Episódios
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Tanmai Gopal (@tanmaigo, CEO/Founder @HasuraQL) discusses the importance of reliability and trustworthiness for both generative and agentic AI. We discuss the pitfalls in existing data pipelines and how to enhance the results.
SHOW: 935
SHOW TRANSCRIPT: The Cloudcast #935 Transcript
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Hasura websiteHasura GitHubTopic 1 - Welcome to the show, Tanmai. Give everyone a quick introduction.
Topic 2 - Our topic today is Reliable and Trustworthy AI Agents. First off, what’s the problem we’re solving for here (define reliability and trustworthiness)? Are we solving for hallucinations? Reliability? Connecting private and Enterprise data to models with fine-tuning or RAG?
Topic 3 - How is reliability or trustworthiness measured? I would imagine this isn’t black and white, but maybe a bit more subjective?
Topic 4 - How do Agentic and GenAI differ, if at all, with this model? I would think that since Gen AI lends itself more to the creative side and Agent AI is very deterministic, the approaches to solving the problem might be different. Thoughts?
Topic 5 - Let’s talk about data pipelines. Today, many organizations take an off-the-shelf frontier or foundational model and then apply a RAG pipeline to it for customization. Sometimes fine-tuning is involved, but in my experience, this is the exception rather than the rule. What is wrong with that architecture today? How is this less reliable?
Topic 6 - Let’s talk about Hasura and PromptQL. As I understand it, you are decoupling query planning from execution, thereby creating a more deterministic AI workflow. Now… that’s a mouthful. Can you break down what this means and explain how the architecture differs?
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Are the dynamics of the hyperscale cloud providers changing? Will the leaders of the last decade continue for the next 3-5 years, or the next decade? Let’s explore how the market is now rapidly changing.
SHOW: 934
SHOW TRANSCRIPT: The Cloudcast #934 Transcript
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Andy Jassy (Amazon CEO) - Thoughts on Generative AI AWS quarterly revenue growth OpenAI struggling to meet deadline for $20B investment (including Microsoft)Inside Microsoft’s complex relationship with OpenAIGoogle Cloud quarterly revenuesBG2 PodcastEast Meets West 2025 (keynote presentation)TECHNOLOGY WAVES CHANGE MARKET DYNAMICS; INCUMBENTS DON’T ALWAYS LEAD
Revenues: AWS ($62B, 2021), Azure ($60B, 2021), GCP ($19B, 2021) Revenues (run rate): AWS ($130B, 2025), Azure ($108B, 2025), GCP ($50B, 2025) AWS is on 3rd CEO since pandemic; market questioning their AI strategyAzure + OpenAI alignment strategy went from strategic to questionedGoogle start-stop AI strategy, but coming together; but company breakup loomingApple AI strategy seems completely unknown (Siri,Apple Intelligence, devices, models?)Oracle seems to be rebounding around Oracle Cloud InfrastructureNew hypercloud AI providers emerging? The Amazon/AWS 20%FEEDBACK?
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Simon Yu (Co-Founder @SpeakeasyDev) discusses the rise of MCP servers, API integration into AI systems, including SDK generation, and the emergence of the Agentic web.
SHOW: 933
SHOW TRANSCRIPT: The Cloudcast #933 Transcript
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Speakeasy websiteSpeakeasy MCP HubThe NewStack article on MCP with SpeakeasyTopic 1 - Welcome to the show, Simon. Give everyone a quick introduction.
Topic 2 - API tooling and platforms for AI has become the hot topic this year. Of course, Agentic AI has made significant contributions to this. What trends do you see that we need to pay attention to? What problems are organizations trying to solve?
Topic 3 - Let’s dig into MCP Servers. The interest in MCP has taken off. At the highest level, why do they exist?
Topic 4 - MCP servers and all the recent announcements around an Agentic Web have me thinking… Do we need to prepare for an Internet where AI agents talk to each other? We had humans and GUIs (web frontends), then we saw the rise of APIs. Is this a third wave or an evolution of the API wave?
Topic 5 - How do SDKs and things like AI API tooling play into all of this? API tools to generate SDKs or AI documentation aren’t new. How does the abstraction of AI change this process?
Topic 6 - In our experience, API-level integrations are challenging to productize. Sometimes it comes down to something as simple as who is going to pay for it, and developers often have a voice and a seat at the table, but don’t have the budget. What has been your experience?
Topic 7 - How does the business side of the house see an advantage to this? What metrics tend to matter and are measurable?
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As Father's Day rolls around, we explore some tales from people taking on their first professional jobs, and how those lessons can relate to managers of newer hires.
SHOW: 932
SHOW TRANSCRIPT: The Cloudcast #932 Transcript
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SOMETIMES WE FORGET HOW CHALLENGING THAT FIRST JOB WAS
Sometimes your first job(s) are just cogs in a machine - you work, but you don’t really careA tale of two first job searches and surrounding activities[Tale 1] Are you qualified for a job? Do you know how to do all the required things?“I’m about to do this first task, and I’m very nervous”“They keep giving me more things to do, and asking if I want more things to do”“Just run with it, own it”We meet 1:1 every week. Sometimes they miss/cancel the meeting. Understanding the entrepreneur’s mindset and challenges[Tale 2] The fork in the road, and the road less travelled. The job that doesn’t pay as well, but provides the necessary experience.FEEDBACK?
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Tobi Knaup (@superguenter, VP/GM of Cloud Native @Nutanix) talks about the evolution of the cloud-native ecosystem, the intersection of AI and Kubernetes, and expectations of the next few years.
SHOW: 931
SHOW TRANSCRIPT: The Cloudcast #931 Transcript
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Nutanix Cloud NativeThe Cloudcast #211 - Mesosphere DCOSTopic 1 - Welcome to the show! Full disclosure for everyone out there, I worked for Tobi at Nutanix. Give everyone a brief introduction and a little about your background.
Topic 2 - This is a throwback for our long-time listeners. We had Ben Hindman on episode #211 almost 10 years ago, when D2IQ was Mesosphere, and we also spoke to Dave Lester, who was at Twitter back in 2014. I’m not going to ask you to catch everyone up on 10 years of your company and the history… but I will encourage everyone to go back and listen to that podcast. It is an excellent snapshot of the early days of cloud native and containers. Today, we will talk a bit about the state of cloud native. The most recent KubeCon EU was a few months ago. What were your thoughts around the event and the current state of the industry?
Topic 3 - What are the most prominent challenges organizations face today with Cloud Native adoption? You hear about the complexity, you hear about Kubernetes is a platform to build platforms… still true?
Topic 4 - Where do you think Cloud Native goes in the next 2-3 years? What technologies or design patterns (besides AI, we’ll talk about that later) evolve, and where does the next round of adoption come from?
Topic 5 - Let’s talk about storage and data services quickly. Data services for K8s is messy, really messy at times. Give everyone an overview of the problem at scale and the challenges, especially in multi-cloud environments, which I’m finding more and more.
Topic 6 - I’d be remiss if I didn’t mention AI and its impact in the space. AI came along and sucked all the air out of the room for a time. How do you think about AI today, now that the dust has settled a bit? Is it just an “app” to run on top? How will AI impact cloud native longer term?
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As we try and predict the future, we explore the possibilities of technology augmenting or replacing the middle of the bell curve. What has history taught us, and where do we expect it to repeat itself or at least rhyme?
SHOW: 930
SHOW TRANSCRIPT: The Cloudcast #930 Transcript
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I Am Disappointed in the AI Discourse (Steve Klabnik)All My AI Skeptical Friends are Nuts (Thomas Ptacek)Pearl Jam - Porch (1991)MOST OF US ARE THE MIDDLE TODAY….BUT WHAT ABOUT TOMORROW?
I’d like to give you free intelligence. Later it might cost something, but it’ll be availableIf you choose not to engage with it, your job prospects might be limited in the futureThere ain’t gonna be any middle anymore - how does that relate to AI?“Hollowing out the middle”We can’t force people to use AI, but it’s going to be in the tools you use dailyYou probably have a choice nowAI won’t have binary impacts on everyone (good/bad), but it’ll have impactsI understand that we don’t understand everyone else’s context, but we do have historical contextFEEDBACK?
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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
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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? Let’s 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
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The McGuffin “Challenge” - 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) - “No Revenue”Set a big ambition for the companyDistract the market from current company challengesDistract your competitors to chase something where they don’t have strengthsProbe the market for ideas - sort of like a “leak”Get 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
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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, let’s dig into model evaluations and hallucinations. Let’s 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, it’s 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 don’t yet have a framework to discuss the technical and business impacts of that spectrum of decisions.
SHOW: 926
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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 don’t want to be bothered to interact with humansWe haven’t yet created a framework to think about the human +/- AI involvement, because we haven’t 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
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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 doesn’t 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 we’ve seen numbers say the uplift is 10-20% productivity.Microsoft claims that 20-30% of their code is now written by AIs.We’ve seen many senior developers speakout that it’s not replacement level technology and they don’t 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 AI’s (agents, etc.)?Is that a good thing for Sr. Dev personalities that didn’t 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 I’ve 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 - Let’s 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 - Let’s 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 “overnight success” 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? We’ve 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 “it is what it is” 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 perksWe’re in an interesting time of challenging economics and pressure from AI Perks are difficult to pull back, because business success isn’t evenly rewardedWhy is positioned as open culture, or strategy, but it’s also about day-to-day behaviorIt is what it is a decision, but it’s 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 “USERS”
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, let’s start with the problem. What problem were you seeing in software development that wasn’t 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 doesn’t 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. It’s an awesome application platform, as long as you aren’t 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 SRE’s, 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 “USERS”
[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 today’s 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 today’s 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 World’s 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 - Let’s begin with some broader concepts - in today’s 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 - Let’s talk a little bit about how you’re 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
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:
CEOs choose more AI, fewer jobsOpenAI plots charging $20,000 per month for PhD-level AgentsWhy AI isn’t 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 isn’t 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 we’re 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..)“How do we replace the employees with AI?”The path to get there isn’t well defined. The politics to get there isn’t defined by technology. Will the current economic downturn accelerate this line of questioning / strategy? The Bed Bath and Beyond 20% coupon test. “The $billion dollar company with one employee.”FEEDBACK?
Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod - Mostrar mais
