P
Papers
Community platform that links ML papers with open source implementations benchmarks and leaderboards to make research more reproducible and accessible.
Pricing Free
Category research
Difficulty Beginner
Type Web App
Status Active
B
BabyAGI
Experimental open source project that explores autonomous task planning and self improving agents often used for demos education and research rather than production systems.
Pricing Free
Category research
Difficulty Beginner
Type Web App
Status Active
Feature Tags Comparison
Only in Papers
mlbenchmarksleaderboardsresearch
Shared
open-source
Only in BabyAGI
agentsautonomousexperimentseducation
Key Features
Papers
- • Task pages: Browse leaderboards datasets methods and metrics for a clear view of the SOTA landscape
- • Paper pages: See official code repos versions and licenses linked directly from publications
- • Filters and compare: Slice by dataset metric task or framework to evaluate methods quickly
- • Community edits: Propose changes and add repos with moderation to keep entries accurate
- • APIs and dumps: Pull structured task and result data for meta analysis and education at scale
- • Trends and guides: Explore curated topics tutorials and learning paths for emerging areas
BabyAGI
- • Core Loop: Generate a task list execute a task evaluate outcome and create new tasks
- • Minimal Codebase: Small readable project
- • Self Improvement: Emphasis on feedback and recursion
- • Community Ecosystem: Many forks and tutorials
- • Extensible Concepts: Combine with retrieval tools and memory
- • Educational Value: Shows agent pitfalls
Use Cases
Papers
- → Find baseline code for a new task and run it quickly
- → Compare methods across datasets and metrics before experiments
- → Build teaching labs with real repos and tasks for students
- → Extract benchmark data for reviews and meta analysis
- → Track trending tasks and papers in a research area
- → Check licenses and versions before reuse in products
BabyAGI
- → Classroom Labs: Demonstrate planning reflection iteration
- → Research Prototypes: Test memory strategies and reflection patterns
- → Internal Workshops: Teach teams how agent loops work
- → Content Experiments: Generate outlines steps critiques
- → Data Tasks: Toy agents that fetch transform summarize
- → Developer Education: Teach stopping criteria and retries
Perfect For
Papers
ml researchers, engineers, students, educators, reviewers and data scientists who need fast paths from papers to code and reproducible benchmarks
BabyAGI
Students, researchers, tinkerers, and engineering teams who want to learn autonomous agent patterns in a small codebase before adopting governed frameworks for production use
Capabilities
Papers
Task leaderboards Basic
Official repos Basic
Filters and metrics Basic
APIs and dumps Intermediate
BabyAGI
Task Queue Basic
Self Improvement Basic
Tools and Memory Basic
Human Oversight Basic
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