Category Archives: Artificial Intelligence

Vibing Vs Buying

In this episode, we get into current market news, including pending IPOs, hardware investment, and IBM’s AI-related announcement with Red Hat, and what this may mean for open weights models and hybrid AI. We also talk about hyper-compressed and ultra-quantized models, specialized hardware, and how memory, compute, and power limits are shaping what can run on device and at the edge. We examine the enterprise software implications and the economics of AI pipelines, including token budgeting, guardrails, human supervision, and the need for validation, governance, and operational control.

Transcript: otter.ai/u/z-PbDS6v8w_4n2Eeey…?utm_source=copy_url

Vibe 3-Step [TechOps]

In this episode we work through a Vibe Coding Operations session using Claude AI inside Digital Rebar as an analysis tool. We describe a three-phase flow of plan, execute, and analyze for inspecting a machine, gathering command output, and reviewing the results. We also talk about troubleshooting an installer problem, differences in user account flag behavior, and improvements in Claude’s debugging across sessions.

Transcript: https://otter.ai/u/-wk5QfPFPgYOxm_dOvTSIezDwTs?utm_source=copy_url

Back After a Break

In this episode, we discuss the rising cost of using AI and how usage-based pricing, model changes, and capacity limits are affecting daily work as AI moves from experimentation into operational use. We also talk about multi-model workflows, hybrid infrastructure, and examples of using hosted models alongside open models locally for tasks such as writing and named entity resolution. We get into the need for enterprises to run their own AI infrastructure, including questions around GPU pooling, routing, reservation, data sovereignty, and service levels.

Vibe Coding Mapping [TechOps]

In this episode, we continue our Vibe Coding experiment. Now that we’ve figured out how to interface with MaaS, this time we wrestle with mapping and how different systems interact with each other. We’re joined by Greg Althaus, RackN CTO, who reviews the project and asks some really great questions. We talk about our decision to restart the experiment, taking the lessons we’ve learned to the newer software available. Enjoy!

Transcript: https://otter.ai/u/NohM8_D7DfbUu9iZc1MSpxi00kg?utm_source=copy_url 

Model Context Protocol Exploration

Today we continue our exploration of vibe coding by digging into the Model Context Protocol, or MCP. We look at how MCPs connect chatbots to backend systems, why natural language matters for complex queries, and what it takes to build smarter, more adaptable interfaces. The discussion covers practical strategies for refining and automating these systems using API docs, making this a solid deep dive into the future of human-to-machine interaction.

Transcript: otter.ai/u/5zn42OkdumP-HXIdi5…?utm_source=copy_url

TechOps Scaling Challenges

In this episode, we talk about scale and the hard realities of system failure in large tech operations. We explore why rare failures become common at scale, and what it takes to build systems that can handle that pressure. From predictive diagnostics to component redundancy, we share practical insights on keeping high-performance and AI infrastructure resilient. This is not theory, it is grounded in real-world lessons from managing complex environments and learning how to plan, isolate, and adapt when things go wrong.

Transcript: otter.ai/u/X8JYiADfPPLEfQ-gge…?utm_source=copy_url

AI Export Controls Work?

We discuss whether or not AI export controls work, but we take a really interesting twist because what we talk about is manufacturing. What we talk about is innovation, and it’s not whether or not you can control AI chips, but what does it actually take to build innovative product? That’s where we really have challenges on export and controls. There’s military manufacturing and goods, and that’s part of what this AI embargo is about. We really talk about how challenging it is to actually build truly innovative manufacturing and what the barriers are.

Transcript: otter.ai/u/k6Thp-TOfwKc_RjXHC…?utm_source=copy_url

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Is 2025 even harder than we expected?

We review 2025 predictions today and dig into why I think this year is going to be both boring and terrifying for a lot of enterprise IT leaders. That, of course, spans Amazon, Reinvent storage, VMware, AI, and Agentic AI – we run the gamut on what is coming and why this is actually going to be a very challenging year.

Transcript: otter.ai/u/H6UvLC-r2zmBO9A5jf…?utm_source=copy_url

Reference: zenoh.io by ZettaScale

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