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.

NelloKudo

Playing TFT on Linux using Waydroid

Just like many other League addicts, ever since the Vanguard incident, TFT has been a no-go....

except it isn't, since the Android version works just as fine, after some tweaks!

---

todbot

# generated by PrusaSlicer 2.2.0+ on 2020-04-02 at 04:02:32 UTC

[print:0.175mm - Monoprice Mini Select v2]
avoid_crossing_perimeters = 0
bottom_fill_pattern = rectilinear
bottom_solid_layers = 3
bottom_solid_min_thickness = 0
bridge_acceleration = 0
bridge_angle = 0
bridge_flow_ratio = 1

iannuttall

AgentOS — Product Spec & Implementation Blueprint

Reconstructed from Danny Postma's talk How I Built My Own AgentOS on Claude's Agent SDK (So You Can Too) (2026). This document is both a product spec for a human and an implementation prompt for an AI coding agent. Build exactly this system. Do not invent features that are not specified here.

Role contracts and prompts in this file are reconstructed from the talk, not his verbatim files. Mark every reconstructed prompt in code comments and docs as such.

---

1. Goal and non-goals

almahmudbd

Free AI API Keys for Agentic Coding

Usage Tips

• Do not subscribe to every service at once. Most free credits expire quickly. Test them one by one and move to the next when your current credits run out.
• Register, verify your account, and claim your credits. Follow each platform’s documentation to connect your coding tools.
• Keep your API keys private. Do not share them publicly. (save or star this gist for future update)

List-1: Top Sites in This Genre

adityaruplaha

Indian Statistical Institute (ISI) Placement Resources

Compiled by Adityarup Laha (MB2402; BS2124) using publicly available information (and the Wayback Machine!). The full dump as of February 26, 2026, 03:30 IST is available at: https://public-files.adityaruplaha.me/isi-placement-archive/2026-02-26_isi-placement-archive.zip

Individual links are provided below for provenance.

ISI Kolkata

Main Programs

Fishhrrr

生日

A Pen by Fishhrrr on CodePen.

License.

Nsarkar-XLR8

deploy.sh — Zero-Downtime NestJS/Node.js Deployment Script

A small, dependency-free redeploy script for Node.js/NestJS apps running under PM2 on a VPS. Pulls the latest code, installs deps, builds, and reloads the app without dropping in-flight requests.

What it does, step by step

1. Checks the deploy directory for uncommitted local changes and aborts if found — prevents accidentally overwriting a manual hotfix someone made directly on the server.
2. Fetches and checks out the target branch (main by default).
3. Runs npm ci (not npm install) — installs exactly what's in package-lock.json, no surprise version bumps in production.

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.

Kyriakos-Georgiopoulos

/*
 * Copyright 2026 Kyriakos Georgiopoulos
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software