What is GEPA? GEPA stands for Graph-based Evolutionary Program Adaptation — a DSPy optimizer that automatically improves the prompts/instructions of a multi-module LLM program through evolutionary search. It iteratively mutates module instructions, evaluates the changes, and keeps the best-performing candidates on a Pareto front. What’s Happening in This Run This file captures a GEPA optimization run on a financial news extraction system that classifies M&A (merger/acquisition) articles and extracts structured data from them.

Background MTP support was recently merged into llama.cpp through the following pull request: MTP support merged into llama.cpp After the merge, I wanted to test MTP models on my mini-PC powered by the AMD AI MAX 395. I tried several approaches, including manually building llama.cpp and using Unsloth GGUF models directly. However, despite multiple attempts, I could not get a stable working setup. I also searched through GitHub issues and asked several AI assistants, including ChatGPT, Gemini, and DeepSeek.

Introduction Last year, I spent some time studying Oracle Banking Microservices Architecture (OBMA), together with enterprise schedulers and orchestration platforms such as Control-M . Part of the work involved understanding how to convert traditional Control-M jobs into Airflow DAGs. During this process, I started to observe an important architectural distinction: Not all workflows are the same. While studying OBMA, I noticed that Netflix Conductor was used as the workflow engine inside the architecture.