Why Speed Matters in AI Research
In the race toward superintelligence, every day counts. While others debate theoretical frameworks, we're building. While others plan perfect architectures, we're iterating. The future belongs to those who ship, not those who speculate.
Ready-to-Go Research Tasks
🔬 Research: More Small vs Few Big Experts
Draw scaling laws comparing architectures with many small experts versus fewer large experts. This is perfect for newcomers and will generate valuable insights for the field.
→ Take this task⚡ Research: Activation Function Ablation
Test SwiGLU, GEGLU, SiLU, GELU, ReLU2 and other activation functions in our MoE architecture. Another great first issue for newcomers to contribute meaningful research.
→ Take this task📊 Research: Batch Scheduling & Curriculum
Implement length bucketing and perplexity curriculum learning. This advanced research task will help optimize training efficiency and model performance.
→ Take this taskHow to Get Started
1. Fork & Clone
Fork the Blueberry LLM repository and clone it locally.
2. Pick Your Task
Browse our open issues and pick one that matches your skills.
3. Build & Experiment
Run experiments, test hypotheses, and push the boundaries of what's possible.
4. Submit PR
Share your findings with the community and help advance superintelligence research.
The Philosophy of Fast Iteration
We believe in the power of rapid experimentation. Every failed experiment teaches us something. Every successful iteration brings us closer to superintelligence. The key is to start building today, not tomorrow.
Don't wait for the perfect plan. Don't wait for more resources. Don't wait for someone else to solve the problem. Pick a task, start coding, and make an impact.