n8n YouTube Clipping Automation — the Version That Actually Works

Why Groq, and where it breaks

Groq's speed is the whole reason this pipeline is usable — waiting minutes per clip on a slower model kills the point of automating it. But Groq has a hard 25MB cap on audio file size, and a real podcast episode blows past that without any preprocessing. The fix was splitting/compressing before the transcription call, not after it failed.

The 70-minute episode that broke everything

The bigger issue showed up on a 70-minute episode: sending the full transcript to Groq in one shot blew through the account's tokens-per-minute cap and the whole run died mid-way. The fix wasn't a bigger plan or a different model — it was splitting the transcript into smaller batched groups and throttling the calls between them, so the pipeline stays under the rate limit instead of hoping it doesn't hit it. Boring fix, but it's the difference between "works on my 10-minute test video" and "works on the actual 70-minute episode I need clipped."

Three smaller bugs that were still worth fixing

None of these were dramatic on their own, but they're the kind of thing that makes automated output look obviously automated if you don't catch them:

• Clips as short as ~15 seconds were slipping through early on — nothing wrong technically, they just looked bad. Raised the floor to 20 seconds and the output got noticeably more watchable.
• A duplicate-title bug where two different clips from the same episode ended up with identical generated titles — an edge case in how titles were generated per-batch instead of per-episode.
• General cleanup on how candidate moments were scored, so the "best" clips picked were actually the ones worth posting, not just the ones that happened to score highest on a narrow metric.

Why I'm not pretending this is unique

People are already paying for versions of this workflow, which tells you the demand is real — but it also means the generic pitch ("AI clips your videos!") is already fully saturated. The part that's actually mine is the specific pipeline: which model, how the transcript gets chunked, and the four bugs above that only show up once you run it against real, long, messy source material instead of a clean demo clip.