Hi everyone! I’m really excited to share some good news. I’ve been selected to join an MBA program in Data Science starting this October! I can’t wait to begin this journey and wanted to celebrate this win with you all.

How to install Linux/Ubuntu without USB bootable stick? Hi: I tried it with the stick but it requires fidgeting with the PC BIOS to alter the booting order to USB. there are lots of confusing guides about it but I just would like to hear someone who has already done it. I will appreciate you feed back. Thank you. Al Puma

https://openai.com/index/introducing-4o-image-generation/ "At OpenAI, we have long believed image generation should be a primary capability of our language models. That’s why we’ve built our most advanced image generator yet into GPT‑4o. The result—image generation that is not only beautiful, but useful." Spelling correctly, desired point of view, accurate editing.

The OpenAI Agents SDK has now support for MCP. This enables you to use a wide range of MCP servers to provide tools to your Agents. https://openai.github.io/openai-agents-python/mcp/

It's definitely cheaper. Better is a value proposition based on cost vs accuracy and the specific domain. CoD is less expensive, but make models come close to full CoT reasoning accuracy, and occasionally even perform even more accurately. https://arxiv.org/abs/2502.18600 """ Large Language Models (LLMs) have demonstrated remarkable performance in solving complex reasoning tasks through mechanisms like Chain-of-Thought (CoT) prompting, which emphasizes verbose, step-by-step reasoning. However, humans typically employ a more efficient strategy: drafting concise intermediate thoughts that capture only essential information. In this work, we propose Chain of Draft (CoD), a novel paradigm inspired by human cognitive processes, where LLMs generate minimalistic yet informative intermediate reasoning outputs while solving tasks. By reducing verbosity and focusing on critical insights, CoD matches or surpasses CoT in accuracy while using as little as only 7.6% of the tokens, significantly reducing cost and latency across various reasoning tasks. """