107 conversations

Mordechai is developing a Python-based web scraping system for the OptiBoard forum. The primary goal is to extract thread titles, links, authors, replies, views, and post content. Challenges encountered include handling dynamic website content, resolving `AttributeError` and `MissingSchema` exceptions, correctly locating HTML elements, managing Selenium WebDriver setup (especially `chromedriver` o

The user is attempting to scrape the OptiBoard website, specifically the 'General Optics and Eyecare Discussion Forum'. Initial attempts using `requests` and `BeautifulSoup` encountered issues finding the correct forum link and later, thread elements, suggesting dynamic content loading or structural changes. The conversation shifted towards using Selenium to overcome these challenges. Several erro

The conversation focused on debugging and fixing a YouTube video pipeline. Initial issues included bot detection errors, incorrect date filtering, and a bug in video discovery logic where the pipeline failed to find videos that yt-dlp clearly identified. Several fixes were implemented, including adding Safari cookie support, correcting date filters to recent ranges (3-30 days), and addressing URL

Mordechai is engaged in a detailed debugging and refinement process for scraping data from the OptiBoard forums. The primary challenge involves inconsistent HTML structures and 'NoneType' errors, indicating that the selectors used by BeautifulSoup are frequently failing. He is exploring various troubleshooting steps, including inspecting HTML, trying different parsers, and implementing more robust

The conversation focuses on refining and proving the functionality of a YouTube pipeline. Multiple options for upgrades were implemented and tested, including embedding model enhancements, Apple Silicon GPU acceleration, multi-core optimization, and parallel processing. The pipeline's ability to discover, process, and store video data, including 768-dimensional embeddings, was repeatedly verified

The conversation focused on a multi-stage process to normalize and enrich YouTube data within a Supabase PostgreSQL database. The primary goal was to increase YouTube ID coverage for videos, especially those with transcripts, by systematically processing various data sources including scraped channel data and watch history. Key challenges involved handling duplicates, special characters in titles,

The conversation focuses on refining YouTube download scripts to address authentication errors, incorrect folder naming, and rate limiting. Key improvements include implementing Chrome cookie authentication, ensuring legacy folder names are used, and adjusting sleep intervals to prevent YouTube's rate limiting. The Pinchflat tool is also introduced as a more robust alternative for managing YouTube

The primary challenge is enriching YouTube video metadata due to missing YouTube IDs in transcript files and YouTube's rate limiting. Initial attempts to extract IDs failed as transcript files contain hashes, not YouTube IDs. Database connection timeouts also plagued the process. The chosen solution involves using browser cookies with yt-dlp to bypass rate limits and searching for videos by title

The user wants to create a CSV file from a YouTube video, extracting specific questions, their corresponding timestamps, and the transcript content related to those timestamps. The goal is to populate three columns: 'Question Name', 'URL with Timestamp', and 'Transcript'. The user has provided a list of questions with their start times.

The user is encountering persistent issues with ChromeDriver version compatibility when trying to scrape the Futurepedia website using Selenium. Despite several attempts to install and update ChromeDriver, the script continues to report a mismatch between the detected ChromeDriver version (120.0.6099.71) and the Chrome browser version (118.0.5993.117). Previous troubleshooting steps included manua

The conversation revolves around fixing and improving a YouTube video downloader pipeline. Initially, the focus was on debugging why the downloader was re-downloading files and not showing progress. Mordechai emphasized the need for a robust system that tracks downloaded MP3s, metadata, and transcripts, and organizes them by channel. The assistant has demonstrated progress by showing successful do

The user requested the download of all MP3 audio lectures for Rabbi Yoram Bogacz's series on 'Alei Shur'. The assistant successfully downloaded 151 shiurim for Volume 1, totaling approximately 1.9 GB, covering pages 7-349. Subsequently, the assistant proceeded to download Volume 2, completing the download of its available pages, though some gaps in the series were noted.

Mordechai is building an automated YouTube content processing pipeline. The core strategy is to prioritize fast MP3 downloads for MacWhisper transcription, followed by a metadata backfill. He's integrating this with Supabase for data storage and a web app using shadcn/ui for management. Key challenges include optimizing download speed, handling duplicates, and ensuring a comprehensive sync of all

Mordechai is building an automated YouTube content capture pipeline. The core goal is to download MP3s from specified channels for transcription via MacWhisper, prioritizing speed. Metadata capture is a secondary, backfillable step. The pipeline is being integrated with Supabase for data storage and a minimalist web UI using shadcn/ui is planned. Key decisions involve prioritizing fast MP3 downloa

Mordechai is focused on building a comprehensive and organized system to archive YouTube content from approximately 80 channels. The primary goal is to download all MP3 audio files for manual transcription, while also fetching metadata. The system needs to track the status of each video (MP3, metadata, transcript availability) and ensure the pipeline is robust, capable of resuming after interrupti

Mordechai is orchestrating the development of a comprehensive YouTube archiving system. The primary goal is to download MP3 audio files for all videos across 83 channels, with the intention of performing manual transcriptions. The system needs to be highly organized, track the status of MP3s, metadata, and transcripts for each video, and prioritize downloads effectively. Several iterations of down

Mordechai is building a robust pipeline for processing YouTube content from local markdown files. The core goal is to extract video URLs, download them as MP3s with specific naming conventions (channel prefix), and integrate local transcription tools like MacWhisper. The process involves using `yt-dlp` for efficient extraction and download, with a focus on automation and avoiding API keys. Key cha

Mordechai is trying to download transcripts from a YouTube channel without using API keys. Initial attempts with `pytube` and `youtube-transcript-api` failed due to YouTube's structural changes and `pytube`'s inability to fetch video lists. Subsequent attempts with `youtube-dl`, `youtube-search-python`, and `scrapetube` also encountered errors, indicating ongoing issues with YouTube's dynamic cont

The user is working on a Python script (`youtube.py`) using the `pytube` library to download YouTube videos from a watch history JSON file. The primary goal is to download audio-only files in .m4a format, organize them into folders named after the respective YouTube channels, and name the files with their video titles. The script has encountered several errors related to JSON parsing, missing keys

Mordechai is building a pipeline to download YouTube videos as MP3s and integrate them with MacWhisper for transcription. The process involves extracting URLs from markdown files using `yt-dlp`, downloading MP3s with parallel processing and error handling, and exploring programmatic integration with MacWhisper. Key decisions include using `yt-dlp`, setting MP3 quality, and structuring the pipeline

The conversation focuses on fixing a YouTube video downloader that was failing to download the complete set of videos from various channels. Initial issues included artificial limits on the number of videos processed per channel, leading to only 10-15% of content being downloaded. The assistant identified and removed these limits, increasing the download capacity significantly. Further problems ar

The user is trying to build a Python script to extract YouTube video transcripts. Initially, the focus was on using the YouTube Data API, but this led to authentication issues (OAuth 2.0, API keys). The user then shifted to using `youtube-transcript-api` and web scraping with `BeautifulSoup` and `requests` to avoid API complexities. Current challenges include correctly parsing video IDs from URLs,

Mordechai is building a YouTube content pipeline to efficiently download and process videos. The initial focus is on extracting YouTube URLs from markdown files, cleaning them, and then batch downloading the videos as MP3s. The process involves using `yt-dlp` for metadata extraction and downloads, with a plan to prefix files by channel name. Future considerations include integrating local transcri

The user is trying to create a Python script to extract questions and their corresponding timestamps from a YouTube video's description, convert these timestamps to seconds, and then save this information along with the video transcript into a CSV file. The process has been iterative, with multiple errors encountered related to timestamp parsing ('float' object has no attribute 'split') and module

The assistant is systematically downloading and converting chapters of 'הגאון מווילנה על משלי' from Wikisource to Markdown. Chapter א was successfully converted from EPUB. Chapter ג was found to be incomplete, so it was saved, and the process continued. The assistant is now attempting to download chapter ד and is exploring more efficient methods for downloading all available chapters after a Pytho

Mordechai is actively debugging and enhancing a video downloader script. The primary focus is on overcoming limitations that prevent downloading all available videos from various channels. Key issues identified include artificial limits on video processing per channel, timeouts for specific channels like TED, and format unavailability for longer podcasts like Lex Fridman's, potentially due to auth

The user is trying to extract specific user prompts from a chat.html file. Initial attempts focused on JSON structure, but the data is embedded within HTML. The process involved using BeautifulSoup to parse HTML, attempting to extract JSON from script tags, and then filtering for user messages. However, the output file remains empty, indicating issues with JSON extraction or filtering logic.

The user is encountering issues with a Scrapy spider not returning any output when attempting to scrape websites like futurepedia.io and mordechaipotash.com. The conversation has focused on debugging the Scrapy code, adding logging, and verifying if the spider is fetching the page content. The user has also been guided on how to use browser developer tools and BeautifulSoup to inspect HTML structu

The user is working on a Python script to extract job duty information from HTML files and convert it into CSV format. The script has evolved to handle different HTML structures and iterate through multiple files in a folder. The current focus is on refining the extraction logic to be more robust against variations in HTML and ensuring all generated CSVs can be merged into a single comprehensive f

The user is refining their YouTube content processing pipeline, focusing on identifying and integrating missing high-value channels. Key issues addressed include YouTube's bot detection and rate limiting, leading to decisions to reduce parallel downloads and use browser cookies for authentication. The pipeline has been expanded by adding numerous new channels, particularly in Torah/Judaism and AI/