TensorCraft - Building an AI/ML Role Preparation Platform with AI Agents

TensorCraft: Building an AI/ML Role Preparation Platform with AI Agents

As the AI/ML job market continues to evolve, I’ve been working on TensorCraft, an educational platform designed to help aspiring AI/ML engineers prepare for technical roles through hands-on learning with AI agents and modern tools. This AI-powered platform integrates agentic workflows to create an interactive learning environment for mastering AI/ML concepts and tools.

Inspiration and Vision

The idea for TensorCraft is inspired by the work of Andrej Karpathy, who has been exploring the potential of edtech AI systems capable of tutoring humans using LLMs and AI agents. While the effectiveness of AI-powered tutoring is still being debated in the education community, it represents an exciting frontier in personalized learning. The ability to leverage AI agents for adaptive, interactive education is a compelling direction worth exploring, and TensorCraft aims to contribute to this emerging field by focusing specifically on AI/ML role preparation.

Project Vision

TensorCraft is an educational platform focused on AI/ML role preparation that helps learners master both theoretical concepts and practical tools. The platform can:

• Learn AI/ML Tools: Interactive tutorials for popular AI/ML frameworks, libraries, and tools
• Practice with AI Agents: Hands-on experience with agentic AI systems and workflows
• Prepare for Technical Interviews: AI-generated practice problems and coding challenges
• Build Real Projects: Guided learning paths for building end-to-end AI/ML applications

Technical Architecture

Core Technologies

Backend Infrastructure:

• FastAPI: High-performance API framework for handling requests
• Redis: Caching and session management for real-time interactions
• LangGraph: Orchestrating complex agentic AI workflows

AI & ML Stack:

• Google AI (Gemini): Primary language model for reasoning and generation
• RAG Pipelines: For dynamic content retrieval and contextual responses
• Multimodal Processing: Handling text, images, and audio inputs

Frontend:

• React.js: Modern, responsive user interface
• Real-time Updates: WebSocket connections for interactive learning

Agentic AI Workflows

The heart of TensorCraft lies in its agentic architecture. The platform uses AI agents to guide learners through complex AI/ML concepts and provide hands-on experience with tools:

# Simplified example of our agentic workflow
from langgraph import StateGraph, Node

class LearningAgent:
    def __init__(self):
        self.graph = StateGraph()
        self.setup_workflow()
    
    def setup_workflow(self):
        # Define the learning workflow nodes
        self.graph.add_node("analyze_query", self.analyze_learner_query)
        self.graph.add_node("retrieve_content", self.retrieve_learning_materials)
        self.graph.add_node("generate_explanation", self.generate_technical_explanation)
        self.graph.add_node("create_practice", self.create_coding_challenges)
        self.graph.add_node("assess_understanding", self.assess_learner_progress)
        
        # Define the workflow edges
        self.graph.add_edge("analyze_query", "retrieve_content")
        self.graph.add_edge("retrieve_content", "generate_explanation")
        self.graph.add_edge("generate_explanation", "create_practice")
    
    def analyze_learner_query(self, state):
        """Analyze what the learner is asking and their skill level"""
        query = state['learner_query']
        # Use Gemini to understand the technical context
        analysis = self.gemini_model.analyze_technical_query(query)
        return {**state, 'query_analysis': analysis}
    
    def retrieve_learning_materials(self, state):
        """Retrieve relevant content using RAG"""
        relevant_content = self.rag_pipeline.retrieve(
            query=state['learner_query'],
            context=state['query_analysis']
        )
        return {**state, 'retrieved_content': relevant_content}

RAG Implementation for AI/ML Learning

Our RAG (Retrieval-Augmented Generation) system is specifically designed for AI/ML role preparation:

1. Content Indexing: Technical documentation, tutorials, and resources are processed and stored
2. Contextual Retrieval: Relevant information is retrieved based on learner queries and skill level
3. Dynamic Generation: Custom explanations and examples are generated
4. Tool Integration: Direct integration with AI/ML tools for hands-on practice

class LearningRAG:
    def __init__(self):
        self.vector_store = WeaviateVectorStore()
        self.gemini = GeminiModel()
        
    def process_technical_content(self, content, content_type):
        """Process and index technical learning materials"""
        # Extract key concepts and create embeddings
        concepts = self.extract_technical_concepts(content)
        embeddings = self.create_embeddings(content, content_type)
        
        # Store in vector database with learning metadata
        self.vector_store.store(
            content=content,
            embeddings=embeddings,
            metadata={
                'concepts': concepts,
                'difficulty_level': self.assess_difficulty(content),
                'content_type': content_type,
                'learning_objectives': self.extract_objectives(content)
            }
        )
    
    def generate_technical_explanation(self, query, learner_profile):
        """Generate explanations tailored to learner level"""
        relevant_content = self.vector_store.similarity_search(query)
        
        prompt = f"""
        Based on the following technical content, create a clear explanation for:
        Learner Level: {learner_profile['level']}
        Learning Goal: {learner_profile['goal']}
        Question: {query}
        
        Content: {relevant_content}
        
        Provide a practical explanation with code examples.
        """
        
        return self.gemini.generate(prompt)

Key Features in Development

1. AI/ML Tool Tutorials

The platform provides interactive tutorials for popular AI/ML tools and frameworks:

• PyTorch & TensorFlow: Hands-on deep learning tutorials
• LangChain & LangGraph: Agentic AI development
• Hugging Face: Working with pre-trained models
• Vector Databases: RAG implementation practice

2. Interview Preparation

AI-powered interview preparation focused on AI/ML roles:

• Technical coding challenges
• System design scenarios
• ML algorithm implementations
• Practical project discussions

3. AI Agent Learning

Direct experience with building AI agents:

• Tool Use: Learning how AI agents interact with tools
• Workflow Design: Creating agentic workflows
• RAG Systems: Building retrieval-augmented systems
• Multimodal AI: Working with different data types

4. Project-Based Learning

Build real-world AI/ML projects:

• End-to-end ML pipelines
• AI agent implementations
• RAG applications
• Multimodal AI systems

Current Progress and Challenges

What’s Working:

• ✅ Core RAG pipeline for technical documentation
• ✅ Basic agentic workflows with LangGraph
• ✅ FastAPI backend with Redis caching
• ✅ React frontend with interactive components
• ✅ Integration with Gemini for content generation

Current Challenges:

• Tool Integration: Seamlessly integrating various AI/ML tools and frameworks
• Personalization: Building adaptive learning paths based on skill level
• Content Curation: Maintaining up-to-date technical content
• Practice Environment: Creating safe sandboxes for hands-on coding

Future Roadmap

Short Term:

• Expand tool integration coverage
• Add more coding challenges
• Implement skill assessment system
• Community features for peer learning

Long Term:

• Mobile application
• Career guidance features
• Industry project simulations
• Integration with job platforms

Technical Insights

Performance Optimization:

• Caching Strategy: Using Redis for session management and content caching
• Async Processing: Leveraging FastAPI’s async capabilities
• Efficient Retrieval: Optimized vector search for quick content access

AI Model Management:

• Model Selection: Using appropriate models for different tasks
• Prompt Engineering: Context-aware prompts for technical content
• Response Quality: Iterative improvement of generated content

Open Source and Community

TensorCraft is being developed with a focus on open-source principles:

• AI agent workflow patterns
• RAG pipeline implementations
• Learning path templates
• Tool integration examples

Conclusion

Building TensorCraft has been an excellent opportunity to combine AI agent development with practical AI/ML education. The platform aims to bridge the gap between learning theory and gaining hands-on experience with modern AI tools and workflows.

The goal is to provide aspiring AI/ML engineers with a comprehensive platform for preparing for technical roles through interactive learning with AI agents.

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Tech Stack: FastAPI, Redis, React.js, Google AI (Gemini), LangGraph, RAG, Weaviate, Python

Interested in AI/ML role preparation or agentic systems? I’d love to connect and discuss ideas!