Applied Compute · Launch Video Breakdown: Hook, Pacing & Motion Design

Scene-by-scene timeline & spoken transcript

1. 0:00 – 0:16

   The Hook

   Introduction to AC2

   “Hi, I'm Yash from Applied Compute, and today we're introducing AC2, the training and inference cloud for open models. We're excited to bring research out of the lab and enable every AI team to train, serve, and continuously improve their LLMs. Let me show you how it works.”

   On screen

   CEO and Co-founder Yash Patil | Applied Compute

   Camera

   Static medium shot with slight digital push-in on presenter.

   Motion

   Lower-third animated logo card with clean typography reveal.

2. 0:16 – 0:36

   Problem Agitation

   Task Setup & Dataset Pipeline

   “Let's say I want to train a code Q&A agent on top of Qwen 3.6 35B because it's fast and cost-efficient. In this case, I've already created a data set from GitHub repos. I also have a custom harness built on top of OpenCode that gives the model access to some special tools I wanted to use.”

   On screen

   github | codeqa-train - 22,488 Tasks | id: codeqa-etcd-8412

   Camera

   Split screen layout with video pip and code windows sliding in.

   Motion

   Curved SVG connection lines linking GitHub repositories to parsed JSON tasks.

3. 0:36 – 1:04

   Product Reveal

   Developer SDK and Scaffolding

   “To get started training with these, I'll create a new AC2 project and implement a few simple abstractions. First, I'll register my data set. Then, because I'm using my own harness, I'll wrap it in the lifecycle functions required by our bring-your-own-harness interface. This lets me directly train with the compaction logic and tools that OpenCode ships with. And finally, I'll define a reward function, which in this example uses an LLM judge to score the model's final answer.”

   On screen

   ac2 project init codeqa | upload_dataset.py | harness.py | grader.py

   Camera

   Smooth center-focus pan across terminal windows and code editors.

   Motion

   Syntax-highlighted code scrolling and typing transitions.

4. 1:04 – 1:27

   Feature Teaser

   CLI Execution & Live Observability

   “Now, I can launch and monitor my run with a single command using the CLI. AC2 comes with optimized configs for all the top models, so you don't have to worry about GPU performance or tuning YAML. We also provide the observability to monitor your training runs live and see how they're progressing. Our post-training stack supports a bunch of different algorithms like GRPO, SFT, and OPSD.”

   On screen

   ac2 train run | Qwen3.5-9B | Kimi-K2.7-Code | NVIDIA Nemotron-3 | GRPO | SFT | OPSD

   Camera

   Floating multi-window cards transitioning to UI dashboard analytics.

   Motion

   Floating model selector badge array and interactive line chart rendering.

5. 1:27 – 1:54

   Feature Teaser

   Automated Agent Intervention (Ari)

   “Let's check back in on our training. Looks like I got a ping on Slack. It's from Ari, our AC2-native research agent. Ari monitors run health, reads through rollouts, and takes actions even when I'm offline. Here, the remote tool call server hosting the codebase appears to have failed. Ari saw the reward tank and decided to restart the run. I can even inspect Ari's execution trace and generate custom reports on data points or do comparisons across runs.”

   On screen

   Slack | Ari | Alert Stalled - Run codeqa-rt-01 | Step 41 | What the model learned during training

   Camera

   Screen overlay zooms smoothly onto Slack notifications and trace metrics.

   Motion

   Push-in camera moves focusing on chart failure spikes and report generation cards.

6. 1:54 – 2:07

   Product Reveal

   One-Click Inference Deployment

   “Once my model has finished training, I'll use AC2 inference to one-click deploy to a fully autoscalable serving endpoint. Now that the model is being served, I can collect traces online to train further with techniques like self-distillation.”

   On screen

   Deploy a model | user-qwen3-30b-a3b-base | Deploy | Requests

   Camera

   Split layout showing dashboard interface next to the speaker.

   Motion

   Cursor click animation triggering real-time deployment throughput charts.

7. 2:07 – 2:24

   Call to Action

   Customer Proof & Outro

   “AC2 is already being used to power models that we train for companies like Microsoft, NVIDIA, Cognition, Harvey, Mercor, DoorDash, and others. If you're interested in learning more about training and serving your own models, please reach out to us.”

   On screen

   Microsoft | NVIDIA | Cognition | Harvey | Mercor | DoorDash | Applied Compute

   Camera

   Cut back to speaker with grid overlay, dissolving into vector end card animation.

   Motion

   Staggered logo badge reveal followed by rotating concentric vector ring resolution.