DEV Community: Sreevanth K G

AI-Assisted FPGA Development: Cursor IDE for Tang Nano 9K Projects

Sreevanth K G — Mon, 06 Jul 2026 09:45:00 +0000

AI-Assisted FPGA Development: Cursor IDE for Tang Nano 9K Projects

FPGA development has traditionally been a steep learning curve—wrestling with HDL syntax, peripheral datasheets, and synthesis quirks. But what if you could leverage AI to accelerate your workflow? This guide walks you through using Cursor IDE (Claude-powered code generation) for Tang Nano 9K development, with practical setup advice and hard-won pitfalls to avoid.

Why Cursor for FPGA?

Tang Nano 9K is an accessible FPGA board, but the toolchain (EDA Suite, Verilog, IP cores) demands deep domain knowledge. Cursor, built on Claude, excels at:

Code generation: Writing synthesizable Verilog from natural language descriptions
Peripheral integration: Generating boilerplate for HDMI, PSRAM, SPI with correct pin mappings
Debugging: Analyzing error logs and suggesting fixes
Documentation: Explaining complex HDL patterns

The caveat? You need domain knowledge to guide and validate AI output. This guide covers both.

Part 1: Setup

Prerequisites

Tang Nano 9K board with USB programmer
EDA Suite (Gowin IDE or command-line tools)
Cursor IDE installed (free tier available)
Verilog basics: Modules, always blocks, instantiation
Tang Nano datasheets for pinouts and IP cores

Step 1: Install Cursor

Download from cursor.com
Launch and authenticate (free tier works for learning)
Create a project folder for your Tang Nano work

Step 2: Set Up Project Structure

Create a standard FPGA project layout with src/, constraints/, sim/, and docs/ directories.

Step 3: Create a Knowledge Base for Cursor

This is critical. Create a knowledge_base.md file with Tang Nano-specific details:

Pin Assignments:

LED: IO_25 (active high)
Button: IO_26 (active low)
HDMI_CK: IO_17, HDMI_D0-D2: IO_18-20
PSRAM_CLK: IO_35, PSRAM_CS: IO_36, PSRAM_MOSI: IO_37, PSRAM_MISO: IO_38

IP Cores:

rPLL: Clock generation (VCO: 600MHz–3GHz)
OSER10: 10:1 serializer for HDMI TMDS
ELVDS_OBUF: Differential output buffer for HDMI
BSRAM: Built-in SRAM (256K bits)
SSRAM_CTRL: PSRAM controller (32MB external)

Common Mistakes:

PSRAM slew rate must be HIGH (not FAST)
PLL VCO max is 3GHz
HDMI TMDS requires 10:1 serialization per lane

Part 2: Workflow Tips

Tip 1: Start with High-Level Prompts

Instead of "generate HDMI code," give full context:

Good: "I'm building HDMI output on Tang Nano 9K with 50MHz input clock. Use rPLL for 74.25MHz pixel clock and OSER10 for TMDS. Pins: HDMI_CK=IO_17, HDMI_D0=IO_18. Generate top-level Verilog module."

Poor: "Generate HDMI code."

Tip 2: Validate Against Datasheets

Cursor generates plausible Verilog, but always cross-check:

Clock domains and timing constraints
Pin voltage levels (3.3V for Tang Nano logic)
IP core parameter ranges against official specs

Tip 3: Iterative Refinement with Tests

Ask Cursor to generate both RTL and testbenches. This catches logic errors early before synthesis.

Tip 4: Leverage Diff View

Use Cursor's inline diff feature to review changes line-by-line before accepting modifications.

Tip 5: Debug Synthesis Errors Together

Paste full error logs into Cursor with context. It can identify missing IP instantiations, parameter mismatches, or constraint issues.

Part 3: Pitfalls to Avoid

Pitfall 1: Over-Trusting AI for Pin Assignments

Always reference pins.cst constraints and cross-check against datasheets before synthesis.

Pitfall 2: Ignoring Timing Constraints

Ask Cursor to generate timing constraints (.cst) alongside HDL. Test timing closure at your target frequency (e.g., 74.25MHz for HDMI).

Pitfall 3: Mixing AI and Manual Code Without Version Control

Use git with meaningful commits. Comment all AI-generated sections clearly in code.

Pitfall 4: Not Keeping Synthesis Logs

Save every synthesis log with timestamps. Ask Cursor to analyze old logs for patterns when debugging recurring issues.

Pitfall 5: Asking for Too Much at Once

Break designs into modules: HDMI driver → PSRAM controller → top-level glue. Test each in isolation first.

Pitfall 6: Forgetting Power and Thermal Constraints

Include power budgets in prompts. Ask Cursor about clock gating and low-power techniques to avoid overheating.

Part 4: Real-World Example

Here's a workflow for building an LED blinker with PLL-generated clock:

Step 1: Write a Detailed Prompt

Specify input clocks, output frequency, pin assignments, and IP requirements.

Step 2: Review Generated Code

Verify PLL parameters are within spec, divider math is correct, and IO voltage levels are appropriate.

Step 3: Synthesize and Test

Run gowin_pack and check for errors. If synthesis fails, paste the error log back to Cursor.

Step 4: Program and Observe

Upload to Tang Nano and verify the LED blinks at your target frequency.

Key Takeaways

Build a knowledge base upfront (pins, IP specs, common mistakes)
Prompt iteratively with context and constraints
Validate relentlessly against datasheets and synthesis logs
Version control your work and mark AI-generated sections
Break problems into modules and test incrementally

With this workflow, you'll accelerate from struggling with Verilog syntax to rapidly prototyping complex designs on Tang Nano 9K.

Tools & Resources

Cursor IDE: AI-assisted code editing
Gowin EDA: Synthesis and place-and-route
GTKWave: Waveform viewing
Tang Nano Docs: Pinouts, IP cores, examples

Conclusion

Cursor is a powerful multiplier for FPGA development—if you know how to guide it. The key is building domain knowledge upfront, prompting with context, validating against specifications, maintaining version control, and breaking problems into testable modules.

Happy building! 🚀

What's your biggest FPGA pain point? Share in the comments—I'd love to hear how Cursor could help you solve it faster.

API vs MCP: Understanding the Difference

Sreevanth K G — Sun, 05 Jul 2026 09:47:10 +0000

API vs MCP: Key Differences

When building AI-powered applications and integrations, understanding the distinction between APIs and MCPs (Model Context Protocol) is crucial. Both enable external connectivity, but they serve different purposes and operate at different levels of abstraction.

What is an API?

An API (Application Programming Interface) is a direct interface to a service or application. It defines the endpoints, methods, and data formats for communication.

Characteristics:

Direct HTTP/REST calls to specific endpoints
Standard request-response model
Authentication typically via API keys or OAuth
Returns structured data (JSON, XML, etc.)
Synchronous or asynchronous depending on implementation

Example:

const response = await fetch("https://api.example.com/posts", {
  method: "POST",
  headers: { "Authorization": "Bearer TOKEN" },
  body: JSON.stringify({ title: "My Post", content: "..." })
});

Use Cases:

Direct service integration (payment processing, email delivery)
Data retrieval and manipulation
Third-party service calls

What is MCP?

MCP (Model Context Protocol) is a standardized protocol that enables AI models (like Claude) to discover and use tools and services in a unified way. It acts as a middleware layer that wraps APIs and provides them to AI models.

Characteristics:

Standardized tool interface for AI models
Model can discover available tools dynamically
Abstracts away direct API complexity
Enables multi-step reasoning with tools
Tools are wrapped in a consistent schema

Example:

const response = await fetch("https://api.anthropic.com/v1/messages", {
  method: "POST",
  body: JSON.stringify({
    model: "claude-sonnet-4-6",
    messages: [{ role: "user", content: "Create a task in Asana" }],
    mcp_servers: [
      { type: "url", url: "https://mcp.asana.com/sse", name: "asana-mcp" }
    ]
  })
});

Use Cases:

AI agents interacting with external tools
Complex workflows requiring AI reasoning
Unified tool discovery for AI models

Side-by-Side Comparison

Aspect	API	MCP
Level	Service endpoint	Protocol/middleware layer
Caller	Direct application code	AI model (via Claude)
Authentication	API keys, OAuth tokens	Configured once, handled by protocol
Discoverability	Manual documentation	AI can discover tools dynamically
Use Case	App-to-service communication	AI-to-service communication
Complexity	Lower (direct calls)	Higher (requires MCP server setup)
Reasoning	Application logic decides flow	AI model decides tool usage

When to Use Each

Use APIs When:

Building traditional applications that need external services
You want direct control over requests and responses
The integration is straightforward and doesn't need AI reasoning

Use MCP When:

Building AI agents that need access to multiple tools
You want the AI model to decide which tools to use
You need flexible, dynamic tool discovery
The workflow involves complex reasoning with external services

Real-World Example

Scenario: Building a task management assistant

With APIs:

// Application decides: create task, then assign to user
const task = await createTaskViaAPI();
const assigned = await assignTaskViaAPI(task.id);

With MCP:

// AI decides: should I create a task? should I assign it? should I add to project?
const response = await claude.ask(
  "Create a task for the Q3 planning in Asana",
  { mcp_servers: [asana_mcp] }
);

The AI model uses reasoning to determine the best sequence and which tools to invoke.

Conclusion

APIs are fundamental building blocks for service-to-service communication
MCPs are higher-level abstractions that enable AI models to intelligently use multiple APIs
Modern AI applications increasingly use MCPs to give models access to tools while maintaining a clean, standardized interface
Understanding both is essential for building next-generation AI-powered systems

Start with APIs for basic integrations, but adopt MCPs when your application needs AI-driven decision-making across multiple services.

Test Post - Connectivity Check

Sreevanth K G — Sun, 05 Jul 2026 09:46:09 +0000

This is a test post to verify dev.to API connectivity and posting functionality.

Status: Working ✓

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