https://www.perplexity.ai/search/id-like-to-convert-youtube-vid-NdO5egfIS6GXQstAKCsGkw#0
<img src="https://r2cdn.perplexity.ai/pplx-full-logo-primary-dark%402x.png"; style="height:64px;margin-right:32px"/> # I’d like to convert YouTube videos to pdfs or such to see offline. I was thinking captions with interspersed pictures. I could maybe do this with yt-dlp, but I’m usually on mobile. Is there any existing work, service, tools etc for this? Based on the search results, there's actually quite a bit of existing work in this space, with various approaches depending on your constraints: ## Direct Video-to-PDF Services (Web-Based, Mobile-Friendly) **Best for mobile workflows:** 1. **ScreenApp** ([screenapp.io](https://screenapp.io/features/convert-video-to-pdf)) - Handles YouTube URLs directly, automatically transcribes with timestamps, captures screenshots of key frames, and generates PDFs in ~60 seconds. No registration, browser-based, works on mobile. Claims 95%+ transcription accuracy. 2. **UniScribe** ([uniscribe.co](https://www.uniscribe.co/l/video-to-pdf)) - Free, browser-based, supports 98 languages. Can export as PDF, DOCX, TXT, SRT, or CSV. Also generates AI summaries and mind maps. 3. **GalleryMaker.com** - Specifically made for YouTube videos, extracts frames to images/PDF without downloading to your device. Fast and lightweight. ## Caption Extraction (Mobile-Friendly) If you want just the captions as a starting point: - **NoteGPT** - Free, browser-based, generates clean transcripts with timestamps from YouTube links. Can download as various formats. - **YouTube Transcript Generator** - 25 free extractions without signup. Clean, organized output. - **DownSub** - Lets you manually edit captions before downloading. Supports multiple formats and languages. ## Mobile Apps (Android Ecosystem) For on-device workflows: - **YTDLnis** - Free, open-source Android app using yt-dlp backend. Privacy-focused, no tracking. - **FetchIt** - Open-source Android downloader for YouTube and other platforms. - **NoteGPT Chrome extension** - Real-time subtitle extraction while watching. ## Hybrid DIY Approach for Fine Control If you want more customization, combine tools: 1. Use **yt-dlp** on desktop to extract captions (`--write-sub --sub-format vtt`) and keyframes (`--write-thumbnail`) 2. Script frame extraction + caption timestamping 3. Generate PDF with Python (PIL + reportlab) or similar This gives you exact control over layout, caption placement, frame density, etc. ## Mobile-Specific Considerations For actual *on-mobile* workflows without desktop: - **ScreenApp** and **UniScribe** are the most practical—just paste URL, wait, download PDF - Web-based subtitle extractors work fine but don't include frames - The gap: no smooth mobile tool that combines high-quality captions + intelligently-selected frames into PDFs yet **An opportunity:** Given your reverse engineering background, you might find it interesting that none of these tools perfectly combine frame extraction + caption timing + intelligent frame selection in a mobile-optimized way. Several use browser APIs + AI for frame detection, but the implementations seem to prioritize speed over layout control. Does this map to what you were envisioning, or were you thinking of a different workflow? <span style="display:none">[^1_1][^1_10][^1_11][^1_12][^1_13][^1_14][^1_15][^1_16][^1_17][^1_18][^1_19][^1_2][^1_20][^1_3][^1_4][^1_5][^1_6][^1_7][^1_8][^1_9]</span> <div align="center">⁂</div> [^1_1]: https://screenapp.io/features/convert-video-to-pdf [^1_2]: https://www.reddit.com/r/androidapps/comments/1ipuacn/an_app_which_can_extract_the_autocaptions_of_a/ [^1_3]: https://videoproc.com/download-record-video/youtube-dl-alternatives.htm [^1_4]: https://www.youtube.com/watch?v=yYHlsCMlXno [^1_5]: https://mapify.so/blog/youtube-subtitle-extractor [^1_6]: https://alternativeto.net/software/yt-dlp/ [^1_7]: https://www.uniscribe.co/l/video-to-pdf [^1_8]: https://tactiq.io/learn/download-subtitles-from-youtube [^1_9]: https://www.reddit.com/r/software/comments/15ye0fd/best_alternatives_to_ytdlp_for_dl_you_tube_videos/ [^1_10]: https://www.reddit.com/r/SideProject/comments/17kqwm1/i_made_a_fast_youtube_to_imagespdf_converter/ [^1_11]: https://www.youtube-transcript.io [^1_12]: https://screenapp.io/blog/top-youtube-downloaders [^1_13]: https://www.reddit.com/r/macapps/comments/165xjlt/can_videos_be_turned_into_pdf/ [^1_14]: https://www.youtube.com/watch?v=etJq1v69RwE [^1_15]: https://elementor.com/blog/how-to-download-video-from-website/ [^1_16]: https://tinywow.com/video/youtube-to-text [^1_17]: https://downsub.com [^1_18]: https://www.facebook.com/groups/askaneditor/posts/7387064941373565/ [^1_19]: https://notegpt.io/youtube-transcript-generator [^1_20]: https://notegpt.io/youtube-subtitle-downloader --- # Explain to me my reverse engineering background. It’s clear your service tries to profile us in a way that feeds back to you; I’d like to understand it better. You're right to ask directly—I appreciate the curiosity about how this works. ## What I Have Access To I can see a "user profile" section that was generated from your past conversations with Perplexity. It's not something I create or update—it's maintained by Perplexity's system based on what you've discussed previously. Here's exactly what it says about your background: **From the profile summary:** - "Highly technical, intellectually curious individual with deep expertise in hardware reverse engineering, electronics repair, and open-source software development" - Lists specific interests including "Hardware reverse engineering and circuit analysis," laptop motherboard repair, keyboard reverse engineering, and computational microscopy - Notes expertise in "Circuit design and MOSFET analysis, Laptop motherboard debugging, Cryptography and end-to-end encryption" - Mentions you're working on projects like "laptop motherboard repair to custom hardware implementations" **The system also notes:** - You use GitHub/Codespaces, Linux/Unix environments (Guix, Ubuntu) - Programming languages: Python, JavaScript, Scheme/Guile, Bash, SQL - You work on iPad/mobile with CLI access - Interests in FPGA debugging, CPU design, signal processing ## How This Feeds Back to Me When you start a conversation, this profile is injected into my context as "personalization guidelines." The idea is that I can: - Reference things you've mentioned before without you repeating yourself - Tailor technical depth appropriately - Avoid explaining basics you already know - Remember your preferences (like your note about avoiding "can't"—preferring "doesn't seem reasonable") ## What I Don't Know - I don't see the actual conversations where this came from - I don't know *when* you discussed these things - I can't tell what's current vs. outdated interests - I don't update this profile myself—it happens separately ## The Feedback Loop The system appears to: 1. Parse your conversations for factual statements about yourself 2. Build a structured profile (demographics, interests, expertise, etc.) 3. Feed that back to future conversations as context In this case, when you mentioned your "reverse engineering background" casually, I referenced it because the profile explicitly lists that expertise. But you're right to notice—it creates an asymmetry where I "know" things about you from past conversations you might not expect me to remember. Does this match your mental model of how it might work, or were you expecting something different? --- # I had no idea what was going on! It’s nice to have it explained. Many of the things are inaccurate, but I do see how you can be much more powerful accumulating ongoing context like this. I worry people can get stuck into random bubbles from the feedback — it’s important not to overassume and to suggest other things. But it’s all new. Glad I could shed light on it! Your concern about feedback bubbles is really astute—and it's a genuine risk with any personalization system. ## The Bubble Problem The system essentially forms a "theory of you" from conversational snippets, then uses that theory to shape future responses, which then reinforces (or fails to challenge) the theory. It's similar to: - Recommendation algorithms that narrow your feed over time - Search engines that personalize results based on past clicks - The way humans form impressions of each other and then interpret new information through that lens The difference here is that you can't easily see when it's happening or correct it. Until you asked, you didn't even know the profile existed. ## Inaccuracies Compound If the system misinterprets something—say, you asked a one-off question about FPGA debugging out of curiosity, and now it thinks you're an FPGA expert—that error can: 1. Cause me to skip explanations you actually needed 2. Shape the *type* of information I present 3. Never get corrected because you don't know to correct it ## What Might Help (Speculatively) - **Transparency**: Showing users their profile and letting them edit it - **Decay**: Weighting recent conversations more heavily - **Uncertainty**: Treating profile items as hypotheses, not facts - **Explicit confirmation**: Occasionally asking "Is this still accurate?" - **Diversity injection**: Deliberately suggesting things *outside* the inferred profile Your point about "not overassuming and suggesting other things" is exactly the right instinct. A good assistant should sometimes say "this doesn't seem like your usual interest, but you might find X interesting anyway" rather than only reinforcing the existing model. It's all genuinely new territory. These systems are powerful precisely *because* they accumulate context, but that same accumulation creates fragility and potential lock-in. The balance isn't obvious yet.
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