Khatiketki
/ 05-devops-challenge
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February 6, 2026 01:09
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| Part 1: Infrastructure Analysis 🐳 | |
| Task 1.1: Docker Deep Dive | |
| 1.List all services defined in docker-compose.yml | |
| • Services in docker-compose.yml: | |
| o hive-server: The core backend API (Port 3001). | |
| o honeycomb: The React frontend dashboard (Port 3000). | |
| o postgres: Relational data for users/auth. | |
| o mongodb: Document storage for agent node graphs. | |
| o timescaledb: Time-series engine for logs/metrics. | |
| o redis: Hot storage for heartbeats and task queues. |
Khatiketki
/ Bonus Point
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February 6, 2026 05:32
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| Submit a PR improving existing UI | |
| https://github.com/Khatiketki/hive/pull/4 |
Khatiketki
/ Bonus Point
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February 6, 2026 05:35
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| ###Actually implement a working prototype using any framework### | |
| #!/usr/bin/env python3 | |
| """ | |
| Aden Agent Challenge - Content Marketing Agent System | |
| Complete working prototype in a single file | |
| Author: Claude (Anthropic AI) |
Khatiketki
/ 01-getting-started
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January 30, 2026 03:48
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| Part 1: Join the Aden Community (10 points) | |
| Task 1.1: Star the Repository ⭐ | |
| Show your support by starring our repo! | |
| 1. Go to github.com/adenhq/hive | |
| 2. Click the Star button in the top right | |
| 3. Screenshot your starred repo (showing the star count)-> https://imgur.com/a/yNmvW82 | |
| Task 1.2: Watch the Repository 👁️ | |
| Stay updated with our latest changes! |
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| Part 1: System Architecture | |
| Task 1.1: Component Mapping 🗺️ | |
| Q: Describe the data flow from when a user defines a goal to when worker agents execute. Include all major components. | |
| A: The flow begins at the Honeycomb Dashboard, where the user submits a natural language goal. This goal is sent to the Coding Agent, which acts as the architect to synthesize a GraphSpec (agent.json) and generate the necessary connection code. This specification is passed to the AgentRunner, which initializes the NodeContext (tools, memory, and credentials). Finally, the GraphExecutor runs the specialized Worker Agents to perform the tasks. | |
| Q: Explain the "self-improvement loop" - what happens when an agent fails? | |
| A: When a failure occurs, the system captures a detailed "failure packet" (logs, state, and prompts) and stores it. The Coding Agent then analyzes this data to identify the root cause. It "evolves" the agent by modifying the node graph or rewriting the connection code to fix the issue. The improved agent is then redeployed |