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.
Official best practices guide for integrating Claude's computer and browser use capabilities, covering screenshot scaling, click accuracy, cache breakpoints, context management with rolling buffer and server-side compaction, prompt injection defenses, thinking effort tuning, and experimental features like batch tools and the advisor tool. Based on internal testing with Claude 4.6 and Opus 4.7, includes concrete code and performance data.
Cloudflare tested Anthropic's Mythos Preview on 50+ internal repos under Project Glasswing. The model excels at chaining low-severity bugs into working exploits and generating PoCs, making validation actionable. Real-world use revealed inconsistent model refusals and signal-to-noise challenges; a generic coding agent proved ineffective. Cloudflare built an eight-stage harness (Recon, Hunt, Validate, Gapfill, Dedupe, Trace, Feedback, Report) using parallel narrow tasks and adversarial review to improve quality. The post argues that beyond faster patching, defenses must limit exploit reachability from the architecture layer.
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.
Most people use Claude as a one-off Q&A, losing context each time. The author shares a systematic setup: personal instructions, projects, reference files, a context file, connected tools like email and calendar, templates, and repeatable workflows. 25 concrete steps transform Claude from a chat window into a reusable work environment. Suitable for technical workers frustrated with inconsistent AI responses.
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.
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 systematically examines the underlying architecture and engineering practices of agent systems. Starting from a stable agent loop, it contrasts workflows with agents, explains five control patterns, and emphasizes that the harness (evaluation baselines, execution boundaries, feedback, and fallbacks) often matters more than the model itself. It details context engineering via layered management and three compression strategies to prevent context rot, ACI‑oriented tool design, a four‑type memory system with consolidation, long‑task state externalization across sessions, protocol‑based multi‑agent coordination, eval frameworks (Pass@k and Pass^k), and event‑driven observability. Finally, it shows how these principles are implemented in OpenClaw, providing a practical reference for engineers building robust agents.
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.
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.
In the age of agentic development, user story quality directly impacts AI output. The article argues teams should invest more time in breaking down stories and writing clear acceptance criteria rather than just estimating story points. A well-defined story includes three parts: Context, Acceptance Criteria, and Technical Hypothesis. Story point estimation is valuable only when forecasting or surfacing team misalignment is needed; otherwise it can be skipped. A good story acts as a good prompt, accelerating development cycles. Relevant for engineering teams using agile/Scrum.
Anthropic launches Dreaming in research preview for Claude Managed Agents: a scheduled process that reviews past sessions and memory to extract patterns, enabling agents to self-improve. Outcomes let developers define rubrics with a separate grader for self-correcting work; internal benchmarks show +10pp task success, +8.4% on docx, +10.1% on pptx. Multiagent orchestration allows a lead agent to decompose tasks to specialist subagents running in parallel with shared filesystem and traceability. Case studies include Harvey (6x completion rate improvement), Netflix (parallel log analysis), Spiral (writing quality via outcomes), and Wisedocs (50% faster document reviews). For engineers building autonomous AI agent systems.
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.
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 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.