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An enterprise AI platform that turns real business needs into ready-to-use AI solutions.
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository for the latest instructions.
Miners submit SKILL.md files (instructions and procedures for AI agents); hardware requirements depend on the model chosen for the specific Challenge's harness, specified in suite.yaml
Validators run evaluation harness against submissions using models specified in each Challenge's suite.yaml. Some validators child-key to sundae_bar's scoring validator rather than re-running full evaluation stack
Review the active Challenge brief and download the public suite bundle (suite.yaml, dataset.jsonl, rubric.txt)
Access sundae_bar Lab challenge page
Develop skill instructions and procedures that teach an AI agent how to solve the Challenge. Submit through sundae_bar Lab while the Challenge is open
Create SKILL.md file
Submit SKILL.md for evaluation; subject to daily limits (rolling 24-hour window) and per-Challenge limits. Monitor remaining submissions shown on Lab
Submit skill and iterate
Download grader rationale and inspect scored results JSON to understand how score was computed across multiple scenarios and graders
Review evaluation results
Obtain suite.yaml, dataset.jsonl, and rubric.txt from the Lab challenge page
Download Challenge suite bundle
Configure the fixed evaluation harness with load_skill tool to inject miner submissions. Use models specified in suite.yaml for that Challenge
Set up evaluation harness
Set up all configured graders (skill_use, scenario_quality, rubric model-judge, novelty_check) with published weights from suite.yaml
Implement graders
Process queued submissions against all dataset scenarios, apply pre-gates (skill_alignment, dataset_derived), compute weighted scores per scenario, and average across scenarios
Run evaluation queue
Submit setWeights transaction to SN121 based on leaderboard ranking (highest score, then earliest qualifying submission). Current #1 miner receives 40% of emissions
Post weights on-chain
Use the same weights from sundae_bar's extensive scoring rather than re-running full evaluation. Does not change incentive mechanism; amplifies the published vote(optional)
Optional: Child-key to sundae_bar validator