| name | explain-diff-html |
|---|---|
| description | Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output. |
Please make me a rich, interactive explanation of the specified code change.
It should have these sections:
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.
Hey r/Piracy and r/FMHY,
I need to share a serious warning about a malicious browser extension being forced on users by filecr.com. Many of you are already skeptical of the site, and you're right to be. I have some proofs of what they're doing.
TL;DR: The CRXLauncher extension, which filecr.com requires you to install for some downloads, is adware. It injects a heavily obfuscated script into websites you visit to perform affiliate marketing fraud (link hijacking & cookie stuffing). UNINSTALL IT IMMEDIATELY.
Magic words:
psql -U postgresSome interesting flags (to see all, use -h or --help depending on your psql version):
-E: will describe the underlaying queries of the \ commands (cool for learning!)-l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)ventoy-1.0.79-linux.tar.gz| import math | |
| class Graph: | |
| def __init__(self, n=0): # a graph with Node 0,1,...,N-1 | |
| self.n = n | |
| self.w = [ | |
| [math.inf for j in range(n)] for i in range(n) | |
| ] # adjacency matrix for weight | |
| self.dp = [ |