Very interesting worth checking out... https://workspace.google.com/blog/product-announcements/introducing-google-workspace-studio-agents-for-everyday-work

Really enjoyed hearing from the speakers and connecting with so many talented, like minded people in the automation community. Already planning follow-up meetings with some promising potential collaborations on the horizon. Events like these remind me why the tech community here is so special it's not just about the tools, it's about the people building with them. Big thanks to Bart Veldhuizen , Marrallisa Kreijkes and Tino Zwirs of n8n for organizing and making it happen! https://www.linkedin.com/feed/update/urn:li:activity:7402086121158955008

25 days to master AI Agents with Gemini 3, Google ADK, and production templates. Daily tutorials with copy-paste code. Start here Read the Introduction to Agents white paper 100% free. 🙌🏻

CFO: "Reduce costs by 20%" R&D: "Without losing taste?" CFO: "Of course." This conversation usually ends in compromises. But what if it doesn't have to? Recent case: → Calories reduced from 783 to 600 kcal → Taste and aroma profile maintained → Costs decreased instead of increased → Compliance maintained How? AI optimization that analyzes thousands of combinations. Finds the sweet spot between all constraints. Cost, taste, nutrition, compliance. No more choosing. Just optimize. Curious and want to see a demo? DM me or leave a message below

Worth reading and keeping an eye on.. Introducing Nested Learning: A new ML paradigm for continual learning We introduce Nested Learning, a new approach to machine learning that views models as a set of smaller, nested optimization problems, each with its own internal workflow, in order to mitigate or even completely avoid the issue of “catastrophic forgetting”, where learning new tasks sacrifices proficiency on old tasks. The last decade has seen incredible progress in machine learning (ML), primarily driven by powerful neural network architectures and the algorithms used to train them. However, despite the success of large language models (LLMs), a few fundamental challenges persist, especially around continual learning, the ability for a model to actively acquire new knowledge and skills over time without forgetting old ones. When it comes to continual learning and self-improvement, the human brain is the gold standard. It adapts through neuroplasticity — the remarkable capacity to change its structure in response to new experiences, memories, and learning. Without this ability, a person is limited to immediate context (like anterograde amnesia). We see a similar limitation in current LLMs: their knowledge is confined to either the immediate context of their input window or the static information that they learn during pre-training. The simple approach, continually updating a model's parameters with new data, often leads to “catastrophic forgetting” (CF), where learning new tasks sacrifices proficiency on old tasks. Researchers traditionally combat CF through architectural tweaks or better optimization rules. However, for too long, we have treated the model's architecture (the network structure) and the optimization algorithm (the training rule) as two separate things, which prevents us from achieving a truly unified, efficient learning system.