| import { | |
| getArtistIdMap, | |
| getFeedItems, | |
| createDocsFromItems | |
| } from './artistUtils' | |
| let artistIdsMap; | |
| let feedItems; | |
| let startingIndex; | |
| let endingIndex; |
| #!/usr/bin/env python3 | |
| """Convert a manual Trakt export (.zip contents) into a Yamtrack import CSV. | |
| Targets Yamtrack v0.25.3's CSV importer (integrations/imports/yamtrack.py). | |
| Key importer facts this script relies on: | |
| - Row order determines bulk-create order: movie, tv, season, episode. | |
| - TV is unique per (user, item); Season per (related_tv, item); Episode and | |
| Movie rows may repeat (that is how rewatches are stored). | |
| - Episodes are linked to seasons via (media_id, season_number) after seasons | |
| are created (update_episode_references), so season rows must precede |
This file documents environment variables visible in the checked-in source of this repository snapshot. It is not guaranteed to be exhaustive for the full product, because many imported files are not present here.
For each variable, this document gives:
Every repo from my series, newest first. I keep this same link updated — save it.
Follow me on Instagram for the next one: @angelotrifanoff.ai
Đây là bài viết dành cho người mới bắt đầu sừ dụng git, phù hợp cho học sinh, sinh viên, người mới ra trường, hoặc mới đi làm.
Yêu cầu:
Git là một hệ thống quản lý phiên bản phân tán (Distributed Version Control System - DVCS) được sử dụng để theo dõi sự thay đổi trong mã nguồn của phần mềm trong quá trình phát triển. Nó cho phép nhiều người làm việc trên cùng một dự án mà không gặp phải vấn đề xung đột mã nguồn.
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
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.
| # Load the contents of the receipt file | |
| receipt_file = open('./receipt_data.bin', 'rb').read() | |
| # Use asn1crypto's cms definitions to parse the PKCS#7 format | |
| from asn1crypto.cms import ContentInfo | |
| pkcs_container = ContentInfo.load(receipt_file) | |
| # Extract the certificates, signature, and receipt_data | |
| certificates = pkcs_container['content']['certificates'] | |
| signer_info = pkcs_container['content']['signer_infos'][0] |