Deterministic timecode alignment prevents cut drift.
Language models select text; a deterministic engine calculates exact frame boundaries by snapping to audio silence thresholds.
How does Passcut guarantee frame accuracy when using AI?
Passcut guarantees frame accuracy by restricting language models to transcript text selection, passing selected word spans to a deterministic audio engine that calculates exact cut timecodes using silence energy analysis.
By snapping edit points to audio silence intervals and aligning them to native video frame boundaries (e.g. 23.976, 24, 29.97, 59.94 fps), Passcut eliminates audio pops, clipped syllables, and frame drift.
Why is deterministic silence snapping superior to LLM timestamps?
Generative AI models struggle with precise mathematical calculations like video timecode offsets, leading to audio clipping when forced to output timestamps directly. Passcut’s hybrid architecture combines AI semantic text selection with deterministic signal processing.
Build Frame-Accurate Rough Cuts
Join the waitlist to test Passcut's deterministic video cutting engine on macOS.
Requires macOS 26 or later and Apple Silicon. No cloud processing, zero spam.
#Frequently Asked Questions
Why doesn't the language model generate timecodes directly?
Language models are probabilistic text generators that hallucinate numbers. Having an LLM emit timestamps causes cut drift and dropped words. Passcut forces the LLM to select transcript words, then computes exact timecodes deterministically.
How does silence snapping work?
Passcut analyzes audio waveform energy around selected transcript word boundaries, snapping cut points to true silence intervals and then aligning them to exact video frame boundaries.
Get Early Access to Passcut
Passcut is currently in pre-launch. Join the waitlist to receive early access as soon as the native macOS build is ready.
Requires macOS 26 or later and Apple Silicon. No cloud processing, zero spam.