We talk about current events, the acquisition of data stacks and the closing of the HashiCorp acquisition by IBM. Later, we dive into the productivity of AI and what’s going on – are companies really getting the benefits that they expect from AI chat bot integrations and what the challenges are?
We touch base on a little bit of something more infrastructure focused, where I give a preview of work I’ve been doing on separating Kubernetes virtualization from Kubernetes development use cases, which is something that we will be talking about more in the future.
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.
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.
How can you execute on a zero trust strategy and what do you need to keep in mind while building it? Today covers the 101 and 201 levels on zero trust.
We had a really good conversation about how it works, what doesn’t work, what you need to be prepared for. Even if you think you understand zero trust, you will get something out of this conversation. And if it’s a new topic for you, you can also benefit from this pragmatic discussion of zero trust, security and application architects.
Joining the podcast this week is Sander Bogaert, VP Engineering at Guardsquare.
About Sander Bogaert
Sander Bogaert leads the technical teams at Guardsquare. He ensures engineering efforts are aligned with the company’s technical vision and helps determine the next steps for existing and new products. Sander joined Guardsquare very early on and built iXGuard from scratch after some initial months working on DexGuard.
About Guardsquare
Guardsquare is the global leader in mobile application protection. Hundreds of customers worldwide across all major industries rely on Guardsquare to secure their mobile applications against reverse engineering and hacking. Built on open source ProGuard technology, Guardsquare software integrates transparently in the development process and adds multiple layers of protection to Android (DexGuard) and iOS (iXGuard) applications, hardening them against both on-device and off-device attacks. Guardsquare is based in Leuven (Belgium) with a US office in Boston, MA.
Chetan Vinkatesh, CEO and Co-Founder of Macrometa provides deep technical insight into their soon to be launched Global Edge Fabric development platform. For more information go to https://www.macrometa.co/product.
