feat(docs): add participant onboarding guide

Author: swarna1101

docs/verifiable-ai-hackathon/0-onboarding-guide.mdx

@@ -0,0 +1,201 @@
+---
+sidebar_position: 1
+unlisted: true
+slug: onboarding-guide
+title: Participant Onboarding
+description: Step-by-step guide for setting up infrastructure, credentials, and cloud instances for the Verifiable AI Hackathon.
+keywords: [flare, ai, confidential-compute, hackathon, google-cloud, onboarding]
+---
+
+import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+
+import CreateInstance from "/static/img/verifiable-ai-hackathon/create-instance.png";
+import MachineConfiguration from "/static/img/verifiable-ai-hackathon/machine-configuration.png";
+import OsAndStorage from "/static/img/verifiable-ai-hackathon/os-and-storage.png";
+import Security from "/static/img/verifiable-ai-hackathon/security.png";
+import Networking from "/static/img/verifiable-ai-hackathon/networking.png";
+import FinalStep from "/static/img/verifiable-ai-hackathon/final-step.png";
+import AddMetaData from "/static/img/verifiable-ai-hackathon/add-metadata.png";
+
+# Participant Onboarding
+
+This guide provides all necessary steps to get onboarded for the **Verifiable AI Hackathon**. Participants will set up their **Google Cloud infrastructure**, acquire necessary **API keys**, and deploy their **Confidential Compute VMs**.
+
+## 1. Setup
+
+1. **Google Account**
+
+   - Create or use an existing Google account
+   - This account will be used for all hackathon resources
+
+2. **Project Registration**
+
+   - Submit your email address during registration
+   - The Flare Infra Team will onboard you to `google-hackathon-project`
+
+3. **Gemini API Key**
+
+   1. Navigate to [Get a Gemini API key in Google AI Studio](https://aistudio.google.com/app/apikey)
+   2. Click on "Create API key"
+   3. Select `google-hackathon-project` from the dropdown
+   4. Click "Create API key in existing project"
+
+4. **VM Instance Creation**
+   - Choose between CLI or Console setup methods below
+
+## 2. VM Instance Setup Methods
+
+### Option A: Using gcloud CLI (Recommended)
+
+Follow these steps to set up a GCP instance using Confidential Space with Cloud Logging.
+
+1. **Install gcloud CLI**
+
+   - Follow the [gcloud CLI installation guide](https://cloud.google.com/sdk/docs/install)
+
+2. **Create VM Instance**
+   Use the following command, replacing variables marked with `<>`:
+
+   ```bash
+   gcloud compute instances create <INSTANCE_NAME> \
+       --confidential-compute-type=TDX \
+       --machine-type=c3-standard-4 \
+       --maintenance-policy=TERMINATE \
+       --zone=us-central1-c \
+       --project=flare-network-sandbox \
+       --network-interface=network-tier=PREMIUM,nic-type=GVNIC,stack-type=IPV4_ONLY,subnet=default \
+       --metadata=tee-image-reference=<IMAGE_URL>,tee-container-log-redirect=true,tee-env-<ENV_VAR_NAME1>=<ENV_VAR_VALUE1>,tee-env-<ENV_VAR_NAME2>=<ENV_VAR_VALUE2> \
+       --provisioning-model=STANDARD \
+       --service-account=83674517876-compute@developer.gserviceaccount.com \
+       --scopes=https://www.googleapis.com/auth/cloud-platform \
+       --tags=<INSTANCE_NAME>,http-server,https-server \
+       --create-disk=auto-delete=yes,boot=yes,device-name=<INSTANCE_NAME>,image=projects/confidential-space-images/global/images/confidential-space-debug-250100,mode=rw,size=11,type=pd-balanced \
+       --shielded-secure-boot \
+       --shielded-vtpm \
+       --shielded-integrity-monitoring \
+       --labels=goog-ec-src=vm_add-gcloud \
+       --reservation-affinity=any
+   ```
+
+   **Required Variables:**
+
+   - `<INSTANCE_NAME>`: Your instance name (e.g., flare-ai-core)
+   - `<IMAGE_URL>`: Image path (e.g., ghcr.io/flare-foundation/flare-ai-core:main)
+   - `<ENV_VAR_NAME1>`: First environment variable name (e.g., GEMINI_API_KEY)
+   - `<ENV_VAR_VALUE1>`: First environment variable value
+   - Add additional environment variables as needed
+
+### Option B: Using GCP Console
+
+Follow these steps to set up a GCP instance using the web console.
+
+1. **Access Console**
+
+   - Go to the [GCP Console](https://console.cloud.google.com/)
+   - Click "Create Instance"
+
+   {" "}
+   <img src={CreateInstance} />
+
+2. **Configure Machine**
+
+   1. Basic Settings:
+
+      - Name: `<project-name>-<team-name>` (e.g., agent-gemini-john)
+      - Region: us-west2
+      - Machine Type: n2d-standard-2 (2 vCPU, 1 core, 8 GB memory)
+
+      <img src={MachineConfiguration} />
+
+   2. Security Settings:
+
+      - Service account: confidential-sa
+      - Access scopes: "Allow full access to Cloud APIs"
+      - Confidential VM service: Enable
+      - Shielded VM: Check "Turn on Secure Boot"
+
+      <img src={Security} />
+
+   3. OS and Storage:
+
+      1. Click "Change"
+      2. Select "Confidential Space Debug 240900"
+      3. Keep other values default
+      4. Click "Select"
+
+      <img src={OsAndStorage} />
+
+   4. Networking:
+
+      - Enable "Allow HTTPS traffic"
+
+      <img src={Networking} />
+
+   5. Advanced Settings:
+
+      1. Click "Add Item" under Metadata
+      2. Add the following items:
+
+         ```bash
+         tee-image-reference: <ghcr.io/john/agent-gemini:main>
+         tee-container-log-redirect: cloud_logging
+         ```
+
+      3. Additional API keys should be prefixed with `tee-env-`
+
+      <img src={AddMetaData} />
+
+3. **Launch Instance**
+
+   - Click "Create" to start the VM
+   - Monitor progress:
+
+     - System logs: Click "Serial Port (console)"
+     - Container logs: Click "Logging" after startup
+
+     <img src={FinalStep} />
+
+## Resource Allocation
+
+The hackathon provides the following resource allocations per track:
+
+| Track              | Prize Share | Compute | Gemini API | BigQuery | Total |
+| ------------------ | ----------- | ------- | ---------- | -------- | ----- |
+| Social AI Agent    | 20%         | $250    | $100       | $100     | $450  |
+| RAG Knowledge      | 25%         | $250    | $100       | $600     | $950  |
+| AI x DeFi          | 35%         | $250    | $100       | $400     | $750  |
+| Consensus Learning | 20%         | $250    | $100       | $300     | $650  |
+
+**Notes:**
+
+- Total credits required: ~$3,000
+- Buffer allocation: 100% ($6,000 total)
+- Resources monitored via Billing → Budget & Alerts
+- Consumption tracked per service
+
+## Service Usage Assumptions
+
+### BigQuery Usage
+
+- 50% team activity at any time
+- Free tier (1 TiB) distributed equally
+- 48-hour usage period
+- Query patterns vary by track
+- Storage costs not included
+
+### Gemini API Usage
+
+- Uses Gemini 1.5 Flash pricing
+- 50% team activity rate
+- 48-hour usage distribution
+- Standard pricing (no long context)
+- No context caching included
+- No Google Search grounding
+
+### Compute Resource Usage
+
+- Confidential Compute: $0.123/hour per instance
+- Total hourly cost: $15.65 (125 teams)
+- 48-hour duration: $750
+- Rounded to $1,000 with equal track distribution

docs/verifiable-ai-hackathon/0-overview.mdx

@@ -17,8 +17,8 @@ const sidebars: SidebarsConfig = {
       type: "category",
       label: "Verifiable AI Hackathon",
       collapsed: true,
-      link: { type: "doc", id: "verifiable-ai-hackathon/overview" },
-      items: ["verifiable-ai-hackathon/overview"],
+      link: { type: "doc", id: "verifiable-ai-hackathon/onboarding-guide" },
+      items: ["verifiable-ai-hackathon/onboarding-guide"],
     },
     {
       type: "category",