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Turn blog posts and X threads into talking head videos

Hacker News

Turn blog posts and X threads into talking head videos

Hi HN, I’m one of the founders of Gling https://gling.ai We’ve been working on AI video editing for a while (mostly helping YouTubers remove silences and bad takes), but we just released a new feature that I wanted to share with you: converting text links directly into talking head videos. The Problem: Repurposing text content (blogs, Twitter threads, LinkedIn posts) into video is tedious. You usually have to summarize the text, write a script, set up a camera/teleprompter, record, and edit. We wanted to collapse that entire stack into one click. How it works: You paste a URL (e.g., a TechCrunch article, blog post, or an X thread), and Gling does the following: Content Extraction: We scrape the meaningful content from the page. Script Generation: We use llm to summarize the content and rewrite it into a natural-sounding video script. Video Synthesis: This is the core update. We generate the visual and audio using elevenlabs and veed fabric, you can either upload your avatar and voice, or select an AI generated one. The Tech Stack: Backend: Node.js on google cloud. Video/Audio: Elevenlabs for audio, Veed fabirc for video, and mediabunny for handling the media files Why we built this: We noticed many of our users were manually reading their own blog posts to camera. We figured we could automate the "talking head" part so creators can test video versions of their written content without the production friction. The Ask: We are looking for feedback on: The "Uncanny Valley": How natural does the movement/voice feel to you? Script Quality: Does the summarization miss key technical context from your links? Use Cases: Would you use this for docs? Marketing? Internal updates? You can try it here: https://app.gling.ai – no login required for the first video. Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · Missing: mac, agents, macos
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, google, users · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: just released, ide, io · Missing: https docs, excited, exist
31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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