36 articles
Get a comprehensive overview of the AI agent landscape. This survey paper explains the core components, structures, and challenges of LLM agents.
Explore Anthropic's Constitutional AI paper. Learn how models can be trained for safety and harmlessness without extensive human feedback.
Dive into the FlashAttention paper. Discover how its I/O-aware algorithm speeds up transformers by optimizing GPU memory usage for long sequences.
Understand Low-Rank Adaptation (LoRA). Learn how this parameter-efficient technique makes fine-tuning massive language models accessible to everyone.
Delve into the Mixture of Experts (MoE) architecture. Understand how sparse models like Mixtral achieve high performance with lower computational cost.
Unpack the QLoRA paper. Learn how 4-bit quantization enables fine-tuning of huge models (up to 65B parameters) on a single consumer GPU.
Explore how AI agents remember information. Learn the differences between short-term and long-term memory and how to implement them for smarter agents.
Take your RAG systems to the next level. Explore advanced techniques like query transformation, reranking, and hybrid search to improve retrieval accuracy.
As agents become more autonomous, safety is critical. Learn foundational principles and practical steps to build safer, more reliable AI agents.
Level up from single agents. Learn the fundamentals of designing multi-agent systems where specialized agents collaborate to achieve a common goal.
Overwhelmed by LLM choices? This guide compares top models to help you select the best foundation for your first AI agent project.
Go beyond basic prompts. Learn how AI agents think and act using core reasoning loops like ReAct and Plan-and-Execute to solve complex tasks.
Is your agent actually working well? Explore essential metrics and frameworks for rigorously evaluating the performance and reliability of your AI agents.
Learn what vector databases are and why they are essential for modern AI agents. A beginner-friendly guide to embeddings, storage, and similarity search.
Start your AI agent development journey right. This guide covers setting up Python, managing API keys, and essential tools for a smooth workflow.
Learn how to give your AI agents new abilities. This guide explains function calling, enabling your LLM to interact with APIs and external systems.
Learn prompt engineering for AI agents: chain-of-thought, few-shot prompting, system prompts, structured output, and ReAct patterns.
Understand the transformer: self-attention, multi-head attention, positional encoding, and how it enables GPT-4 and Claude — explained with code.
A clear, developer-friendly explanation of what large language models are, how they work, and why they matter for building AI applications.
Understand what makes an AI agent different from a chatbot. Covers the Perceive-Plan-Act loop, tool use, memory, and why agents matter for developers.
Understand RAG (Retrieval-Augmented Generation): how it works, why it solves LLM hallucination, and when to use it. Includes a minimal working example.
Learn how AI agents fail across three impact radii — commit delay, team flow friction, and maintainability rot — and how shift-left prevents them.
Master the two control loops of harness engineering: feedforward Guides that steer agents before action and feedback Sensors that correct them after.
Choose the right LLM backend for your multi-agent system. Compare Ollama, vLLM, and LM Studio, plus 2026 API pricing and hybrid routing strategies.
NLAH replaces code-based harnesses with natural-language contracts. Learn the ICLR 2026 IHR runtime, context rot prevention, and model vs harness debate.
Harness engineering wraps LLMs with runtime controls. Learn the Agent = Model + Harness formula and why it decides agent quality more than the model itself.
Measure multi-agent system quality with modern benchmarks. Covers ADP data standards, SWE-bench, HAL leaderboard, and how to design your own eval suite.
Compare centralized and distributed multi-agent topologies. From ChatDev's waterfall to AgentNet's DAG — learn when each architecture fits your system.
How agents communicate, share state, and stay observable. Covers message passing, shared memory patterns, Tools vs Skills separation, and distributed tracing.
Compare three orchestration paradigms: LangGraph's DAG state machine, CrewAI's role-based crews, and AutoGen's async messaging. Choose the right pattern.
Learn what multi-agent systems are, how they evolved from single-agent LLMs, and why specialized agent teams outperform monolithic AI models.
Breakdown of 'Attention Is All You Need' (Vaswani et al., 2017) — the transformer paper that underlies every modern LLM including GPT-4 and Claude.
Chain-of-Thought prompting (Wei et al., 2022) explained — the step-by-step reasoning technique that unlocked complex LLM reasoning and powers modern AI agents.
Breakdown of the original RAG paper (Lewis et al., 2020) — the retrieval-augmented generation architecture behind every modern knowledge-grounded AI system.
The ReAct paper (Yao et al., 2022) explained — the Thought/Action/Observation loop that powers LangChain, LlamaIndex, and most production AI agent frameworks.
Toolformer (Schick et al., 2023) explained — how LLMs learn to use external tools through self-supervised training, influencing GPT-4 function calling.
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