ReelStudio – open-source Instagram Reel transcription with Whisper

Hacker News

Hey HN, I built an open source tool to transcribe Instagram Reels using OpenAI's Whisper model. GitHub: https://github.com/stym06/reelstudio How it works: - Paste a Reel URL → Worker downloads via yt-dlp → Whisper transcribes → Get shareable transcript page Tech stack: - Next.js 14 frontend - Go worker (polls PostgreSQL for jobs) - faster-whisper for transcription - No login required The architecture is simple: the frontend creates a task in PostgreSQL, a Go worker polls for pending tasks, downloads the video, runs Whisper, and saves the transcript. The frontend polls until complete and redirects to the transcript page. I built this because I wanted to repurpose Reel content into blog posts and needed accurate transcripts with timestamps. Existing tools either required accounts, had watermarks, or were expensive. Self-hosting is straightforward - just need PostgreSQL, the Next.js server, and the Go worker with Python/Whisper installed. Would love feedback on the architecture or any feature suggestions. The worker is designed to be horizontally scalable if anyone wants to add Redis/queue support.

Visit product View on Hacker NewsJan 26, 2026

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

75%75% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · 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.

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

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

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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.

Correct prediction on native model

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