Quick poll for my fellow developers:
When building AI-powered applications, what's your biggest bottleneck?
πΈ Model accuracy/performance
πΈ Data quality and preprocessing
πΈ Integration with existing systems
πΈ User experience design
#AI#Development#Poll
For me, it's been handling edge cases in real-world data that never appeared in training. My stamp verification model was 99% accurate in testing, but production taught me humility π
3/ Error Handling: AI responses can be unpredictable - build robust fallbacks
4/ Streaming: Real-time responses keep users engaged (learned this building my calculator!)
What challenges have you faced with AI agents?
#AgenticAI#MachineLearning#letsconnect#freshers#job
π§΅ Thread: Building your first AI agent system - key components you need to know
1/ State Management: Your agent needs to remember context across interactions
2/ Tool Integration: Agents become powerful when they can call external APIs and functions
#AgenticAI#MachineLearning
π§΅ 3 lessons learned from building production-ready FastAPI backends:
1/ Always implement proper error handling - your users will thank you
2/ Use Pydantic models for request/response validation - saves hours of debugging
#FastAPI#BackendDevelopment#tweet#letsconnect
Currently debugging my Agentic Calculator project at 2 AM π
There's something oddly satisfying about watching FastAPI + Langgraph work together to process real-time calculations. The multi-agent system is finally streaming responses smoothly!
#FastAPI#Langchain#LateNightCoding