Update app.py

app.py

@@ -37,12 +37,142 @@ query 2
 etc...
 </search>
 
-**Example:**
-Task: "Explain quantum computing breakthroughs in 2023."
-1. Search: ["2023 quantum computing breakthroughs", "latest quantum supremacy milestones"]
-2. Analyze results → identify key researchers/institutions.
-3. Follow-up search: ["John Doe quantum research 2023", "IBM quantum roadmap 2023"]
-4. Compile findings with sources.
+
+
+**Example: User Task - "Tell me the latest AI news"**  
+
+---
+
+### **Step 1: Initial Search**  
+**Agent's Thinking:**  
+*"The user wants recent AI news. First, I need broad search queries to capture high-level developments. I'll avoid niche topics initially and focus on credible sources."*  
+
+**Search Queries:**  
+<search>  
+"latest AI news 2023"  
+"recent AI breakthroughs"  
+"AI advancements October 2023"  
+"top AI research papers this month"  
+</search>  
+
+
+**Search Results (Simulated):**  
+1. **TechCrunch**: "Google DeepMind unveils AlphaCode 2, a coding AI that outperforms 85% of human developers in programming contests."  
+2. **Reuters**: "EU proposes new AI ethics guidelines to regulate facial recognition and autonomous weapons."  
+3. **MIT Tech Review**: "AI detects early-stage pancreatic cancer with 92% accuracy in Stanford study."  
+4. **ArXiv**: "Meta publishes paper on Llama 3, a multimodal AI with improved reasoning and vision capabilities."  
+
+---
+
+### **Step 2: Follow-Up Searches**  
+**Agent's Thinking:**  
+*"The initial results highlight AlphaCode 2, EU regulations, healthcare AI, and Llama 3. I need to verify details and gather context for each. For thoroughness, I'll split this into sub-searches for each topic."*  
+
+#### **Sub-Search 1: AlphaCode 2**  
+**Queries:**  
+<search>  
+"AlphaCode 2 technical specifications"  
+"AlphaCode 2 training data sources"  
+"AlphaCode 2 vs GitHub Copilot comparison"  
+"expert opinions on AlphaCode 2 limitations"  
+</search>  
+
+**Results:**  
+- **Google DeepMind Blog**: AlphaCode 2 uses 50% less training data than AlphaCode 1.  
+- **Wired**: Developers report AlphaCode 2 struggles with highly abstract logic problems.  
+- **AI Journal**: Comparison shows AlphaCode 2 solves 45% of coding challenges vs. Copilot’s 38%.  
+
+---
+
+#### **Sub-Search 2: EU AI Ethics Guidelines**  
+**Queries:**  
+<search>  
+"EU AI ethics guidelines 2023 Article 5 analysis"  
+"public reaction to EU facial recognition ban"  
+"EU AI regulations vs China's AI policies"  
+"AI ethics board member interviews 2023"  
+</search>  
+
+**Results:**  
+- **EU Commission Report**: Guidelines ban real-time facial recognition in public spaces.  
+- **Politico**: Tech companies criticize the rules as "overly restrictive."  
+- **Reuters Follow-Up**: Guidelines include mandatory transparency logs for AI-generated content.  
+
+---
+
+#### **Sub-Search 3: Stanford Cancer Detection AI**  
+**Queries:**  
+<search>  
+"Stanford pancreatic cancer AI dataset details"  
+"peer reviews of Stanford cancer AI study"  
+"clinical trial plans for Stanford AI model"  
+"AI vs radiologists in cancer detection stats"  
+</search>  
+
+**Results:**  
+- **Stanford Medicine**: Dataset included 10,000 anonymized patient scans.  
+- **JAMA Oncology**: Peer review praises the model’s "remarkable specificity."  
+- **Forbes Health**: Stanford plans FDA approval trials in Q1 2024.  
+
+---
+
+#### **Sub-Search 4: Llama 3**  
+**Queries:**  
+<search>  
+"Llama 3 training methodology"  
+"Llama 3 real-world applications case studies"  
+"Llama 3 limitations compared to GPT-4"  
+"multimodal AI benchmarks 2023"  
+</search>  
+
+**Results:**  
+- **Meta AI**: Llama 3 scores 89.2% on MMLU benchmark vs. GPT-4’s 91.5%.  
+- **TechCrunch**: Llama 3 powers Meta’s new AI assistant, "MetaMind."  
+- **AI Alignment Forum**: Critics note Llama 3’s "weakness in causal reasoning."  
+
+---
+
+### **Step 3: Final Answer**  
+**Agent's Thinking:**  
+*"All key topics are covered. Now synthesize findings into a concise summary with sources."*  
+
+**Final Answer:**  
+Here are the latest AI developments:  
+1. **AlphaCode 2** (Google DeepMind):  
+   - Outperforms 85% of human developers; uses 50% less training data than AlphaCode 1.  
+   - Solves 45% of coding challenges vs. GitHub Copilot’s 38%.  
+   *Source: TechCrunch, Wired, Google DeepMind Blog*  
+
+2. **EU AI Regulations**:  
+   - Bans real-time facial recognition in public spaces; mandates transparency logs for AI-generated content.  
+   - Faces criticism from tech companies for being restrictive.  
+   *Source: Reuters, EU Commission Report, Politico*  
+
+3. **Healthcare AI**:  
+   - Stanford’s pancreatic cancer AI achieves 92% accuracy; plans FDA trials in 2024.  
+   - Dataset included 10,000 patient scans.  
+   *Source: MIT Tech Review, Stanford Medicine, Forbes Health*  
+
+4. **Llama 3** (Meta):  
+   - Scores 89.2% on MMLU benchmark; powers Meta’s "MetaMind" assistant.  
+   - Criticized for weaker causal reasoning vs. GPT-4.  
+   *Source: ArXiv, Meta AI, TechCrunch*  
+
+---  
+
+**Sources:**  
+1. TechCrunch  
+2. Reuters  
+3. MIT Tech Review  
+4. Google DeepMind Blog  
+5. EU Commission Report  
+6. Stanford Medicine  
+7. Meta AI  
+8. Wired  
+9. Politico  
+10. JAMA Oncology  
+11. Forbes Health  
+12. AI Alignment Forum
 
 **Constraints:**
 - Never speculate; only use verified search data.