@xoxo_psychoo here you are sir atumera.com/hackathon/apply

hey Mateo, we have given option for extension till 26 for everyone

Project showcase + winners reveal: Sep 29, 20:00 Athens / 17:00 UTC, just before the Watercooler 🎉 discord.com/events/423160867… Join the server: discord.gg/6CZEhWrt4 Finalists emailed Sep 27. Can’t demo live? We can show your submitted video.

A fantastic submission for the @livepeer x @origin_trail track 🔥

Give one creative brief to a media team. Make sure every role improves the next version. I built 𝐋𝐈𝐕𝐄𝐑𝐋𝐎𝐎𝐏 for the @Embody_Net Livepeer Agent Hackathon, submitting to the Livepeer Agent + OriginTrail DKG track. LiverLoop is an autonomous media production loop: Generate → Evaluate → Improve → Remember. One Director turns a creative brief into a production plan by discovering the capabilities currently available through Livepeer Agent. The Orchestrator executes the plan. The Critic evaluates the finished artifact against the brief. If something fails, the Director chooses a targeted retry instead of regenerating everything blindly. The loop can coordinate video generation, image generation, audio, FFmpeg transformations, trimming, muxing and subtitle overlays. Every capability call uses authenticated server-side access, with provider fallbacks and key rotation for reliability. The browser never receives the credentials. Livepeer returns real media artifacts, URLs and estimated costs. LiverLoop stores them, probes the files with mediainfo, checks their actual format, duration, dimensions and streams, then makes the final artifacts playable and downloadable from the dashboard. Previous versions stay preserved instead of being overwritten. The system also checks generated footage for unwanted baked-in text before allowing a version to pass. When a run completes, LiverLoop packages the brief, plan, artifacts, evaluations, retry decisions and lessons into a knowledge asset. OriginTrail DKG stores that memory so future runs can retrieve previous lessons and improve their next plan. How to use it: 1. Open the app and start a run 2. Describe your brief: format, duration, audience, style and CTA 3. Watch the loop plan, generate, evaluate and improve in real time 4. Download the final artifact, or browse earlier versions 5. Open Provenance to audit every event, cost and decision > One Director. > One Critic. > One orchestration loop. > A network of media capabilities. > Downloadable artifacts. > Explainable retries. The key word is Generate. Evaluate. Improve. Remember. Live app: liverloop.world GitHub: github.com/Saber1Y/LiverLoop full demo: piped.video/oBlLtjnV4uw Built with Livepeer Agent, OriginTrail DKG, Next.js, FFmpeg and mediainfo. #Livepeer #OriginTrail #AI #GenerativeAI

Project showcase + winners reveal: Sep 29, 20:00 Athens / 17:00 UTC, just before the Watercooler 🎉 discord.com/events/423160867… Join the server: discord.gg/6CZEhWrt4 Finalists emailed Sep 27. Can’t demo live? We can show your submitted video.

The MCP credit issues should now be resolved. For your free $200 in Livepeer Creative MCP credits, or help accessing them, email qiang@livepeer.org and mention the hackathon. We’re here to help—good luck with the final stretch! 🙌

Livepeer Agent Hackathon builders: submissions are due today at 23:59 Athens (UTC+3)! 🛠️ Have your repo, video demo & code ready: atumera.com/hackathon/submit Need until Sep 26? Email george@atumera.com. No submissions after Sep 26, 23:59 Athens.

I’ve been thinking about how strange it is that we spend months building software, then when it’s time to explain what we built, we basically start from zero. The story is already there. Buried in the repo. So I built EchoPitch. You give it a GitHub repository. It investigates the code, figures out what the product actually does, and verifies the claims it wants to make against implementation evidence. Then it plans the story, produces the media through Livepeer, reviews its own work, and assembles the final launch film. The part I cared about most was trust. I didn’t want an agent that reads a README and turns marketing copy into prettier marketing copy. If EchoPitch can’t find enough evidence for a claim, ClaimLock doesn’t let that claim into the film. And when the film is done, the evidence doesn’t disappear. You can still trace what was said, why it was said, what was generated, what it cost, and what survived review. Repository → evidence → story → film. Your software already tells a story. EchoPitch directs it. echopitch-studio-ten.vercel.… Built for the @Livepeer Agent Hackathon. 🎬 @Embody_Net

Myself and @floozi_official built a Video generating AI for the @Embody_Net & Livepeer Agent Hackathon. It was a pleasure working with you @floozi_official 👏🫡

A few days ago, George (@Embody_Net) reached out to me about the Livepeer Agent Hackathon, and I decided to actually build an n8n workflow around Livepeer Agent rather than just read the docs. The idea sounded simple: Telegram → receive a product image + brief → n8n → Livepeer Agent → image-to-video → return the finished video What started as a straightforward automation turned into a pretty interesting exercise in debugging AI infrastructure. The first issue I hit was media handoff. I initially passed the Telegram-hosted image URL directly into the image-to-video request. The request reached the provider, but the provider rejected the asset with: file_download_error “Failed to download the file. Please check if the URL is accessible…” So I changed the architecture. Instead of asking the model provider to fetch the Telegram file: Telegram → download image into n8n → upload image to Livepeer → receive a Livepeer-hosted asset URL → pass that URL into the video generation request That worked. Then I hit MCP surface routing. I called run_capability on the creative MCP surface. The server rejected it before execution and explicitly told me that run_capability was available on the raw surface: /api/mcp/raw So now different parts of the workflow had to understand different MCP surfaces rather than treating the Agent as one uniform endpoint. Then authentication became another interesting layer. I created credentials and tested authenticated requests, but the deployment I was hitting returned: “This deployment is keyless — Livepeer Agent does not accept an API key here…” So I removed the Authorization header and went back to the keyless connection for that deployment. At that point I realized I was no longer debugging an “AI video generation” problem. I was debugging an integration contract. The next question was: What exactly does create_media return? Because video generation is asynchronous, the workflow isn't simply: create_media → video It becomes something more like: create_media → job → wait → get_create_media(job_id) → inspect status → wait → retrieve final asset And this is where things got particularly interesting. My workflow reached the polling stage, but the data coming back through MCP wasn't giving my parser the job identifier in the form I expected. I could have kept guessing. Instead, I changed the workflow. The latest version is intentionally a diagnostic pipeline whose job is to capture the raw create_media response before I write another line of polling logic around assumptions. Then I hit another boundary. I tried exact model dispatch with: run_capability(ltx-25-i2v-fast) and received: “Demo budget store unavailable — try again shortly.” The response also explicitly said the request was refused before dispatch and that nothing was billed. So the provider wasn't even reached in that attempt. This is the part of AI infrastructure engineering that I find fascinating. The hard part isn't always the model. The hard part is everything around the model. For an automation system like n8n, the actual integration contract is: authentication + MCP surface discovery + tool schemas + binary media handoff + async job lifecycle + job identifiers + polling semantics + asset URLs + error semantics Every one of those has to be predictable if the workflow is eventually going to run unattended. And that changes the question I'm asking. I'm no longer asking: “Can Livepeer generate a video?” The more interesting question is: “Can an external automation system reliably treat Livepeer Agent as a deterministic media execution layer?” That's what I'm testing. I'm deliberately not hiding the failures behind a polished demo. I'm logging the actual runtime errors. I'm changing one integration boundary at a time. I'm separating provider failures from MCP failures. I'm separating MCP failures from n8n expression/state problems. And I'm refusing to assume what an API returned until I inspect the actual response. That's how I prefer to debug systems where multiple services are involved: Observe first. Normalize second. Automate third. There are also signs that this area is still actively evolving. Livepeer's own Agent documentation describes multiple MCP surfaces, asynchronous media jobs, model selection behavior, and the need to inspect returned capability/job information rather than assuming a model or result. The documentation also explicitly describes run_capability as the exact-dispatch path. So my takeaway so far isn't “Livepeer doesn't work.” It's more nuanced: The underlying media-generation capability is interesting. But when you integrate an AI media agent into an external orchestration system, the documentation needs to make the entire runtime contract easy to follow: auth → endpoint/surface → tool → schema → async lifecycle → output contract → error handling → billing behavior That's the part I'm validating right now. George, thanks for reaching out and pushing me to actually build this instead of just reading about it. I'm still debugging the workflow, and the next checkpoint is simple: Capture the real create_media response. Identify the exact job lifecycle. Wire the polling layer against the actual response. Then finish the end-to-end Telegram → Livepeer → video automation. I’ll share the final architecture once I have it running. #Livepeer #LivepeerAgent #AI #AIVideo #MCP #n8n #Automation #AIEngineering #GenerativeAI #BuildInPublic #DevTools #AIAgents

advice to hackers building, always take what you built in your last hackathon and use it in the next. Think of a way that your last thing can integrate with the new theme of the hackathon. Do that few times and you get progressivelly exremely competent at delivering under a single repo instead of building pieces of software that don't compound.

we like our work very much sir, keep it up!

🔥

this was generated by nue motion, powered by @Livepeer

@theProcessXCII did one of our best submissions. The frame extractor utility is absolutely fantastic❤️‍🔥

Quick 1-min Demo for @workflow_xAI - A Thread🧵 the problem, Workflow [powered by @Livepeer] solves for me is removing the tedious effort of jumping through different tabs, for different tasks. sometimes, i have to resize/extend a reference image, sometimes i have to re-imagine or recreate a certain shot/frame, sometimes it's getting an optimized prompt for a particular model, but by bringing all of that into one place. i have elliminated a chunk of time-wasting in my process, which optimizes my workflow, hence the name 'Workflow'. and i believe it can do the same for creators like me. here is a quick one minute walkthrough, sped up, but i will post the full versions in the threads below, so you can see it in real time.

Hey lune, no worries at all. Submissions are extended till 24 of September. If you need a bit more time than that, let us know and we will be happy to accommodate! Happy building! ✨ ⚒️

Made with @Livepeer agent ❤️

New @Livepeer agent mcp is 🔥 storage.googleapis.com/story…