribution towards unwanted nation states and actors. Specifically, easyjson is developed by a Russian firm that is under sanctions. The podcast duo discuss the implications and how to protect apps from sub-dependency threats. This leads to a deep dive into breaches and whether a breach has an effect on the industry, company, or individual. Current regulations and certifications can be lost, but does not always have the effect we would expect.

Ajahn Amaro gave this Dhamma talk on 28 March 2025 at Amaravati Buddhist Monastery, UK. The post Unmoved and ReMoved: The Mind Free From Dependencies appeared first on Amaravati Buddhist Monastery.

Ajahn Amaro gave this Dhamma talk on 28 March 2025 at Amaravati Buddhist Monastery, UK. The post Unmoved and ReMoved: The Mind Free From Dependencies appeared first on Amaravati Buddhist Monastery.

Dependencies Destroy Agility and PredictabilityWhy do organizations desire Agile?Two of the most common items I hear organizations and executives identify as reasons for adopting Agile methods and practices are to increase their delivery speed and to become more predicable. When will X be done? There are valid financial reasons for these goals such as: when can we expect to book revenue for product X?How to connect with AgileDad:- [website] https://www.agiledad.com/- [instagram] https://www.instagram.com/agile_coach/- [facebook] https://www.facebook.com/RealAgileDad/- [Linkedin] https://www.linkedin.com/in/leehenson/

Anthony Fu, Framework Developer at Nuxt Labs, discusses the shift to ESM-only formats in JavaScript development. He covers the controversy surrounding ESM, the advantages of moving from CJS to ESM, and what this transition means for the future of web development. Tune in to learn why now is the ideal time for this change, and how it benefits developers! Links https://antfu.me https://bsky.app/profile/antfu.me https://github.com/antfu https://x.com/antfu7 https://www.linkedin.com/in/antfu https://antfu.me/posts/move-on-to-esm-only 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? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). 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 understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Anthony Fu.

American-Made, all natural, powerful wellness tech patented to reverse aging was designed for Navy SEALs. This incredible Star Trek-like breakthrough has now hit the mass market. Join Brad Wozny, Michael Jaco and serial entrepreneur Connie Lucas as they unpack these radical, MAHA blessed wearable med bed patches which activate your stem cells -- they work so well even RFK Jr, Mike Tyson, NCAA teams and Warrior Grannies in 100 countries are wearing it with testimonies pouring in non-stop!   Given the power of this technology, we invite all listeners in the audience to share this far and wide. . X39 - http://www.catchthelifewave.com/promo The MAHA-Approved, POWERFUL X39 wellness Patch naturally activates your STEM CELLS! Developed for Navy

Play audio-only episode | Play video episode Click above to play either the audio-only episode or video episode in a new window. Episode Summary (See the "Resources" section below to download the handout with the "Three Top Dependency Management Mistakes and How to Avoid Them".) Dependency management is a crucial yet often overlooked aspect of project success. In this episode, Bryan Barrow, author of Dependency Management, shares his expertise on how project managers can effectively navigate and manage dependencies to ensure timely and efficient project delivery. With decades of experience in helping organizations tackle complex projects, Bryan emphasizes the importance of communication, collaboration, and proactive planning.

Play audio-only episode | Play video episode Click above to play either the audio-only episode or video episode in a new window. Episode Summary (See the "Resources" section below to download the handout with the "Three Top Dependency Management Mistakes and How to Avoid Them".) Dependency management is a crucial yet often overlooked aspect of project success. In this episode, Bryan Barrow, author of Dependency Management, shares his expertise on how project managers can effectively navigate and manage dependencies to ensure timely and efficient project delivery. With decades of experience in helping organizations tackle complex projects, Bryan emphasizes the importance of communication, collaboration, and proactive planning.

In this episode of Crazy Wisdom, Stewart Alsop sits down with Diego Basch, a consultant in artificial intelligence with roots in San Francisco and Buenos Aires. Together, they explore the transformative potential of AI, its unpredictable trajectory, and its impact on everyday life, work, and creativity. Diego shares insights on AI's role in reshaping tasks, human interaction, and global economies while touching on his experiences in tech hubs like San Francisco and Buenos Aires. For more about Diego's work and thoughts, you can find him on LinkedIn or follow him on Twitter @dbasch where he shares reflections on technology and its fascinating intersections with society.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction to the Crazy Wisdom Podcast00:20 Excitement and Uncertainty in AI01:07 Technology's Impact on Daily Life02:23 The Evolution of Social Networking02:43 AI and Human Interaction03:53 The Future of Writing in the Age of AI05:27 Argentina's Unique Linguistic Creativity06:15 AI's Role in Argentina's Future11:45 Cybersecurity and AI Threats20:57 The Evolution of Coding and Abstractions31:59 Troubleshooting Semantic Search Issues32:30 The Role of Working Memory in Coding34:46 Human Communication vs. AI Translation35:46 AI's Impact on Education and Job Redundancy37:37 Rebuilding Civilization and Knowledge Retention39:54 The Resilience of Global Systems41:32 The Singularity Debate45:01 AI Integration in Argentina's Economy51:54 The Evolution of San Francisco's Tech Scene58:48 The Future of AI Agents and Security01:03:09 Conclusion and Contact InformationKey InsightsAI's Transformative Potential: Diego Basch emphasizes that artificial intelligence feels like a sci-fi concept materialized, offering tools that could augment human life by automating repetitive tasks and improving productivity. The unpredictability of AI's trajectory is part of what makes it so exciting.Human Adaptation to Technology: The conversation highlights how the layering of technological abstractions over time has allowed more people to interact with complex systems without needing deep technical knowledge. This trend is accelerating with AI, making once-daunting tasks more accessible even to non-technical individuals.The Role of Creativity in the AI Era: Diego discusses how creativity, unpredictability, and humor remain uniquely human strengths that current AI struggles to replicate. These qualities could play a significant role in maintaining human relevance in an AI-enabled world.The Evolving Nature of Coding: AI is changing how developers work, reducing the need for intricate coding knowledge while enabling a focus on solving more human-centric problems. While some coding skills may atrophy, understanding fundamental principles remains essential for adapting to new tools.Argentina's Unique Position: The discussion explores Argentina's potential to emerge as a significant player in AI due to its history of technological creativity, economic unpredictability, and resourcefulness. The parallels with its early adoption of crypto demonstrate a readiness to engage with transformative technologies.AI and Human Relationships: An AI-enabled economy might allow humans to focus more on meaningful, human-centric work and relationships as machines take over repetitive and mechanical tasks. This could redefine the value humans derive from work and their interactions with technology.Risks and Opportunities with AI Agents: The development of autonomous AI agents raises significant security and ethical concerns, such as ensuring they act responsibly and are not exploited by malicious actors. At the same time, these agents promise unprecedented levels of efficiency and autonomy in managing real-world tasks.

Are you someone who has been forced to let go of someone you love, because they hurt you, were toxic, lacked accountability and often caused you to doubt your worthiness? If so, this episode of Breakdown to Breakthrough with Life Coach and award-winning author Lisa A. Romano is for you. Dear One, in this episode, you will learn why it is better to walk alone than with a fool by your side. This podcast episode is not intended to bolster a sense of moral superiority; instead, it has been designed to help you come out of the denial of the consequences of unhealthy emotional dependencies.  When you embark on the emotional and spiritual healing journey, you are required to confront unhealthy attachments for your personal growth and personal empowerment.  Welcome to the practical wisdom of Lisa A. Romano. May you be blessed, uplifted, and your mind expanded by these teachings. Embark on the path to conscious awakening, emotional healing, and transformation with Lisa's Conscious Healing Academy, which includes a 3 tier coaching system that assists with one's awakening, emotional intelligence, and mental and emotional mastery.  12 Week Breakthrough Program (Level One - The Awakening) 8 Week Master Your Reality (Level 2 -- Deliberate Creating) Soul School - (Level Three -- Ascending Ego) To learn more, contact Lisa and her team members here; Contact Website Spotify Award Winning Books  Facebook Support Group