From the SnipChamp team
Video workflows, AI agents, and the tools content teams actually need.
Why edit instructions don't survive the handoff
You recorded three hours of footage. You watched it back, made notes in your phone, sent a voice memo to the editor. Two weeks later the cut is wrong — and no one can explain why.
Read →EngineeringMCP agents and video: how AI reads your notes and executes cuts
The Model Context Protocol lets an AI agent connect to a running application, read its state, and take actions. Here is what that looks like for a video editing workspace.
Read →WorkflowThe 20 GB problem: why upload-first tools break content teams
Most cloud video tools require you to upload your footage before you can do anything. For a team producing 30 hours of raw recordings a week, this is not a workflow — it is a waiting room.
Read →TutorialBrowser-native video editing: what it is and why it matters
Most video tools require an upload before you can do anything. Browser-native editing changes that — the entire edit engine runs inside your tab, with no server involved.
Read →WorkflowAuto subtitles for content teams: accuracy, workflow, and what to expect
Automatic subtitles have improved dramatically, but they still require a workflow. Here is how content teams can use auto-transcription effectively without losing hours to correction.
Read →AIAI agents in post-production: what actually works today
The promise of AI in video editing is automation without loss of control. Here is an honest assessment of where AI agents add value in post-production today — and where they still need a human in the loop.
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