Pixelora ChannelOps
A review-first YouTube operating system for AI
What it does
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.
Keep source notes, decisions, approvals, and next actions in one local workflow with an offline Generate, Approve, Reject console.
If you already publish at least weekly with ChatGPT or Claude, ChannelOps keeps your YouTube sources, approved prompts, revisions, decisions, and next action in one local operating loop. You keep every public decision. Double-click the included console. No Pixelora login, installation, API, or account connection. Record one channel promise, the active video, and the exact revision under review. Export the next structured handoff for a compatible ChatGPT or Claude conversation. Approve that revision or reject it with a required reason. Upload authorization remains separate. The console keeps one active job visible. Rejection produces a usable next-revision reason. Approval binds to the exact…from pixelora-ai-operator-kits.j2ggzn7c8z.chatgpt.site
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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