My results after tracking: Cursor saves me 2 hours a day on boilerplate, costs me 1 on debugging its bad ideas.

After a rigorous 90-day tracking period across my integration projects, I've quantified Cursor's impact on my workflow with hard numbers. The headline is a net positive, but the details reveal a significant trade-off. On average, the AI-assisted composition—generating API client stubs, data transformation functions, and boilerplate configuration—reclaims approximately **2 hours per calendar day**. However, I then lose roughly **1 hour per day** diagnosing, debugging, and ultimately rewriting the subtly flawed or contextually inappropriate code it suggests. This yields a net gain of 1 hour, but it's a cognitively expensive hour.

The savings are most pronounced in predictable, pattern-heavy domains:
* Generating full Axios/`fetch` wrappers for new REST APIs, including TypeScript interfaces from OpenAPI specs.
* Writing repetitive data mapping functions (e.g., transforming `{user_name: "..."}` to `{userName: "..."}`).
* Creating initial configuration skeletons for `zapier-cli` projects or `n8n` node code.

Yet, the incurred cost arises from a category of errors I term "syntactically correct but semantically dangerous" suggestions. For example:

```javascript
// Cursor's suggestion for a 'safe' array chunking function
function chunkArray(arr, size) {
const chunks = [];
for (let i = 0; i < arr.length; i += size) {
chunks.push(arr.slice(i, i + size));
}
return chunks;
}
// Problem: This is fine for simple arrays, but when I fed it an array of
// complex objects with mutating methods, the shallow copy caused state side effects
// later in my pipeline. It required a deep clone, which it didn't infer from context.
```

The primary pitfalls I've cataloged include:
1. **Inadequate Error Handling:** It will suggest the core logic but consistently omit robust error catching and retry mechanisms essential for production integrations.
2. **Context Blindness:** It doesn't grasp the larger architectural patterns of my project (e.g., that we use a specific logger or circuit breaker pattern) and generates generic code.
3. **Library Hallucination:** In middle-tier code, it will occasionally reference non-existent methods from popular libraries (`axios.hyper()` was a recent fictional example).

My workflow has adapted. I now use Cursor strictly as a **first-pass acceleration engine**, followed by a mandatory **validation phase**. I never accept its output without a critical review of state management, error flow, and alignment with existing project conventions. The tool is invaluable for overcoming blank-canvas syndrome and generating the initial 70% of boilerplate. However, the final 30%—the part that ensures reliability, resilience, and maintainability—requires experienced, human oversight. It's a powerful force multiplier for a senior developer, but a potential minefield for a novice.

API first.