Tag Archives: LLMs

Is Limiting LLMs possible?

How do we limit and regulate LLMs and AI? We approach this at multiple angles and look through what it’s like to regulate this type of technology.

If you’re interested in the limits of any technology, and specifically how AI gets regulated, and where we’re likely to impose legislative barriers or restrictions on this, then this will be a fascinating podcast for you.

Transcript: otter.ai/u/8IsFB-H-U3XzpQ751l…?utm_source=copy_url
Photo by Pixabay: www.pexels.com/photo/black-andro…white-book-39584/

Rob’s Hot Take:

In the Cloud 2030 Podcast episode from October 19th, Rob Hirschfeld delves into the topic of limiting large language models (LLMs) in AI and explores the potential legal frameworks for regulating artificial intelligence and technology. The conversation highlights the intriguing idea that Section 230, a core governing principle of the internet that exempts internet service companies from extensive content moderation, could play a pivotal role in shaping technology use. Hirschfeld suggests that changes to Section 230 might serve as a critical component in influencing the control and regulation of emerging technologies like AI. Listeners are encouraged to check out the full October 19th episode for a detailed exploration of these regulatory considerations and can join ongoing discussions at the2030.cloud.

CoDev With LLMs?

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: otter.ai/u/u3bArfIvx40oUXnRLt…?utm_source=copy_url
Photo by Pixabay: www.pexels.com/photo/aeroplane-a…e-aviation-33224/

Rob’s Hot Take:

In the Cloud 2030 Podcast episode from August 31st, Rob Hirschfeld discusses the potential of using large language models to enhance DevOps and development outcomes. The conversation emphasizes the possibilities of leveraging AI to improve codebases, facilitate refactoring, and encourage code reuse by tapping into the knowledge embedded in existing code bases. Hirschfeld envisions a future where AI assists developers in reducing technical debt, maintaining code more efficiently, and consolidating code intelligently, ultimately leading to improved development practices. The episode explores the challenges and investments required for realizing these outcomes, encouraging listeners to delve into the full podcast for a comprehensive understanding. To engage in further discussions, interested individuals can explore the Cloud 2030 podcasts and join the conversations at the2030.cloud.

LLMs adding to Technical Debt? Maintenance?

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:
www.linkedin.com/pulse/how-google…h-debt-abi-noda/
devops.com/are-llms-leading-de…o-a-tech-debt-trap/

Transcript: otter.ai/u/ngUClgtMmLLKXCFalD…?utm_source=copy_url
Image: www.pexels.com/photo/anonymous-w…y-gloves-3846440/

Cloud2030LLMsChatGPTTechnical DebtMaintenanceCoding

Rob’s Hot Take:

In the Cloud 2030 podcast episode on August 17th, Rob Hirschfeld discusses the distinction between technical debt and system maintenance costs, emphasizing the importance of understanding the ongoing effort needed to maintain and improve systems. He points out that overlooking the maintenance costs while building a system leads to technical debt. Hirschfeld raises the question of whether large language models and AI can change the equation of system maintenance, a topic yet to be explored fully in the podcast.