Explainers that
land on the word
Give it a topic or your own script. It writes, narrates, measures where every spoken word actually lands, and generates the animation against those measurements. No manual timing, anywhere.
Built for teacherswho would rather explain than keyframe
0 of 4 cues fired · beat “layers”
For people whose job is making something click
Lecturers
Turn a week's reading into a ninety-second primer students actually watch before the seminar.
YouTubers
Ship the diagram-heavy explainer without spending three days nudging keyframes in an NLE.
Teams explaining themselves
Onboarding, architecture, incident write-ups — the things everyone means to animate and never does.
The idea everything rests on
Audio is the clock
The naive way is to animate first and hope the voiceover fits. Here the order is inverted: narration is synthesized first and becomes the single source of truth. Every word is measured, and both the animation and the captions are written against those measurements.
What the engine measured
- Cue times
- 0 / 4 fired
- Alignment quality
- 96.8%
- Now speaking
- TCP/IP
Nothing here is a guessed run_time. The scene asks the timeline when “TCP” happens and animates to that number. Rewrite a sentence and every cue moves with it.
Nothing hidden
Ten stages, and you can watch every one
Each stage reports what it did and how long it took. When something breaks, it tells you which stage and what to change — not a stack trace.
- 01
Script
Writes or loads the narration script and checks it against the authoring rules.
- 02
Narrate
Synthesizes every beat, concatenates, and fits the result to the target duration.
- 03
Transcribe
Runs word-level speech recognition over the narration. Skipped when the TTS backend supplies its own timings.
- 04
Align
Matches recognized words back onto the script so every cue gets a real timestamp.
- 05
Scene code
Generates one Manim scene per beat, written against the measured timeline.
- 06
Captions
Builds the karaoke .ass subtitle file from the word timings.
- 07
Render
Renders each scene independently, then joins them.
- 08
Mux
Muxes video with narration and burns the captions.
- 09
Verify
Probes the finished file: resolution, fps, loudness, duration, alignment.
- 10
Finalize
Records the outcome and cleans up the workspace.
Transcribe is conditional. Pick a voice backend that returns its own word timings and speech recognition is skipped entirely — nine stages instead of ten, and the progress bar knows it before the job starts.
Why AI video usually looks like slop
Generic AI video tools
- Stock footage loosely related to the words
- Narration recorded first, animation guessed after
- Captions drift a beat behind the voice
- Edit one sentence, re-time the whole video
- You cannot see why it chose anything
Loooped
- Diagrams generated from the actual explanation
- Every word measured before a frame is drawn
- Captions cut from the same measurements
- Edit a sentence, cues move themselves
- Every scene is readable Python you can inspect
Describe the thing. Get the explainer.
Start with a topic and let it write the script, or paste your own and keep every word.