LLM Agents
Technical comparison of memory architectures for AI agents: vector databases for semantic search, graph structures for relationships, and event logs for temporal reasoning. Each approach offers distinct advantages for agent performance.
LLM Agents
New research introduces grounded test-time adaptation for LLM agents, enabling dynamic learning during deployment by anchoring adaptations to factual knowledge and environmental feedback.
AI Agents
Analysis of how AI agents handle human tasks reveals critical performance patterns: exceptional speed paired with systematic flaws in reasoning, tool use, and decision-making that impact real-world deployment.
Meta-Learning
MIT researchers introduce SEAL, a framework enabling AI systems to autonomously improve through self-evaluation and learning. The approach combines meta-learning with self-supervised techniques to create systems that identify weaknesses and refine their capabilities.
deepfakes
A deepfake fraud case demonstrates how sophisticated voice and video synthesis can bypass AI-powered verification systems, exposing critical vulnerabilities in corporate authentication protocols and raising urgent questions about digital trust.
Generative AI
A technical deep dive into the major families of generative AI models—from GANs and VAEs to diffusion models and transformers—that power today's synthetic media, deepfakes, and AI video generation tools.
GANs
Wasserstein GANs revolutionized generative AI through mathematical innovations that solved training instabilities. Understanding these breakthroughs reveals how modern image and video synthesis became possible.
Agentic AI
Learn to build a modular agentic AI system using a planner-tools-executor-summarizer architecture with FastAPI and OpenRouter. This technical guide walks through creating autonomous AI assistants with multi-step reasoning capabilities.
Transformers
Understanding the attention mechanism is essential for grasping how modern AI generates video, text, and synthetic media. This technical guide breaks down the architecture that powers everything from GPT to deepfake generators.
Neural Networks
Dropout is a powerful regularization technique that prevents overfitting by randomly deactivating neurons during training. This counterintuitive approach forces networks to learn robust, distributed representations that generalize better to unseen data.
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.
Voice AI
Learn how to architect voice AI assistants with autonomous reasoning capabilities, combining speech processing, LLM agents, and planning frameworks for intelligent multi-step responses.