https://docs.anthropic.com/en/docs/claude-code/settings#environment-variables
Apologies for the snarky title, but there has been a huge amount of discussion around so called "Prompt Engineering" these past few months on all kinds of platforms. Much of it is coming from individuals who are peddling around an awful lot of "Prompting" and very little "Engineering".
Most of these discussions are little more than users finding that writing more creative and complicated prompts can help them solve a task that a more simple prompt was unable to help with. I claim this is not Prompt Engineering. This is not to say that crafting good prompts is not a difficult task, but it does not involve doing any kind of sophisticated modifications to general "template" of a prompt.
Others, who I think do deserve to call themselves "Prompt Engineers" (and an awful lot more than that), have been writing about and utilizing the rich new eco-system
| Stable Diffusion is an AI art generation model similar to DALLE-2. | |
| Here are some prompts for generating art with Stable Diffusion. | |
| Example: | |
| - A ghostly apparition drifting through a haunted mansion's grand ballroom, illuminated by flickering candlelight. Eerie, ethereal, highly detailed, digital painting, artstation, concept art, moody lighting. | |
| - portait of a homer simpson archer shooting arrow at forest monster, front game card, drark, marvel comics, dark, intricate, highly detailed, smooth, digital illustration | |
| - pirate, concept art, deep focus, fantasy, intricate, highly detailed, digital painting, matte, sharp focus, illustration | |
| - red dead redemption 2, cinematic view, epic sky, detailed, concept art, low angle, high detail, warm lighting, volumetric, godrays, vivid, beautiful | |
| - a fantasy style portrait painting of rachel lane / alison brie hybrid in the style of francois boucher oil painting, rpg portrait |
| import torch | |
| import numpy as np | |
| import k_diffusion as K | |
| from PIL import Image | |
| from torch import autocast | |
| from einops import rearrange, repeat | |
| def pil_img_to_torch(pil_img, half=False): | |
| image = np.array(pil_img).astype(np.float32) / 255.0 |
| import time | |
| import torch | |
| from torch import cuda | |
| def gpu_utilization(type='gpu'): | |
| import subprocess | |
| if type=='gpu': | |
| # utilization.gpu |
Tutorial and tips for GitHub Actions workflows
| { | |
| "version": "0.2.0", | |
| "configurations": [ | |
| { | |
| "name": "Example", | |
| "type": "node", | |
| "request": "launch", | |
| "runtimeExecutable": "node", | |
| "runtimeArgs": ["--nolazy", "-r", "ts-node/register/transpile-only"], |
| npm init vite@latest demo-project -- --template react-ts |