cloud2030

We dive deep into the technical subject of governance and policy enforcement, including the tools, techniques and processes that you need to be aware of to do a good job with policy and governance enforcement. We cover how to get started, what to think about, what to be aware of, and chip away at your governance and policy challenges including developer development portals, infrastructure pipelines and DevSecOps. Transcript: https://otter.ai/u/ND90jKHwbklUBOAwT1XkgEo2pbs?utm_source=copy_url Image by Dall-E prompt “please make a carton that shows a regulator who is managing cloud and IT assets using impractical tools”

We dive into the dynamics of open source projects and monetization today, specifically starting around the TerraForm and open tofu split. That topic is one that we love to chew over and potentially over analyze, but today’s discussion is different. We go into how ecosystems are built both in open and proprietary and cloud systems, and look at sort of a historical perspective on what makes a project successful from an ecosystem perspective. We also dive into why some projects work like that, and why some projects don't. Today’s episode gives a new take on some of the dynamics going on in the open source communities through the lens of what happened with Open Tofu and TerraForm. Transcript: https://otter.ai/u/ONDvgS9yGMrSN-bXMTy9KMC-XPs?utm_source=copy_url Photo by James Wheeler: https://www.pexels.com/photo/lake-pebbles-under-body-of-water-1574181/

We discuss innovation, a favorite topic of ours, today. Instead of diving in for a structured conversation, we dove at the bait that was offered by Marc Andreessen in his techno optimist manifesto. If you haven't read it, I would suggest taking a moment to read it before you listen to the rest of the podcast, but you do not have to! It is definitely an interesting opinion piece about the power of innovation, which is why it was a good input for our discussion. We have our own unique perspective and a robust discussion about how innovation should work that tees up further conversations about the three horizons model for innovation. References: https://a16z.com/the-techno-optimist-manifesto/ Transcript: https://otter.ai/u/6qOpnFW0LMvh-rvZfwko93SC5CY?utm_source=copy_url Photo by RDNE Stock project: https://www.pexels.com/photo/woman-in-black-jacket-sitting-beside-woman-in-gray-sweater-7413891/

What does it take to implement governance and compliance, because they are process controls much more than individual technologies. Today we discuss that a lot of the talks seem to be about governance and compliance, and we have a fascinating discussion about governance compliance and Kubernetes. The idea that Kubernetes is maturing, losing the drama that is a hallmark of its first decade now and moving into a focus on managing how to control and have security, compliance and normality. Yet all of those things have a degree of tension with the vendors and users, which puts single choice compliance and governance in direct conflict with open source competitive ecosystems. This makes for a fascinating conversation where we touch on some really important issues for the industry. Transcript: https://otter.ai/u/mAkvsYgMYMp_W8BizkoSxliYgrg?utm_source=copy_url Image: Generated by Dall-E

How do we limit and regulate LLMs and AI? We approach this at multiple angles and look through what it's like to regulate this type of technology. If you're interested in the limits of any technology, and specifically how AI gets regulated, and where we're likely to impose legislative barriers or restrictions on this, then this will be a fascinating podcast for you. Transcript: https://otter.ai/u/8IsFB-H-U3XzpQ751l2fs_c3-vE?utm_source=copy_url Photo by Pixabay: https://www.pexels.com/photo/black-android-smartphone-on-top-of-white-book-39584/

What goes on behind the scenes with AI, and specifically data center infrastructure and hardware? We discuss broad ranging concerns, opportunities and market blockers around AI. We also address how deeply it can impact innovation companies' privacy legislation from the frame of hardware and automation. Today’s discussion leads us to a larger question of what unlocks innovation in general that we will address in future podcasts. Links: https://research.aimultiple.com/wp-content/webp-express/webp-images/uploads/2023/10/Total-funding-for-ai-chip-makers.png.webp Transcript: https://otter.ai/u/3FUaZ3m8JabYLyJZGHZnpjgd81Y?utm_source=copy_url Photo by Tim Samuel: https://www.pexels.com/photo/woman-handful-of-unhealthy-chips-6697286/

If you follow cloud2030 discussions or any of my podcasting over the last decade, Edge is a very interesting topic to me. Today’s episode is a short update on the state of the edge from a very specific position. In this discussion, I walk through with Josh why edge has been hard for us to nail down from a technology perspective. This is something of special interest to RackN as we keep honing and refining our IT edge infrastructure technology set. Transcript: https://otter.ai/u/OtzOtPvoyiAKZdxJjmE4q9HpSfo?utm_source=copy_url Photo by Khoa Võ: https://www.pexels.com/photo/unrecognizable-man-sitting-on-rooftop-edge-against-cloudy-sundown-sky-5780744/

The TerraForm fork, now known as the OpenTofu project, is our first topic in today’s episode. We discuss what's going on with that, the challenges, as well as the potential pressures from HashiCorp that created this whole situation. How do we get experts to recover their authority and how do we look at organizations like that? We have about 20 minutes of really involved conversation about the book, Death of Expertise by Tom Nichols, from the previous podcast. If you haven't heard our first part of the conversation, I suggest you go back and listen to our full Death of Expertise podcast. We cover two topics, one of them short term and one of them long term. So it’s a nice, balanced industry discussion around what the fork means, what its impacts are and a little bit of recap. There's some really spicy opinions around 32 minutes in if you want to jump forward, we resume our discussion about death of expertise. Transcript: https://otter.ai/u/zGUYDP6DynzxPBNLM9dcePneb7Q?utm_source=copy_url Photo by lil artsy: https://www.pexels.com/photo/person-about-to-catch-four-dices-1111597/

What are the potentials for biasing LLM models? We dive into biases both in good ways and in bad ways. Is the expertise that we're feeding into these models is not sufficient to actually drive the outcomes that we're looking for? We're going to be eliminating humans out of the loop in a relatively short period of time. Both outcomes, at the moment, feel equally probable, which is troubling. We dive into how and why that happens, what's going on, and some concrete tips for how you can improve your prompting to avoid these same pitfalls. Transcript: https://otter.ai/u/v3MaWiCWEe-G1ar2O0KpriJf9vU?utm_source=copy_url Photo by Marta Nogueira: https://www.pexels.com/photo/pink-and-blue-background-divided-diagonally-with-two-matching-colored-pencils-placed-on-opposite-colors-top-down-view-flat-lay-with-empty-space-for-text-17151677/

We continue our book group series today about the Death of Expertise by Tom Nichols, which is very dense with a lot of provocative and thought provoking comments, topics and ideas. It was so interesting that we decided we needed two sessions to fully unpack this. This is part one, which is about how expertise as a society is handled, how social media changes and the cyclical nature of confidence in our institutions, and how technology is shaped in buying patterns in use by expertise. If you’re interested, please participate in part two of the discussion! We also talked about the Dunning Kruger effect, the idea that the less you know about something, the more confident you are, and that gaining knowledge makes you more knowledgeable but also less falsely confident in how you present yourself. It’s a more complex topic than that very short summary. “It is difficult to get a man to understand something, when his salary depends on his not understanding it.” ― Upton Sinclair, I, Candidate for Governor Transcript: https://otter.ai/u/m--7wT4fRjdodT3qRuXs6N8VB4E?utm_source=copy_url Image is book cover

Can large language models effectively supplant developers and DevOps engineers? Today we go deeper into how the models can be trained, if they can be trusted, and what is the upside or positive use case in which we really turn LLMs into the type of weighing person experts that they have the potential to be versus simply something that turns up the volume on how fast you generate code. We also talked about the downsides of that type of model and the potential upsides of how powerful using these tools as assistants could emerge to be as a key aspect here to transform and improve the outcome for work. Transcript: https://otter.ai/u/u3bArfIvx40oUXnRLtSXwAx_Iuw?utm_source=copy_url Photo by Pixabay: https://www.pexels.com/photo/aeroplane-aircraft-airplane-aviation-33224/

We recap the Google Next event and do a deep dive into the interesting topics Google was announcing at their flagship cloud event. We looked at what they were doing for AI, but also some new services offerings around what's going on with Cloud. Then we pulled back and went into generative DevOps, something we're going to be diving into more and more as we explore how to better use large language models in DevOps and automation building/ Fascinating topics and some probing questions at the very end of this discussion. Transcript: https://otter.ai/u/1l0Q6SuFHV7EIG1kBqrWM3wlOec?utm_source=copy_url Photo by Tima Miroshnichenko: https://www.pexels.com/photo/a-person-holding-a-container-full-of-money-7009477/

What’s going on from VMWare to Broadcom to HashiCorp and their license changes. We discuss current topics, even to the sad news about Kris Nova passing during a mountaineering expedition. If you'd like to catch up on the tech news, then this topic hopefully has aged well and you will enjoy it! Transcript: https://otter.ai/u/J18ecRKKwc8As_TLyChs_IdI_Ao?utm_source=copy_url Photo by Oziel Gómez: https://www.pexels.com/photo/man-wearing-gray-long-sleeved-shirt-and-brown-shorts-holding-black-dslr-camera-on-mountain-925263/

How do Edge and Compute and SaaS and cloud influence everything that we do? We covered topics from VMware explorer and talked a lot about Edge. That led to AI ml, which led to another topic, which led to another topic. If you enjoy hearing about how interconnected our technology and choices are, everything from Bitcoin to edge, and cloud and government interaction, this is the podcast for you because we cover pretty much all of it and connect it together. Remember that on September 14, we are having one of our quarterly book club meetings on the death of expertise. Resources: https://www.fiercewireless.com/wireless/telcos-and-road-digital-promised-land Transcript: https://otter.ai/u/wf2OahZkgp9tzF7eJu0-V08-8jY?utm_source=copy_url Photo by Aksonsat Uanthoeng: https://www.pexels.com/photo/close-up-photo-of-assorted-color-of-push-pins-on-map-1078850/

How do you regulate large language models? We look at the challenges of regulating these AI approaches and how governments and companies can approach it. We untangle how these models work, and dive into the mechanics of what information is controllable. We walk through concrete information that is a benefit to you here as our listener, and incentive for you to join us in future conversations as we continue to unravel it. In addition, John Willis was on the panel today, and he started us off with a story about API's, Amazon, Jeff Bezos, and O'Reilly from the warmup. So you'll get a short bonus story by John Willis before we start. References https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act-aida-companion-document https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence#:~:text=Parliament's%20 priority%20is%20to%20make,automation%2C%20to%20prevent%20harmful%20outcomes https://www.trade.gov/market-intelligence/uk-ai-regulations-2023 https://content.naic.org/cipr-topics/artificial-intelligence Transcript: https://otter.ai/u/dBRQBFNz8d01taQ-iMelFGdR2Zk?utm_source=copy_url Image: https://www.pexels.com/photo/measuring-guitar-pick-3988555/

What is technical debt, and how does it apply to large language models? We dive into a really interesting conversation that goes from technical debt into system and code maintenance, which is probably a much better way to think about the challenges we have in maintaining the infrastructure systems, code, data and data lakes that we have to deal with on an everyday basis. How do we maintain, store, track and update the LLMs themselves? How do we know and manage which model is being used when we retire a model? References: https://www.linkedin.com/pulse/how-google-measures-manages-tech-debt-abi-noda/ https://devops.com/are-llms-leading-devops-into-a-tech-debt-trap/ Transcript: https://otter.ai/u/ngUClgtMmLLKXCFalDxFNsNdVr4?utm_source=copy_url Image: https://www.pexels.com/photo/anonymous-worker-in-heavy-duty-gloves-3846440/

A coming Data Darkage is on its way, where we're watching Reddit, Twitter and other companies take what used to be publicly available information and put it behind a paywall or gate. Because of the way large language models are using this data and the value of the data, we are expecting to see that trend accelerate. This will have profound implications for how we think of, share, and use data in the coming years. Transcript: https://otter.ai/u/e1XCyhSa9V81bgMpbox8ujZObuo?utm_source=copy_url Photo by Pollianna Bonnett: https://www.pexels.com/photo/young-brunette-woman-with-sunglasses-crouching-on-blue-chair-17687131/

Hashicorp made a license change into a BSL, a business license which is not open source that allows or makes code available, but instead restricts the use of Hashicorp products to people who are effectively paying customers or enterprise customers. If you're embedding or repackaging the software or competing with Hashicorp, you are prohibited from using it. We spent this podcast looking into why, how, and what implications there were, as well as historical precedents. References * https://www.runtime.news/hashicorp-closes-a-door/ * https://opentf.org * https://blog.gruntwork.io/the-future-of-terraform-must-be-open-ab0b9ba65bca * https://spacelift.io/blog/spacelift-latest-statement-on-hashicorp-bsl * https://ir.hashicorp.com/news-releases/news-release-details/hashicorp-announces-first-quarter-financial-results-fiscal-0 * https://www.hashicorp.com/license-faq#competitive-product-bsl-coverage * https://www.linkedin.com/posts/rhirschfeld_hashicorp-closes-a-door-activity-7097631727665233920-MxcP/ Photo by BİLAL KARADAĞ: https://www.pexels.com/photo/yk-1-17939409/ Transcript: https://otter.ai/u/ZjTzZZiYh_dXri3rSkjkD50GPuc?utm_source=copy_url

We talk about DataOps, but if you’re expecting this to be DevOps for data - you are mistaken. Today we talk about engineering data through the idea of data stewardship or how you manage and control the data. Beyond permissions and access into the costs and how things are stewarded, how logs are handled, who controls how much access and how quickly people have access, where you're putting the data to improve its effectiveness, and more. There is so much going on above the infrastructure, but below that actual analytics, this conversation will open your mind to a whole layer of challenges related to governing and managing data. Transcript: https://otter.ai/u/SDaRomv41JtQYM7a6C1Hy7cOpy8?utm_source=copy_url Photo by Taryn Elliott: https://www.pexels.com/photo/person-holding-cupcake-with-white-icing-on-top-4099125/

This is the second installment of our book group, which is a discussion about Investments Unlimited. We have one of our authors, and a great all around DevOps enthusiast, John Willis, on the call with us. As you might expect, while we talk about the book and John gives a lot of background and details about the book, we treat it with the classic cloud2030 style, and bring in AI, large language and advanced DevOps. We take the topics of the book to the next level, and frame it in the moment of the year, looking beyond and into how the concepts of compliance, validation, team coordination and risk assessment are incorporated into the coming AI and how it changes in our landscape. Sources Book https://www.amazon.com/Investments-Unlimited-Compliance-Thriving-Digital/dp/1950508536 https://techstrong.ai/aiops/the-rise-of-shadow-ai/ https://guidehouse.com/insights/financial-services/2021/public-sector/garp-three-lines-of-defense Transcript: https://otter.ai/u/uC9c3xJS4oATQx7BrYeHAOjFHsY?utm_source=copy_url Image is book cover

Is Kubernetes actually creating the amount of cloud portability of infrastructure agnosticism that we hope it will? If we're using the same platform across multiple clouds, multiple infrastructures, multiple management teams, does that actually create portability? It's a key question for us in building cloud architectures, making decisions about the architect and about how we build architect applications, and even how we structure our teams. In this conversation, we get to the heart of answering that question, but our answers might surprise you. Transcript: https://otter.ai/u/ASE9dLnP8qo2jednZB5CoDhqLVc?utm_source=copy_url Image: https://www.pexels.com/photo/black-and-white-border-collie-puppy-in-brown-bucket-3908821/

The Red Hat changes in how they publish the source code for CentOS sent a stream specifically, but unlike all the other conversations that I've heard, we dive into how enterprises can inoculate themselves from this type of disruptive change. We also address what it means for the ecosystem of vendors and how we can build better software in response to the potential fragmentation of Red Hat, Linux Enterprise Linux or Enterprise Linux distros. This was a surprising conversation, because we addressed a lot of important trends in how companies depend on Linux stability, and what they could do. If you are in this boat with all of us looking at how to have stable long term secure infrastructure using Linux, you will love this podcast. Sources: https://www.theregister.com/2023/07/10/oracle_ibm_rhel_code/ https://www.zdnet.com/article/oracle-takes-on-red-hat-in-linux-code-fight/ https://www.suse.com/news/SUSE-Preserves-Choice-in-Enterprise-Linux/ https://www.zdnet.com/article/why-snap-and-flatpak-make-linux-a-better-os-for-the-average-user/ Transcript: https://otter.ai/u/dmrcCulS0X1CC4YGHOWtzMdXyio?utm_source=copy_url Image: https://www.pexels.com/photo/photo-of-people-sitting-beside-table-3182755/

We use ChatGPT to live create DevOps, automation, Ansible, TerraForm, Python, and interact with different clouds to get advice on how to set up clouds. This discussion includes a screen share session, so if you're listening to this audio there will be times when we are talking about something you can't see but I do make a point of working to explain what we're doing. There’s also a video of the screen share session if you prefer. Video: https://youtu.be/hU7pUDfliGk Transcript: https://otter.ai/u/MPvT7SP0FCSe02asm8_iSunXz6o?utm_source=copy_url Image: https://www.pexels.com/photo/pink-background-with-speech-bubble-1111369

What does it take to make a good decision? We discuss an interesting take on this as we integrate the topic of how to sell into situations, and selling is the ultimate drive in a decision. Our conversation mixes the challenges of making decisions as a leader with the challenges of selling into organizations where people have to make a decision to choose your product. It also includes tips on how to frame decisions, how to position decisions, and more. If you engage in projects and selling them to your boss, supervisor, company, peers, reports, etc - you need to be able to understand why it is important to move forward and make a decision or change. If you're not able to sell, you’re not well equipped for being a leader. Transcript: https://otter.ai/u/1SRbrNbmISOgTUyV2Xp8AZtmKZU?utm_source=copy_url Image: https://www.pexels.com/photo/man-looking-at-a-vintage-car-3056057/

What can we expect generative AI to generate and is it going to produce good code? Today we talk about Gluecon and generative DevOps and the different concepts and capabilities around it. What impact is it going to have on developers? How do we control that? Today’s discussion was in preparation for our session on June 13, where we're going to group program GPT to see what type of DevOps coding skills we can prompt. We talked about the necessity of prompting in this session and covered some tips to help you think about how to be a better prompt engineer, a skill set that everybody's going to need to have in the next months if not years. Transcript: https://otter.ai/u/lsye_htH0-wksAOrqgbUu5TCtpU?utm_source=copy_url Image: https://www.pexels.com/photo/man-welding-round-window-frame-2965260/

What actually is used to describe the provenance and information that comes with our data? Today we discuss metadata and the governance, security, hint, domains, date that accompany data in ways that in some senses are more important than data. How can we move, change and transform data? We had a really robust conversation about how access to data is so critical in actually understanding how data is used. This is a topic we struggle with: figuring out the naming, how things work, and the context. More than anything else, the context makes things challenging. As you listen to this, think through how challenging it is to define data, data governance, and using data effectively. Transcript: https://otter.ai/u/VE2u_jTwG_h4z9ewfI0bya7kvmg?utm_source=copy_url Image: https://www.pexels.com/photo/cheerful-black-teacher-with-diverse-schoolkids-5905918/

What would our systems look like if we didn't have Kubernetes? We started this discussion with platform engineering and its associated challenges. In talking about platforms, we covered ways in which people can consume infrastructure more effectively. That segwayed directly into ways in which Kubernetes could be changed under the covers, used for virtualization use for non traditional containerized automation. This episode is a pretty thorough review of alternatives to Kubernetes, and the ways in which Kubernetes misses the mark. Transcript: https://otter.ai/u/0qX-qloVOzAQ47LgpS2A9yXsoWs?utm_source=copy_url Image: https://www.pexels.com/photo/wrecked-ship-2336927/

How do you deal with the weakest link in security? Today we talk through how we can secure systems, all the way from what technical processes put in place to the people involved to legal enforcement, and who pays the price when data is compromised? There's a lot to digest here that comes back to thoughtful ways in which we can deal with the weakest link in the systems. How do we create robust security models? Transcript: https://otter.ai/u/mkup2hKSzyP0PkpxkkoPbnQrNgw?utm_source=copy_url Image: https://www.pexels.com/photo/brown-thread-2072872/

The book Data Cartels by Sarah Lambda serves as a starting point for our discussion today. https://www.amazon.com/Data-Cartels-Companies-Monopolize-Information/dp/1503633713 A dense and thoughtful book, it is straight up the alley of the type of conversations of the2030.cloud has. Our analysis of the book and the challenges it provides - the data compliance governance, the legality, the threat, and broader implications of what Dr. Lambda lays out - are all really important. Today’s podcast is understandable and interesting without having had to go through and read the book, but I still recommend that you do. Transcript: https://otter.ai/u/T5CJzO8pMrpGnLVGo48fOcHcuj4?utm_source=copy_url Image: https://www.pexels.com/photo/lady-justice-and-a-gavel-6077123/

How can you execute on a zero trust strategy and what do you need to keep in mind while building it? Today covers the 101 and 201 levels on zero trust. We had a really good conversation about how it works, what doesn't work, what you need to be prepared for. Even if you think you understand zero trust, you will get something out of this conversation. And if it's a new topic for you, you can also benefit from this pragmatic discussion of zero trust, security and application architects. Transcript: https://otter.ai/u/Z1SO1WuJYT3JmEctZe1-SzlQ_Nw?utm_source=copy_url Image: https://www.pexels.com/photo/wooden-chair-on-a-white-wall-studio-963486/

Software bills of materials are one of the most critical, modern software development practices that people should be doing but don't. They have significant impacts in improving security, provenance, reproducibility, and license compliance. The benefits of having a good software bill of materials in our technology industry are incredibly high, both as a producer and a consumer. And yet, this is one of those places where I feel like we have really fallen behind. Transcript: https://otter.ai/u/J3VtBLiQMAuZgfMCOz9K62uKjYg?utm_source=copy_url Image: https://www.pexels.com/photo/photo-of-monumental-sculpture-3810915/

We dig into a topic written about by Eric Norlin or SK ventures about technical debt and AI. In this episode, we discuss the consequences of generative AI could be radically transforming the way in which we generate code and deal with code that has been generated in technical debt. We explore some fascinating concepts about how fast we can iterate, how we change the dynamics of building software, building automation, and the expertise required to architect systems. This leads pretty far down in the path towards disruptive thinking, and how this could reshape the entire industry. Source: https://skventures.substack.com/p/societys-technical-debt-and-softwares Transcript: https://otter.ai/u/MEtVkoNnZeCu0JHa30-jPOcRrj4?utm_source=copy_url Image: https://www.pexels.com/photo/piggy-bank-beside-a-flower-4886900/

We continue our discussion of what would the environment look like without Kubernetes? We started with the idea of what if Kubernetes went away, what if there was a copyright or a trademark or an API issue that made us have to abandon Kubernetes altogether? In this episode we played what if scenarios, exploring what made Kubernetes unique, and if parts of Kubernetes or parts of the architectural model could exist outside of Kubernetes? What would be necessary? We identified enough parts of Kubernetes individually where we saw how it itself is an interesting convergence of some core technologies. Nothing new except in the combination of those architectural paradigms, designs and open source models. Through this, we dig into why Kubernetes is so powerful in the market. Transcript: https://otter.ai/u/L7GZRD8-wEnzLIJL5_nLCPfbogY?utm_source=copy_url Image: https://www.pexels.com/photo/wreck-of-sunken-ship-in-middle-of-bay-12102304/

NOTE: This is Rob's Gluecon topic on 5/24. Save $300 if you register with speaker300 at https://www.gluecon.com We dive into the question of whether or not generative AI can be used to productively change DevOps automation and the control of infrastructure. We've discussed the closed loop side of using AI to manage infrastructure in the past, but this episode we really dive into the idea of creating automation and using generative AI. Transcript: https://otter.ai/u/VtnznHgydT3_6QSJpkSb3uU2btY?utm_source=copy_url Image: https://www.pexels.com/photo/tossing-fried-rice-in-a-frying-pan-6937457/

We talk about improving the time it takes to make decisions - called time to decision, a topic that we like to address quite a bit. We started with the news of the day around AI, ml Chaffee GP, and learning models. We asked ourselves if AI/ML and generative AI could change the way expertise is used to make decisions and improve the time to the decision for experts. What type of implications would that have in the market? If you've been tracking this subject, I know you will find this exciting and interesting. Transcript: https://otter.ai/u/L3Kb_I4fe0ggAr9nDEhyECSkt5A?utm_source=copy_url Image: https://www.pexels.com/photo/person-touching-black-two-bell-alarm-clock-1198264/

Today we look at what it takes to have much more collaborative building of automation, templates and shared components that are necessary to really drive platform engineering, and not just between teams at the same company. We make components for infrastructure automation that bridges the industry because they can be shared much more broadly, similar to the way we share modules in coding languages. We dug into what it takes to make that type of environment work in automation, and what are the prerequisites of the environment? How do we structure automation? Transcript: https://otter.ai/u/IPyFDG19OCH-UekM7Pbfbg1XJx4?utm_source=copy_url Image: https://www.pexels.com/photo/doctor-operating-a-patient-4421494/

How do we sustain open source? Today we discussed how the commercial models and sustaining models around open source are changing and evolving. We also included some conversations about whether or not generative AI might actually change the economics around that part of open source. We hit on top projects, open source hardware, open source, operating systems platforms, a whole gambit, and how it fits together into a sustainable model for the users, companies, enterprises, and really everybody. We all use open source to one extent or another. We have our book club coming up on data cartels, we're going to be discussing it on May 4th and I hope you take the time to read and come join us. Transcript: https://otter.ai/u/MugVjZkIebPMCepjlVb69YQoX9k?utm_source=copy_url Image: https://www.pexels.com/photo/wood-road-landscape-mountain-16179008/

A conversation about platform migration turned into an interesting topic about the end of expertise and the changing of the way we think about expertise in a variety of contexts. How can platform improvement be radically transformed by the use of AI? We discuss entering a world where the lock that we've had in a platform, or the longevity of a platform, is radically transformed by the ability to review, scan, test, correct, and transport the data included in that system. The expertise needed to handle platform migration might be entering a new era in which it's radically reduced. What are the implications of those transformations? We address a wide range of the impacts of knowledge, AI, and generative machine learning. Transcript: https://otter.ai/u/4nL-a5_7dMhYBsvSFP3w8ABdXvc?utm_source=copy_url Image: https://www.pexels.com/photo/shepherd-dog-with-flock-of-sheep-5384726/

How has Kubernetes changed our industry? Today's discussion is part of a multi podcast conversation in which we're going to think about ways in which Kubernetes could go away, or could influence other technologies in such a way to be transformative. We went down the path of what we have learned from Kubernetes and how it influences other aspects of IT operations, architecture and design, and explored the impact that the expectation for declarative immutable operational constructs will play into other aspects of our system. We also discuss micro LS microkernels and how operations are staged to talk about the need for declarative OS, banking on this idea that what Kubernetes has built extends into other areas. Chat GPT Summary: “The conversation is part of a multi-podcast series focused on exploring ways in which Kubernetes could influence other technologies, as well as the potential consequences if it were to disappear. During the discussion, the group delved into the lessons learned from Kubernetes and its impact on various aspects of IT operations, architecture, and design. One key takeaway was the importance of declarative immutable constructs in managing the complexities of modern IT systems. The group also explored the potential for microkernels to revolutionize system design and emphasized the need for declarative operating systems. Overall, the discussion highlighted the transformative role that Kubernetes has played in shaping the IT industry and underscored the importance of adopting a declarative, immutable approach to managing complex IT systems.” Transcript: https://otter.ai/u/7SMjDGwHTMLmfaCACkVrqwrB2k4?utm_source=copy_url Image: https://www.pexels.com/photo/submarine-moored-in-a-harbor-14707646/

Every time we look at data analytics and data systems, the idea of having a way to manage control and explain the data is actually as important as the data itself. This episode is all about metadata, specifically, metadata related to data analytics analysis, Big Data computation, sort of the data lake metadata problem. Today we discuss the challenges of data management, but also the potential of understanding so much more about how data is used. If you are an IT professional or a data professional, you will find this conversation about how we're going to draw inferences, manage and control all of that data that were collected fascinating. Transcript: https://otter.ai/u/dFF6CfBYOMR3QFydaeeqsKk9UW4?utm_source=copy_url Image: https://www.pexels.com/photo/woman-standing-beside-wall-mirrors-6208385/

Emily Friedman's DevOpsDays Ukraine presentation about rethinking the software development lifecycle or SDLC sparks our conversation today. She describes looking at it as a multi-dimensional cross functional discipline, that actually accounts for six different vectors of capabilities that need to be factored in - a resilient and robust look at the SDLC. Watch her YouTube: We found that the model does not cover all of the things that we've been discussing as important things to consider in building, deploying, and making software resilient and reliable, most specifically software, build materials, or s bombs. Transcript: https://otter.ai/u/zp0uAu_xis_1fb66sSZlywN0QRo?utm_source=copy_url Image: https://www.pexels.com/photo/photo-of-orange-pinwheels-3580452/

What are the human and management factors that go into building great platform engineering? And what are the efforts of control having too much control or too much flexibility, not enough collaboration, not creating space for innovation, and changing inside what's inside these platform engineering efforts? Today, we discuss centralized versus decentralized platform engineering, or as came up in the conversation about platform engineering, it's the opposite of Java Enterprise, version and platform. As you're doing this type of work interacting with platform teams should influence how you design and authorize the effort to make that work. What type of slack you need to put in the system and what type of authority needs to be given to the platform engineering team. Transcript: https://otter.ai/u/ySGeMTU_qFeeBbENz4-L7FFG-Yo?utm_source=copy_url Image: https://www.pexels.com/photo/keyboard-keys-lot-373072/

We check in on data gravity to see how generative AI and conversations about metadata and thinking on data lakes impacts data gravity thinking in general. Data gravity is a concept that has been propagated by David McCrory, a friend of mine, who defined this idea that data itself, the aggregation of data, the use and transit of data has a gravitational effect. That it pulls more data to it as well as workloads towards it. We jumped right into impacts of data gravity in this conversation. Transcript: https://otter.ai/u/qKf75W8OvZHkMtVQRM1M2vi5_qE?utm_source=copy_url Image: https://www.pexels.com/photo/action-animal-bronco-bucking-33251/

Is hardware going to be innovative and change? Brian Cantrell brings up oxide computing and some of their design motivation. Today we discuss our skepticism about some of his points, as well as the impacts for cloud distributed Compute hardware design mainframes, cloud, repatriation, and a whole bunch of topics about next generation thinking in Compute infrastructure management and applications. We are officially starting our cloud2030 book group and I hope you will join us - we are going to be reading Data Cartels by Sara Landon, followed by Investments Unlimited by John Willis and crew. Book Clubs Links: May 4 > Data Cartels https://www.amazon.com/Data-Cartels-Companies-Monopolize-Information/dp/1503633713 Early July > https://www.amazon.com/Investments-Unlimited-Compliance-Thriving-Digital/dp/1950508536 Transcript: https://otter.ai/u/S7CRv2J9fmOjAc8HM_TG9PM3eUw?utm_source=copy_url Image: https://www.pexels.com/photo/a-man-standing-on-gray-hot-air-balloon-9128460/

How does AI chat and generative AI have the potential to disrupt everything we know about social media? Today we talk Twitter versus mastodon. We spend most of our time talking about the power, influence and simple use cases for generative AI. Is this going to break Mastodon, Twitter and other forms of social media? We have a pretty compelling conversation about that, too. If you're a fan of Mastodon and Twitter, jump forward to about 30 minutes in when we really start getting down to that topic. Stay tuned for our agenda as a bonus extra in the back half of the podcast. Discussed Links: https://jounce.ai/ https://techcrunch.com/2023/02/28/jack-dorsey-backed-twitter-alternative-bluesky-hits-the-app-store-as-an-invite-only-app/ https://www.primal.com/about-us/ https://techcrunch.com/2023/03/01/addressing-criticism-openai-will-no-longer-use-customer-data-to-train-its-models-by-default/ Transcript: https://otter.ai/u/cVUTxHkYrJ_BhfZetm91mvxlstI?utm_source=copy_url Image: https://www.pexels.com/photo/bird-sitting-on-top-of-an-elephant-12118214/

What is generative AI and what are people now just generically calling ChatGPT? We put these things in a technical frame, meaning can we use generative AI to improve our programming, testing or automation? What does it take to use these concepts in ways that iteratively improve IT infrastructures. We review the state of chat, ChatGPT, AI infrastructure and things like that. Transcript: https://otter.ai/u/nFCSMPFyUVHO50I0jGP2HXCas7c?utm_source=copy_url Image: https://www.pexels.com/photo/woman-leaning-on-washing-machine-15625100/

Is platform engineering effective at hiding complexity from developers? Today we tear apart what platform engineering is doing, how it came about and what it's trying to be. We discuss what companies are trying to accomplish with platform engineering - how can successful efforts improve outcomes for development teams and operations teams by improving collaboration in contracts? Why and how is that important, and what do those efforts entail? Transcript: https://otter.ai/u/bVd1_IwEqFD-uEYxsyuffhNTeG4?utm_source=copy_url Image: https://www.pexels.com/photo/woman-posing-with-mirror-underwater-15674505/

How can the intersection of generative AI machine learning and artificial intelligence be applied to environments using digital twins? Today we discuss digital twins and artificial intelligence. How can we improve the simulations, the systems, the interactions that we build? How can we correctly model complex components of everything from cars to pumps in ways that allow us to then build on top and build more intelligent systems. We come up with some grounded examples. Mentioned: https://projectarrow.ca/ Transcript: https://otter.ai/u/A79s08jpJ4-UPyT3132EfU-5DaY?utm_source=copy_url Image:https://www.pexels.com/photo/two-bernese-mountain-dogs-lying-on-floor-9040438/

Platform engineering is quite buzzy and has a lot of hype at the moment. Today we dug behind the hype to acknowledge how the term is being used and misused. We cover why this is a topic of interest, how it’s driving customer thinking around operations and development teams, how it's working to establish standard operating procedure around infrastructure and operations to support a business, and how those needs drive the evolution of our technology, infrastructure and design thinking. Transcript: https://otter.ai/u/GksMnMIZV0cyM6iwWj_sAd8yNhU?utm_source=copy_url Image:https://www.pexels.com/photo/person-in-black-vest-holding-fan-of-cards-6255293/

We break down the edge compute cluster by the Chick-fil-A team, and we talk about how they use Kubernetes, specifically K3s in 2500 of their restaurants to build an IoT and restaurant management system. This system uses Intel Knucks, a commodity commercial residential grade hardware. It’s an update on a four year old Kubernetes story with a lot of buzz, and they show how they have been successful building this system. If you're interested in Kubernetes, Edge DevOps and distributed systems, this episode has a lot to enjoy. Transcript: https://otter.ai/u/i0lBYq9PNQevn0AIvXNX9BpnpbM?utm_source=copy_url Image: https://www.pexels.com/photo/person-in-black-vest-holding-fan-of-cards-6255293/