#meta-learning
6 related articles
·3 min
Loopcraft Explained: Loop-Stacking Architecture Design in AI Agent Development
Deep dive into Loopcraft loop-stacking architecture for AI Agent development, covering retry, self-validation, and meta-learning loops to boost reliability.
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Tutorials·2 min
LangChain Few-Shot Prompt Templates in Practice: A Complete FewShotPromptTemplate Tutorial
Learn LangChain FewShotPromptTemplate: core parameters, implementations for text completion and chat models, and practical use cases like batch file renaming to reduce LLM hallucinations.
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Product Reviews·2 min
JCode: Deep Dive into a Multi-AI Agent Collaborative Programming Framework
Deep dive into JCode, an open-source Coding Agent Harness designed for multi-Agent collaboration. Features Agent Memory, Swarm collaboration, multi-Provider access, and self-evolution with just 14ms first-frame latency and 117MB for 10 sessions.
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Tutorials·4 min
Four Core Rules Anthropic Engineers Use to Prompt Claude Code
Anthropic engineers share how to properly use Claude Code at AI Code Summit: build composable Skills systems instead of one-off prompts. Master four core rules for skills that get smarter over time.
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Tutorials·3 min
Hermes Agent in Practice: A Complete Breakdown from ReAct Loop to Autonomous Skill Evolution
Deep dive into Hermes Agent's four progressive cases: terminal ReAct loop, Feishu AI assistant, four-layer persistent memory, and three-stage Skill evolution with DeepSeek support.
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Product Reviews
Three AI Agents Tested Head-to-Head: W…
·3 min
Three AI Agents Tested Head-to-Head: Which One Handles E-Commerce Livestream Data Analysis Best?
Testing three AI Agents on e-commerce livestream data analysis: local deployment memory limits, costly overseas APIs, and how a cloud-based multi-model solution delivers a complete business workflow.
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