Front-End Clouds and Web Development

Malte Ubl (@cramforce, CTO @Vercel) talks about Front-End web development and clouds, how AI impacts front-end infrastructure, and impacts to the business.

SHOW: 819

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

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

SHOW NOTES:

• Vercel (homepage)
• Elevate your frontend with AI
• V0.dev

Topic 1 - Our topic for today is front-end development and front-end clouds, and we’ll also explore AI-generated front ends. Our guest is Malte Ubl, CTO at Vercel. Malte, welcome to the show. Give everyone a quick background on your time at Vercel and Google Search.

Topic 2 - Let’s start at the start. We hear all the time about front-end developers and back-end developers. What is your definition of a front-end and, more specifically, a front-end cloud?

Topic 3 - What are the most common use cases you see today? What problem are you solving when you talk to organizations?

Topic 4 - How much of a front-end is typically integrated with 3rd party services, and how much is build your own?

Topic 5 - Where does AI fit into this from a developer's perspective? Are we to the point where we can develop a front-end from a prompt?

Topic 6 - Let’s talk about the business side of this. Do companies typically have a “front-end problem”? If so, what does it look like, and how do they know?

FEEDBACK?

• Email: show at the cloudcast dot net
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Identifying Leadership Traits to Follow

What are the key characteristics and red flags with today’s leaders in the tech industry? How to identify the leadership skills, the team building skills, and the execution skills.

SHOW: 818

SHOW TRANSCRIPT: The Cloudcast #818

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:

WHERE ARE THE LEADERS IN THEIR CAREERS? 

• Are they a 1st time manager?
• Have they managed distributed teams? Managed multiple teams?
• Have they managed a P&L? 
• Have they had significant success in the past? What was their role?

LEADERSHIP SKILLS, TEAM BUILDING, EXECUTION AND RED FLAGS

• Are you joining an established “team” or group that’s worked together before?
• Is the leader repeating a previous success, in a similar or different domain?
• What motivates the leader? What is their view on competition?
• What are the leader’s best skills? Do they recognize this? 
• Does the leader augment their skills with the team around them? Do they recognize this?
• Does your personality mesh with the team you’re joining, or are you expecting to be a change agent?

FEEDBACK?

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Cloud News of the Month - April 2024

Aaron (@aarondelp) and Brian (@bgracely) talk about all the major news stories in Cloud and AI from April 2024

SHOW: 817

TRANSCRIPT: Cloudcast #817 - CNOTM - April 2024

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

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

SHOW NOTES: 

• LINKS TO ALL ARTICLES FOR APRIL 2024 CLOUD NEWS

Segments Covered in the Show:

• Good Old Fashioned Cloud News
• The AI Innovation Continues - Speed Round
• Trend 1 - 2024 is going to be a year of big announcements
• Trend 2 - We’re starting to see some (early) reality set in with AI
• Trend 3 - Lots of things are being lost between Broadcom/VMware and AI discussions

FEEDBACK?

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Open Source and Business

Every few years we have to be reminded that open source isn’t a business model. Let’s talk about the business dynamics that everyone seems to keep forgetting.  

SHOW: 816

SHOW TRANSCRIPT:  The Cloudcast #816

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:

• Adam Jacobs discusses open source and business (The Changelog)
• “figure out how to play nice with competition” (Twitter)
• The will never be another Red Hat - Economics of OSS (a16z)

OPEN SOURCE IS A LICENSE, NOT A BUSINESS MODEL

• There are rules around software licenses (e.g. Apache, GPL, etc.)
• There are no rules about how people feel about software, creators or maintainers

FREE, FREE TIERS, EXTENSIONS, CLONES

• Red Flags: Writes most of the code, took VC funding (multiple rounds)
• Green flags: Lots of diverse (companies) contributors
• Yellow flags:  Foundation owns copyright
• “There’s the business side and there’s the hippie side of OSS”
• “I have endless ambitions”
• “I didn’t build a forever entity”
• “When is the rug pull going to happen?”
• If a company takes VC funding, is open source anything more than a marketing vehicle?
• “Docker figured it out and now they are doing like $100M”. Did they? 
• When is OSS personal, and when is it a company?
• Will there never be another Red Hat, or just not another Linux?
• How much is too much when determining if a company should give things away for free?

FEEDBACK?

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Sizing AI Workloads

John Yue (CEO & Co-Founder @ inference.ai) discusses AI workload sizing, matching GPUs to workloads, availability of GPUs vs. costs, and more.

SHOW: 815

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

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

SHOW NOTES:

• Inference.ai (homepage)
• TechCrunch post
• SiliconAngle post on ChatGPU

Topic 1 - Our topic for today is sizing and IaaS hosting for AI/ML. We’ve covered a lot of basics lately, today we’re going to dig deeper. There is a surprising amount of depth to AI sizing, and it isn’t just speeds and feeds of GPUs. We’d like to welcome John Yue (CEO & Co-Founder @ inference.ai) for this discussion. John, welcome to the show

Topic 2 - Let’s start with sizing, I’ve talked to a lot of customers recently with my day job, and it is amazing how deep AI/ML sizing can go. First, you have to size for training/fine-tuning differently than you would for the inference stage. Second, some just think, pick the biggest GPUs you can afford and go. How should your customers approach this? (GPU’s, software dependencies, etc.)

Topic 2a - Follow-up question what are the business side, what are the business parameters that need to be considered? (budget, cost efficiency, latency/response time, timeline, etc.)

Topic 3 - The whole process can be overwhelming and as we mentioned, some organizations may not think of everything. You recently announced a chatbot to help with this exact process, ChatGPU. Tell everyone a bit about that and how it came to be.

Topic 4 - This is almost like a match-making service, correct? Everyone wants an H100, but not everyone needs or can afford an H100.

Topic 5 - How does GPU availability play into all of this? NVIDIA is sold out for something like 2 years at this point; how is that sustainable? Does everything need to run on a “Ferrari class” NVIDIA GPU?

Topic 6 -  What’s next in the IaaS for AI/ML space? What does a next-generation data center for AI/ML look like? Will the Industry move away from GPUs to reduce dependence on NVIDIA?

FEEDBACK?

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The Maintenance Episode

For some strange reason, “maintenance” has been in the news quite a bit lately. Is there ever a time when maintenance is enjoyable, or appreciated? 

SHOW: 814

SHOW TRANSCRIPT: The Cloudcast #814

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 increased the price of longer-running EKS clusters by 6x
• Broadcom changes VMware licensing from perpetual to subscription
• Broadcom offers security patches to perpetual license customers
• Increasing the Kubernetes support window to 1 year
• Discovering the XZ backdoor (Oxide and Friends, podcast)

IS MAINTENANCE EVER APPRECIATED OR ENJOYABLE?

• Spent the day surrounded by maintenance activities (oil, AC, power-wash)
• The costs of maintenance are real and opportunity
• Maintenance often goes unappreciated and unseen
• Naming: Release Notes, Technical Debt, Chaos Engineering

TECHNICAL DEBT VS. MAINTENANCE

• Should we encourage a lack of maintenance vs. innovation as a priority?
• Should we encourage active maintenance with lower hard costs?
• Is there a way to put respect on maintenance? (e.g. OSS maintainers)
• Do we undervalue maintenance (e.g. Backup/Recovery, DisasterRecovery, etc.)?
• What maintenance best practices do you use? What are the good and bad of them?

FEEDBACK?

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Synthetic Data for AI

Kalyan Veeramachaneni (@kveeramac, CEO/Founder @DataCebo) discusses the generation and value proposition of synthetic data for GenAI.

SHOW: 813

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

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

SHOW NOTES:

• DataCebo (homepage)
• Synthetic Data Vault - SDV
• TechCrunch Article
• MIT News Article

Topic 1 - Our topic for today is synthetic data. While the concept and need for synthetic data has been around for a long time, it isn’t a topic that typically comes to the forefront and something we haven’t talked about until today. Today is a bit of crossing the streams between developers and testing data and using GenAI to achieve this goal. For this, we’re joined by Kalyan, CEO and Co-Founder of DataCebo. Welcome to the show

Topic 2 - First, for those not familiar, what is synthetic data? What is the use case and need? What problem is it solving today?

Topic 2a - Hopefully, listeners out there are making the connection to the advantages of GenAI for synthetic data, but take us through your original concept at MIT and the history of Synthetic Data Vault (SDV).

Topic 3 - We recently did a show on the security and privacy of training LLMs where we covered the need to mask PII for the training of models for compliance. I can also see bias issues coming into play or maybe training data that doesn’t exist in the real world (weather models example). What are some of the use cases that you’ve seen require synthetic data sets. Are there certain industries (healthcare, financials, etc.) that benefit?

Topic 4 - You were designing this based on GenAI before GenAI was “cool”. How has the rise of LLMs impacted this space?

Topic 5 - If I understand this correctly, organizations would put generative AI on a problem to describe a need for a data set, the model would then evaluate the available data and create a quality synthetic or “fake” dataset. How would the organization verify the quality of the dataset? How would they validate that a synthetic data set is as good as the original data?

Topic 6 - Let’s talk about resources for a bit. When I think of GenAI and training, I think of large amounts of hardware and in particular GPU’s that might have limited availability. Is that true here? Also, is this on-prem or in the cloud, or both? 

FEEDBACK?

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