win3zz

GitHub Search Syntax for Finding API Keys/Secrets/Tokens

As a security professional, it is important to conduct a thorough reconnaissance. With the increasing use of APIs nowadays, it has become paramount to keep access tokens and other API-related secrets secure in order to prevent leaks. However, despite technological advances, human error remains a factor, and many developers still unknowingly hardcode their API secrets into source code and commit them to public repositories. GitHub, being a widely popular platform for public code repositories, may inadvertently host such leaked secrets. To help identify these vulnerabilities, I have created a comprehensive search list using powerful search syntax that enables the search of thousands of leaked keys and secrets in a single search.

Search Syntax:

(path:*.{File_extension1} OR path:*.{File_extension-N}) AND ({Keyname1} OR {Keyname-N}) AND (({Signature/pattern1} OR {Signature/pattern-N}) AND ({PlatformTag1} OR {PlatformTag-N}))

Examples:

**1.

karpathy

LLM Wiki

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.

The core idea

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.

markasoftware

############ If you are using DOCKER all-in-one image, create Dockerfile like:         ################
############ FROM openproject/openproject:16                                           ################
############ COPY ./enterprise_token.rb app/models/enterprise_token.rb                 ################

############ If you are runing a manual installation:                                  ################
############ REPLACE app/models/enterprise_token.rb in the source code with this file! ################
############ also be sure to RESTART OpenProject after replacing the file.             ################

############ If using some other set up (eg docker-compose), read the comments on      ################
############ https://gist.github.com/markasoftware/f5b2e55a2c2e3abb1f9eefcdf0bfff45    ################

jossef

Insta360 Studio - Serial Number for Download Access

Sharing my serial number for the Insta360 X3 camera. The company's poor website design requires proof of ownership to use the editor software, which makes no sense since only those who bought the camera would want to download it. 🤦‍♂️

This is my Insta360 X3 S/N. You're welcome to use it to work around that annoying prompt:

IAQEB2208SEC7T

EncodeTheCode

class NetClient {
    constructor(serverUrl) {
        this.ws = null;
        this.serverUrl = serverUrl;

        this.connected = false;
        this.authenticated = false;

        this.seq = 0;

en0ndev

How to Remove "Open in Visual Studio" in the Context Menu

Also you can watch the video.
https://www.youtube.com/watch?v=8S7s-p_enSY

---

STEP 1

• Press Win+S keys, and search regedit then open Regedit (Registry Editor).

STEP 2

EncodeTheCode

class NetClient {
    constructor(url) {
        this.url = url;
        this.ws = null;

        this.connected = false;
        this.authenticated = false;

        this.seq = 0;

enkiusz

#!/usr/bin/env python3

import serial
import struct
import sys

with serial.Serial(sys.argv[1], 115200) as ser:
	while True:
		pkt = ser.read(10)
		#print("PKT: ", pkt)

webcane

LLM Knowledge Compiler

A pattern for building persistent knowledge bases using LLMs.

This is an idea file, designed to be copied into your own LLM agent (OpenAI Codex, Claude Code, OpenCode, Pi, etc.). Its purpose is to communicate the architecture, while leaving implementation details to the agent and the user.

The core idea

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 rediscovers knowledge from scratch on every question. Nothing accumulates.