Cursor & Crypto APIs

Overview

Solo developers can dramatically speed up their Next.js application development by leveraging AI-powered tools and up-to-date data sources. This guide explores advanced workflows in the Cursor AI code editor—inline chat, multi-file editing, prompt engineering—and how these features can turbocharge productivity. It also covers best practices for integrating free Crypto/Web3 APIs (for real-time crypto, Web3, and Bitcoin data) into a Next.js + TypeScript + Prisma stack, with practical tips and code snippets.

Cursor IDE Workflows for Solo Developers

Cursor is an "AI-first" IDE that integrates a powerful AI assistant directly into the coding workflow. Its features like inline AI prompts, context-aware chat, and multi-file edits allow a single developer to generate and refactor code quickly using natural language. Below, we detail how to maximize Cursor's capabilities in a Next.js/TypeScript/Prisma project.

Inline AI Prompting and Code Generation (Composer)

One of Cursor's core features is the Inline Composer (Cmd/Ctrl + K), which opens a prompt bar for generating or modifying code via natural language. You can describe a task in plain English and have Cursor write the code for you at the cursor location. For example, highlight a section and prompt: "Convert this callback-based function to async/await", or ask: "Write a Next.js API route that fetches Bitcoin prices from an API." Cursor will suggest code changes, visible in a diff view for review.

• Be Specific: "Create a Prisma model for User with fields id, name, email (unique), createdAt timestamp" yields a precise schema.
• Request Refactoring: "Optimize this query using Prisma transactions" returns an editable diff.
• Instant Apply: Review and apply changes with one click—the "play" button merges the AI-generated diff into your codebase.

AI Chat: In-IDE Assistant for Debugging and Q&A

Cursor provides an AI Chat panel (Cmd/Ctrl + L) where you can have a conversation with an AI that is aware of your codebase. This is extremely useful for debugging, getting explanations, or brainstorming implementation approaches without leaving the IDE. The chat is context-aware: it automatically includes the current file and even specific code you've selected in the conversation, so you can ask questions like, "Why am I getting a type error on this Prisma query?" and the AI will examine the code and respond with an explanation or fix suggestion.

Multi-File Context and Refactoring at Scale

Cursor's multi-file context awareness means the AI can understand your entire project structure, not just the file you're editing. You can explicitly bring other files into the conversation using @filename. Multi-file editing lets you apply a single instruction to multiple files at once, saving huge amounts of time on refactoring and consistency.

Prompt Engineering Strategies in Cursor

• Be Specific with Tasks: Break down complex tasks into clear, single-purpose prompts. E.g., "Generate a Next.js API route that returns the current Bitcoin price using CoinGecko API."
• Use Step-by-Step Instructions: Guide the AI with ordered steps, e.g., "Write tests first, then implement the code to make those tests pass."
• Leverage Project Context: Remind the AI of relevant context, libraries, or conventions in your prompt.

Additional Cursor Features to Leverage

• Autocompletion and Imports: AI-powered autocomplete generates multi-line code suggestions and auto-imports modules as you type.
• AI-Generated Tests & Docs: Instantly generate unit tests and doc comments for your functions.
• YOLO Mode (Autonomous Coding): Let the AI execute changes without manual approval for each diff—great for rapid prototyping (use with caution).

Understanding Cursor's AI Model Ecosystem

Cursor AI is an AI-powered code editor that supports various large language models (LLMs) to assist with coding, reasoning, and other tasks. Each model has unique strengths, making it suitable for different purposes like coding, creative writing, or research. Below, we explore the models available in Cursor and recommend which to use based on your needs.

Key Points

• Research suggests Cursor AI integrates models like Anthropic's Claude, DeepSeek, Google's Gemini, and its own Cursor Small, each excelling in coding and related tasks.
• It seems likely that Claude 4 Opus is great for complex coding and reasoning, while Claude 3.5 Sonnet suits general coding and creative writing.
• The evidence leans toward DeepSeek models being efficient for technical coding and AI research, and Gemini 2.5 Pro for large-scale, multimodal tasks.
• Cursor Small appears ideal for basic, lightweight coding assistance, given its low cost and small size.

Survey Note: Detailed Analysis of AI Models

This survey note provides an in-depth exploration of the AI models integrated within Cursor AI, an AI-powered code editor, focusing on their strengths, capabilities, and specific use cases for tasks like coding, creative writing, and research. The analysis is grounded in recent research and benchmarks, reflecting the state of AI as of June 14, 2025.

Anthropic Models (Claude)

Anthropic's Claude family is prominently featured in Cursor, with models ranging from lightweight to highly capable. They are known for their depth over breadth, prioritizing deep reasoning and specialized capabilities like code generation.

• Claude 4 Opus/Sonnet: High-performance models for agentic tasks, complex coding, and reasoning. Opus is the most capable for highly complex problems.
• Claude 3.7 Sonnet: Balances capability and performance, optimized for real-world applications like instruction following.
• Claude 3.5 Sonnet: A great all-rounder for most tasks, excelling in coding, multistep workflows, and chart interpretation.
• Claude 3.5 Haiku & Claude 3 Opus: Lighter-weight, cost-effective models for quick, efficient tasks or those needing deep reasoning on a smaller scale.

DeepSeek Models

DeepSeek models are known for their efficiency and high performance, particularly in technical coding, AI research, and multilingual support. They use a Mixture of Experts (MoE) architecture to reduce computational costs.

• DeepSeek V3 & R1: Excel in reasoning, coding, and logical inference, rivaling top models at a lower cost.
• DeepSeek-Coder-V2: A coding-focused model supporting 338 languages, ideal for multilingual projects.
• DeepSeek-VL2: A competitive multimodal model for tasks involving text and images.

Google Models (Gemini)

Google's Gemini models are integrated for their large context windows and strong multimodal capabilities, making them ideal for handling extensive documents or codebases.

• Gemini 2.5 Pro: A powerful model with up to a 1M token context window, excelling at agentic tasks and reasoning over large-scale, complex information.
• Gemini 2.5 Flash: A faster, high-throughput model also with a 1M context window.

Cursor Proprietary Models

Cursor develops its own models optimized for the platform.

• Cursor Small: A lightweight, free model likely optimized for basic coding assistance, autocompletion, and other quick, in-editor tasks.

Comparative Analysis

Recommendations for Specific Tasks

Based on the strengths outlined, here are detailed recommendations for which model to use for your specific task in Cursor.

Coding

• General-Purpose Coding: Use Claude 3.5 Sonnet for its balance of capability and efficiency.
• Complex Projects: Choose Claude 4 Opus for its deep reasoning and precision.
• Multilingual Coding: Opt for DeepSeek-Coder-V2, which supports over 300 languages.
• Lightweight Edits: Try Cursor Small or Claude 3.5 Haiku for quick, cost-effective tasks.

Creative Writing

• Long-Form Content: Use Gemini 2.5 Pro for its large context window to maintain coherence.
• Shorter Pieces: Claude 3.5 Sonnet is a versatile and cost-effective option.

Research & Analysis

• Extensive Documents: Gemini 2.5 Pro is ideal for its ability to process large amounts of information.
• Accuracy-Critical Tasks: Use DeepSeek R1 for its strength in logical inference.

Multimodal & Technical Tasks

• Multimedia Applications: Gemini 2.5 Pro and DeepSeek-VL2 support multimodal inputs.
• AI Research & Technical Coding: DeepSeek V3 offers performance rivaling top models with high efficiency.

This analysis, based on recent research and benchmarks as of June 14, 2025, suggests that the choice of model in Cursor depends on the task's complexity, cost considerations, and required capabilities. These recommendations aim to guide users in leveraging Cursor's diverse model ecosystem effectively for coding, creative writing, research, and beyond.

Summary of API Integration Tips

• Choose the Right API: CoinGecko's free API is an excellent default for price data. For broader info, consider CryptoCompare or blockchain.com for network data.
• Avoid Overuse: Respect rate limits and use server-side caching and revalidation.
• Secure Your Keys: Keep API keys in .env and never push them to git or expose them client-side.
• Test and Handle Errors: Always handle fetch failures and validate external data before saving.
• Stay Updated: The crypto world evolves quickly—keep an eye on API docs and use Cursor's web search for alternatives if needed.

Current Cursor Instructions

# ─── Core Architecture ──────────────────────────────────────────────────────
- USE Next.js 14+, React functional components (TSX), strict TypeScript, and Prisma ORM (db code in /lib/db).
- FOLLOW atomic-design folders (atoms/ molecules/ organisms/ templates/ pages); separate presentation, logic, data.
- ENFORCE progressive-enhancement: site must render core content without JS; layer JS features afterward.
- WRITE semantic HTML5 + ARIA; meet WCAG 2.1 AA.

# ─── Styling & Design System ────────────────────────────────────────────────
- STYLE with styled-components; expose theme + design-tokens (colors, spacing, typography) via ThemeProvider.
- DEFINE mobile-first breakpoints: 375 px, 768 px, 1024 px, 1440 px.
- EXPORT critical CSS above-the-fold; lazy-load non-critical styles.

# ─── Performance Targets ───────────────────────────────────────────────────
- HIT Lighthouse ≥ 90; Core Web Vitals LCP < 2.5 s, INP/FID < 100 ms, CLS < 0.1, TTFB < 200 ms.
- SPLIT code with dynamic imports + route-based chunking; enable React.lazy + Suspense where possible.
- LAZY-LOAD below-the-fold images; serve AVIF/WEBP; generate <source> sets automatically.
- ADD resource hints (preconnect, preload hero assets, dns-prefetch APIs); apply immutable cache-control headers.

# ─── Conversion UX ──────────────────────────────────────────────────────────
- USE F- or Z-pattern hierarchy; primary CTA ≥ 60 px touch area, contrast ≥ 3:1.
- PROVIDE inline form validation + real-time feedback; microcopy next to inputs.
- ANIMATE key conversion elements with Intersection Observer; 200 ms ease-out, prefers-reduced-motion respected.
- SHOW social-proof blocks with quantifiable metrics (e.g., "10 k users").

# ─── Analytics & Tracking ──────────────────────────────────────────────────
- FIRE event-based tracking (GA4) for every user interaction; define micro vs. macro funnels.
- HANDLE UTMs server-side; enable cross-domain + enhanced ecommerce where relevant.

# ─── Security Hardening ────────────────────────────────────────────────────
- SET strict Content-Security-Policy, X-Frame-Options, HSTS, and granular CORS allowlist.
- SANITIZE & validate all inputs; enable CSRF tokens; RATE-LIMIT form posts (≤ 5/min/IP).

# ─── SEO Essentials ────────────────────────────────────────────────────────
- ADD canonical <link>, meta tags, and JSON-LD (Website, Organization, Product).
- AUTO-GENERATE sitemap.xml (lastmod) & robots.txt; maintain h1-h6 hierarchy.

# ─── Deployment / CI-CD ────────────────────────────────────────────────────
- ISOLATE env vars (local | staging | prod); secrets via Vercel.
- RUN pre-commit lint, test, type-check; fail pipeline on error.
- OPTIMIZE build: tree-shaking, source-map-extraction, bundle-analyzer when NODE_ENV=analyze.
- LOG errors to Sentry; configure 308 redirects for legacy URLs.

# ─── Crypto / Web3 Data Layer ──────────────────────────────────────────────
- FETCH live crypto prices & news from CoinGecko (no-key) or CryptoCompare/NewsData (key) in server routes.
- CACHE responses in Prisma; revalidate ISR pages every 60 s.
- EXPOSE /api/prices & /api/news endpoints; front-end consumes via SWR.

# ─── Deliverables ──────────────────────────────────────────────────────────
- DOCUMENT exported functions with JSDoc/TSdoc; auto-generate typed API docs.
- INCLUDE README (setup, env vars, architecture rationale), Lighthouse report, breakpoint screenshots.