There will be 3 tracks in which to hack on. Below are the specifications for each one. 

Student Track 

• Educational Impact on Akash: Solves a student or learning-focused problem using compute deployed on Akash, improving affordability, access, or learning outcomes.

• Clarity, Usability & Learning: Clear student-friendly UX, understandable architecture, and explanation of how Akash enables the solution versus centralized cloud.

• Outcome Must be a working application that uses Akash GPUs directly through Akashml APIs, addresses a clear use case, and be an open source repo.

Agent Track

• Create an autonomous or semi-autonomous AI agent deployed on Akash that continuously operates (not a one-off script) and is capable of task execution, monitoring, orchestration, or decision-making.
• The agent must take verifiable real-world actions such as authenticated API calls, multi-step tool usage, job scheduling, stateful resource management, or interactive user workflows — beyond simple prompt/response behavior.
  
• Demonstrate persistent state, fault tolerance, and compute independence by running the agent entirely on Akash Infrastructure (no fallback to centralized cloud compute), including redeploys or restarts without loss of core functionality.

• Outcome: Must be a working agent hosted on Akash Console or use a hosted model via the akashml API while meeting the criteria above.

Template Track

• Build a reusable Akash deployment template (SDL, scripts, or starter repo) for a common use case (LLM hosting, vector DBs, agents, web apps).
  
  

• Leverage and reference the Awesome Akash GitHub repository — templates should be inspired by, extend, or improve existing Awesome Akash templates.
  
  

• Focus on developer experience: easy setup, clear documentation, configurable parameters, and fast deployability.

• Outcome: Template(s) must be hosted on Akash Console or use a hosted model via the akashml API while meeting the criteria above.