폴더의 원시 영상을 드롭합니다. 어시스턴트 편집기처럼 이야기. .fcpxml]를 타임라인으로 바로 얻으세요. - 모든 컷, 전환, 재회, 캡션 결정은 당신이 검사, 거부 할 수있는 서면 합리적과 함께 제공, 또는 그냥 그렇게 말하기로 결정합니다. · 다른 절반 벌거 벗은 AI 편집기는 예기치 않은 두 번째로 떨어질 것입니다. ** 3개의 인식 차선 — Parakeet ONNX 연설, Florence-2 1개의 fps, CLAP 오디오 이벤트는 에이전트 커스터드 어휘에 대해 점수를 매겼습니다. - 단일 [merged_timeline.md로 옮겼습니다. 이 에이전트는 픽셀 대신 end-to-end와 이유를 읽습니다. Word-precise EDL 앵커는 캐시 된 성적에 대해 helpers/find_quote.py]에서 옵니다. - greps 없음, off-by-one. 상태-of-the-art 오픈 모델, 다른 사람이 배송, 실행 ** 100 % 기계. 모든 커트 경계는 수출의 앞에 근원 클립에 대하여 확인됩니다 — mid-word 커트 및 시각적인 discontinuitiesblock FCPXML. · 구름 없음. 까만 상자 없음. 워터 마크 없음. 구독 없음. 개정에 따라 50은 개정 1과 동일합니다. · **이것은 소설이 아닙니다.

Drop raw footage in a folder. Talk to it like an assistant editor. Get an .fcpxml straight into your timeline — every cut, transition, retime, and caption decision comes with a written rationale you can inspect, reject, or iterate on by just saying so.

Not another half-baked AI editor that demos pretty and falls apart the second you point it at real footage. Three perception lanes — Parakeet ONNX speech, Florence-2 visual captions at 1 fps, CLAP audio events scored against an agent-curated vocabulary — interleaved into a single merged_timeline.md the agent reads end-to-end and reasons over instead of watching pixels. Word-precise EDL anchors come from helpers/find_quote.py against the cached transcripts — no greps, no off-by-one. State-of-the-art open models, composed in a way nobody else has shipped, running 100% on your machine. Every cut boundary is verified against the source clips before export — mid-word cuts and visual discontinuities block the FCPXML.

No cloud. No black box. No watermark. No subscription. Iteration on revision 50 costs the same as revision 1.

This isn’t a novelty. It’s the assistant editor you wish you could hire — doing real assembly work, on your machine, in your NLE. If you edit for a living, it saves you hundreds of hours.

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premiere-agent

Edit videos by conversation — runs 100% locally and exports straight into Premiere Pro (and Resolve, and Final Cut) with J cuts, L cuts, dissolves, and the rest of the cut vocabulary you’d actually use in an NLE.

Drop raw footage in a folder, chat with your agent (Claude Code — local CLI or cloud workspaces, or Codex CLI / IDE / app), get cut.fcpxml + cut.xml back — XML-only delivery, the cut lives in your NLE. Text + on-demand visuals, no frame-dumping — three perception lanes so the LLM can reason about speech, abstract sounds, and visible objects, not just one of them.

What it does

It’s a conversational video editor — you talk to it like an assistant editor (“trim the filler, J/L the dialogue, timelapse the build sequence, ship it”) and you get back an XML cut you import straight into your NLE.

Drop your raw clips in a folder, run claude or codex in that folder, and the agent walks the loop with you: it inventories the sources, asks a few questions about what you’re going for, runs a one-time preprocess that reduces hours of footage to three small text timelines, proposes a strategy, waits for your OK, writes the cut, self-evaluates every boundary against the source clips, and only then drops cut.fcpxml + cut.xml + master.srt next to your sources. Don’t like a cut? Tell it. Want a different pace? Tell it. The preprocess is cached, so iteration is just re-reasoning over the same timelines — no re-watching, no re-transcribing.