Tags: machine-learning
All the articles with the tag "machine-learning".
Dense vs MoE - Choosing Between Dense Models and Mixture of Experts
Updated: at 10:59 AMDense vs MoE explained: how dense and Mixture of Experts models differ in compute, memory, latency and cost, and a practical rule for when to run each, including on local hardware.
Is ChatGPT an LLM? Not Exactly - It's an Agent Built on One
Updated: at 12:59 AMIs ChatGPT an LLM? Not exactly. ChatGPT is an agent system built on GPT models, with memory, tools and orchestration around them. Here is the difference and why it matters if you build with AI.
Agentic RAG and Context Engineering for AI Agents
Updated: at 04:21 PMWhat is agentic RAG and how does it differ from traditional RAG? Understanding context engineering for agents, dynamic context retrieval techniques, and agentic retrieval methods. Learn to build AI systems that actively manage knowledge for better reasoning.
Memory-Based Agent Learning - The Path to Truly Autonomous AI
Updated: at 04:21 PMExploring how memory-augmented AI agents like Memento are revolutionizing autonomous learning without requiring expensive model fine-tuning, marking the first steps toward truly self-evolving AI systems.
Building Multi-Agent Research Systems - Lessons from Anthropic's Architecture
Updated: at 12:59 AMHow multi-agent research systems work and what it takes to build one: the orchestrator-worker pattern, prompting subagents, evaluation, token costs, and production pitfalls, drawing on Anthropic's published design.
Claude Prompt Engineering Best Practices - What Works with Claude 4
Updated: at 10:59 AMPractical Claude prompt engineering best practices, written for Claude 4: be explicit, give context, show examples, control the output format, and let Claude verify its own work. With code examples and a migration guide from earlier Claude models.
AI Adoption Trends - What's Actually Working in Production
Updated: at 09:12 AMAn analysis of current AI adoption trends, examining what's working in production, common pitfalls, and the patterns that separate successful AI implementations from failed experiments.
Move 37 and Agents
Updated: at 11:59 AMExploring the significance of AlphaGo's Move 37 and its implications for the future of AI agents, highlighting how unexpected innovations in artificial intelligence could revolutionize problem-solving across various domains.
DeepSeek R1 - Rewriting the Rules of AI Training
Updated: at 09:59 AMDiscover how DeepSeek R1 shattered AI training conventions by achieving 71% accuracy on AIME with zero supervised data. This breakthrough reveals how pure reinforcement learning spontaneously develops advanced reasoning, potentially eliminating massive data requirements and democratizing AI development. Essential reading for ML engineers and AI researchers seeking the next evolution in model training techniques.
Sequence to Sequence Learning - A Decade of Neural Networks
Updated: at 03:22 PMAn exploration of Ilya Sutskever's reflections on a decade of progress in sequence-to-sequence learning, examining the evolution of neural networks and their implications for the future of AI development.