Holy... I'm still at 17. My tech portfolio has been obsolete. Let's put up a blog that shares the updates and new features since JDK 9 forward.

AI agents are finding their inner speech. By "talking to themselves" to plan and self-correct before taking an action, they mirror the way we use internal monologues to navigate complex choices. This shift from reactive automation to deliberate reasoning marks the evolution of AI from a simple tool into a truly metacognitive collaborator.

I was originally studying LLM on Hugging Face (https://huggingface.co/learn/llm-course) and notice that transformer occupied significant portion of their learning materials so I decided to look into it deeply

AI agents are finding their inner speech. By "talking to themselves" to plan and self-correct before taking an action, they mirror the way we use internal monologues to navigate complex choices. This shift from reactive automation to deliberate reasoning marks the evolution of AI from a simple tool into a truly metacognitive collaborator.

Building AI Agent from first principles with a durable understanding that frameworks alone cannot, This post structures a learning path that separates the core theory from the "from scratch" implementation, using resources that are independent of major agent frameworks, such as LlamaIndex.

If the Universal Approximation Theorem expresses the unprecedented power of AI, the Halting Problem poses the opposite - its computational limit.

AI is not just a consumer of linguistic data; it is a producer of new philosophical questions. It forces us to be more precise about what we mean by "meaning," "understanding," and "thought," turning centuries of abstract debate into a pressing, practical challenge.

A fascination with things from the past, like ancient languages, is a common and deeply rooted human interest. There are a number of reasons for this, ranging from the intellectual and practical to the emotional and psychological.