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21 articles
Learn how to access and use Tencent's Hy3 Mixture-of-Experts model through OpenRouter, including understanding MoE architecture, using long-context capabilities, and experimenting with reasoning tasks.
Explore the seven types of agent memory that enable AI systems to maintain context, learn from experience, and perform complex, long-term tasks.
Learn how to load, test, and evaluate the VibeThinker-3B reasoning model using Hugging Face transformers and Python.
An advanced tutorial on Salesforce CodeGen demonstrates how to generate, validate, and refine Python functions using large language models, incorporating syntax checking, unit tests, and safety measures.
Learn to build a simplified version of Google's agentic RAG framework with Sufficient Context Agent for handling multi-hop queries and improving factuality accuracy.
Learn to set up and run inference with NVIDIA's Nemotron 3 Ultra, a 550B parameter hybrid Mamba-Transformer model designed for long-running AI agents with extended context windows.
This explainer explores agentic AI—intelligent systems that perceive, reason, and act autonomously in enterprise environments. Learn how these platforms are transforming business operations through automation and cross-system orchestration.
Learn to compress instruction-tuned language models using FP8, GPTQ, and SmoothQuant quantization techniques with llmcompressor, and benchmark their performance.
Learn how to build and extend AI agents using the new Cline SDK, including creating basic agents, plugins, and subagents.
Learn how to implement basic LLM distillation techniques to train smaller, more efficient models that mimic larger pre-trained models.
Learn to build an autonomous coding system using LLMs, similar to Xiaomi's MiMo-V2.5-Pro, that can execute long-running tasks with minimal token consumption.
This explainer article explains what Large Language Models (LLMs) are and why it's important for AI development to focus on what regular people actually want, not just technical breakthroughs.
