Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns. Source: tropes.fyi by ossama.is
Discover gists
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
Comparison of Bash, Elvish, NuShell, Murex, es-shell, fish, xonsh, PowerShell, Oils (Oil/OSH/YSH) and Ion shells
(Originally generated by ChatGPT and reviewed/edited by Claude; now corrected/expanded based on reader feedback. YMMV.)
(generated by chatgpt 4o and reviewed/edited by claude 3.5 sonnet, YMMV)
| Feature | Bash | Elvish | NuShell | Murex | es-shell | Fish | Xonsh | PowerShell | Oil
Most MCP servers just wrap CRUD JSON APIs into tools — I did it too with scim-mcp and garmin-mcp-app. It works, until you realize a tool call dumps 50KB+ into context.
MCP isn't dead — but we need to design MCP tools with the context window in mind.
- Shall i implement it?
- No ...
Use mksquashfs to create a compressed image from a directory. Depending on how compressible the files are you could put on much more than 4.7 GB on a single disc.
mksquashfs /path/to/my/directory image.sqfs
You can use a different compression algorithm with e.g. -comp zstd or append multiple times to one archive to fill it up to almost the allowable size.
Migrating a standalone GitHub repository into a larger monorepo while preserving the invaluable commit history is a common challenge for development teams. This process allows for better code sharing, simplified dependency management, and streamlined builds.
The most effective and recommended method involves using the powerful git filter-repo tool, followed by a strategic merge into the destination monorepo.