Obiklip – read transcripts, find and create video segments quickly

Hacker News

I'm a video editor, but not very professional. I mostly edit speech and podcast videos to highlight interesting parts and make short clips. I've found that the most time-consuming task is finding interesting segments in a full-length video. It usually takes me 2-3 hours of continuous listening just to find the 'timestamps' where the interesting points start and end. I'm referring to source videos that are 30 min to 1 hour long. Nowadays, getting transcriptions for videos is easy with AI. So, why not use that to find segments? Reading through a transcription is much quicker and easier than listening to the whole video. You can't predict what the speaker in the video will say, so you need to listen, go back and forth, and keep repeating this process until you finish the video. So, I created Obiklip. Now I can go through each line of the transcription, listen to each line, read or skim through it. It saves me hours! Of course, there's still detailed post-production editing to do, but finding points to highlight is no longer a pain. As the only developer for this project, I'm happy to share this and hear feedback from the community! P.S. It's a free software. But if you need to buy a license, use the "HELLOHN" coupon. You'll get 50% off.

Visit product View on Hacker NewsOct 17, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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 · 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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