Large Language Models (LLMs) like GPT-3 and GPT-4 are transforming various industries by providing advanced capabilities in customer support, content generation, healthcare, education, and finance. These models are trained on colossal datasets exceeding 1 trillion words. Despite their remarkable abilities, there is ongoing debate within the AI community regarding their limitations, particularly in achieving Artificial General Intelligence (AGI). Prominent figures such as @ylecun argue that LLMs are not sufficient for AGI without significant architectural improvements. The trend towards optimizing LLMs for agentic workflows is expected to enhance their performance significantly. Additionally, the introduction of OpenCoder, an open-source code LLM, aims to address transparency issues in code-specific language models. OpenCoder is built on a transparent data process pipeline and reproducible dataset, providing a robust initiative for developers. Recent releases also include Qwen2.5-Coder checkpoints of 32B and 7B.