M2N2 is a model merging technique that creates powerful multi-skilled agents without the high cost and data needs of retraining.

Memp takes inspiration from human cognition to give LLM agents "procedural memory" that can adapt to new tasks and environments.

Global enterprises Block and GlaxoSmithKline (GSK) are exploring AI agent proof of concepts in financial services and drug discovery. 

AI is brilliant at polishing and rephrasing. But like a child with glitter glue, you still need to supervise it.

The open source framework provides the data and training recipe for building powerful computer-use agents that challenge proprietary systems.

Chain-of-Thought isn't a plug-and-play solution. For developers, this research offers a blueprint for LLM testing and strategic fine-tuning.

Ultimately, model makers and enterprises are focusing on the wrong issue: They should be computing smarter, not harder.

How to close the loop between user behavior and LLM performance, and why human-in-the-loop systems are still essential in the age of gen AI.

While OpenAI’s GPT-5 is highly-performant, capable and an important step forward, it features just faint glimmers of true agentic AI. 

We could soon find ourselves deferring to AI assistants that botsplain our every experience in real time. Is this empowerment or deferral?