Build your AI stack
Tools, MCP servers, and skills that work together — from editor to production.
AI Coding Tools
View all →ChatGPT
ChatGPT is a large language model-based chatbot developed by OpenAI, launched in November 2022. It uses the GPT-4 architecture to generate human-like text responses across conversation formats. The model supports multi-modal inputs including text, images, and voice interactions. A free tier is available with GPT-3.5, while ChatGPT Plus provides access to GPT-4 with faster response times and plugin capabilities. It serves as a versatile tool for writing, analysis, coding assistance, and creative tasks.
Gemini
Gemini is Google's family of multimodal AI models designed to compete with OpenAI's GPT series. Formerly known as Bard, it rebranded to Gemini in 2024 and directly integrates with Google services. The Ultra 1.0 model achieved state-of-the-art performance on multiple benchmarks. Gemini is available through the Google AI app, web interface, and integrates with Gmail, Docs, and other Google Workspace applications.
Claude
Claude is Anthropic's AI assistant based on the Constitutional AI and RLHF-aligned methodology. Launched in 2023, Claude emphasizes helpful, harmless, and honest interactions. It supports extremely long context windows of up to 200K tokens, making it effective for analyzing lengthy documents. Claude 3.5 Sonnet represents the mid-tier model with strong coding and reasoning capabilities. The iOS app and web interface provide easy access across devices.
DeepSeek
DeepSeek is a Chinese AI company that gained prominence in 2025 with its DeepSeek-V3 model, achieving performance comparable to leading US models at significantly lower training costs. The company released DeepSeek-R1 in January 2025, an open-source reasoning model that competes with OpenAI's o1. DeepSeek's models are available through their web interface, API, and have been integrated into various applications. Their open-source approach has democratized access to frontier-level AI capabilities.
Cursor
Cursor is an AI-first code editor built on VS Code, launched in 2023 by Anysphere. It integrates AI capabilities directly into the coding workflow with features like code completion, natural language commands, and pair programming. Cursor 0.5 introduced Agent capabilities that can autonomously modify codebases. The editor supports Python, JavaScript, TypeScript, Go, Rust, and other major languages. It offers a free tier with 1000 code completions and paid plans for extended usage.
GitHub Copilot
GitHub Copilot is Microsoft's AI coding assistant integrated directly into IDEs like VS Code, JetBrains, and Vim. Powered by OpenAI's GPT-4 and specialized code models, it provides real-time code suggestions, entire function implementations, and documentation generation. Copilot Chat enables conversational debugging and code explanation. Launched in 2021 as a technical preview, it became generally available in 2022. Business plans offer team management, policy controls, and SAML SSO integration.
Windsurf
Windsurf is an AI-powered code editor by Codeium, launched in 2024. Its signature feature is Cascade, a chat interface that maintains project context across editing sessions. Windsurf distinguishes itself with SUPERCLINE, a context engine that tracks cursor position and project state for highly relevant suggestions. The editor is built on the same foundation as Codeium's enterprise tooling, emphasizing speed and privacy. A free tier exists alongside Pro and Enterprise plans.
Midjourney
Midjourney is an independent AI image generation lab that operates primarily through a Discord bot. Launched in 2022, it produces highly artistic and stylized images from text prompts. Users interact via Discord commands, with generation happening on Midjourney's servers. Version 6 (V6) released in late 2024 offers improved coherence, text rendering in images, and photorealism capabilities. The platform has developed a distinctive aesthetic that has influenced digital art and design communities.
MCP Servers
More →Qdrant MCP Server
Official Qdrant MCP server implementation that gives AI agents a semantic memory layer backed by Qdrant vector search. It exposes MCP tools for storing information and retrieving relevant context, so assistants can persist and recall facts across sessions instead of relying only on short chat history.
Ollama MCP Server
Community-maintained Model Context Protocol bridge that exposes Ollama's local HTTP API—model listing, pulls, chat, and OpenAI-compatible completions—to MCP clients such as Claude Desktop and Cursor. Published on npm as `ollama-mcp-server` (maintained fork of NightTrek/Ollama-mcp); requires a running Ollama daemon reachable at `OLLAMA_HOST` (default `http://127.0.0.1:11434`).
Shopify Dev MCP
Official Shopify Dev MCP server from the Shopify AI Toolkit: connects Claude Code, Cursor, VS Code, Gemini CLI, Codex, and similar clients to Shopify developer documentation, GraphQL schemas, and validation workflows without guessing API shapes. Runs locally via npx using the @shopify/dev-mcp package; Shopify documents that no authentication is required for this developer-resources server. Part of Shopify's broader AI Toolkit alongside plugins and optional skill bundles.
piLoci MCP
piLoci MCP is a self-hosted memory server for AI agents that exposes project-scoped memory storage and retrieval through the Model Context Protocol. Built to run on Raspberry Pi 5, it provides semantic recall, project listing, and user identity tools. Teams connect Claude Desktop, Codex, and other MCP clients to share persistent context without sending memory data to cloud services.
Webflow MCP Server
Connect any LLM to your Webflow sites via the Model Context Protocol. Manage pages, collections, CMS items, e-commerce products, forms, and users through natural language — enabling AI-driven site management and content workflows.
Cloudflare MCP
Bridges AI agents to Cloudflare Workers, KV storage, R2 object storage, and D1 databases for edge deployment inspection and management. Agents can check Workers status, inspect KV namespaces, query D1 databases, and monitor R2 buckets directly from the coding environment.
Claude Code Skills
More →Creating and maintaining Cursor skills
Defines how to author, revise, and validate SKILL.md files so agent skills stay executable, scoped, and testable. It focuses on turning vague know-how into reusable operational instructions with clear triggers, deterministic steps, and verification checks.
Designing with LLM structured outputs
This skill covers when and how to ask an LLM for machine-readable payloads: define a JSON Schema (or the vendor's equivalent), enable the structured-output feature your provider documents, validate responses in application code, and handle refusals or validation errors explicitly. It applies to tool-calling agents, extraction pipelines, configuration emitters, and any workflow where brittle text parsing creates production risk.
Maintaining Cursor Project Rules
Follow Cursor's official Rules documentation when you want persistent Agent guidance tied to a repository. Project rules encode architecture expectations, risky-folder guardrails, or repeatable workflows; Cursor applies them via Always Apply, intelligent relevance, glob-scoped attachments, or manual @mentions. Use .mdc frontmatter for finer control and reference templates with @file instead of pasting large snippets.
Structured AI meeting notes
Converts raw meeting transcripts into structured, actionable notes with decision logs, assigned action items, and key context preserved for future AI retrieval. This skill bridges the gap between what was discussed in a meeting and what AI agents need to know when acting on outcomes days or weeks later.
Incident response
Structured process for handling production incidents from detection to resolution and post-mortem. Covers severity assessment using P0-P3 grading, team coordination with a designated incident commander, communication templates for stakeholders and users, and structured post-mortem requirements to drive organizational learning from every significant outage.
Context-Aware QA Skill
Context-Aware QA is a prompting technique where an AI model is instructed to retrieve and cite authoritative sources before answering factual questions. By combining retrieval-augmented generation (RAG) with explicit verification instructions, it dramatically reduces hallucinations in production AI systems.