I'm not going to be maintaining this document anymore. I'm leaving it as-is since much of the FAQ section is still accurate and has yet to be incorporated into other resources.
Use CanI.RootMy.TV to find an exploit for your TV.
I'm not going to be maintaining this document anymore. I'm leaving it as-is since much of the FAQ section is still accurate and has yet to be incorporated into other resources.
Use CanI.RootMy.TV to find an exploit for your TV.
See how a minor change to your commit message style can make a difference.
git commit -m"<type>(<optional scope>): <description>" \ -m"<optional body>" \ -m"<optional footer>"
| export default [ | |
| "Reticulating splines...", | |
| "Generating witty dialog...", | |
| "Swapping time and space...", | |
| "Spinning violently around the y-axis...", | |
| "Tokenizing real life...", | |
| "Bending the spoon...", | |
| "Filtering morale...", | |
| "Don't think of purple hippos...", | |
| "We need a new fuse...", |
WAPT-https://github.com/KathanP19/HowToHunt/blob/master/CheckList/Web_Checklist_by_Chintan_Gurjar.pdf
Authenication-https://github.com/HolyBugx/HolyTips/blob/main/Checklist/Authentication.pdf
Oauth Misconfiguration-https://binarybrotherhood.io/oauth2_threat_model.html
File Upload-https://github.com/HolyBugx/HolyTips/blob/main/Checklist/File%20Upload.pdf
https://www.m-vave.com/productinfo/1431195.html
The M-VAVE SMK-37 PRO is a MIDI keyboard (controller) with a built-in DX7 compatible FM tone generator.
It would be so much more valuable if the firmware would be open source for the community to modify and improve.
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.
People
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