Talking the Future of Tech
We dive into the chaos created by Broadcom’s acquisition of VMware. In this episode, we discuss what Broadcom is doing, why it’s a problem, how enterprises are reacting, and what alternatives are on the market.
We cover the whole mess in all its glory, and even provide some love for Broadcom.
Resources:
www.thestack.technology/vmware-is-kil…isor-and-nsx/
www.siderolabs.com/platform/saas-for-kubernetes/
Transcript: otter.ai/u/SO8PD-p8AHwwsKfGsN…?utm_source=copy_url
Image by DALL-E
Departing from our typical podcast format, today’s episode is part of a presentation that I’ve been preparing about comparing 125 year old house building architecture to modern DevOps. We also analyze as things that work and don’t work.
There are a lot of home maintenance stories and comparison notes. Particularly in the back half of the episode we get into how this type of challenge relates to Operations Management.
Refereces: nationalpost.com/news/canada/afte…oard-sewer-pipes
Transcript: otter.ai/u/jf8at50nf0KKQG7Drl…?utm_source=copy_url
Image by DALLE: Victorian house with the second floor redesigned in a modern style, featuring extensive use of glass. Each image also includes the porch with rockers and a poodle.
After a brief hiatus, thecloud2030 group is back and deep in tech, talking about things that we think are going to come on the tech front, sans AI.
In this episode, we take some time to go through Kubernetes, hardware, software, bill of materials, and some governance. This includes a smattering of predictions to get your year started off with a bang.
From there, we are going to be moving into our tech-ops series. Find more details about that in today’s outro!
Resources:
www.theregister.com/2023/12/27/bruc…erens_post_open
developersalliance.org/open-source-l…ty-is-coming/
Transcript: otter.ai/u/UQyqHKJ9oNd1SquAWW…?utm_source=copy_url
Image by DALLE: cartoon images of a robot reviewing a long bill of materials on a scroll of paper.
Cloud20302024SBOMCloudAutomationInfrastructureOSSOpen Source
How can companies, enterprises and individuals become more innovative? We investigate the idea of innovation and disruption and continue past where we were with the three horizons model in our previous discussions.
Today’s podcast focuses on breaking this apart into adapting an agile disruption, the use of standardization and the cognitive dissonance of innovation, even digging down into distinguishing between inventions and applications.
Transcript: otter.ai/u/XCidE-0xG7zNmIC7FK…?utm_source=copy_url
DALL-E Prompt: a factor of rubber chickens getting stretching and upgraded into cybertoys
This is our annual year review and prediction episode and it is a doozy. We talk through what has been an incredibly busy year in Open Source, cloud repre, repatriation, AI, ML, chatGPT.
We laid down some really interesting insights and then looked forward not just into 2024 but two years of predictions and trends that we see happening. We cover what we think will be shaking, shaping and shaking the market.
References
basecamp.com/cloud-exit
world.hey.com/dhh/the-big-cloud-exit-faq-20274010
Transcript: otter.ai/u/13Wdve1WQVCPjXtsf8…?utm_source=copy_url
DALL-E Prompt: futeristic graphic of a hall of mirrors with 2023 turning into 2024 and the cast of the wizard of oz
How do we apply the principles of lean to data science and data engineering? We discuss this broadly into using AI and machine learning more generally.
This is a topic that we had discussed over the summer and wanted to come back to six months later because so much has changed and transformed in the industry. What does agile lean process control look like in an infrastructure automation platform? How can we make these very difficult and challenging components of data and data management, more agile, more lean?
I think you will get a lot out of this conversation considering our current hypercharged AI ml and LM environment.
Transcript: otter.ai/u/1ZuALgSXcPw-bIf2GO…?utm_source=copy_url
DALL-E Prompt: please create a picture of a very large truck stuck under a low bridge. please label the truck as ai and the bridge as lean
If you haven’t had a chance to join in on our book groups, I strongly recommend you take a look at the upcoming books we are reading! Today we discussed Data Science and Context, which is a relatively academic book by a series of doctors, PhDs, Specter, Norvig, Wiggins and Wing. The book gets into some really fascinating analysis techniques, addressing both the practical and ethical implications of data science applications.
We discuss the biases inherent in the book, the things that are missing and potentially disruptive to the core assumptions of the book. So even if you haven’t read this book, I think you will find the discussion fascinating.
This week I’m keeping our warm up discussion about open AI in the podcast. So you will get about 10 minutes of bonus content before the book group discussion as a warm up and it is very related. Our conversations about what has been going on with open AI, their board and Q* are directly related to the concluding ideas in our discussion about Data Science and Context.
Transcript: otter.ai/u/qYBKNhDBKqaghEaxE-…?utm_source=copy_url
Image: Data Science In Context cover
How can digital identity be used to build better trust and systems in our daily transactions? There are really significant challenges and consequences to having a national guaranteed identity – a single identity provider.
Knowing who you’re interacting with, in every form, in every situation is not as simple as you might think. There’s a lot of analogues to physical identity that are worth considering.
What would it mean for us to not have privacy? Does identity mean we don’t have privacy in our interactions? Who can we trust and what authority do they have?
Transcript: otter.ai/u/o_43fyGjxu24Ur5rpz…?utm_source=copy_url
Image by Dall-e prompte: a cartoon like image of a humanoid robot looking into a mirror and seeing a masked pirate version of itself
What incidental, or accidental, surveillance state is being created by all of the video and listening devices that are now embedded in our world?
Today we talk through the ramifications of those networks being in private hands in which companies can actually review, analyze and monetize data from these systems. For example – autonomous vehicle cameras and delivery van cameras. This episode discusses the ramifications of this example and more.
References:
arstechnica.com/tech-policy/2023/…s-invade-privacy/
www.vzbv.de/en/court-prohibits-…ivacy-infringements
last-chance-for-eidas.org/
Transcript: otter.ai/u/NszGAX95R70ydlW_fJ…?utm_source=copy_url
Image by Dall-e prompt: “1950s era cartoon of an autonomous car with a lot of cameras spying on people”
What is innovation? Today we continue this discussion, specifically drilling into the three horizons model for creating growth and value.
We spend a lot of time talking about how companies innovate using that model, what it means and what are examples of it? How does that spark take place? We bridge you further down the innovation learning process in this meeting.
Transcript: otter.ai/u/He9h2NVxazKMDN9a13…?utm_source=copy_url
Image by Dall-E prompt “please create a close up picture of a flock of birds navigating between three different horizons. the birds are smart and know which why they need to go”