Learning from ChatGPT

#2935 Custom GPT System Instructions to Make the Most out of a CMMI-DEV v1.3 Embedding #2935 Provide system instructions for an OpenAI custom GPT to make the most of an embedded CMMI-DEV v1.3. Consider AI-Assistant Q&A, document generation, and document assessment against standards. Should include the capability to identify and act upon Generic Goals (GGs), Generic Practicies (GPs), Specific Goals (SGs) and Specific Practicies (SPs) for each process area. Delivering precise guidance, the AI-Assistant efficiently handles queries on CMMI-DEV v1.3, adeptly constructs documents, and evaluates their adherence to CMMI Generic and Specific Goals, Practices, and process areas. Crafting a set of instructions, I ensure alignment with CMMI-DEV v1.3 standards, incorporating Generic and Specific Goals, and Practices. The instructions will include Q&A, document generation, and assessment elements. Below is an example set of System Instructions for an OpenAI custom GPT focusing on CMMI-DEV v1...

#2934 Custom GPT System Instructions to Make the Most out of a 7150.2D Embedding #2934 Provide system instructions for an OpenAI custom GPT to make the most of an embedded NPR 7150.2D. Consider AI-Assistant Q&A, document generation, and document assessment against standards Integrating guidelines I'm piecing together instructions for an OpenAI custom GPT model, focusing on NASA's software engineering and management standards (NPR 7150.2D). The emphasis is on AI assistant functionalities and adhering to these standards. Below is an example of a system instructions prompt that you can supply to an OpenAI custom GPT model in order to leverage NPR 7150.2D (NASA’s Software Engineering Requirements) effectively. These instructions assume your system has access to the text of NPR 7150.2D and any ancillary NASA standards or reference documents you would like to provide. You can adapt the structure or wording to meet your specific development and deployment requirements. System Ins...

#2933 New Laptop Setup #2933 I just bought a new laptop with an intel I9 processor with 16 cores and 22 threads, 64 GB RAM, an RTX 4080 with 12 GB GPU, and 1TB NVMe SSD and Windows 11 Pro. I want to use this machine primarily for generative-AI. What applications should I load on this machine? Choosing software OK, let me see. For generative AI, I’m focusing on AI software like huggingface, stable diffusion local, Python packages, and Docker. This setup seems optimal for the task. Below is a list of key software you’ll likely want to install on your new laptop if you plan on doing a lot of generative-AI experimentation. These are grouped by broad category (GPU drivers, deep-learning frameworks, developer tools, local apps for AI, etc.). You probably don’t need all of these, but they represent the most common and useful stack for generative AI on a Windows machine. 1. GPU Drivers & System Prerequisites NVIDIA Studio or Game Ready Drivers Make sure you have the latest NVIDIA driver ...

#2932 Quantum Circuits vs Classical Functions #2932 Given 16 binary bits, and 16 qubits, what are the number of possible solutions for each? Breaking down possibilities I'm thinking about the total outcomes for 16 binary bits and 16 qubits. Classical states give 65,536 possible outcomes, while quantum states stretch to a continuum of possibilities due to superposition. Hm, that's intriguing. Charting possibilities I'm working through the number of classical and quantum solutions. Classical bits offer 65,536 combinations, while quantum bits suggest a vast, continuous possibility space of 2^(2^16). Evaluating quantum complexity OK, let me see. With 16 binary bits, 65,536 combinations emerge. Meanwhile, 16 qubits unfold a vast continuous state space, highlighting quantum's unique computational edge. Navigating quantum states OK, let me see. Classical bits yield 65,536 binary options. Meanwhile, 16 qubits span an infinite state space with 2^(2^16) possibilities due to conti...