AI Coding
77 guides tagged “AI Coding”.
- Agent Skills vs MCP vs AGENTS.md: What's the Difference? AGENTS.md sets standing rules, Agent Skills package one procedure, and MCP connects tools and data. They solve three different problems.
- How to Audit an Agent Skill Before You Install It Read what the skill tells the agent to run: download-and-execute steps, broad file reads, permission changes, and hidden network calls.
- How to Hand a Task to Another Coding Agent A handoff needs a written state file: what changed, what was decided, what is open, and the exact command that continues the work.
- How to Keep a Coding Agent On Task for Hours Long runs need a written plan, commit checkpoints, an explicit stop condition, and a budget. Without them the agent expands the task until it fails.
- How to Review a 2,000-Line AI Pull Request Review in passes: intent and scope, then risky surfaces, then tests, then the diff. Do not read a large change top to bottom.
- How to Set a Pull Request Size Budget for AI Agents Give the agent a files-and-lines budget before it starts, enforce it in CI, and split by concern when it is exceeded.
- How to Stop an Agent From Gaming Its Own Tests Agents optimise for the signal you give them. Make the tests independent, review test changes as strictly as code, and verify by mutation.
- How to Use an AI Code Reviewer Without Trusting It Run AI review as a filter for specific defect classes, verify every finding against the code, and keep the approve decision with a human.
- How to Version and Pin Agent Skills Across a Team Treat skills like dependencies: commit them, pin them, review changes, and record which version each project uses.
- How to Write a SKILL.md File an Agent Actually Follows Write one narrow procedure per skill, put the trigger conditions in the description, and end every step with a check the agent can run.
- What Are Agent Mods (and Should You Use Them)? A mod is a packaged bundle of agent customisation: instructions, commands, and hooks installed as one unit. Convenience and risk arrive together.
- What Are Agent Skills (and What Is a SKILL.md File)? An Agent Skill is a folder containing a SKILL.md that tells an agent how to perform one job. The agent loads it only when the task matches.
- What Is a Review Packet (and Why Agents Should Produce One)? A review packet is the evidence an agent hands over with a change: intent, decisions, risks, and how to verify it. It makes review possible at speed.
- What Is Agent Drift (and How Do You Catch It)? Agent drift is a run slowly leaving the task: scope grows, conventions slip, and the plan stops matching the diff. It is caught at checkpoints, not at the end.
- What Is Agent Memory (and Why Does Your Agent Forget)? Agent memory is whatever the harness carries across sessions: files, summaries, and retrieval. Context inside a session is not memory.
- What Is Agentic Engineering (and How Is It Different From Vibe Coding)? Agentic engineering is directing agents inside an engineering process: specs, review, tests, and limits. Vibe coding skips the process.
- What Is Mid-Response Steering for a Coding Agent? Mid-response steering lets you correct an agent while it is still working, instead of cancelling the run and starting over.
- What Is Plan Mode in a Coding Agent? Plan mode makes the agent propose steps before it edits anything, so you can reject a wrong approach while it is still cheap to change.
- What Is Risk-Based Code Review? Risk-based review sends scrutiny where a mistake is expensive: authorization, data, money, and irreversible operations. Everything else gets lighter checks.
- What Is the Agentic AI Foundation (AAIF)? The Agentic AI Foundation is a Linux Foundation body that hosts vendor-neutral agent standards, including MCP, AGENTS.md, and Agent Skills.
- Why Did My AI Coding Bill Spike? Usually a long agent run re-sending context, a large file read repeatedly, or a retry loop. Find the expensive action before changing plans.
- Why Does My Agent Keep Editing the Wrong File? Duplicate files, generated copies, and generic names make several paths look equally plausible. Name the path in the task and remove the duplicates.
- Why Does My Agent Ignore AGENTS.md? Usually the file is not being read, is too long to matter, or states preferences instead of checkable rules. All three are fixable.
- Why Does My Agent Say the Tests Pass When They Fail? The agent is usually reporting a command it did not run, a subset it did run, or output it misread. Require the command and the raw result.
- Why Is Code Review the Bottleneck in Agentic Coding? Agents produce code faster than teams can read it, so the constraint moves from writing to reviewing, and unreviewed code is not an asset.
- Why Is My Coding Agent Stuck in a Loop? A loop means the agent has no way to tell whether its last attempt worked. Give it a check it can trust, or stop the run.
- How to Write Acceptance Criteria an AI Agent Can Verify An agent satisfies the literal text it is given. Criteria must describe observable behaviour, include boundaries, and avoid adjectives.
- What Is Prompt Caching (and How Much Does It Save)? Prompt caching reuses the model's processed prefix, charging less for repeated input. It only works when the stable part comes first.
- Spec-Driven Development: A Worked Example A complete spec for one endpoint: goal, contract, rules, acceptance criteria, and non-goals. The same structure scales to a migration.
- Spec-Driven Development Tools Compared Spec tooling falls into plain markdown, structured toolkits, IDE-native workflows, and registries. They trade flexibility for enforcement.
- Spec-Driven Development vs TDD: Which Comes First? TDD drives design with failing unit tests; spec-driven development fixes behaviour and contracts first. They operate at different altitudes.
- What Is a Design Doc (and Do You Need One)? A design doc records the problem, options, decision, and trade-offs. It is where architecture reasoning survives after the chat window closes.
- What Is Cross-Site Scripting (XSS)? XSS lets attacker-controlled JavaScript run in another user's browser. Learn the three types, why AI-generated React still ships it, and how to prevent it.
- How to Test AI-Generated Code (Before It Reaches Production) AI-generated code needs a higher testing bar, not a lower one. Learn how to write tests you can trust, find the gaps, and verify before merge.
- The Beginner Vibecoder Playbook: A Weekly Routine for Building With AI A one-page weekly routine for beginner vibecoders: stay motivated, learn deliberately, build confidently, own decisions, verify changes, and recover calmly from mistakes.
- How to Build a Productive AI Coding Routine Build a repeatable AI coding routine around focused sessions, clean context, small changes, and verification instead of endless prompting.
- How to Build Confidence as a Beginner Using AI Coding Assistants Build real confidence as a beginner with AI coding by collecting evidence, learning one concept at a time, and taking responsibility for a small slice of the project.
- How to Learn Software Engineering While Building With AI A practical learning loop for beginners using AI: understand one concept, apply it to a real feature, test it, explain it, and remember the lesson.
- How to Recover When AI Makes a Mistake in Your Code A calm incident playbook for AI coding mistakes: contain the change, preserve evidence, roll back safely, verify the fix, and prevent a repeat.
- How to Recover When an AI Build Gets Messy When repeated AI prompts stop producing progress, reset the project with evidence, a smaller problem, and a deliberate debugging loop.
- How to Stay Motivated When AI Coding Makes Everything Feel Easy A practical mindset guide for vibecoders: turn the excitement of fast prototypes into a repeatable habit of shipping, checking, and learning.
- How to Think Like an Owner When AI Writes the Code Move from prompt-writer to product owner: use constraints, evidence, security boundaries, and user outcomes to decide what AI-generated software should do.
- Does AI Still Hallucinate (and Why)? Yes, AI models still hallucinate. The 2026 AI Index measured hallucination rates from 22% to 94% across 26 models. Why it happens and what you can do about it.
- Can AI-Generated Code Be Detected? No reliable way to detect AI-generated code exists. Detectors claim high accuracy but mislabel human code and are easy to evade. What actually works instead.
- Which Code Hosting Platforms Ban AI-Generated Code? Codeberg and SourceHut restrict AI-generated code, Stack Overflow bans ChatGPT answers. What each policy says, when it takes effect, and how enforcement works.
- How to Prevent AI Code Detectors From Flagging Your Code AI code detectors flag style, not origin. Learn what makes code look machine-generated, how to write code that doesn't get flagged, and when hiding it is the wrong move.
- What Is an AI Agent? An AI agent is a program that uses a language model to decide and act — calling tools, reading files, and iterating until a task is done. Learn how they differ from chatbots.
- What Watermarks Do LLMs Leave in Generated Code? Text watermarks like SynthID-Text and Claude's watermark exist — but code has no deployed watermark yet. Here's how watermarking works and why code resists it.
- How to Choose the Right AI Coding Tool for Your Workflow Compare the 8 best AI coding tools of 2026: Claude Code, Cursor, GitHub Copilot, OpenAI Codex, Windsurf, Gemini CLI, Lovable, and Bolt. Decision guide for vibecoders of every skill level, budget, and project type.
- How to Coordinate Multiple AI Coding Agents on One Codebase Running multiple AI agents in parallel sounds fast, but without coordination they overwrite each other's work. Learn six patterns that keep parallel agents safe: spec-driven decomposition, git worktrees, role splits, model routing, verification gates, and sequential merges.
- How to Optimize Token Usage When Coding with AI Every AI coding session burns tokens. Learn the five levers that cut token spend 50-80%: prompt compression, caching, model routing, output control, and context pruning. Practical techniques for vibecoders and teams.
- Self-Hosted AI Coding Models in 2026: The Practical Review The gap between open-weight and proprietary coding models is closing fast. Compare Kimi K3, GLM-5.2, Qwen3-Coder, Gemma 4, DeepSeek V4, and more. Benchmarks, hardware requirements, and honest recommendations for every budget.
- Path Traversal in AI-Generated Code: How Your File Download Endpoint Gets Hacked AI assistants build file-serving endpoints with string concatenation that attackers exploit with ../. Here are the vulnerable patterns your AI generates, the encoding tricks that bypass naive fixes, and the one correct solution.
- How to Write Comments That AI Assistants Actually Read and Use AI assistants read your comments to understand intent and constraints. But they ignore noise and copy bad patterns. Here's what to write — intent over mechanics, constraints over descriptions, and contracts over implementation.
- How to Structure Files So AI Agents Don't Break Your Architecture AI agents generate code in the wrong files, break module boundaries, and turn your project into a monolith. Here's how to structure directories and files so AI agents generate code that fits your architecture.
- How to Name Things So AI Assistants Generate Better Code AI assistants pattern-match on your existing names. Inconsistent naming produces inconsistent AI-generated code. Here's the naming system — descriptive, searchable, consistent — that makes AI assistants generate code that fits your project.
- How to Stop AI Assistants from Duplicating Code Across Your Project AI assistants can't see your entire codebase, so they duplicate validation, formatting, and helper functions everywhere. Here's how to find duplication, extract shared code, and prevent the AI from copying itself again.
- How to Work With an AI Coding Assistant Without Creating a Mountain of Tech Debt AI assistants generate code fast — and generate tech debt faster. Duplicated logic, dead code, over-engineered abstractions. Here's the system for catching these before they accumulate: review, deduplicate, simplify, delete.
- How to Debug an AI-Generated API That Returns Wrong Data Your AI built an API endpoint. It returns 200 OK with wrong data. Here's how to trace request → handler → response when you didn't write the handler — isolate the broken function, instrument it, and get a targeted fix.
- How to Debug AI-Generated Code: A Complete System for When You Don't Understand What the AI Wrote Your AI wrote 300 lines and you don't know what any of it does — and now it's broken. Here's a repeatable debugging system: isolate, bisect, instrument, explain. No prior understanding required.
- How to Debug AI-Generated Database Queries That Return Wrong Results Your AI wrote a database query. It runs, but returns the wrong data — missing rows, wrong counts, stale results. Here's how to extract the raw SQL, test it directly, and fix it without the AI rewriting your whole data layer.
- How to Find Which AI-Generated Change Broke Your App Using Git Bisect Your AI made 15 commits yesterday and now something's broken. git bisect pinpoints the exact commit in minutes without reading any code — just a yes/no test. Step-by-step for AI builders.
- Why Does the AI's Code Keep Failing Linting (and How to Fix It)? Your AI assistant generates code that fails every lint rule. Here's why, which rules to enforce in CI, and how to auto-fix the AI's output before it merges.
- How to Make Your Codebase AI-Friendly: A Complete Guide Your AI assistant is only as good as the codebase it reads. Structure files for context windows, name things consistently, and write comments the AI actually uses. Complete practical guide for making any codebase AI-friendly.
- How to Simplify Overly Complex AI-Generated Functions (Without Breaking Them) AI assistants over-engineer everything — factory patterns for one implementation, abstract classes for nothing, 5 nested conditionals for a yes/no. Here's how to simplify AI-generated code to match the actual complexity of the problem.
- How to Refactor AI-Generated Code Without Breaking Your App Your AI assistant wrote a 400-line function and you need to clean it up. Here's the step-by-step refactoring loop that keeps tests green — one small change at a time, with the diff you can actually review.
- How to Find and Remove Dead Code Your AI Assistant Left Behind AI assistants leave dead functions, unused imports, and abandoned abstractions everywhere. Here's how to find dead code with grep and IDE tools, delete it safely, and stop the AI from generating more.
- What Is Multi-Agent Coding (and Why Do Agents Conflict With Each Other)? Multi-agent coding runs several AI agents on one codebase in parallel — and they step on each other's changes. Learn the failure modes and how to manage them.
- Why Did My AI-Generated Terraform Config Almost Delete Production? AI-generated Terraform can look correct while lacking prevent_destroy, state safety, and plan review. Learn the specific mistakes and how to guard them.
- How to Write a Spec an AI Coding Agent Can Actually Follow Write specs that produce reliable AI-generated code: explicit goals, edge cases, non-goals, and acceptance criteria. A practical, template-driven guide.
- What Is SQL Injection (and Why Does AI-Generated Code Keep Writing It)? SQL injection lets attackers run arbitrary database commands through your app. Learn how it works and why AI-generated code repeatedly introduces it.
- What Is Context Engineering? Context engineering is the skill of curating what an AI model sees so it produces the right output. Learn why it's replacing prompt engineering.
- What Is Spec-Driven Development? Spec-driven development writes a specification before code, so AI agents build from explicit intent. Learn how it fights intent drift and context decay.
- What Makes a Codebase "AI-Friendly"? An AI-friendly codebase gives agents clear conventions, modular files, and tests so they make fewer mistakes. Learn the traits that make AI coding work.
- What Is Vibe Coding (and Where Does It Break Down)? Vibe coding is building software by prompting AI and accepting what works, without reading every line. Learn what it is and exactly where it fails.
- How to Debug AI-Generated Code When You Don't Understand It Debugging code you didn't write is a skill. Learn the method: read the error, reproduce minimally, bisect the change, and interrogate the code.
- How to Review AI-Generated Code Like a Senior Engineer A practical checklist for reviewing AI-generated code: correctness, security, edge cases, and intent. Review what the assistant skips, not just what it wrote.