Omegalabs Bittensor Subnet

F 47 completed
Ai Ml
library / python · small
115
Files
18,436
LOC
0
Frameworks
8
Languages

Pipeline State

completed
Run ID
#367425
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
59.67
Framework unique
Isolation
Last stage change
2026-05-10 03:35:17
Deduplication group #48784
Member of a group with 18 similar repo(s) — canonical #12205 view group →
Top concepts (2)
Project DescriptionLibrary
If a scraper extracted this row, it came from Repobility (https://repobility.com)

AI Prompt

Create a project structure for the OMEGA Labs Bittensor Subnet. This system is designed to build the world's largest decentralized multimodal dataset for AGI research. I need to implement functionality for both Minners and Validators. Specifically, include setup guides and scripts for running both roles, including an auto-updating validator script. The project should also detail the SN24: $\Omega$ Focus Videos Submission process, covering everything from task completion and recording to scoring and marketplace listing. Please structure the documentation using Markdown and include necessary Python files for core logic.
python library decentralized ai multimodal bittensor agi validator miner
Generated by gemma4:latest

Catalog Information

The OMEGA Labs Bittensor Subnet aims to create the world's largest decentralized multimodal dataset for accelerating Artificial General Intelligence (AGI) research and development.

Description

This project is a groundbreaking initiative that democratizes access to a vast and diverse dataset capturing human knowledge and creation. It enables researchers to accelerate AGI development by leveraging a decentralized multimodal dataset. The subnet allows users to contribute, submit, and score videos, with miners and validators incentivized through a complex scoring algorithm.

الوصف

هذا المشروع هو مبادرة رائدة تهدف إلى إنشاء أكبر قاعدة بيانات متعددة الأبعاد غير المركزية في العالم لتعزيز البحث والتطوير للذكاء العام الحاسوبي. يهدف المشروع إلى إدماج الوصول إلى قاعدة بيانات واسعة النطاق ومتنوعة ت.capture من المعرفة البشرية والخلق، مما يسمح للمبحوثين بسرعة تعزيز التطوير الذكاء العام الحاسوبي.

Novelty

9/10

Tags

artificial-general-intelligence multimodal-dataset decentralized-data research-platform aggregation-of-human-knowledge

Technologies

aws-sdk huggingface numpy openai pinecone pytorch

Claude Models

claude (unknown version)

Quality Score

F
47.0/100
Structure
54
Code Quality
54
Documentation
69
Testing
35
Practices
31
Security
24
Dependencies
60

Strengths

  • CI/CD pipeline configured (circleci)
  • Consistent naming conventions (snake_case)
  • Containerized deployment (Docker)
  • Properly licensed project

Weaknesses

  • 4 bare except/catch blocks swallowing errors
  • Potential hardcoded secrets in 4 files
  • 2022 duplicate lines detected \u2014 consider DRY refactoring
  • 6 'god files' with >500 LOC need decomposition

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Add a linter configuration to enforce code style consistency
  • Replace bare except/catch blocks with specific exception types
  • Move hardcoded secrets to environment variables or a secrets manager

Security & Health

9.3h
Tech Debt (B)
A
OWASP (100%)
FAIL
Quality Gate
A
Risk (13)
Generated by Repobility's multi-pass static-analysis pipeline (https://repobility.com)
MIT
License
9.7%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
70.6%
markdown
12.9%
html
7.8%
text
6.3%
yaml
1.6%
shell
0.8%
json
0.0%
toml
0.0%

Frameworks

None detected

Concepts (2)

Source-of-truth: Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Repobility · open methodology · https://repobility.com/research/
auto_descriptionProject Description![OMEGA](https://omegatron.ai) ![License: MIT](https://opensource.org/licenses/MIT)80%
auto_categoryLibrarylibrary70%

Quality Timeline

1 quality score recorded.

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