After 1000+ shows of The Cloudcast, when critical lessons have we learned about the industry, our our journey, and tips for anyone in tech.

SHOW: 995

SHOW TRANSCRIPT: The Cloudcast #995 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS"

SHOW NOTES:

Lesson 1 - Why did we create The Cloudcast? [Career expansion]

Lesson 2 - How did we learn all the topics we covered? [Skills development]

Lesson 3 - What have been the biggest Cloud trends we’ve covered [Trends]

Lesson 4 - How did you think about technology transitions over the last 10-15 years? [Change]

Lesson 5 - How important was it for you to learn the business side of cloud? [Follow the money]

Lesson 6 - What structural changes do you think Cloud will have on IT in 10 years? [Legacy]

Lesson 7 - What lesson from an episode do you think about the most frequently?

Lesson 8 - What surprised you the most about the Cloud 1.0 era?

Lesson 9 - What did you expect to happen, which never really happened?

Lesson 10 - What were the most important lessons you personally learned? [Lessons Learned]

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15 years ago, when we started The Cloudcast the definition of cloud was pretty clear cut. But now in 2026, the rules are all different and a new era of Cloud is upon us. Let’s explore what the next generation might provide.

SHOW: 994

SHOW TRANSCRIPT: The Cloudcast #994 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

CHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"

SHOW NOTES

• AWS in 2026: The year of proving they still know how to operate (This Week in AWS)

WHAT IS AND ISN’T CLOUD IN 2026?

• Cloud isn’t a single company
• Cloud isn’t just public cloud
• A CIA cloud isn’t a Gov cloud isn’t your Enterprise Cloud
• Multi-cloud is a real thing that needs technology
• Google, the people that invented “cloud” are a real company in cloud
• Defining cloud by purity, or IaaS/PaaS/SaaS terms doesn’t really matter
• Does data locality matter more in an AI-Cloud era?
• Do we begin to see price wars over the core Cloud 1.0 primitives?
• Are we in the 9th inning of Cloud, and the 1st inning of what’s next?
• AI has replaced SaaS in immediacy, so does that have impact on Cloud services?
• Does the lack of application portfolio finally come back to bite AWS?
• Can niche cloud services emerge (e.g. is Vercel another Digital Ocean, or something unique?)

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Adam Kamor (@tonicfakedata, Founder & Head of Engineering) talks about building RAG (Retrieval Augmented Generation) systems in production for AI data.

SHOW: 992

SHOW TRANSCRIPT: The Cloudcast #992 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"

SHOW NOTES:

• Tonic.ai website
• Tonic Validate Product Page
• Tonic Validate GitHub

Topic 1 - Adam, welcome to the show. Give everyone a brief introduction.

Topic 2: Our topic today is RAG systems, specifically RAG in production. Let’s start with customization sources and types. When it comes to customizing off-the-shelf LLMs, RAG is one option, as is an MCP connection to a SQL database, and there is pre- and post-training, as well as fine-tuning. How does an organization decide what path is best for customization?

Topic 3 - RAG came on the scene as the savior for organizations that want to use customer AI without the need for fine-tuning and additional training. It has either gone through or is currently still in the trough of disillusionment. What are your thoughts on RAG's evolution and the challenges it faces?

Topic 4 - Let’s walk through the basics of validation. Once you set up RAG, how would an organization know it works? How is accuracy measured and validated? Are you looking for hallucinations? Context quality?

Topic 5 - What is Tonic Validate, and where does it fit into this stack? Is it in band? Out of band? Built into the CI workflow?

Topic 6 - Accuracy is one aspect, but we hear more and more about ROI for Enterprises. How should ROI, risk, and compliance be measured?

Topic 7 - Where and how does security fit into all of this? Also, your thoughts on synthetic data for training vs. real data?

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