- Export your account from Authenticator app then use other phone to scan the QR code displayed
- Your text from QR code has form of:
otpauth-migration://offline?data=thisIsSomeDataInBase64AndUrlEncoded%2B%3D - 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')
- Copy the result string (without quotes) to https://protobuf-decoder.netlify.app/ you will get

- Notice content at Byte range 0-12 is your secret key, copy that string
23 f8 58 9a 0d a6 93 f1 22 ecto https://cryptii.com/pipes/hex-to-base32  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
124816source - if the tolino is on system version 15, search for code
1123581321source - if the tolino is on system version 16, search for code
112358132fbsource
Note: "searching" as in the search for books on the tolino itself (the magnifying glass in the top bar)
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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.
| { | |
| "$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" | |
| }, |
| x-typebot-common: &typebot-common | |
| depends_on: | |
| postgresql: | |
| condition: service_healthy | |
| # redis: | |
| # condition: service_healthy | |
| maildev: | |
| condition: service_started | |
| x-typebot-env: &typebot-env |
| %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); |
| """ | |
| 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 |
| Adobe Acrobat Pro DC License Key |