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.

Lessona.ai's answer:

Lessona is a complete lesson planning system built around the curriculum a teacher actually has to follow. Unlike credit based tools, every plan is unlimited. It works across all three planning horizons: long term annual overviews, medium term unit and term plans, and short term daily lessons. It also builds presentation ready slide decks with illustrations and labelled diagrams, handles differentiation, and never stores student names. Teachers who feel uncertain about technology can use it because it behaves like a planning companion, not a black box.

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.

Lessona.ai's answer:

Most planning tools cap how much you can create or lock features behind credits. Lessona gives you unlimited plans for one simple price. It aligns to your region's current curriculum framework rather than a generic template, includes a calendar, supports differentiation, and produces ready to teach slide decks alongside the written plan. It was built with a practising primary teacher, so it fits the real rhythm of a classroom week rather than the workflow of a software company.

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.

Lessona.ai's answer:

Primary, intermediate, and secondary school teachers who plan lessons every week and want to reclaim their evenings and weekends. The first market is New Zealand, with Australia, the United Kingdom, the United States, and Canada to follow. The core user is a classroom teacher who values their time, cares about curriculum alignment, and may be cautious about new technology.

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.

Lessona.ai's answer:

Lessona was founded by Stephen and Emma Milner. Emma is a primary school teacher who lived the reality of spending evenings and weekends planning lessons, and Stephen builds the product around what she and her colleagues actually need. The goal is simple: give teachers back the hours that lesson planning quietly takes from their lives, while keeping every plan aligned to the curriculum they are required to teach.

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.

Lessona.ai's answer:

Lessona is built with Next.js and TypeScript, styled with Tailwind CSS, and deployed on Vercel. Lesson and slide generation is powered by large language models from Anthropic and Google.