AI-Youtube-Shorts-Generator
Open-source alternative to Opus Clip, Vidyo.ai, Klap & SubMagic. Turn long-form YouTube videos into viral 9:16 shorts using LLM highlight detection, Whisper transcription, and auto vertical cropping, free, no watermarks, no per-clip credits.
Runs with mockssource: GitHubhomepagePythonMITcommit 9c7a33e7b927
Python, MIT licensed. The project labels itself: 2short ai alternative, ai clip generator, ai clipping, auto clip, auto crop, highlight detection, klap alternative and llm.
AI-Youtube-Shorts-Generator runs, with stand-ins for the services it depends on. An Argusic agent installed it in 0.0 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
Both API mode (with mock MuAPI server) and local mode (with mock LLM) complete end-to-end: download, whisper transcription, highlight selection, and vertical crop produce valid output files. api_ok=True local_ok=True clip_valid=True
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 92 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- under a minute
- Cold machine to finish
- 10 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- API mode with mock MuAPI server: generate_shorts returned 1 clip with clip_url via POST /youtube-download, /openai-whisper, /gpt-5-mini, /autocrop. Local mode: faster-whisper tiny transcribed 10s test video (1 segment, 10s audio in ~2s), mocked LLM returned 1 highlight, ffmpeg+OpenCV cropped to 404x720 9:16 mp4 with aac audio, ffmpeg -v error -i output/short_01.mp4 -f null - returned 0 with no stderr output.
- Model tokens used
- 409,153
- Exact commit tested
- 9c7a33e7b927
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
- Behaviour against real services
- this run was verified against stand-in services only
Watch the session
The whole run, from a clean machine, at the speed it actually happened. Nothing is cut and nothing is sped up.
A recording of attempt 1, run . The verdict above comes from a different attempt, so read this as one session and not as the current result. The terminal recording it was made from is the evidence, stored with a sha256 fingerprint; the video is a rendering of it. Nothing loads from the video host until you press play.
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
+ except Exception: + pass + here = _os.path.dirname(_os.path.abspath(__file__)) + repo_root = _os.path.normpath(_os.path.join(here, "..", "..")) + candidate = _os.path.join(repo_root, "assets", "haarcascade_frontalface_default.xml") + if _os.path.isfile(candidate): + return candidate + raise RuntimeError( Both modes passed all checks. Here's the complete report: tokens used 409,153 Both modes passed all checks. Here's the complete report:
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Installed in 0.0 minutes.
- Nothing broke on the way: zero errors between clone and running.
- Licensed MIT, as reported by its host.
- Measured 3 times, so the result is not a one-off.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 3/3 | Runs with mocks | 92.00 | 0.08 | |
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.08 | |
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.07 |
Topics (from GitHub)
2short-ai-alternativeai-clip-generatorai-clippingauto-clipauto-crophighlight-detectionklap-alternativellmopen-sourceopus-clip-alternativereelsshorts-makersubmagic-alternativetiktokvertical-videovideo-editingvidyo-ai-alternativeviral-clipswhisperyoutube-shorts
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/ai-youtube-shorts-generator)Questions
- Does AI-Youtube-Shorts-Generator run?
- AI-Youtube-Shorts-Generator runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 0 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test AI-Youtube-Shorts-Generator?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 9c7a33e7b927. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test AI-Youtube-Shorts-Generator?
- The run that produced this verdict cost $0.08: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does AI-Youtube-Shorts-Generator take to install?
- 0.0 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does AI-Youtube-Shorts-Generator need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing AI-Youtube-Shorts-Generator?
- Nothing did, in the recorded run: zero errors between clone and running.
- Where is the evidence for AI-Youtube-Shorts-Generator?
- All 3 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.