#Agent Tuning
776 related articles
·3 min
From RAG to Agent Tuning: A Complete Guide to Deploying LLM Agents
A deep dive into LLM Agent frameworks covering RAG, Agent core components (tools, memory, planning), and Agent Tuning workflows with cost considerations for production deployment.
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·3 min
Agent Fine-Tuning in Practice: A Complete Guide to Training Your Own AI Agent
A deep dive into Agent Tuning: from LLM hallucination and staleness issues to RAG vs. Agent architecture, the 4-step fine-tuning process, and cost analysis for building your own AI agent.
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Fine-Tuning Cosmos Models in One Day w…
·2 min
Fine-Tuning Cosmos Models in One Day with Agent Skills — Breaking 90% Accuracy
NVIDIA uses Autonomous Coding Agents and Agent Skills with TAO to fine-tune Cosmos visual reasoning models in one day, achieving over 90% accuracy.
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Tutorials·3 min
Agent Tuning: A Complete Guide to Training LLMs with Agent Capabilities
A deep dive into Agent Tuning principles and practices, covering why Agent training is needed, the evolution from Prompt to RAG to Agent, development workflows, and cost assessment for private deployment.
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·2 min
Transformer²: Achieving Co-Design of Robot Morphology and Control with a Unified Architecture
Deep dive into how Transformer² uses a unified Transformer architecture to integrate robot morphology design and motion control into one model, enabling task-driven end-to-end co-design for embodied AI.
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·2 min
Stop Blindly Using Adam: The Hidden Pitfalls and Practical Boundaries of Optimizers
Deep analysis of Adam optimizer failure mechanisms in RL and deep Transformer training, revealing the mathematical roots of loss burstiness from second moment estimation, with practical solutions.
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·2 min
Agenta: A Detailed Look at the Open-Source Self-Hosted Claude Cowork Alternative
Agenta is an open-source AI Agent collaboration platform supporting self-hosted models and any Agent framework, positioned as an open-source Claude Cowork alternative.
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·3 min
Lessons from Claude Code's 80% Prompt Reduction: Less Is More in Prompt Engineering
Anthropic cut Claude Code's system prompt by 80% and got better performance. Learn why verbose prompts hurt, how to streamline them, and key takeaways for AI developers.
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·3 min
Lessons from Claude Code's 80% Prompt Reduction: Less Is More in Prompt Engineering
Anthropic cut Claude Code's system prompt by 80% and got better results. Learn why verbose prompts hurt performance, how to streamline them, and key lessons for AI developers.
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·2 min
Training AI to Play Devil May Cry 3 with Reinforcement Learning: Challenges and Practices in Action Game AI
An indie developer trains AI to autonomously play Devil May Cry 3 using reinforcement learning. Explore the core challenges of action game AI including sparse rewards, high-dimensional action spaces, and real-time decision-making.
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·2 min
Training AI to Play Devil May Cry 3 with Reinforcement Learning: Challenges and Practices for Action Game AI
An indie developer trains AI to autonomously play Devil May Cry 3 using reinforcement learning. This article analyzes the core challenges including sparse rewards, high-dimensional action spaces, and real-time decision-making.
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·3 min
PIRL Framework Explained: A Smooth Transition from Open-Loop Exploration to Closed-Loop Reinforcement Learning
Deep dive into the PIRL reinforcement learning framework: how to smoothly transition from open-loop exploration to closed-loop RL, mitigating the exploration-exploitation dilemma and improving sample efficiency.
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·2 min
Google's Beyond Zero: An Enterprise Security Framework That Goes Beyond Zero Trust for the AI Era
Deep dive into Google's Beyond Zero security concept, exploring how enterprises can move beyond traditional Zero Trust models in the AI era to address prompt injection, data poisoning, and other emerging threats.
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·3 min
AI Game Development Showdown: Claude, Kimi K3, and ChatGPT Recreate Classic Games — Who Wins?
Bilibili creator tests Claude Fable 5, Kimi K3, and ChatGPT Codex recreating Hollow Knight, Cuphead, and Zelda — comparing code quality, collision detection, and Boss design.
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·4 min
LangChain Guardrails Explained: Building Safe and Controllable AI Agents
A detailed guide to LangChain Guardrails covering layered ecosystem architecture, middleware implementation, deterministic and model-driven protection for building production-grade secure AI Agents.
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·3 min
Deep Dive into Microsoft's AI Security Tools: Does Performance Really Surpass the Competition?
Microsoft launches enterprise AI security tools claiming superior performance. This deep analysis examines core capabilities, ecosystem advantages, and risks to guide enterprise security decisions.
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·3 min
Learning AI Large Language Models: A Complete Roadmap from Beginner to Practitioner
A systematic breakdown of the AI LLM learning roadmap covering prompt engineering, AI Agent development, RAG knowledge bases, model fine-tuning, and hands-on projects for beginners.
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·5 min
Learn AI Agent Development from Scratch: A 3-Step Practical Path to Get Started Quickly
How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.
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·3 min
Backend Developer Transition to AI/LLM: 4 Core Skills and a Practical Learning Roadmap
Complete guide for backend developers transitioning to AI/LLM engineering. Covers the 4 core skills—Python, RAG, Fine-tuning, and Agents—with a phased learning roadmap and practical project advice.
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·3 min
Kimi K3 Deep Dive: How a 3 Trillion Parameter Open-Weight Model Reaches the Frontier
Deep dive into Kimi K3: the largest open-weight model at 3 trillion parameters, surpassing Opus-level models in Agentic coding with 896-expert MoE architecture, 1M token context, at Sonnet pricing.
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