Man, your title is misleading. I was curious to see what Google had suddenly built. Hyperframes + ffmpeg can be used in any coding editor, so where did Antigravity come from? 😕

Genuinely impressive diagram, and the flow you wrote underneath is the right shape. Sense, decide, act, learn with a human only on exceptions is exactly how autonomous ops should look. A few things I would add or pressure test, from having watched where these break. The example is mostly deterministic, which is its strength, so I would tighten where the LLM actually sits. Reorder quantity is usually a formula, sales velocity, lead time, safety stock, not a judgment call, so an LLM there adds cost, latency and a chance of a confidently wrong number. Keep the AI for the genuinely fuzzy calls, anomaly reasoning, demand shifts, supplier issues, and let deterministic math own the reorder. That is the recurring lesson in this whole thread, an LLM belongs only where an IF statement cannot go. What I would add is the unhappy path, because that is what separates a demo from real autonomy. What happens when the supplier API times out or rejects the order. What stops a bad trigger reordering five times in a loop. Where is the idempotency key so a webhook firing twice does not double order. Where is the cap, a hard ceiling on spend or quantity that no AI decision can exceed without a human. Right now the diagram shows the flow when everything works. Production is mostly the handling of when it does not. Two smaller ones. Your notify only if abnormal is smart, but define abnormal explicitly or you get alert fatigue or silent failures. And the learning loop is the hardest piece to make real, updating strategies from outcomes needs labeled results and evaluation, otherwise it quietly drifts. Most systems that claim self learning are really just logging. So my honest take, the architecture is sound and the autonomy is real for the happy path. Make the reorder deterministic, put a hard spend cap and retry and idempotency logic around the action layer, and define your alert thresholds, and you move from an impressive diagram to something you would actually trust to run unattended. Nice work, this is a genuinely strong mental model.

Solid plan. The one thing to flag up front is that the hardest part here is not the automation, it is the social platform APIs. That is where projects like this quietly die, so weight your planning there. Most platforms require app review before you can post on a client's behalf. Meta covers Instagram and Facebook but demands business verification and review, and Instagram only publishes through a Business account linked to a Page. TikTok, LinkedIn and X each have their own hurdles. Budget weeks for approvals, not an afternoon. Your questions in order. OAuth flow, the client approves on the platform, then you exchange the code server side for tokens, never in the frontend since it uses your app secret. Storage, keep tokens encrypted in your database tied to client and platform, Supabase with row level security fits well. Refresh, do not rely on n8n for this in a multi client system, its credential model is built for your own accounts, so own the refresh logic yourself. That answers your backend question too. Put a real backend between the frontend and n8n. Let Supabase Edge Functions or a small Node service own auth, token storage, refresh and client isolation, and hand n8n a valid token at run time. Let n8n do what it is good at, the publish orchestration. Do not make it your whole production backend for a system holding many clients' credentials. For isolation, tag every token, post and log with a client id and filter at the database layer with row level security, so a code bug cannot leak one client's access to another. Biggest lesson, build one platform end to end first, usually Instagram via Meta, get the full approve, token, publish, refresh loop working for a single client, then clone the pattern. Launching six platforms at once means six review processes stalling you in parallel. Worth knowing as a plan B, not your foundation: middleman tools like Blotato give you one API key to publish across all major platforms, with OAuth, refresh and rate limits handled for you, plus official n8n nodes. It skips every app review queue, but you would be renting your core publishing layer and inheriting their pricing, limits and uptime, which for a white label agency system is exactly the part you want to own. Keep it in your back pocket if review timelines block you, but build to own the plumbing if you can.

Solid first ship by the way, solving your own real pain is exactly how the best nodes get built.

Honest answer, n8n is what gets you hired right now, and it is not close. Businesses buy what they can name, scope and put a budget on, and n8n is a known quantity with a known deliverable. A Claude Code agent is harder for a non technical buyer to picture or trust, so it rarely shows up in the job post even when it would do the job better. But the either or is the trap. Sell what they ask for, deliver it with the newer stuff. n8n is the keyword that gets you in the door, Claude Code is the edge that lets you finish the same job in half the time and take on the ones other freelancers cannot. The client never asks how you built it, only that it works, so you get paid on the recognized name and win on the hidden capability. Keep in mind that asked for by name is a lagging indicator too, a tool lands in job posts long after it matured, so the people learning the new stuff now are the ones the market asks for by name later. One more thing, do not get pulled into shiny object syndrome. There is a fresh buzzword every week, AI OS being the latest, and honestly 90% of the people throwing that term around could not tell you what it actually is. Clients do not buy buzzwords, they buy a problem that stays solved. Think of it like commuting to work. You need a reliable car that starts every morning, not an F1 car. The F1 is faster and far more impressive, but it is useless for the commute, costs a fortune and needs a pit crew. Chase the trophy tech for fun on your own time, and get paid solving the boring commute, because that is what businesses are actually buying.