Shadow Inbox's answer

Bunny Honey Club AI

Shadow Inbox's answer

Shadow Inbox doesn't just match keywords, it understands context. Our Gatekeeper AI reads the full post, the subreddit, and the poster's intent before scoring a lead. An SEO agency tracking "traffic" won't see posts about highway accidents. Only genuine buying intent reaches your dashboard, and you only spend credits on leads you actually want to pursue.

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.

Shadow Inbox's answer

Most social listening tools are built for brand monitoring, not lead generation. They're enterprise-priced, noisy, and require a team to manage. Shadow Inbox is built specifically to find ready-to-buy prospects on Reddit and Hacker News, deliver them in real time, and let you reach out before your competitors even see the post.

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.

Shadow Inbox's answer

Agencies, freelancers, B2B SaaS founders, and consultants who need high-intent leads without spending thousands on paid ads. Anyone who sells a service and wants to reach people at the exact moment they're asking for help.

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.

Shadow Inbox's answer

Built by an agency owner who spent too many hours manually scrolling Reddit looking for clients. After validating the idea by posting on Reddit itself and getting overwhelming positive feedback, Shadow Inbox was born. The meta-joke: the tool found its own first users on Reddit before it existed.

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.

Shadow Inbox's answer

Next.js, Supabase, Vercel, Kimi 2.5, Gemini 3.1 Pro, Claude Sonnet 4.6, Claude Opus 4.5, Deepseek, Stripe, Resend

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.