Audit this Go codebase and aggressively simplify AI-generated, defensive, overly abstract, or unnecessarily generic code.
The goal is to make the code look like it was written by an experienced Go engineer:
- simple
- explicit
- strongly typed
- boring
| #!/usr/bin/env bash | |
| set -euo pipefail | |
| show_help() { | |
| cat << 'EOF' | |
| Passbolt SSH Wrapper (Custom Fields / Secret Metadata) | |
| USAGE: | |
| passbolt-ssh [OPTIONS] [USER@]RESOURCE_NAME [-- SSH_OPTIONS...] |
Audit this Go codebase and aggressively simplify AI-generated, defensive, overly abstract, or unnecessarily generic code.
The goal is to make the code look like it was written by an experienced Go engineer:
| #!/usr/bin/env python3 | |
| #ODI DFP-34X-2C2 MAC_KEY key generator by rajkosto | |
| import sys | |
| import string | |
| import hashlib | |
| args = sys.argv | |
| if len(args) != 2: | |
| sys.exit("Usage: odi_keygen.py YOURMACADDR") |
This document attempts to consolidate all available information regarding the VBIOS ROMs for the venerable AMD Instinct MI50 - especially the 32 GB variant
Sources include
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Let's attempt to figure out the model architecture from DLSS5's binary! See https://gist.github.com/madebyollin/f87b506b2779c2aeae5e44c81ef0fdee for context.
Warning
The remainder of this gist was authored by Codex 5.6
Ever wondered how ChatGPT or Claude are trained? You can train your own language model in under 5 minutes. Here's how.
Before we dive in, you might ask: "Why bother training my own when ChatGPT exists?"
Fair question. Here's why: