Episodes
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Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss the top stories in Cloud and AI from May 2025, including Google I/O, Microsoft Build, OpenAI, and Jony Ive.
SHOW: 929
SHOW TRANSCRIPT: The Cloudcast #929 Transcript
SHOW VIDEO: https://youtube.com/@TheCloudcastNET
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Link to May 2025 News and ArticlesFEEDBACK?
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Is it ever a good time for a company to announce vaporware? Letâs discuss the pros and cons of pre-announcements, vaporware, and the strategy of products that may or may not ever exist.
SHOW: 928
SHOW TRANSCRIPT: The Cloudcast #928 Transcript
SHOW VIDEO: https://youtube.com/@TheCloudcastNET
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The McGuffin âChallengeâ - Software Defined Talk (May 30, 2025)McGuffin (Wikipedia)IS IT EVER A GOOD TIME TO PRE-ANNOUNCE SOMETHING? OR VAPORWARE ON PURPOSE?
Russ Hanneman (Silicon Valley) - âNo RevenueâSet a big ambition for the companyDistract the market from current company challengesDistract your competitors to chase something where they donât have strengthsProbe the market for ideas - sort of like a âleakâGet the market to pump your valuation, so you can take other actionsStall your customers from potentially buying a competitive offerFEEDBACK?
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Anand Kannappan (@anandnk24, CEO @PatronusAI) talks about evaluating AI models for hallucinations, managing data quality, automating the process, and optimizing models.
SHOW: 927
SHOW TRANSCRIPT: The Cloudcast #927 Transcript
SHOW VIDEO: https://youtube.com/@TheCloudcastNET
CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw
NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"
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Patronus AI websiteTopic 1 - Welcome to the show, Anand. Give everyone a quick introduction.
Topic 2 - Our topic today is Preventing AI Model Hallucinations. Before we dig into that, I wanted to ask about your time as Lead Data Scientist at Meta. What was it like to be early into that organization, and what did you take away from your time there?
Topic 3 - Ok, letâs dig into model evaluations and hallucinations. Letâs start at the beginning. How do model hallucinations come about?
Topic 4 - When evaluating models for hallucinations, how does a developer or a data scientist know fact from fiction? Due to its size, complexity, and number of parameters, itâs not feasible to simply fact-check and manually verify inputs to outputs. How is this process evaluated and automated with some level of confidence? Additionally, numerous benchmarks are available. What are your thoughts on the usefulness of the benchmarks?
Topic 5 - How does the concept of data quality play into this? How would we know when a model was given insufficient or improper data vs. a hallucination
Topic 6 - We often hear about how frontier models are running out of training data, and increasingly, synthetic data is being used. Does this impact hallucinations in any way?
Topic 7 - The last item I wanted to ask you about, Partonus AI, also pertains to model optimization. Can you explain that process?
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The industry is debating whether AI should be viewed as augmenting or replacing human-centric tasks. But we donât yet have a framework to discuss the technical and business impacts of that spectrum of decisions.
SHOW: 926
SHOW TRANSCRIPT: The Cloudcast #926 Transcript
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The final 5% of AI successHOW DO WE THINK ABOUT THE INVOLVEMENT OF HUMANS WITH AI SYSTEMS?
What do we think AI systems should be capable of?What do we think human systems should be capable of?We know how to quantify error, but do we really know how to quantify error?Almost every question about AI comes down to augment vs. replace, and yet humans tend to skew towards the replace angle. We like the thought of humans in the loop, but often donât want to be bothered to interact with humansWe havenât yet created a framework to think about the human +/- AI involvement, because we havenât really created it for general-purpose automation yet either.FEEDBACK?
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Joel Christner, (@joelchristner, Founder/CEO at @viewyourdata) discusses the complexities of data management in AI, structured and unstructured data, the importance of RAG pipelines and vector databases.
SHOW SUMMARY: Aaron and Joel discusses the complexities of data management in AI, focusing on the concept of universal data representation. They explore the challenges organizations face with structured and unstructured data, the importance of RAG pipelines and vector databases, and the implications of data privacy in regulated industries. The conversation also touches on managing model versions and the emerging patterns in AI tooling that can help enterprises effectively utilize AI technologies.
SHOW: 925
SHOW TRANSCRIPT: The Cloudcast #925 Transcript
SHOW VIDEO: https://youtube.com/@TheCloudcastNET
CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw
NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS"
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View.io websiteTopic 1 - Welcome to the show, Joel. Give everyone a quick introduction.
Topic 2 - Our topic today is everything data and how to represent it and embed it into AI systems. First, what is the challenge with data, structured or unstructured, in organizations today and what is behind the concept of Universal Data Representation
Topic 3 - Industry or customer specific data today is big challenge for organziations, especially in highly regulated industries such as healthcare, financial services, etc. The most prevalent solution I am seeing is taking an existing foundational model and then adding a RAG pipeline vs. the cost and time to fine tuning. What are you seeing?
Topic 4 - Even when companies have good data, that doesnât mean that data makes it into the AI pipeline correctly, this is where the embedding problem and your concept of Universal Data Representation comes into play, correct?
Topic 5 - But, once you get the first model out, then what? How should the data and models be handled over time? How do you create a platform and a continuous feedback loop to improve the results over time?
Topic 6 - What are the most successful use cases you are seeing today with your customers?
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Where does the next phase of AI-assistants for software development go next? Is it an evolution of developer productivity, or a complete rethinking of the barriers and limitations for broader software development?
SHOW: 924
SHOW TRANSCRIPT: The Cloudcast #924 Transcript
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WHERE DO AI DEVELOPER-ASSISTANTS GO NEXT?
A year ago it felt like co-pilots were one of the entry point use-cases for AI.Since then weâve seen numbers say the uplift is 10-20% productivity.Microsoft claims that 20-30% of their code is now written by AIs.Weâve seen many senior developers speakout that itâs not replacement level technology and they donât trust it.Companies like Cursor have a $9-10B valuation. Windsurf just got purchased for $3B by OpenAI.Microsoft has Co-Pilot (based on OpenAI models). Google and Amazon are rumored to be launching their own.Lots of companies are launching agents (IBM, Salesforce, Oracle, etc.), and lots of agent frameworks now exist. So where does it go next? Is it just wide-spead adoption of developer productivity? Is it specialized functions within developer workflows? (e.g. CI/CD, documentation, security evaluations, bug fixes, long-term maintenance, etc.)How far are we from teams being just a few architects, leads, Sr. Devs, and then teams of AIâs (agents, etc.)?Is that a good thing for Sr. Dev personalities that didnât want to focus on soft-skills?Does that allow for greater experimentation against feature-requests or stories, since they can create more, test more, etc.?Do we start to see companies create skunkworks teams/groups that try to adopt this approach? Are product managers ready to have zero-backlogs and the demand for new ideas to increase?FEEDBACK?
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Siqi Chen (@blader, CEO/CFO @Runwayco), talks about his journey from JPL developer to Founder of a financial planning and analysis (FP&A) startup. We focus on how to build products that customers crave and how a customer-centric view differs from traditional product management.
SHOW: 923
SHOW TRANSCRIPT: The Cloudcast #923 Transcript
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Runway websiteBehind What Seems Like an Overnight Success (video)Topic 1 - Welcome to the show, Siqi. First, your combination of technical and business/financial background is fascinating. How did you go from coding at NASA to Head of Product at Zynga to CEO/CFO for a finance platform startup? Give everyone a quick introduction.
Topic 2 - One thing Iâve noticed as a trend in your background is the core concept of building. What has been your philosophy in building products? How do you build products that customers demand?
Topic 3 - Letâs talk about AI and AGI for a moment. We hear all the time how disruptive this will be. What are your thoughts here, and how do we develop both adaptability and resiliency to new technologies?
Topic 4 - Letâs talk FP&A (financial planning & analysis). Our core listeners out there tend to skew more towards the tech and infrastructure side, but a core theme of this show is always to be learning as much of the business as possible to apply those concepts. As someone with a background in both worlds, plus now running an FP&A startup, what do you wish folks on the technical side of the house knew more about to make their jobs easier?
Topic 5 - We posted a link in the show notes for a video you did on the âovernight successâ of Runway. It was a good representation and origin story of how something can go viral with the right mindset and product-market fit. Tell everyone about that as Runway approaches 5 years now.
Topic 6 - What is your biggest challenge in the FP&A space today? Is it AI? Weâve seen a lot of AI disruption in coding, legal, and other areas requiring deep data pool insights. Is this any different?
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Tech CEOs are making bold proclamations, from WhyQ to It is what it is. How will companies navigate this spectrum as they seek innovation, accountability and profitability throughout 2025?
SHOW: 922
SHOW TRANSCRIPT: The Cloudcast #922 Transcript
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Amazon 2024 CEO Letter to Shareholders - A Why CultureUber CEO says âit is what it isâ about changing benefitsWHEN DOES WHY BECOME IT IS WHAT IT IS?
Communicating is difficult, especially as the company grows in size (or is remote)Communicating change is difficult, even when communication channels are strongOutside of finance, tech tends to pay at the high end of salaries and perksWeâre in an interesting time of challenging economics and pressure from AI Perks are difficult to pull back, because business success isnât evenly rewardedWhy is positioned as open culture, or strategy, but itâs also about day-to-day behaviorIt is what it is a decision, but itâs also accountability and continued viabilityMost leaders are going to have to manage between Why and It is what it is in 2025Most workers are going to have to work between Why and It is what it is in 2025FEEDBACK?
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Aaron Delp, Brian Gracely, and Brandon Whichard discuss the top stories in Cloud and AI from April 2025, including OpenAI, MCP, VibeCoding and Hyperscaler earnings.
SHOW: 921
SHOW TRANSCRIPT: The Cloudcast #921 Transcript
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Link to April 2025 News and ArticlesFEEDBACK?
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As more companies begin to adopt AI into their workforce and day-to-day processes, it will be interesting to watch how their learning curve is spread across knowledge workers.
SHOW: 920
SHOW TRANSCRIPT: The Cloudcast #920 Transcript
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AI Horseless Carriages (AI user-experiences)HOW WILL WE VIEW AN AI AGENT IN THE CONTEXT OF HUMANS OR âUSERSâ
The low-hanging fruit, simple on-ramp is the key to early AI adoption Google and Microsoft are already showing revenue increases, likely through the productivity apps bundlingExpect prices to increase slowly, but frequently as adoption happens and companies get used to the knowledge worker productivity increases (or expectations)Curious how knowledge workers are adopting, sharing, increasing their learning curveSharing still seems to be lacking within the AI tools. Not just sharing of an individual task, but sharing of learning curves, best practices, datasetsIs there a dataset collection opportunity? This feels like Big Data or Data Lake 5.0.FEEDBACK?
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Mark Freydl (CEO/Founder @codiacio) talks about the challenges of blending IaaC, DevOps and Platform Engineering to drive efficient software development lifecycles.
SHOW: 919
SHOW TRANSCRIPT: The Cloudcast #919 Transcript
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Codiac websiteCodiac at Tech Field DayTopic 1 - Welcome to the show, Mark. Give everyone a quick introduction.
Topic 2 - Before we dig into the tech, letâs start with the problem. What problem were you seeing in software development that wasnât being solved with IaaC, Platform Engineering, DevOps, etc.? Where does SDLC fit into this?
Topic 3 - Have microservices helped or hurt? We hear all the time about the loose coupling of microservices and benefits towards production, but doesnât that also make it harder to develop? How do you recreate an environment where you are dependent on a bunch of microservices in a development pipeline?
Topic 4 - I get the feeling this is all about removing friction. But where and how? I see Kubernetes as a blessing and curse many times. Itâs an awesome application platform, as long as you arenât the one that has to do the care and feeding on it. Thoughts?
Topic 5 - The goal here I believe is a closed loop system that is beneficial for developers and SREâs, but how do you balance closed loops vs. extensibility and abstraction of different platforms to the systems that are truly write once.
Topic 6 - How does the culture and relationships in the org have to change to meet the changes in the tech?
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There is a lot of excitement around AI Agents. Here are five questions we're asking ourselves about how AI Agents will be used and managed.
SHOW: 918
SHOW TRANSCRIPT: The Cloudcast #918 Transcript
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Anthropic warns that fully AI employees are a year awayAuthZ Agents ChaosWhy we need to get AuthZ rightAI Agent Gateway and AI Agent MeshAuth in the age of AI Agent (Cloudcast Eps. 885)Trust will make or break AI Agents Introducing the Agent2Agent Protocol (A2A - Google)Introducing the Model Context Protocol (MCP - Anthroopic)Top 9 AI Agent FrameworksHOW WILL WE VIEW AN AI AGENT IN THE CONTEXT OF HUMANS OR âUSERSâ
[Who] How is an AI agent represented in the context of a person, a group, an organization? [What] How will we audit Agent created data or content? Will we need a way to label Agent-created information?[Where] Will we have agents that are focused on non-human-interactions (e.g. optimizations) vs. human-interactions (e.g. empathy)?[When] Are todayâs auth systems able to manage AI agents, or will they need to be re-engineered to deal with different scale, granularity of actions?[Why] Why will we choose an Agent (as the implementation) over another form or working on a problem?[How] Are todayâs auth systems able to manage AI agents, or will they need to be re-engineered to deal with different scale, granularity of actions?FEEDBACK?
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Taylor Pechacek (Head of Product, API Collaboration Core @getpostman) talks about APIs, AI, and MCP (Model Context Protocol). We cover what is top of mind for developers and what has shifted in the industry recently.
SHOW: 917
SHOW TRANSCRIPT: The Cloudcast #917 Transcript
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Postman - The Worldâs Leading API PlatformOpenAPI SpecificationMCP - Model Context Protocol (Anthropic)Topic 1 - Welcome to the show. Tell us a bit about your background, and what you focus on these days at Postman? Tell us a little bit about Postman, for our audience that might not be familiar.
Topic 2 - Letâs begin with some broader concepts - in todayâs world, where does a developer interact with software vs. an API - what are the most important things they have to think about?
Topic 3 - What are the standards that are most important for developers these days? Where does OpenAPI Spec fit into that space?
Topic 4 - APIs are now so pervasive. What are the best practices for API usage patterns and governance? What are some of the things that Postman is doing to improve this for developers and companies?
Topic 5 - Letâs talk a little bit about how youâre seeing AI change how developers work, and if/how AI is impacting APIs.
Topic 6 - Help us understand how you expect to see APIs interacting with AI Agents. What will the bigger picture look like for these interactions, and how will they be different than current API interactions?
Topic 7 - Can we finish by talking about MCP (Model Context Protocol) a little bit? This is a new standard that is being adopted by many companies.
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Instead of focusing on augmenting existing teams and processes with AI, are we beginning to see more companies looking to replace teams with AI? Will economic pressure increase this likelihood?
SHOW: 916
SHOW TRANSCRIPT: The Cloudcast #916 Transcript
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CEOs choose more AI, fewer jobsOpenAI plots charging $20,000 per month for PhD-level AgentsWhy AI isnât giving Salesforce a boostThe one person $Billion dollar companyHUMAN-AUGMENTED vs HUMAN-REPLACEMENT IS A COMPLEX AI STRATEGY
Most AI projects today as either augmenting or replacing humansTechnology has augmented, shifted or replaced human tasks for a while, this isnât anything newManagement/owners have tried to replace workers with technology for a long timePricing will tell you something about the goal of the technology (e.g. seat vs. task)Now weâre starting to see the psychology of aligning AI to business goals - augmenting, shifting or replacingThe hyperbole of AI funding is to convey direct lines to AI success (whatever success is - AGI, No Coders, No Operations, No Entry-Level, etc..)âHow do we replace the employees with AI?âThe path to get there isnât well defined. The politics to get there isnât defined by technology. Will the current economic downturn accelerate this line of questioning / strategy? The Bed Bath and Beyond 20% coupon test. âThe $billion dollar company with one employee.âFEEDBACK?
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Evan Powell (@epowell101, CEO at DeepTempo) talks about the intersection of CyberSecurity, Deep Learning and new AI techniques to improve operational security.
SHOW: 915
SHOW TRANSCRIPT: The Cloudcast #915 Transcript
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DeepTempo websiteThe Promise of CyberSecurity Foundation ModelsEvan on The Cloudcast #165Topic 1 - Welcome back to the show, Evan. Itâs hard to believe itâs been over ten years since we last spoke in your StackStorm days. Give everyone a quick introduction.
Topic 2 - Today's topic is the intersection of Deep Learning and CyberSecurity. Letâs start at a high level. As someone who has founded several companies, whatâs interesting enough in this space for you to jump in?
Topic 3 - I talk to many customers about AI and use cases. If they donât have a leading use case, I often ask where they have a lot of data, and they want insights data. The needle in the haystack scenario. It could be text, unstructured data, log files, data lakes, just about anything. Is this scenario correct when it comes to CyberSecurity? What are the leading use cases?
Topic 4 - Iâve been reading and hearing how AI has been a boon for hackers; what are your thoughts on the ever-lasting cat-and-mouse game in the security space and how it has evolved with AI?
Topic 5 - Letâs dig into the tech; at DeepTempoâs core, you use Deep Learning and specific foundational models. You wrote a great blog recently on Cybersecurity models. When folks hear the term foundational or frontier model, they think of LLMs and GenAI. How is this different?
Topic 6 - Is this a SaaS model? On-prem? Where does the tech sit and why?
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Are companies starting to get concerned that AI isnât meeting expectations? Concern is a part of any new technology adoption curve, but letâs explore some areas where expectations might not be meeting results.
SHOW: 914
SHOW TRANSCRIPT: The Cloudcast #914 Transcript
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Why AI isnât meeting expectations (The Artificial Intelligence Enterprise)IS THREE YEARS ENOUGH TIME FOR ANY TECHNOLOGY TO TAKE OVER THE WORLD?
Costs are still high, and positive ROI is still evolvingThe technology stack and standards are still evolvingEnterprise expectations are being confused with consumer expectationsAI predictions and timelines are overly aggressiveHow is anymore measuring AI success? The AI future is already here, itâs just unevenly distributedâAI Firstâ strategies are following âCloud Firstâ strategies - unevenly and distributedMost people donât like to talk about the augment vs. replace issueMiscellaneous stuff:
ChatGPT is the fasting growing tech ever AGI will be here at any momentFor the first 18+ months, it was only an OpenAI + NVIDIA marketAll software development will be done by AIThere will be $1B companies with 1 personGenAI, Frontier Models, Open Source Models, Agents, etc.Deep Seek, MCP, etc.FEEDBACK?
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Dan Lee, (@_dan_lee_, CEO Nooks.ai, @JoinNooks) talks about agentic AI vs. AI assistants, their differences, and we explore ROI in this space vs. GenAI and common paths to adoption.
SHOW: 913
SHOW TRANSCRIPT: The Cloudcast #913 Transcript
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Nooks.ai websiteNooks Founders on Forbes 30 under 30 for AITopic 1 - Welcome to the show, Dan. Give everyone a quick introduction.
Topic 2 - Our topic today is AI Agents vs. Assistants. Letâs start there. Many out there are hearing the terms and will often think an AI Agent is equal to an AI Assistant. Whatâs the difference?
Topic 3 - One of the big problems Iâve seen with AI adoption vs. hype is the ROI. GenAI hit the scene and brought AI into the mainstream, but the world is quickly moving on to Agentic AI. Iâve mainly seen this play out because the ROI on âchatbotsâ just isnât there most of the time and also everyone is still scared for data leaks/privacy and hallucinations.
Topic 4 - Weâll get into Nooks.ai in a bit but before we do, I wanted to point out that your company started prior to the big breakout of GenAI. How did you see the market back then and has LLMâs and transformer models in general changed your approach to AI Assistants?
Topic 5 - When taking customers through the adoption process for AI Assistants, what are the typical steps? How do you go about getting consensus and buy-in from the various orgs in the company? Are there common hurdles folks need to consider?
Topic 6 - With Nooks, youâve decided to conquer the domain of sales and sales prospecting. What was your thoughts here in deciding where and how to remove friction from sales? Is this decision and thought process transferable to other areas? Any universal considerations?
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How is it possible for the 900lb gorilla of an industry to stumble or fall from it's heights? Let's explore how emerging trends can knock industry leaders off the top of the mountain.
SHOW: 912
SHOW TRANSCRIPT: The Cloudcast #912 Transcript
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ITâS DIFFICULT TO STAY THE BIGGEST COMPANY FOREVER
Merchant Silicon happened for Networking - Will it happen for AI?Cloud Providers happened - Will the opposite happen for AI? What unknown or unexpected events will happen over the next 3-5 years?Data Center Power Requirements?Geopolitical Activities?FEEDBACK?
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Aaron Delp, Brian Gracely and Brandon Whichard discuss the top stories in Cloud and AI from March 2025 including recent M&A activity.
SHOW: 911
SHOW TRANSCRIPT: The Cloudcast #911 Transcript
SHOW VIDEO: https://youtube.com/@TheCloudcastNET
CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw
NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"
SHOW SPONSORS:
Try Postman AI Agent Builder TodayCut Enterprise IT Support Costs by 30-50% with US CloudSHOW NOTES:
Link to March 2025 news and articlesSEGMENTS COVERED IN THE SHOW:
Recent M&AGood Old Fashioned Cloud NewsThe AI Innovation ContinuesFEEDBACK?
Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod -
What happens when you consider moving from a technical role to a lesser technical role? What are the trade-offs? What new skills do you need to evolve?
SHOW: 910
SHOW TRANSCRIPT: The Cloudcast #910 Transcript
SHOW VIDEO: https://youtube.com/@TheCloudcastNET
CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw
CHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"
SHOW SPONSORS:
Try Postman AI Agent Builder TodayCut Enterprise IT Support Costs by 30-50% with US CloudSHOW NOTES:
Software Defined Interviews - Episode with Brian GracelyWHAT HAPPENS DURING AND AFTER AN ACQUISITION?
Moving from an IC to Leadership role is a complex choiceMoving from a technical to a less technical role is a difficult choiceWhat happens if youâre not an expert in the room?What happens when you donât have all the answers?How do you build your network to keep you informed?The economics of technology are more complicated than you thinkFEEDBACK?
Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod - Show more
