This guide reveals 17 hidden features of Claude that most users never use, including Projects, Artifacts, Extended Thinking, Memory, Claude in Chrome, Cowork, Scheduled Tasks, Skills, CLAUDE.md, Claude Code, Claude Design, and Prompt Caching. Each feature comes with setup instructions and ready-to-use prompts. Perfect for anyone wanting to turn Claude from a simple chatbot into a full productivity system.
Most Claude users don't know about CLAUDE.md — a plain-text file placed in a project folder that Claude reads automatically at the start of every session, permanently setting your preferences, context, and behavioral rules. This guide provides 21 concrete instructions across five parts: communication style (no filler, admit uncertainty, match length to task), behavior (ask before big changes, only change what was requested, summarize changes), user context (background, project, writing voice), memory & continuity (log decisions in MEMORY.md, session summaries, track failures), and developer-specific rules including Andrej Karpathy's 4 golden rules (don't assume, simplest solution, don't touch unrelated code, flag uncertainty), which reportedly boosted coding accuracy from 65% to 94%. For anyone who wants to stop repeating themselves and get more consistent, on-brand output from Claude.
This article provides a complete, copy-paste-ready setup for making Claude Code automatically catch, fix, and learn from its own mistakes. It covers a self-growing CLAUDE.md for project rules, PostToolUse hooks for auto-formatting and type-checking, Stop hooks for running tests on completion, PreToolUse hooks for blocking dangerous operations, and cross-session memory. The included settings.json config reduces back-and-forth from 45 minutes to 10 unattended minutes per feature. Audience: engineers using Claude Code or AI coding assistants.
A beginner-friendly primer covering 20 core AI concepts split into four parts: foundational mechanisms, how LLMs work, how models improve, and how real systems are built. Uses simple analogies and visuals to explain neural networks, transformers, RAG, agents, and more. No code or deep implementation details — a quick reference for building mental models.
Claude Code now supports dynamic workflows, enabling parallel orchestration of tens to hundreds of subagents within a single session for large-scale engineering tasks. It handles end-to-end jobs like codebase-wide bug hunts, migrations across hundreds of files, and security audits. Workflows dynamically plan, fan out, cross-validate, and converge results. Example: Bun was ported from Zig to Rust in 11 days, producing ~750k lines with 99.8% test pass rate. Workflows show plans before execution, can resume after interruption, but consume significantly more tokens. Available in research preview for Max, Team, Enterprise users.
Anthropic introduces Dynamic Workflows, a primitive that turns task orchestration into JavaScript scripts executed by a deterministic runtime. The script manages loops, branching, and intermediate results, so only the final answer enters the main Claude context—solving the bottleneck of context overflow and attention dilution when coordinating hundreds of parallel tasks. A deep dive into architecture, primitives, and execution model is paired with a real-world Bun-to-Rust migration (11 days, 750K lines, 99.8% test pass) and a personal 133-session analysis. Comparisons with n8n/Coze/Dify show Workflow's advantage: Turing-complete code offers more expressiveness than visual DAGs, and the orchestration can be generated on‑the‑fly by a model. It shines for codebase audits, large migrations, and adversarial verification but comes with high token costs and current preview limits. Target audience: engineers tackling massive, automated coding tasks.
The author argues that prompt engineering is giving way to 'context engineering'—building the environment of information (identity, knowledge, memory, tools, processes) that enables a model to produce consistent results with minimal prompting. A five-layer framework is detailed, with practical steps for Claude users: set custom instructions, upload knowledge files, actively craft memory, connect MCP tools, and encode processes as Skills. The piece is opinionated and lacks empirical evidence but offers actionable guidance for those heavily using Claude.
AI engineering has undergone three paradigm shifts: from Prompt Engineering (2023–2024) to Context Engineering (2025), and now to Harness Engineering (early 2026). Harness Engineering combines evaluation feedback loops, architectural constraints, and memory governance. Anthropic’s evaluator agent turned a 20‑minute useless artifact into a 6‑hour complete game; OpenAI built a million‑line system with zero human‑written code in five months, enforcing architectural boundaries via CI/linters. Two academic papers fill the memory layer: (S)AGE uses Byzantine‑fault‑tolerant Proof of Experience consensus to double agent calibration accuracy; a longitudinal study shows that 3 lines of prompt plus memory matches 200 lines of expert prompt in performance, yet only the memory group improves over time. Essential for engineers building multi‑agent systems.
This article shows how to automatically improve Claude Skills using Karpathy's autoresearch method. It works by giving an agent a yes/no checklist and letting it iteratively test, tweak, and keep only beneficial changes. The author improved a landing page copy skill from 56% to 92% pass rate in four rounds, with visible changelogs. The method applies to any measurable task—define a checklist and let the agent run. Includes download links and practical examples for engineers building AI workflows.
Vercel distills 10+ years of React and Next.js optimization into a structured repo with 40+ rules across 8 categories, ordered by impact from eliminating waterfalls to JavaScript micro-optimizations. Each rule includes code samples and impact ratings, and compiles into an AGENTS.md document consumable by AI coding agents.
At Open House, ClickHouse announced three major observability updates: ClickStack Cloud (serverless, managed, private preview), AI Notebooks (beta), and an open-source ClickStack MCP server. AI Notebooks replace linear chat with persistent, branchable investigation workspaces, exposing every query and step. The MCP server provides semantic investigative tools to external agents; internal benchmarks show 25% fewer tool calls, 2.5× consistency improvement, and 20% higher evaluation scores vs. raw SQL MCP. The server also supports bi‑directional orchestration: agents can create dashboards and persist results. The design philosophy is “bring your own agents,” with SQL as an escape hatch when pre‑built tools fall short. The post includes setup instructions and a demo. For infrastructure/SRE engineers evaluating ClickHouse-based observability.
Software engineering is splitting into two groups: those who use AI to remove drudgery and invest in higher-level thinking, and those who outsource their reasoning to AI, simulating competence without building it. This 'outsourced thinking' is a new failure mode that erodes judgment over time. The real value of engineers lies in framing problems, making tradeoffs, and creating clarity—skills AI cannot own. Early-career engineers are especially at risk of skipping essential skill formation. Leadership must learn to differentiate polished output from genuine technical depth. The article argues that organizational health depends on recognizing this divide.
ClickHouse has released official Agent Skills: an open-source set of 28 prioritized best-practice rules covering schema design, query optimization, and data ingestion, packaged using Anthropic's Agent Skills specification. Users can add them locally with `npx skills add clickhouse/agent-skills`. AI agents (e.g., Claude Code) automatically invoke these rules when appropriate, helping avoid common pitfalls like wrong ORDER BY, non-scalable JOINs, or missing materialized views. The Apache 2.0-licensed repo welcomes community contributions.
A podcast interview with Creao's founders explores Harness Engineering—building self-healing, self-improving systems around LLMs. True AI-First companies restructure around AI as the primary producer: development cycles shrink from weeks to a day, product managers are dismantled, and cross-team alignment is automated. Junior engineers adapt faster than seniors; the future rewards architecture + product + marketing generalists. The 'Agent Economy' means content may be produced for AI consumers. A 25-person team rebuilt their architecture in two weeks. Full transcript available.
Based on six months of intensive use of Claude Code, the author breaks down its functionality into six layers (CLAUDE.md/rules/memory, Tools/MCP, Skills, Hooks, Subagents, Verifiers) and provides design principles, anti-patterns, and configuration examples for each. The article focuses on context engineering (token cost structure, layered loading strategy, compaction pitfalls), tool design, Hooks for mandatory enforcement, Subagents for context isolation, prompt caching, and verification loops. Ideal for engineers wanting to move from ad‑hoc chat to a disciplined agent engineering workflow.
This free series examines AI-assisted software engineering at enterprise scale. While individual coding speed has skyrocketed, many teams have not seen delivery improve—some have even slowed down. The author argues that current AI coding assistants optimize a single role, but software is shipped by teams with many non-coding roles. The next frontier is lifecycle orchestration, not better code generation. The series is structured in four parts, publishing three times a week with no paywall. It is aimed at engineering leaders, architects, and developers interested in AI engineering.
The author refactored a tangled CSS codebase into a clean architecture using Claude Code and Playwright, ensuring zero visual changes across seven phases. A Playwright script captured 9 app states, and after each phase, Claude compared screenshots to baseline, catching regressions like a line-height shift. The result: layered CSS with modern reset, unified button classes, and CSS variables. The post details state enumeration, script writing, and AI-driven diffing, and discusses trade-offs with dedicated tools. Essential reading for front-end developers tackling legacy CSS.
Andrej Karpathy's three observations about LLM behavior—making silent assumptions, overcomplicating code, and performing careless side effects—inspired a single CLAUDE.md file with four principles: think before coding, prioritize simplicity, make surgical changes, and execute goal-driven. Each principle directly addresses a specific pain point. The file is ready to drop into any project to guide AI coding assistants toward more disciplined output. For every Claude Code user who has experienced these issues but struggled to articulate them.
Over 40% of agentic AI projects fail, not because of models, but due to poor risk controls, architecture, and business value. This article presents ten engineering principles: threat modeling, strictly typed tool contracts, least-privilege execution, compact context engineering, governed retrieval, deterministic orchestration, separated memory, reliability mechanics, full observability, and continuous governance. Each principle provides concrete implementation details and real-world numbers (e.g., prompt injection appears in 73% of deployments), guiding teams to build secure, scalable production-grade agents.
Bassim Eledath maps the progression of AI-assisted coding into 8 levels, from tab-complete and AI IDEs to context engineering, compounding engineering, MCPs & skills, harness engineering with automated feedback loops, background agents, and autonomous agent teams. Each level builds on the previous, with practical insights on closing the gap between model capability and practice. He argues that plan mode is fading, multi-model dispatching yields better results, and true autonomous teams are still experimental. The piece serves as a roadmap for engineers looking to leverage AI more effectively.
Coding agents are fundamentally reshaping the EPD (Engineering, Product, and Design) collaboration model. With the cost of implementation plummeting, the traditional PRD→mockup→code waterfall is dead, replaced by a review-centric process where prototypes are rapidly generated and then scrutinized. Generalists who wield coding agents gain unprecedented leverage; system thinking and product sense become essential for everyone. The bar for specialization rises, and roles converge into either builders or reviewers. Ultimately, anyone with a deep grasp of both product and technology can thrive, blurring traditional role boundaries.
A summary of a roundtable on the future of software engineering, featuring leaders from Stripe, NVIDIA, Microsoft, and others. Key insights: closed-loop development creates compounding gains; test-first is the new default; human code review is fading; comments are written for AI readability; long-horizon tasks remain unsolved; developer tooling is being displaced first; hiring values experimentation over raw skill; human-authored context files help, agent-authored ones can hurt. Candid trade-offs and real-world practices are shared.
With SKILL.md format standardised across 30+ agent tools, the real challenge is content design. This article distills five recurring patterns from ecosystem-wide practices: Tool Wrapper (on-demand library context), Generator (template fill-in for consistent output), Reviewer (checklist scoring by severity), Inversion (agent-led interview before acting), and Pipeline (strict multi-step with gate conditions). Each pattern includes working ADK code, helping developers build reliable agents.
A distilled summary of ByteDance TRAE team's 20 internal AI coding practice manuals. The core argument is that the bottleneck in AI coding efficiency is not model capability but context engineering. The article details six methodologies: Context Engineering, Skills, Spec Coding, Rules, MCP, and Agentic Coding, backed by experimental data (e.g., 32 real bug fixes: 100% success with Skills vs 59% without). Suitable for frontline developers, tech leads, and engineering managers.
A tutorial on using Claude Code Hooks to automate shell commands at lifecycle events, replacing unreliable prompt instructions. Covers 5 key events (PostToolUse, PreToolUse, etc.), 3 hook types (command/prompt/agent), and config file structure. Provides 5 ready-to-use examples: desktop notification, auto-formatting, file protection, context recovery after compaction, and commit message linting. Exit code 2 blocks dangerous actions and feeds stderr back to Claude. For developers seeking reliable Claude Code workflows.
This article covers how Claude Code navigates large codebases using agentic search instead of RAG indexing, avoiding stale index issues but requiring good context configuration. It details the 'harness' ecosystem around the model—CLAUDE.md, Hooks, Skills, Plugins, MCP servers, LSP integration, and subagents—and presents three configuration patterns from successful deployments: making the codebase navigable, maintaining CLAUDE.md as models evolve, and assigning ownership for rollout. A practical guide for teams adopting Claude Code at scale.
The CTO of an agent platform shares their journey of rebuilding the entire engineering workflow around AI: 99% of production code is written by AI, shipping features within a day. The article critiques the superficial “AI-assisted” approach and introduces “harness engineering,” detailing their tech stack, self-healing feedback loop, and the new engineer roles of Architect and Operator. Real-world results include 3–8 deployments per day. Valuable for teams and CTOs seeking genuine AI integration.
Thariq Shihipar argues for using HTML instead of Markdown when working with Claude Code. HTML can represent tables, SVG, designs, and interactions—far denser information than Markdown. HTML docs are more readable, shareable, and can include interactive elements. Claude Code can pull context from codebases, Slack, git history to generate rich HTML reports, prototypes, and review interfaces. Concrete use cases cover planning, code review, design, reporting, and custom editing tools, with reusable prompt examples. For developers seeking to make Claude Code outputs more engaging and actionable.
This guide covers 17 hidden Claude features: persistent memory via Projects, interactive app building with Artifacts, step-by-step reasoning in Adaptive Thinking, long-term user profiling with Memory, role-based prompts, a browser agent (Claude in Chrome), desktop file-system access (Cowork), scheduled tasks, installable skills, CLAUDE.md project rules, terminal coding with Claude Code, visual design with Claude Design, and 90% cost reduction through Prompt Caching. Each includes where to find it and a ready-to-use prompt.
This guide provides 18 actionable steps to fully leverage Claude. It covers setting up Projects and Custom Instructions for persistent context, shifting your mindset to treat Claude as a thinking partner rather than a search engine, and using advanced techniques like style cloning, Extended Thinking, and token-saving prompts. Ready-to-use templates are included for Feynman-style learning, travel planning, expense analysis, and business idea stress-testing. A key insight: simply specifying output length can cut token usage by 40-60%. Aimed at users who want to go beyond basic Q&A and make Claude work for them.