ftahmed

This show how to extract OTP secret from Google Authenticator

1. Export your account from Authenticator app then use other phone to scan the QR code displayed
2. Your text from QR code has form of: otpauth-migration://offline?data=thisIsSomeDataInBase64AndUrlEncoded%2B%3D
3. Convert the data (all after otpauth-migration://offline?data=) to base64 by F12 in Chrome/Edge, in Developer console enter the following: decodeURIComponent('thisIsSomeDataInBase64AndUrlEncoded%2B%3D')
4. Copy the result string (without quotes) to https://protobuf-decoder.netlify.app/ you will get
5. Notice content at Byte range 0-12 is your secret key, copy that string 23 f8 58 9a 0d a6 93 f1 22 ec to https://cryptii.com/pipes/hex-to-base32 ![image](https://user-images.githubusercontent.com/2195141/24266984

hasezoey

Install KOReader on a Tolino Device

Tolino Debug Menu codes

For KOReader (or any other APK) to be installed on a Tolino device, the debug menu has to be enabled:

• if the tolino is on system version 14, search for code 124816 source
• if the tolino is on system version 15, search for code 1123581321 source
• if the tolino is on system version 16, search for code 112358132fb source

Note: "searching" as in the search for books on the tolino itself (the magnifying glass in the top bar)

rajatkumar1011

Get $1000+ Worth of Free AI API Credits with AgentRouter and 13 Other Platforms — [Full Claude Desktop Setup]

Most people are paying for Claude Pro when they don't have to. These 14 platforms give you free API credits on signup, some with daily top-ups too. You plug your key into Claude Desktop's developer mode and get the same Opus 5 / Opus 4.8 experience — no subscription needed.

Note: GitHub account must be at least 1 year old to Sign Up.
[Some platforms may require more than one year.]

Free Credit Platforms

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.

mpvii

{
  "$schema": "https://opencode.ai/config.json",
  "model": "relax-beta/DeepSeek-V4-Pro",
  "providers": {
    "relax-beta": {
      "name": "Relax AI (Beta)",
      "package": "@opencode/ai/providers/openai-compatible",
      "settings": {
        "baseURL": "https://api.beta.relax.ai/v1"
      },

zerofltexx

Limine + LUKS2 + TPM2 Auto-Unlock Guide (CachyOS/Arch Linux)

Working configuration for:

• Limine bootloader
• LUKS2 encrypted root partition
• btrfs with Snapper snapshots
• dracut (required for btrfs snapshot boot)
• systemd-based initramfs
• Secure Boot enabled
• TPM2 auto-unlock using PCR 0+7

itxtoledo

x-typebot-common: &typebot-common
  depends_on:
    postgresql:
      condition: service_healthy
    # redis:
    #   condition: service_healthy
    maildev:
      condition: service_started

x-typebot-env: &typebot-env

BandarHL

%hook TFSKeychain
- (NSString *)providerDefaultAccessGroup {
    NSDictionary *query = [NSDictionary dictionaryWithObjectsAndKeys:
                           (__bridge NSString *)kSecClassGenericPassword, (__bridge NSString *)kSecClass,
                           @"bundleSeedID", kSecAttrAccount,
                           @"", kSecAttrService,
                           (id)kCFBooleanTrue, kSecReturnAttributes,
                           nil];
    CFDictionaryRef result = nil;
    OSStatus status = SecItemCopyMatching((__bridge CFDictionaryRef)query, (CFTypeRef *)&result);

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

myself659

Adobe Acrobat Pro DC License Key