LLM Architecture
Comprehensive technical analysis of retrieval-augmented generation and fine-tuning strategies for LLMs, exploring when to use each approach, their technical trade-offs, and emerging hybrid architectures that combine both methodologies.
Neural Networks
Liquid Neural Networks introduce adaptive, continuous-time dynamics that outperform traditional RNNs with fewer parameters. The architecture offers real-time processing capabilities crucial for video analysis and autonomous systems through dynamic state adjustments.
Neural Networks
Deep dive into the mathematical foundations of neural network training: how loss functions measure model performance and optimization algorithms like SGD, Adam, and RMSprop guide learning through gradient descent.
Synthetic Data
Large language models are revolutionizing how AI systems are trained by generating synthetic datasets that overcome data scarcity challenges. This technical approach is transforming model development across domains including synthetic media generation.
Machine Learning
Explore practical techniques for building supervised machine learning models when annotated training data is unavailable, including weak supervision, self-supervised learning, and synthetic data generation approaches.
AI Agents
Technical deep dive into designing agentic AI systems with human-like memory decay, self-evaluation capabilities, and persistent personalization. Explores implementation strategies for creating adaptive AI agents that learn and forget contextually.
AI Agents
New research introduces adaptive data flywheel methodology using Monitor-Analyze-Plan-Execute control loops to systematically improve AI agent performance through continuous feedback and refinement.
Deep Learning
Deep dive into the technical foundations of training neural networks, covering backpropagation, gradient descent, optimization algorithms, and the mathematical principles that power modern AI systems from video generation to deepfakes.
Neural Networks
Deep dive into activation functions, the mathematical components that enable neural networks to learn complex patterns. Explores ReLU, sigmoid, tanh, and modern variants with technical implementation details.
OCR
DeepSeek-OCR introduces efficient optical character recognition with competitive performance at reduced computational costs, demonstrating how architectural innovations can democratize AI capabilities previously requiring massive resources.
Symbolic AI
Exploring the technical foundations of symbolic AI and how it differs from neural networks. This deep dive examines knowledge representation, logical reasoning, and hybrid architectures that combine symbolic and subsymbolic approaches.
Agentic AI
Comprehensive research paper examines security vulnerabilities in autonomous AI agents, detailing attack vectors, defense strategies, and evaluation methods for protecting agentic systems from adversarial threats.