Blog · AI Development
AI Development
The AI coding tools, editors and workflows changing how developers build software.
AI Code Review in 2026: How to Use It Well
How to use AI for code review — what it catches (bugs, security smells, weak tests) and where it fails (intent, false positives), the 2026 tools, prompt patterns, and how to keep a human accountable — each with why.
AI DevelopmentAI Development Workflow in 2026: Integrating AI Into Your Process
How to integrate AI across your development process — mapping it to the whole SDLC, the plan → implement → verify loop, spec-driven development, context files (AGENTS.md/CLAUDE.md), MCP, CI review, guardrails, and measuring impact honestly.
AI DevelopmentBest AI Coding Assistants in 2026: 11 Tools Compared
The best AI coding assistants of 2026 — Cursor, Claude Code, GitHub Copilot, Windsurf, Gemini Code Assist and more — compared honestly on what they do, which models they run, and who each is best for.
AI DevelopmentClaude vs ChatGPT vs Gemini for Coding in 2026
A fair comparison of Claude, ChatGPT and Gemini for coding in 2026 — each model family’s coding strengths, genuine weaknesses, tools (Claude Code, Codex, Gemini CLI) and who each is best for, with guidance on choosing.
AI DevelopmentHow to Build AI Agents in 2026: A Practical Guide
A practical guide to building AI agents — the tool-use loop, ReAct-style reasoning, planning and reflection, multi-agent systems, MCP, frameworks (LangGraph, CrewAI, agent SDKs), guardrails, cost control, observability and security.
AI DevelopmentHow to Build with LLM APIs in 2026: A Developer’s Guide
A developer’s guide to building with LLM APIs — the messages shape, tokens, streaming, tool use, structured outputs, embeddings, prompt caching, cost control and error handling — each with why and a code example, plus the 2026 model landscape.
AI DevelopmentHow to Debug Code with AI in 2026: 14 Techniques
Debug faster with AI — paste the full stack trace, give a minimal reproduction, ask for ranked hypotheses, have it write a failing test, use agentic tools that run the code, and verify every suggested API — each with why and a prompt example.
AI DevelopmentHow to Use AI for Coding in 2026: 14 Practices That Work
A practical guide to using AI for coding — front-loading context, spec-first prompting, small reviewable steps, AI for tests and debugging, agentic vs autocomplete modes, and reviewing every diff — each with why it matters.
AI DevelopmentPrompt Engineering for Developers in 2026: 14 Techniques
Practical prompt engineering for developers — specificity and context, system prompts, few-shot examples, structured JSON outputs, tool use, and the 2026 shifts (reasoning models, effort budgets, less over-steering) — each with why and an example.
AI DevelopmentRetrieval-Augmented Generation (RAG) Guide 2026
A practical RAG guide — what retrieval-augmented generation is, the ingest → chunk → embed → store → retrieve → generate pipeline, chunking, embeddings, hybrid search, reranking, evaluation, and RAG vs fine-tuning vs long context.
AI has moved from novelty to daily tool for a lot of developers — writing first drafts, explaining unfamiliar code, generating tests, and increasingly acting on their own inside an editor. This category tracks that shift with a practical bias: which tools are worth your time, how to prompt them well, and how to build with the same models yourself.
The writing splits into two halves. One is using AI to code faster and better — assistants, code review, debugging with a model at your side. The other is building AI features — working with LLM APIs, retrieval-augmented generation and agents — with runnable patterns you can open in the editor and adapt to your own keys and data.
The field moves quickly, so the focus is on ideas that last: how these tools actually work, where they help versus where they mislead, and how to keep human judgement in the loop. Hype gets left out; what ships gets kept.