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 →Snowflake-managed MCP Server
Snowflake documents a Snowflake-hosted Model Context Protocol (MCP) endpoint that fronts governed Snowflake data and Cortex workloads without provisioning a separate MCP bridge VM. Administrators declare tools with SQL (`CREATE MCP SERVER`)—for example Cortex Search queries, Cortex Analyst chat-style messages, Cortex Agent executions, parameterized SQL runners, or custom tools backed by Snowflake-native functions—and clients authenticate using Snowflake OAuth against the MCP revision pinned in Snowflake release notes.
n8n MCP Server Trigger
The MCP Server Trigger is a first-party n8n core node that turns an n8n workflow into a Model Context Protocol server endpoint. Instead of chaining conventional trigger nodes, it connects only to tool nodes so remote MCP clients can list tools and invoke them over long-lived Server-Sent Events or streamable HTTP transports (stdio is explicitly unsupported). Each node exposes separate test and production MCP URLs, optional bearer or header authentication, and documentation explains how to proxy Claude Desktop through `npx mcp-remote` plus queue-mode caveats for multi-replica webhook deployments.
DuckDB MCP community extension (`duckdb_mcp`)
The DuckDB-distributed community extension `duckdb_mcp` embeds MCP client and server capabilities directly inside DuckDB. Installers load it via `INSTALL duckdb_mcp FROM community` followed by `LOAD duckdb_mcp`, after which SQL can attach remote MCP servers (stdio/TCP/WebSocket transports), enumerate resources (`mcp_list_resources`), invoke remote tools (`mcp_call_tool`), and wrap responses with `read_csv`/`read_json`/`read_parquet` URIs routed through `mcp://`. In reverse direction, DuckDB can publish tables, queries, and execution-bound tools (`mcp_publish_table`, `mcp_publish_query`, `mcp_publish_execution_tool`) while `mcp_server_start` exposes them to external MCP-compatible clients.
Neon MCP Server
Official Neon MCP integration exposes Neon Postgres projects to MCP-capable assistants via Streamable HTTP (`https://mcp.neon.tech/mcp`), legacy SSE (`https://mcp.neon.tech/sse`), or a locally launched `@neondatabase/mcp-server-neon` package. Documentation lists tools for project and branch lifecycle, SQL execution, migration rehearsal branches, slow-query diagnostics, Neon Auth provisioning, Data API setup, and embedded Neon docs retrieval—each mapped to Neon API operations.
Apify MCP Server
Apify documents an official Model Context Protocol server hosted at https://mcp.apify.com that speaks Streamable HTTP in line with the current MCP specification; Apify warns that SSE transport was deprecated for removal April 1, 2026. Hosted clients authenticate through browser OAuth or by supplying Bearer tokens sourced from Console → Settings → Integrations (`APIFY_TOKEN`), can pin tool bundles via URL query (`?tools=actors,docs,apify/rag-web-browser` style examples reproduce Apify wording), optionally append `telemetry-enabled=false`, and benefit from inferred structured-output schemas surfaced for Actor tooling on hosted endpoints unlike the default stdio server. When MCP clients refuse remote transports, docs recommend `npx -y @apify/actors-mcp-server` with `APIFY_TOKEN` for stdio, Node.js ≥18, and adherence to documented per-user throughput (Apify cites up to thirty requests per second across Actor runs plus storage/documentation calls). Specialized payment modes (open x402 on Base plus Skyfire) appear as optional adjunct pages inside the broader integration handbook.
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.
Claude Code Skills
More →OWASP GenAI LLM Top 10 (v1.1) threat review checklist
Maps the authoritative OWASP "Top 10 for Large Language Model Applications" (version 1.1) taxonomy—LLM01 Prompt Injection through LLM10 Model Theft—into an actionable readiness checklist for architects red-teaming Retrieval-Augmented Generation, Agents, plugins, training pipelines, or hosted inference gateways. Official project pages summarize each risk bucket (prompt injection bypassing safeguards, unchecked outputs enabling downstream exploits, poisoned corpora distorting reasoning, abusive workloads starving capacity, brittle supply-chain dependencies, sensitive data resurfacing inside generations, excessively privileged plugins/agents/autonomy, misplaced trust producing compliance failures, loss of proprietary model weights via API abuse). The skill pairs each category with tangible controls (policy, monitoring, toolchain limits) anchored to genai.owasp.org releases rather than anecdotes.
Postmortem trigger and root-cause taxonomy
Distills Appendix C (“Results of Postmortem Analysis”) from Google’s SRE workbook: it explains why Google catalogs standardized postmortem fields—linking outages to observable triggers versus deeper root-cause categories—so reliability leaders can prioritize systemic fixes rather than anecdotal fixes. The appendix cites a multi-year corpus (labeled 2010–2017 in the workbook) highlighting that binary pushes accounted for roughly 37% of outage triggers while configuration pushes were about 31%, with additional slices for user-behavior spikes, pipelines, upstream providers, performance decay, capacity, and hardware. A companion table correlates outages with qualitative root causes such as faulty software (~41%), development-process gaps (~20%), emergent complexity (~17%), deployment planning weaknesses (~7%), and network failures (~3%). Teams use these distributions to sanity-check whether their incident queues skew differently and to steer investment into the failure classes that statistically dominate historically.
Example SLO document authoring
Operationalizes Appendix A from Google’s SRE workbook by translating the illustrative “Example Game Service” SLO dossier into a checklist teams can mimic: articulate the user-facing workload, nominate rolling measurement windows (the appendix uses four weeks), pair each subsystem with tightly defined SLIs (availability from load balancers excluding 5xx, latency percentile gates, freshness for derived tables, correctness via probers, completeness for pipelines), cite explicit numerator/denominator language, rationalize rounding policies, quantify per-objective error budgets, and cite the sibling error budget policy for enforcement.
Error budget policy drafting
Translates Google’s worked example error-budget policy into a repeatable playbook for tying release tempo to measured reliability: define goals (protect users from repeated SLO misses while preserving innovation incentives), spell out what happens when the rolling window consumes its budget (freeze changes except urgent defects or security work), codify outage investigation thresholds, and document escalation paths when stakeholders disagree about budget math.
NIST AI Risk Management Framework (AI RMF 1.0) lifecycle checklist
Anchors facilitation workshops to NIST's voluntary Artificial Intelligence Risk Management Framework (AI RMF 1.0, formally NIST.AI.100-1 with DOI https://doi.org/10.6028/NIST.AI.100-1): the playbook issued alongside the Framework emphasizes structuring programs around the mutually reinforcing core functions GOVERN → MAP → MEASURE → MANAGE rather than improvising unrelated security tickets. NIST contemporaneously publishes companion assets such as the Trustworthy AI Resource Center playbook (airc.nist.gov), roadmap, crosswalks, and—for generative workloads—the Generative Artificial Intelligence Profile (NIST AI 600-1, July 26, 2024, DOI https://doi.org/10.6028/NIST.AI.600-1)—so teams can reconcile novel failure modes against documented categories of trustworthiness. This operational skill folds those authoritative layers into scripted prompts for cross-functional councils that must evidence documentation, escalation paths, quantitative trustworthiness analyses, prioritized mitigations, and alignment with externally referenced stakeholder expectations—not marketing slides.
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.