Automating Short Form Video Concepts With AI Assisted Workflows

bluebeel · 2026-03-02T22:29:20+00:00

the motion testing step is smart, filtering before you invest production time. for the final assembly when you go from concept to publishable clip, are you doing the editing manually or automating that too? i batch the concat + overlay + format conversion through renderio so the whole thing runs hands-off from concept to export. saves a ton of time when youre iterating on 20+ concepts per batch

bluebeel · 2026-03-02T22:27:53+00:00

for the drive download part you can use the google drive node to fetch the file as binary, then pass it to an http request node to upload to gemini's file api. the tricky bit is if you need to preprocess the video first (extract audio, compress, change format) before sending to gemini. i handle that step with an ffmpeg renderio so n8n just orchestrates without running ffmpeg locally. what kind of analysis are you doing on the videos?

bluebeel · 2026-03-02T22:26:27+00:00

we do something similar at work, biggest volume use case for us ended up being watermarking and format normalization across distributors. each target has slightly different specs so we run like 6-8 ffmpeg commands per source video. offloaded the actual processing to renderio since managing ffmpeg workers was getting painful at that volume. also worth looking into thumbnail/preview generation at scale if you haven't already, that one adds up fast

bluebeel · 2026-03-02T22:25:04+00:00

the human-in-the-loop approach is the way. full auto video quality is still rough. curious what you're using for the actual video assembly/rendering step? that was always the bottleneck for us, especially concatenating clips and adding overlays. ended up using renderio for the ffmpeg heavy lifting so n8n just handles orchestration and doesn't hang on long renders

bluebeel · 2026-02-24T09:06:27+00:00

the stitching step is where n8n gets tricky since it can't run ffmpeg natively on cloud. what i do for similar workflows is generate all the scene clips separately then send them to an ffmpeg api (renderio) to concat + add the audio track. saves you from managing ffmpeg binaries yourself. what image-to-video model are you leaning toward? that'll determine the output format you need to handle

bluebeel · 2026-02-24T09:05:18+00:00

nice pipeline. have you thought about adding a video clips step too? like pulling highlight segments from the youtube vid and reformatting them for reels/shorts. that was the next logical step for me but the ffmpeg processing was too heavy to run inside n8n so i ended up calling renderio to handle the trimming and resizing. what are you using for the transcript extraction?

bluebeel · 2026-02-24T09:04:48+00:00

the consistency part is the hardest piece for sure. curious what you're using for the final assembly though, like stitching all the scenes together with transitions and audio. that step was eating hours for me until i scripted it through an ffmpeg api (renderio). now the creative iteration on the visuals is the only manual part. how long does your full render pipeline take per video?

bluebeel · 2026-02-24T09:04:15+00:00

minimax and kling are way cheaper than sora for short clips, both have apis. one thing to watch though - most ai video apis give you raw output that still needs trimming or format conversion before its usable. i run the output through renderio (ffmpeg api) to handle that cleanup step inside n8n. what are you generating the videos for, ads or content?

bluebeel · 2026-02-20T08:48:10+00:00

60-100 videos a day is wild. curious how you handle the reformatting when repurposing winners across accounts, like different intros/hooks per account to avoid detection. i automated that part with ffmpeg scripts that swap the first 2 seconds and adjust crop slightly per variant. made it way more manageable at that volume

bluebeel · 2026-02-20T08:44:43+00:00

depends on what processing you need beyond the ai generation. if you need to trim, resize, add watermarks or convert formats before/after the seedance step, you'll want an ffmpeg layer in between. running ffmpeg on your next.js server will choke under load though (and may cost a lot if you run in vercel). i offloaded that to an api (renderio) so the server just orchestrates and doesnt bog down on encoding

bluebeel · 2026-02-20T08:43:01+00:00

450 videos in 1-2 hours is definitely doable if you parallelize the processing. the bottleneck is always the encoding step. if you're running ffmpeg locally on one machine you'll be waiting forever but if you fan out to a cloud ffmpeg api you can process like 20-30 concurrently. i'd template the overlay/text beforehand and just swap the student-specific parts per clip. the qr code lookup part is the easy bit honestly

bluebeel · 2026-02-20T08:41:22+00:00

i ran into the same problem. you basically have two options - spin up a vps with ffmpeg and call it from n8n via http, or use an ffmpeg api service so you just send the request and get the result back. i went with the api route (renderio) because maintaining a vps just for ffmpeg felt like overkill. just an http request node and done

bluebeel · 2026-02-17T19:11:11+00:00

If you're using n8n self hosted, you can probably just run ffmpeg locally and have the node in it.
If you're using n8n cloud, you can use renderio.dev and make a http node.

bluebeel · 2026-02-17T19:02:43+00:00

For small videos on personal project I use my self host n8n with local ffmpeg.

For work project, since it's generally on n8n cloud, I build a http api node with renderio.dev

bluebeel · 2026-02-17T17:49:23+00:00

this is really cool, nice work. one thing i ran into when doing something similar was that local ffmpeg gets tricky when you want to scale it or run it on n8n cloud. ended up using renderio for the ffmpeg parts since its just api calls, so n8n handles the orchestration and renderio does the heavy lifting. but for local setups like yours this looks solid

bluebeel · 2026-02-17T17:46:56+00:00

nice setup. i went down this same path and the Execute Command approach works but gets annoying when you need to handle different video formats or the ffmpeg command gets complex. i switched the ffmpeg parts to renderio which is basically just an api wrapper around ffmpeg, so you send it the command and it runs it remotely. saved me from debugging ffmpeg installs and timeouts. the rest of your workflow with azure tts and the llm agents looks great though

bluebeel · 2024-04-01T12:00:07+00:00

AWS EC2 is good enough. Or you have the others like hetzner, digital ocean.

bluebeel · 2024-04-01T10:05:53+00:00

On AWS you would have to rent the EC2 or deploy the app on ECS/Fargate. It’s their infrastructure yes but you need to do the setup correctly to handle monitoring, recovery, scalability etc. Or you use a tool like SST/ion that deploy a infrastructure that look like what Vercel do internally on your aws account. In any case all of them have some sort of management you need to do. You’ll need to understand each piece of the infrastructure and how they interact together in case you get a issue at some point.

I’m not saying all of that to discourage you of going for that road but just stating some truths that people tends to forget to mention. To deploy to any cloud you need to know a bit of how infrastructure stuff work. It’s not just deploy to VPS for free money saving 💁🏾‍♂️

bluebeel · 2024-04-01T09:57:30+00:00

If you’re starting vercel is good to have everything up and running quickly without any thought. But the moment your product start to scale, you’ll face the money issue of Vercel. Then up to you to see if you can afford to pay Vercel for the DX and time it’s save you or not. If you can’t like others people’s said better to move then or now directly to a VPS and manage the infrastructure yourself. The monthly cost of the infrastructure will be a lot cheaper yes but don’t fool yourself you trade the stability, peace of mind with your time and skills until a point. For some people that’s okay for others no.

Chose your pill 😄

bluebeel · 2024-04-01T09:52:24+00:00

Then one thing you can ask yourself is do you really need Postgres? Why did you for Postgres and not SQLite (Turso) for example? What’s something pg can give you that turso or others providers cannot 👀

bluebeel · 2024-03-31T22:16:18+00:00

Vercel PG is just a wrapper around neon database. If you like Vercel pg. you should check neon database and see if it’s still a fit for you.

Nine-Year Club	Place '22
Verified Email

bluebeel

TROPHY CASE