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/

John Willis, panelist on the episode, shares a bonus story about APIs and Jeff Bezos. The podcast explores the challenges of regulating large language models (LLMs) and discusses the EU's AI Act, UK's adoption of EU regulations, and the National Association of Insurance Careers' realistic approach to LLM regulation. They also discuss consent, ownership, and the legality of using scraped data. The importance of data provenance, metadata management, and similarity search in traditional databases is highlighted. They delve into transforming artifacts into monetary value and invite the listener to join future discussions on regulating LLMs.

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/