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TranscriptFetch
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TranscriptFetch is one API for getting text out of video and web content.
Send a URL from YouTube, TikTok, Instagram, X or Facebook and get back clean, timestamped text. Send any web page and get clean Markdown. One endpoint, one response shape, one API key.
Most short-form video has no caption track to download. TikTok's auto-captions are opt-in per upload, Instagram never publishes a downloadable track, and a large share of captions on both platforms are burned into the video frames where no parser can read them.
When there is no caption track, TranscriptFetch transcribes the audio instead. Same endpoint, same response, so your code never branches on which method produced the text.
text field for feeding a model or a search indexsegments array with per-cue start times and durations, so subtitles and jump-to-moment links are a formatting step rather than another integration100 free credits on signup, no card required. One credit per successful response. Failed, blocked and empty results are never charged, which matters on short-form video where a meaningful share of any batch is music with no speech in it.
Dash for macOS TranscriptFetchNo TranscriptFetch videos yet. You could help us improve this page by suggesting one.
TranscriptFetch's answer:
Most short-form video has no caption track to download. TikTokโs auto-captions are opt-in per upload, Instagram never publishes a downloadable track, and many captions on both are burned into the video frames where no parser can read them. TranscriptFetch transcribes the audio when no caption track exists, on the same endpoint, with the same response shape. Your code never branches on which method produced the text. It also covers YouTube, TikTok, Instagram, X and Facebook plus any web page as clean Markdown, so a pipeline spanning several sources is one integration rather than five.
TranscriptFetch's answer:
Three reasons. Coverage: one API key and one response shape across five video platforms and the open web, instead of stitching together a library per platform. Reliability: requests run through rotating infrastructure, so code that works locally keeps working from a server, which is where most open-source approaches break. Billing that matches reality: one credit per successful response, with failed, blocked and empty results never charged. That last point matters on short-form video, where a meaningful share of any batch is music with no speech in it. There is also an MCP server, so AI agents can fetch transcripts as a tool without a custom integration.
TranscriptFetch's answer:
Developers and technical teams building on video and web content. The common cases are RAG and retrieval pipelines that need video as text, AI agents that need to read a link mid-conversation, content teams repurposing short-form video at scale, and media monitoring and research tools. It is an API first, so the buyer is usually the person writing the integration rather than an end user. The free browser tools exist for one-off transcripts and for evaluating output quality before writing any code.
TranscriptFetch's answer:
It started with discovering there is no good way to get the text of a video. YouTubeโs official Data API will confirm a caption track exists and then refuse to hand it over, because captions.download requires the video ownerโs OAuth token. The popular open-source libraries work until you deploy them, at which point platforms start refusing datacenter IPs. And YouTube is the easy case: TikTok and Instagram publish no caption file at all. Every workaround solved one platform, worked locally, and broke in production. TranscriptFetch is the version that handles the failure cases as first-class behaviour rather than edge cases.
TranscriptFetch's answer:
Next.js with TypeScript and Tailwind on the front end and API layer, Clerk for auth with SHA-256 hashed API keys, Neon Postgres with Drizzle ORM, Redis for caching, and Stripe for billing. The extraction layer is a Python and FastAPI service. Speech-to-text uses Whisper-class models. The MCP server is published in the official Model Context Protocol registry with a DNS-verified namespace.
Once you get use to it, you won't be able to imagine your life without Dash. It will save you a bit of time every day. Many times.
As a bonus you can use the "snippets" feature as a generic text-expander. That saves me tons of time when writing emails, too.
p.s. aText is not exactly a direct competitor; however, I replaced it through the snippets feature of Dash.
Based on our record, Dash for macOS seems to be more popular. It has been mentiond 94 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Dash for MacOS (proprietary, paid) has the documentation for over 200 APIs and over 100 cheat sheets, and the ability to generate documentation for packages for Swift, Python, Ruby, PHP, Java, Go, Rust, Scala, Dart, Haskell, Hex, Clojure. - Source: dev.to / 2 months ago
This isn't a new idea for developer tools. DevDocs, Zeal, and Dash have offered offline documentation browsing for years. What's new is applying this architecture to AI agents โ giving your coding assistant the same offline, instant, version-accurate access to docs that you'd want for yourself. - Source: dev.to / 6 months ago
"the IDE had to be discoverable right away (which it was) and self-contained to offer you a complete development experience" This right here was the key to super flow state. Lightning fast help (F1), very terse and straightforward manuals. I have tried to replicate this with things like Dash (https://kapeli.com/dash), to some degree of success. The closest thing I had to this in windows was probably Visual Studio... - Source: Hacker News / 10 months ago
You're absolutely right about the root cause being outdated AI knowledge bases/training data. I agree, my solution doesn't address that directly. Where this actually shines is with local LLMs (Ollama, etc) - smaller models, no API costs, fully offline, and the AI gets fresh docs without waiting months for model retraining cycles. Your point about convincing major providers to integrate something like Dash... - Source: Hacker News / about 1 year ago
Https://kapeli.com/dash for MacOS supports man pages just like any of its many other documentation sources. Just prefix the search query with `man:`. Absolute hall of fame app IMO. - Source: Hacker News / over 1 year ago
Zeal - A free, open-source offline documentation browser that puts documentation for every major language and framework one instant search away, on Linux and Windows.
SocialFetch.dev - Social media scraping API for public profiles, posts, comments, videos, transcripts, and metrics from TikTok, Instagram, YouTube, X, LinkedIn, and more. Pay-as-you-go credits, 100 free to start.
DevDocs - Open source API documentation browser with instant fuzzy search, offline mode, keyboard shortcuts, and more
TranscriptAPI.com - Get YouTube video transcripts with a simple API call or through Model Context Protocol. Fast, reliable, and easy to integrate into your applications.
Velocity - Velocity gives your Windows desktop offline access to over 150 API documentation sets provided by...
iTerm2 - A terminal emulator for macOS that does amazing things.