TextWisely's answer

TextWisely is built specifically for macOS users who write a lot and don’t want to break their flow. Unlike browser-based tools or clunky extensions, TextWisely works natively across any Mac app – from Slack and Notes to Mail and Safari – without requiring you to copy-paste into ChatGPT or switch contexts.

It’s fast, always available via a shortcut, and designed with real-world writing tasks in mind – like replying to emails with full context, fixing grammar instantly, or changing your tone before sending a message. It doesn’t just polish your text – it removes the friction around writing.

In short: TextWisely brings GPT-quality writing help into your daily Mac workflow without getting in your way.

TranscriptFetch's answer:

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 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.

TextWisely's answer

TextWisely was born from a personal itch 🧩

⚠️ Sending numerous Emails and Slack messages daily, I found myself constantly switching apps and re-entering the same prompts into ChatGPT to refine my writing.

💡 This inspired me to create TextWisely to boost writing speed, reduce context switching, and improve quality with instant text actions: ☑️ Grammar corrections, ☑️ Email drafts, ☑️ Structured writing, ☑️ Tone changes, ☑️ Translations ...and more

👉 It's a native macOS desktop app – shortcut-heavy for power users, but also click-friendly for those just starting out.

🚀 Give it a try: https://textwisely.ai

TranscriptFetch's answer:

It started with discovering there is no good way to get the text of a video. YouTube’s official Data API will confirm a caption track exists and then refuse to hand it over, because captions.download requires the video owner’s 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.

TextWisely's answer

Most writing tools force you to copy, paste, and jump between apps – or lock you into a browser extension that only works in certain places. TextWisely is different.

It’s a native macOS app that works everywhere you write – Slack, Mail, Notes, Google Docs, you name it. Just hit a shortcut, and you can instantly rewrite, correct, or respond to text without breaking your flow.

You get: 🔄 No context switching – stay in the zone, ⚡ Super fast – opens instantly, runs smoothly, 📩 Email context support – attach threads, get smart replies, 🔒 Built for privacy – nothing is stored on our servers, ✍️ Natural tone control – adjust writing style on the fly

If you care about speed, fluidity, and staying focused while writing – TextWisely is built for you.

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:

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.