WorthLens.ai's answer

WorthLens.ai delivers expert-level appraisals for antiques, coins, jewelry, and collectibles in under 30 seconds — combining AI trained on millions of auction records with live gold and silver spot prices and Red Flag authenticity detection. Unlike generic AI assistants, every report is structured: price range, confidence score, key value factors, comparable references, material analysis, and selling guidance. And unlike traditional appraisers who charge $50–$300+ and take days, WorthLens costs as little as $0.49 per report and runs 24/7.

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.

WorthLens.ai's answer

Speed, depth, and honesty. Other tools either give you raw auction listings (WorthPoint, Invaluable) or a generic guess (ChatGPT) — WorthLens identifies the item from your photo, synthesises comparable data, factors in today's metal prices, and returns a full structured report. Every report includes a confidence level, so you always know how much to trust the estimate, and a dedicated Red Flag section when signs of inauthenticity appear. No subscription, credits never expire, and new users get 3 free appraisals with no credit card required.

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.

WorthLens.ai's answer

Collectors, auction bidders, estate sale shoppers, flea market resellers, antique dealers, and investors — anyone who buys or sells items whose value isn't obvious. Also: people who inherited jewelry, coins, or antiques and have no idea what they're worth. Users range from first-time sellers checking a single item before listing on eBay, to professional dealers using WorthLens as a low-cost first filter on unfamiliar categories.

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.

WorthLens.ai's answer

WorthLens.ai was born from a simple observation: most people buying or selling collectibles are flying blind. A traditional appraiser costs $50–$300+ and takes days. Google shows asking prices, not what items actually sold for. ChatGPT guesses without real market data. We built WorthLens.ai to close that gap — expert-level valuation intelligence made accessible to anyone with a phone, in the exact moment they need it: at a flea market, before a bid, or standing in front of an inherited box of jewelry.

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.

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.