This demo shows an Agent Development Kit (ADK) implementation that leverages the CaMeL framework for enhanced security and controlled data flow in LLM agents. CaMeL (Defeating Prompt Injections by Design) protects the model against prompt injection attacks by explicitly separating control and data flows in the query given to the agent. Additionally, CaMeL enables fine-grained access control; in other words, it is possible to define precise rules that are deterministically enforced over data flows between tool calls.
Am I the only one that thinks that being "agent wrangler" actually makes building things more fun?
To me, the interesting parts in building is taking a real problem, mapping it to a set of "things" that need to be built, decompose them into treatable chunks, and defining how they should interact. This is where intelligence comes in. if agents want to take the rest, please do! I can focus on making better products.
The law says that US cloud providers are fined if they continued to provide services to Bytedance.
As far as we know, Tiktok is operated on US servers by Oracle. While it might have been possible to find another cloud provider and move all US data there, I can see them not wanting to do that given that there was no point if the app isn't distributed in the US anymore.
I have been interested in Prolog since my time at the University, and I loved the idea of logic programming.
For "proper" Prolog, in 2024 it is a niche language alive in specific constraint solving applications, but not really used outside of that. I haven't seen anyone attempting at using prolog as a general purpose language since the 90'.
Datalog and logic-inspired languages tend to pop up here and there as domain-specific languages.
Rego is a recent incarnation which had good adoption for k8s and other "modern" systems. However, when trying to get people in my org to adopt it in practice, I saw engineers struggle with the paradigm when complexity grows to more than toy problems.