AI Tools and AI Development: The Complete 2026 Guide

Every AI tutorial on the site in one place — coding assistants, local models, RAG, MCP, AI app architecture, AI video generation, and AI workflows for Excel and Flutter.

Coding Liquids cover featuring Sagnik Bhattacharya for the Complete AI Tools and AI Development Guide 2026.

This is the master AI hub. It covers AI coding assistants you run inside your IDE (Claude Code, Copilot, Cursor, Windsurf, Gemini CLI), local open-weight models (Gemma 4, DeepSeek), RAG and agent architecture, MCP servers, AI video generation with Gemini Omni and Seedance, and the AI workflows that overlap with Excel and Flutter — those are also linked from the Excel hub and Flutter hub respectively.

Who This Guide Is For

If you are new to AI tools and trying to figure out which one to start with, read the frontier model comparison first, then pick a coding assistant from the AI Coding Tools section. If you are a developer building AI apps, jump to the Building AI Apps section — it covers the production patterns that matter (tool calling, evaluation, caching, structured outputs). If you are a creator or marketer looking at AI video, head to the Gemini Omni and Seedance sections. If you are privacy-sensitive or cost-conscious, the Local and Open Models section covers Gemma 4 setup and when free local models beat paid APIs.

How to Navigate This Guide

The hub is organised into nine sections. AI Coding Tools covers the IDE assistants — Claude Code, Copilot, Cursor, Windsurf, Gemini CLI — across VS Code and Android Studio. Local and Open Models covers Gemma 4 setup and local-first AI workflows. Frontier Model Comparisons ranks ChatGPT, Claude, Gemini, and the open vs API decision. Building AI Apps and Agents covers RAG, tool calling, evaluation, cost control, and production patterns. MCP (Model Context Protocol) covers servers, agents, and remote architecture. AI Video Generation (Gemini Omni) covers Google's Omni video workflow, prompts, Flow, editing, image-to-video, Shorts, Excel-adjacent video, and model comparisons. AI Video Generation (Seedance) covers the full Seedance toolchain — setup, prompts, settings, use cases, and comparisons against Sora, Kling, and Veo. AI for Excel cross-lists the AI workflows that target Excel specifically (also on the Excel hub). AI for Flutter cross-lists the AI workflows that target Flutter specifically (also on the Flutter hub).

Key takeaways

  • Pick your AI coding tool by where you work: Claude Code or Copilot in VS Code, Gemini Code Assist or Claude Code in Android Studio.
  • Local Gemma 4 is genuinely usable for everyday coding and analysis — the trade-off is hardware setup, not output quality.
  • RAG quality depends more on chunking strategy and reranking than on which model you use.
  • MCP is the protocol layer that lets one AI assistant talk to many tools and data sources — worth learning if you build AI apps.
  • For Seedance, put subject, motion, and camera direction early in the prompt so the model sees the important control signals first.
  • For Excel and Flutter AI workflows, see the dedicated cross-links in the last two sections — they are also indexed on those respective hubs.

AI Coding Tools (VS Code and Android Studio)

Claude Frontier Models (Fable 5, Mythos 5, Opus 4.8)

Local and Open Models (Gemma 4)

Frontier Model Comparisons

Building AI Apps and Agents

MCP (Model Context Protocol)

AI Video Generation (Gemini Omni)

AI Video Generation (Seedance)

AI for Excel (also on the Excel hub)

AI for Flutter (also on the Flutter hub)

Frequently asked questions

Which AI coding assistant is best in 2026?

There is no single best assistant — pick by where you work and what you need. Claude Code and GitHub Copilot lead inside VS Code; Gemini Code Assist and Claude Code are the strongest choices in Android Studio; and for a free, private option, local Gemma 4 is genuinely usable for everyday coding. Match the tool to your IDE and your privacy requirements rather than chasing a single winner.

Can I run AI models locally for free?

Yes. Open-weight models such as Gemma 4 run locally through Ollama or LM Studio with no per-token cost and no data leaving your machine. The trade-off is hardware rather than output quality: you need a reasonably modern GPU or an Apple Silicon Mac for comfortable speeds, and the largest hosted models still lead on the hardest reasoning tasks.

What is MCP and do I need it?

MCP (Model Context Protocol) is an open standard that lets one AI assistant connect to many external tools and data sources through a common interface, instead of a bespoke integration for each. You need it if you are building AI apps or agents that must read files, call APIs, or query databases; for everyday chat use you do not, because the assistant already ships with its own tools.

What is the best AI video generator in 2026?

It depends on the shot. Seedance is strong on prompt control and consistent characters, Google's Veo and Gemini Omni excel at cinematic motion and audio, and Sora and Kling each have their own strengths. For most creators the deciding factors are motion realism, how much prompt control you need, and pricing — compare a few on the same prompt before committing.

Do I need to know how to code to build an AI app?

For a serious app, yes — you need enough programming to wire up API calls, handle errors, and manage data, even though the AI writes much of the code. The concepts that matter most are prompt design, retrieval-augmented generation (RAG), tool calling, and evaluating outputs; those, not raw model choice, decide whether an AI feature works reliably in production.