cloud2030

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/

Today we discuss the implementation of governance and compliance in Kubernetes. The conversation explores the challenges of maintaining security and compliance while navigating the tensions between single choice compliance and open source ecosystems. The podcast also touches on the significance of compliance in small and medium enterprises, the resistance to tech adoption, transitioning from prototypes to production in Kubernetes, and the growing demand for governance tools in the industry. The speakers also delve into the issue of big corporations leveraging open source projects and the importance of multiple perspectives in governance discussions.

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/

The podcast discusses the impact of data center infrastructure and hardware on AI, covering concerns, opportunities, and market blockers. They explore the importance of proximity to water sources for cooling, the lack of dystopian talk around AI advancements, and the role of generative AI in unifying office suite and backend pieces. The speakers also touch on data center issues, political will, and the significance of innovation in sales.

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/