by simon-willison
Claude Cowork repackages Claude Code's powerful agentic capabilities for general audiences through accessible design rather than technical innovation—a pragmatic approach to unlock untapped value.
by geoffrey-huntley
A hands-on Go workshop that builds a coding agent incrementally through six files, each adding one capability - proving agents need only simple primitives composed in a loop.
by simon-willison
Reasoning models have made LLM-generated code undeniably good, and 2026 will bring both a major security incident from coding agents and the resolution of the sandboxing problem.
by humanlayer-team
Systematic context management—through frequent intentional compaction and a Research-Plan-Implement workflow—enables productive AI-assisted development in complex production codebases.
by langchain
Context engineering—filling the context window with the right information at each step—determines agent performance more than model choice or complex frameworks.
by jediah-katz
Coding agents perform better when they pull context on demand rather than receiving everything upfront—files serve as a simple, future-proof abstraction for this dynamic retrieval.
by thariq-shihipar
Bash is the most powerful agent tool. The Claude Agent SDK packages Claude Code's battle-tested patterns—tools, file system, skills, sandboxing—for building coding and non-coding agents alike.
by philipp-schmid
As AI models converge in benchmark performance, the infrastructure managing them—Agent Harnesses—becomes the competitive differentiator for building reliable, multi-day workflows.
The Pragmatic Engineer Podcast
Chip Huyen explains how AI engineering differs from ML engineering, walking through the practical developmental path from prompts to RAG to fine-tuning.
by chip-huyen
A practitioner's guide to building applications on foundation models, covering prompt engineering, RAG, finetuning, agents, and evaluation.
by trey-grainger, doug-turnbull, max-irwin
The holy grail for AI-powered search lies at the intersection of semantic search, personalized search, and domain-aware recommendations—systems that understand the domain, the user, and can match arbitrary queries to any content.
by gergely-orosz, martin-fowler
Martin Fowler argues AI represents the biggest shift in software engineering since assembly to high-level languages—not because of abstraction level, but because we now work with non-deterministic systems.
by chip-huyen
A comprehensive guide explaining the three-phase process (pretraining, supervised fine-tuning, RLHF) used to train models like ChatGPT.
by stephen-wolfram
Explains how ChatGPT works by breaking down neural networks, embeddings, and training—connecting modern AI to foundational questions about language and thought.
The Diary of a CEO • Guest: tristan-harris
Tristan Harris warns that AI companies are racing to build a 'digital god' that could automate all human cognitive labor, with insiders believing this will happen within 2-10 years while publicly downplaying the risks.
by andrej-karpathy
A comprehensive introduction to how large language models work, covering the entire pipeline from internet data collection through tokenization, neural network training, and inference.
by mo-gawdat, steven-bartlett
Mo Gawdat, former Chief Business Officer at Google X, warns that AI represents humanity's greatest existential challenge—bigger than climate change—and outlines his 'three inevitables' for why we're approaching a point of no return.
by carl-assmann
CLI tool that enables LLM conversations through markdown files in your preferred editor, storing chat history as readable documents.
by dex-horthy
A practical framework for getting AI coding agents to work reliably in brownfield codebases through context engineering, intentional compaction, and the Research-Plan-Implement workflow.
by varin-nair
Frontier models cap out at 1-2 million tokens, yet enterprise codebases span several million. Factory's solution: a five-layer context stack that delivers the right information at the right time.
by antonio-gulli
A practical guide presenting 21 design patterns for building AI agents, covering prompt chaining, tool use, multi-agent collaboration, and self-correction techniques with examples in LangChain, CrewAI, and Google ADK.
Patterns and practices for building autonomous AI agents with prompt chaining, routing, and reflection
by andrej-karpathy, dwarkesh-patel
Karpathy argues we're building 'ghosts' that imitate internet documents rather than evolved animals, and that practical AI agents will take a decade—not a year—to fully mature.
by maham-codes
A practical guide to building AI agents using prompt chaining and basic primitives instead of heavy frameworks.
by mark-anthony-cianfrani
A first-principles guide to building AI agents in TypeScript, covering LLM integration, conversation memory, tool calling, and agentic loops.
by erik-schluntz, barry-zhang
Anthropic's guide to building agentic LLM systems, advocating for simple composable patterns over complex frameworks.
by andrej-karpathy
Andrej Karpathy's practical guide to using LLMs effectively: understanding them as lossy internet zip files, managing token windows, selecting models across providers, and leveraging thinking models for complex problems.
by mark-winteringham
Practical guide to using generative AI for test design, synthetic data generation, and automation without the hype.
by yacine-mahdid
Prompt chaining breaks complex LLM tasks into sequential steps, each with a specific job and structured input/output, trading latency for reliability.
by humanlayer-team
A manifesto for building production-grade LLM agents, arguing that effective agents combine mostly deterministic software with strategic LLM decision-making rather than naive 'loop until solved' patterns.
by simon-willison
Simon Willison's comprehensive year-in-review analyzing how reasoning models, autonomous agents, and Chinese AI competition fundamentally reshaped the landscape in 2025.
Techniques for providing AI agents and LLMs with optimized context
by humanlayer-team
Guidelines for crafting an effective CLAUDE.md file, emphasizing brevity, universal applicability, and progressive disclosure to maximize Claude Code's instruction-following capacity.
by andrew-qu
Stripping a text-to-SQL agent down to a single bash tool produced a 3.5x speedup, 100% success rate, and 37% fewer tokens—proving that simpler agent architectures outperform elaborate tooling.
by ryan-x-charles
Markdown has become a general-purpose programming language—AI agents like Claude Code compile structured specifications into working applications.
by geoffrey-huntley
Coding agents require only 300 lines of code in a loop with LLM tokens - understanding these fundamentals transforms you from AI consumer to producer.