Update README.md

README.md

@@ -142,30 +142,8 @@ Evaluated on three key benchmarks: **DMind Benchmark** (Web3 Native Logic), **Fi
 
 The evaluation compares DMind-3 (21B) against top-tier frontier models (GPT-5.1, Claude Sonnet 4.5) and other efficient models. Despite its optimized size, the Max model demonstrates exceptional efficiency, particularly in specialized domain tasks where it outperforms significantly larger generalist models.
 
-### 8. 🚀 API Access & Quick Start
 
-Access to DMind-3 is provided via a secure, high-throughput API. Enterprise clients can also opt for private VPC deployment.
-
-**Example API Call (Python):**
-```python
-import dmind_api
-
-dmind_api.api_key = "YOUR_API_KEY"
-
-response = dmind_api.Completion.create(
-    model="dmind-3",
-    prompt="Model the potential contagion effects of a 50% drop in stETH collateral value across the top 5 lending protocols on Ethereum and Base. Provide a probabilistic assessment of liquidation cascades.",
-    max_tokens=2048,
-    temperature=0.2, # Low temperature for analytical tasks
-    top_p=1.0
-)
-
-print(response.choices[0].text)
-```
-
-[→ View Full API Documentation](https://docs.dmind.ai/max/api)
-
-### 9. ⚖️ Limitations & Disclaimer
+### 8. ⚖️ Limitations & Disclaimer
 
 - **Not a Financial Advisor (NFA)**: DMind-3 is a powerful analytical tool for generating insights and modeling risks. It is not a registered financial advisor. All outputs should be independently verified and are not a solicitation to trade.
 - **Probabilistic Nature**: All forecasts are probabilistic and based on the data available up to the knowledge cutoff. The model cannot predict black swan events and is subject to the inherent unpredictability of markets.