Hyacehila's Blog

A technical evolution map for medical AI, from black-box predictors toward traceable medical agents.

Models have crossed the good-enough threshold. In 2026, the scarce resource is not the next bigger foundation model, but applications that enter workflows and daily life.

Agent Skills are popular because they package capabilities with low friction. They are a useful layer of context engineering, but not a new universal protocol or final agent form.

The shift from RL agents to LLM agents is not a simple model replacement; learning signals move from numeric spaces into language spaces, reshaping priors, evaluation, and memory.

Starting from CoALA’s cognitive architecture, this post discusses Workflow, Agent, Supervisor, Agent Team, and MAS boundaries, plus the engineering value of frameworks such as LangGraph.

An introduction to MCP’s Host, Client, and Server layers, stdio and Streamable HTTP transports, and the workflow for building and debugging MCP servers and clients.

A discussion of why statistics drifted away from applied settings while pursuing theoretical completeness, and how data science and machine learning reconnect with learning from data.

A review of 60 years of data science, from John Tukey’s prediction to David Donoho’s broader view, discussing how statistics, computer science, and machine learning converged.

Hypothesis testing is powerful but assumption-bound; statistical significance does not make a conclusion true, and anyone without statistical literacy can accidentally commit statistical malpractice.

Are calculations precise while charts are rough? Francis Anscombe’s quartet challenges that belief and shows why exploratory data analysis matters in statistical inference.