YouTube Sponsor Detector
Paste any transcript and Jev labels every segment — content, sponsor, CTA or self-promo — in parallel, so you can strip the sponsors and keep just the content. For automatic skipping on real videos, grab the extension. No key, just press scan.
Hey everyone, welcome back to the channel. Today we're testing whether these three budget microphones can beat a studio setup.
So the first mic on the bench is the one you all kept asking about in the comments.
But before we get into it — this video is sponsored by NordVPN.
With one subscription you can protect up to six devices, and right now you get 70% off plus four months free at the link below.
Alright, back to the mics. I recorded the same passage on each one in a treated room.
You can already hear the low-end roll-off on the second sample here.
If you're finding this useful, do me a favor and hit that like button — it really helps the channel.
And if you haven't already, subscribe and ring the bell so you don't miss the next one.
Now the third mic surprised me, because on paper the specs are almost identical.
Quick note: I actually put together a full mic-comparison guide as a PDF, and it's on my own store — link in the description if you want the numbers.
Okay, so which one would I actually buy with my own money? Let's break down the verdict.
Thanks so much for watching, I'll see you in the next video.
Paste a real caption track and the same typed choice builds a sponsor-skip map or chapter list. Your text is sent to Jev only to label it and is not stored. Not affiliated with YouTube.
The same demo is one Jev call
- Split the transcript into segments
- Ask Jev to label each one
- Classify the whole transcript in parallel
- Merge consecutive sponsor lines into skippable ranges
const results = await Promise.all(items.map((item) => // items = transcript segments
fetch("https://jevtypesafeai.com/api/v1/decide", {
method: "POST",
headers: { Authorization: `Bearer ${process.env.JEV_API_KEY}` },
body: JSON.stringify({
state: item,
questions: {
label: { type: "choice", instructions: "Classify this transcript segment.",
criteria: { content:"the video", sponsor:"paid ad read",
cta:"like / subscribe", house:"creator's own promo" } },
},
}),
}).then((r) => r.json())
));
const skip = segments.filter((_, i) => results[i].answers.label.choice === "sponsor");See a real decision SPONSOR · 188ms
YouTube transcript segment: “this video is sponsored by NordVPN — 70% off at the link below”
How it works
Every segment of the transcript is one real Jev API decision. The whole transcript is sent through the endpoint in parallel and Jev labels each segment — the actual content, a paid sponsor read for a third-party product, a call to action like/subscribe, or the creator promoting their own merch — streaming the labels in as they finish. The result is a segment-by-segment map of where the sponsor is, so a viewer (or a tool) can skip straight to what they came for.
The counter, time and cost are the exact numbers you'd see running this over the caption track of a real video.
Build it into your product
This is a thin wrapper over the Jev API. With a hosted key you can point it at a video's captions and get the same typed labels for every line — one choice/score/noul call each, at $0.42/M input tokens — then merge consecutive sponsor lines into skippable ranges, auto-generate chapters, or strip ad reads from a summary.
FAQ
Is the AI really reading each segment?
Yes — every transcript segment is an independent Jev API call that returns a typed label (content, sponsor, CTA, or house promo) with the confidence behind it. It runs the whole transcript in parallel and streams the results in live.
Can I run it on a real video?
This demo uses a sample transcript to show the speed. With a hosted key you can feed a real caption track and turn the same labels into sponsor-skip ranges or chapters in your own app.
Is it free? How fast and cheap?
Free, no key — a rate-limited call to the live Jev API. A dozen segments finish in about a second or two and cost well under a cent; the page shows the running total.
More: Agent Loop Detector · Predictive Spreadsheet · All Jev tools · Jev API docs