Pixelora ChannelOps

A review-first YouTube operating system for AI

4 followers

A review-first YouTube operating system for AI

4 followers

ChannelOps turns scattered YouTube planning, production, QA, approval, publishing prep, and measurement into one file-based workflow for ChatGPT or Claude. It includes 28 working files and a local Generate / Approve / Reject console. No API, subscription, account connection, autonomous upload, or performance guarantee.

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Launch tags:Productivity•Artificial Intelligence•YouTube

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Maker

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Hi Product Hunt — I built ChannelOps after seeing the same problem repeat in AI-assisted creator work: every chat starts with useful ideas, but the operating state, source notes, approval decision, and next action disappear across sessions. ChannelOps gives one YouTube channel a file-based operating loop: - Generate exports the next AI work packet. - Approve binds the decision to the exact revision under review. - Reject requires a reason and turns it into the next revision brief. - Publishing authorization remains separate. The download contains 28 working files for channel setup, strategy, research, production, rights-aware checks, QA, owner review, publish preparation, measurement, recovery, and handoff. The offline console runs locally in a browser and does not connect to YouTube, ChatGPT, Claude, or any server. This is not a video generator or autonomous posting tool. The buyer still supplies the AI account and production tools, reviews facts and rights, creates or edits the media, and controls every public action. I would value direct feedback on two questions: Is the first five-minute setup clear, and does the console make the boundary between AI preparation and owner approval obvious?

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2mo ago

Forum Threads

p/pixelora-channelops

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2mo ago

What part of your AI-assisted YouTube workflow do you rebuild most often?

I keep seeing the same failure mode in recurring creator work: the AI conversation produces useful output, but the source notes, approved prompt, revision decision, and next action are scattered when the next session starts.

I built ChannelOps around a simple boundary: AI prepares the next work packet; the creator still reviews, approves or rejects the exact revision, and separately authorizes anything public.

For people already using ChatGPT or Claude for a weekly YouTube workflow, which state is hardest to preserve between sessions sources, approved prompts, revision history, rights checks, or the next action?

I am the maker of Pixelora ChannelOps and am using this thread to learn where the workflow breaks in real use. No automation or performance claim is implied.

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