20 articles
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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