I built Magiclip – an all-in-one AI studio

Hacker News

Hi HN I’ve been working on a tool I personally needed as someone who edits a lot of video content. The problem is simple: Modern video editing requires 8+ different tools, all slow, all noisy, all repetitive. Subtitles here. Audio cleanup there. Silence removal elsewhere. Upscaling in another tool. AI voice in a different one. A clip extractor somewhere else… So I built Magiclip.io — a single interface that automates the most boring parts of editing. What it does today Auto-subtitles (fast & accurate) Silence removal AI voice-over Audio enhancement Image upscaling Clip extraction from long videos Thumbnail generation Quick TikTok/Reels format conversion And more coming The idea isn’t to replace full editors. It’s to remove the friction of things we repeat 100 times. Upload → Magic → Download. No timeline, no project files, no complexity. Why I built it I edit content frequently, and the workflow felt unnecessarily painful. Magiclip is my attempt to reduce editing from hours to seconds by batching the most common tasks behind simple endpoints. What I’d love feedback on What other tasks should be automated? Anything in the UX that feels off or slow? Any feature you’d want exposed through an API? Live link https://magiclip.io Happy to answer anything about the architecture, the pipelines, or the reasoning behind the features.

Visit product View on Hacker NewsNov 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, tasks · Missing: mac, agents, macos

84%84% 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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, video · Missing: mobile apps, ios, entrepreneurs

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

32%32% 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

19%19% 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.

Incorrect prediction on native model

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