🚧 Challenges We Faced

Model Fine-Tuning:
Adapting the DNABERT model to precisely predict optimal guide RNA sequences required extensive experimentation. Achieving accurate tokenization, embedding generation, and iterative sequence optimization involved rigorous validation cycles. Each adjustment had to be biologically meaningful and computationally efficient, demanding continuous refinements to enhance prediction reliability.

IoT Integration:
Implementing real-time IoT monitoring introduced complexities, particularly in maintaining reliable data synchronization between environmental sensors and our backend infrastructure. Ensuring continuous and accurate data flow for conditions like temperature and humidity required resolving latency and connectivity issues, crucial for experimental reproducibility.

Blockchain Deployment:
Developing robust smart contracts on Base blockchain to securely handle genomic data presented unique challenges. We had to carefully design contracts for sample provenance, experimental auditing, access control, and workflow automation. Ensuring immutability, traceability, and precise permission management demanded thorough planning, testing, and debugging.

Groq Integration:
Leveraging Groq’s rapid inference capabilities for real-time interactions required meticulous prompt engineering and system optimization. Selecting suitable Groq models and fine-tuning the integration for responsiveness was essential, especially for complex tasks like interpreting lab commands and generating blockchain guidance. Balancing speed and accuracy, while effectively using Groq’s LPUs, was particularly challenging.

System Integration:
Merging AI, IoT, blockchain, and Groq into one cohesive platform required significant effort. Coordinating these diverse technologies, each with distinct protocols and requirements, demanded rigorous debugging and seamless orchestration. Ensuring smooth interaction .