lay offs, lay offs, lay offs....

[ManOffSteel]

3 minutes ago, baabaa said:

ee city anna?

Dallaspuram

[ManOffSteel]

3 minutes ago, kevinUsa said:

fake news by H1 people to scare others canada lo recruiters asking are u willing to move to usa if job is given ani.

what are u saying..  they are also paying good salaries too  upto 150-200k per year 

Ante ante

[citizenofIND]

Seems like, Every client doing, just saw a ticket. Tellodu working since 15 years laid off. Since ticket is there, aware else no idea what’s happening in own client.

[pakeer_saab]

36 minutes ago, Coconut said:

Fake News anna...lot of high paying jobs available...

aade cheppedi "manam IT work cheste paisal iche job not HAND JOB"

[pakeer_saab]

30 minutes ago, tollywood_hater said:

emo anna , ee sari raasi palalu antha baga levu .... kompa teesi vallu mana raasi vallu emo ani doubt 

raasi phalalu eppudu alaage untaayi.. choose vaadi batti untadi

[pakeer_saab]

9 minutes ago, kevinUsa said:

fake news by H1 people to scare others canada lo recruiters asking are u willing to move to usa if job is given ani.

what are u saying..  they are also paying good salaries too  upto 150-200k per year 

induku kaadu Canada povaddu ra anindi

brain freeze ayipoddi neeku ainattu

[Iriswest]

24 minutes ago, kevinUsa said:

fake news by H1 people to scare others canada lo recruiters asking are u willing to move to usa if job is given ani.

what are u saying..  they are also paying good salaries too  upto 150-200k per year 

Ekkada ento kuda cheppu.

[desimanager]

Contractors safee temporarily..

ftes hard time layoff iyte 

[Tellugodu]

2 minutes ago, desimanager said:

Contractors safee temporarily..

ftes hard time layoff iyte 

Why do you think? 

[desimanager]

1 minute ago, Tellugodu said:

Why do you think? 

In case of layoff contractors will have breathing time .. 

FTe 60 day period lo job kotali ga 

[kevinUsa]

21 minutes ago, Iriswest said:

Ekkada ento kuda cheppu.

yes ERP meda nannu linkedin lo asked 

[allbakara]

56 minutes ago, kevinUsa said:

fake news by H1 people to scare others canada lo recruiters asking are u willing to move to usa if job is given ani.

what are u saying..  they are also paying good salaries too  upto 150-200k per year 

Adhi trap bro…border security check daggara moosi 10gadaniki plan

[VanceChilukuri]

github copilot lo Claude 3.7 Sonnet model select cheskoni code write chesthe layoffs kaka inka emavthadi ? evadaina use chesada claude 3.7 ee DB lo? padi mandi chesey pani okkadey cheyyocchu if you use it.

[Paamu]

Single job a ..... or multi chesi anni poyayaa ?

[kevinUsa]

18 minutes ago, VanceChilukuri said:

github copilot lo Claude 3.7 Sonnet model select cheskoni code write chesthe layoffs kaka inka emavthadi ? evadaina use chesada claude 3.7 ee DB lo? padi mandi chesey pani okkadey cheyyocchu if you use it.

please write a python code to create awebsite like perplexity

 code by claude 3.7 sonet   

I'll help you write Python code to create a website similar to Perplexity, which is a search engine that uses AI to provide comprehensive answers. Here's a basic implementation using Flask and some AI components:

 

Claude Sonnet 3.7

 

 

I'll help you write Python code to create a website similar to Perplexity, which is a search engine that uses AI to provide comprehensive answers. Here's a basic implementation using Flask and some AI components:

Code

python

Copy Code

# Create a simple Perplexity-like web app using Flask import os from flask import Flask, render_template, request, jsonify # Create a basic Flask app structure app_structure = """ perplexity_clone/ ├── app.py ├── static/ │ ├── css/ │ │ └── style.css │ └── js/ │ └── main.js └── templates/ ├── index.html └── results.html """ print("Creating app structure...") # Create directories os.makedirs("perplexity_clone/static/css", exist_ok=True) os.makedirs("perplexity_clone/static/js", exist_ok=True) os.makedirs("perplexity_clone/templates", exist_ok=True) # Create app.py app_py = """ from flask import Flask, render_template, request, jsonify import time import random app = Flask(__name__) # This would be replaced with actual AI/search functionality def generate_ai_response(query): # Simulate AI processing time time.sleep(1) # In a real app, this would call an LLM API or search service sources = [ {"title": "Source 1", "url": "https://example.com/1", "snippet": "Relevant information from source 1..."}, {"title": "Source 2", "url": "https://example.com/2", "snippet": "Relevant information from source 2..."}, {"title": "Source 3", "url": "https://example.com/3", "snippet": "Relevant information from source 3..."} ] return { "answer": f"Here is an AI-generated answer about '{query}'. This would be a comprehensive response synthesized from multiple sources.", "sources": sources } @app.route('/') def index(): return render_template('index.html') @app.route('/search') def search(): query = request.args.get('q', '') if not query: return render_template('index.html') return render_template('results.html', query=query) @app.route('/api/answer') def get_answer(): query = request.args.get('q', '') if not query: return jsonify({"error": "Query is required"}), 400 # Get AI-generated response response = generate_ai_response(query) return jsonify(response) if __name__ == '__main__': app.run(debug=True) """ with open("perplexity_clone/app.py", "w") as f: f.write(app_py) # Create index.html index_html = """ <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>AI Search Engine</title> <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}"> </head> <body> <div class="container"> <div class="search-container"> <h1>AI Search Engine</h1> <p class="tagline">Get AI-powered answers to your questions</p> <form action="/search" method="get" class="search-form"> <input type="text" name="q" placeholder="Ask anything..." required> <button type="submit">Search</button> </form> </div> <div class="features"> <div class="feature"> <h3>AI-Powered Answers</h3> <p>Get comprehensive answers generated by AI</p> </div> <div class="feature"> <h3>Source Citations</h3> <p>See where information comes from</p> </div> <div class="feature"> <h3>Real-time Results</h3> <p>Get the most up-to-date information</p> </div> </div> </div> </body> </html> """ with open("perplexity_clone/templates/index.html", "w") as f: f.write(index_html) # Create results.html results_html = """ <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Results - AI Search Engine</title> <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}"> </head> <body> <div class="results-container"> <header> <div class="logo"> <a href="/">AI Search Engine</a> </div> <form action="/search" method="get" class="search-form-small"> <input type="text" name="q" value="{{ query }}" required> <button type="submit">Search</button> </form> </header> <main> <div class="query-info"> <h2>{{ query }}</h2> </div> <div class="answer-container"> <div id="loading">Generating AI response...</div> <div id="answer" style="display: none;"></div> <div id="sources-container" style="display: none;"> <h3>Sources</h3> <div id="sources-list"></div> </div> </div> </main> </div> <script src="{{ url_for('static', filename='js/main.js') }}"></script> <script> document.addEventListener('DOMContentLoaded', function() { fetchAnswer('{{ query }}'); }); </script> </body> </html> """ with open("perplexity_clone/templates/results.html", "w") as f: f.write(results_html) # Create style.css style_css = """ * { margin: 0; padding: 0; box-sizing: border-box; font-family: 'Arial', sans-serif; } body { background-color: #f9f9f9; color: #333; } .container { max-width: 800px; margin: 0 auto; padding: 100px 20px; text-align: center; } .search-container h1 { font-size: 2.5rem; margin-bottom: 10px; color: #4285f4; } .tagline { font-size: 1.2rem; color: #666; margin-bottom: 30px; } .search-form input { width: 70%; padding: 15px; font-size: 1rem; border: 1px solid #ddd; border-radius: 24px 0 0 24px; outline: none; } .search-form button { padding: 15px 25px; font-size: 1rem; background-color: #4285f4; color: white; border: none; border-radius: 0 24px 24px 0; cursor: pointer; } .features { display: flex; justify-content: space-between; margin-top: 80px; } .feature { flex: 1; padding: 20px; background-color: white; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1); margin: 0 10px; } .feature h3 { color: #4285f4; margin-bottom: 10px; } /* Results page styles */ .results-container { max-width: 1000px; margin: 0 auto; padding: 20px; } header { display: flex; justify-content: space-between; align-items: center; padding-bottom: 20px; border-bottom: 1px solid #eee; } .logo a { font-size: 1.5rem; font-weight: bold; color: #4285f4; text-decoration: none; } .search-form-small { display: flex; width: 60%; } .search-form-small input { width: 100%; padding: 10px; font-size: 1rem; border: 1px solid #ddd; border-radius: 4px 0 0 4px; outline: none; } .search-form-small button { padding: 10px 15px; font-size: 1rem; background-color: #4285f4; color: white; border: none; border-radius: 0 4px 4px 0; cursor: pointer; } .query-info { margin: 20px 0; } .answer-container { background-color: white; padding: 30px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1); } #loading { text-align: center; padding: 20px; color: #666; } #answer { line-height: 1.6; margin-bottom: 30px; } #sources-container { border-top: 1px solid #eee; padding-top: 20px; } #sources-container h3 { margin-bottom: 15px; color: #4285f4; } .source-item { margin-bottom: 15px; padding-bottom: 15px; border-bottom: 1px solid #eee; } .source-item:last-child { border-bottom: none; } .source-title { font-weight: bold; margin-bottom: 5px; } .source-title a { color: #1a0dab; text-decoration: none; } .source-title a:hover { text-decoration: underline; } .source-snippet { color: #666; font-size: 0.9rem; } """ with open("perplexity_clone/static/css/style.css", "w") as f: f.write(style_css) # Create main.js main_js = """ async function fetchAnswer(query) { try { const response = await fetch(`/api/answer?q=${encodeURIComponent(query)}`); const data = await response.json(); if (response.ok) { displayAnswer(data.answer); displaySources(data.sources); } else { displayError(data.error || 'Failed to generate answer'); } } catch (error) { displayError('An error occurred while fetching the answer'); console.error(error); } } function displayAnswer(answer) { document.getElementById('loading').style.display = 'none'; const answerElement = document.getElementById('answer'); answerElement.innerHTML = `<p>${answer}</p>`; answerElement.style.display = 'block'; } function displaySources(sources) { const sourcesContainer = document.getElementById('sources-container'); const sourcesList = document.getElementById('sources-list'); sourcesList.innerHTML = ''; sources.forEach(source => { const sourceItem = document.createElement('div'); sourceItem.className = 'source-item'; sourceItem.innerHTML = ` <div class="source-title"> <a href="${source.url}" target="_blank">${source.title}</a> </div> <div class="source-snippet">${source.snippet}</div> `; sourcesList.appendChild(sourceItem); }); sourcesContainer.style.display = 'block'; } function displayError(message) { document.getElementById('loading').style.display = 'none'; const answerElement = document.getElementById('answer'); answerElement.innerHTML = `<p class="error">${message}</p>`; answerElement.style.display = 'block'; } """ with open("perplexity_clone/static/js/main.js", "w") as f: f.write(main_js) # Create a README file with instructions readme = """ # Perplexity-like AI Search Engine This is a basic implementation of a Perplexity-like AI search engine using Flask. ## Features - Search interface similar to Perplexity - AI-generated answers (simulated in this demo) - Source citations - Responsive design ## How to Run 1. Install the required packages:

pip install flask

2. Navigate to the project directory:  

cd perplexity_clone

3. Run the Flask application:  

python app.py

4. Open your browser and go to:  

http://127.0.0.1:5000/

## Next Steps for a Production-Ready Application  

1. Integrate with a real LLM API (like OpenAI, Claude, etc.)  
2. Add a real search API (Google, Bing, etc.)  
3. Implement user authentication  
4. Add conversation history  
5. Implement streaming responses  
6. Add database for storing queries and results  
7. Deploy to a production server with proper scaling  

## Note  
This is a simplified demo. A production-ready Perplexity clone would require more sophisticated components, including:  
- Advanced NLP models  
- Web crawling and indexing  
- Real-time data fetching  
- Citation verification  
- User accounts and personalization  
"""  

with open("perplexity_clone/README.md", "w") as f:  
 f.write(readme)  

print("Files created successfully in the 'perplexity_clone' directory.")  
print("\nTo run the application:")  
print("1. cd perplexity_clone")  
print("2. pip install flask")  
print("3. python app.py")  
print("4. Open http://127.0.0.1:5000/ in your browser")  

Output

output

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Creating app structure... Files created successfully in the 'perplexity_clone' directory. To run the application: 1. cd perplexity_clone 2. pip install flask 3. python app.py 4. Open http://127.0.0.1:5000/ in your browser

I've created a basic Perplexity-like search engine using Flask. The application includes:

1. A clean search interface with a prominent search bar
2. Results page that displays AI-generated answers
3. Source citations section
4. Responsive design for different screen sizes

To run the application:

1. Navigate to the perplexity_clone directory
2. Install Flask: pip install flask
3. Start the server: python app.py
4. Open http://127.0.0.1:5000/ in your browser

This is a simplified version that simulates AI responses. For a production-ready application, you would need to integrate with actual LLM APIs (like OpenAI or Claude), implement real search functionality, add user authentication, and deploy to a proper server.