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Choose from industry-vetted challenges. Build local, push to GitHub, and earn cryptographic proof of your engineering skills.

Recipe Finder with AI Substitutions

fullstackBeginner365d access
99onwards

Enter what's in your fridge, get recipe suggestions, and ask the AI to suggest ingredient substitutions for anything missing. Practical multimodal CRUD plus AI.

  • Build ingredient-based recipe matching using overlap scoring logic
  • Highlight matched vs. missing ingredients in a recipe card UI
  • Design a prompt that contextualizes AI suggestions within a specific recipe
  • Stream AI substitution suggestions in real time in response to user needs

Job Application Tracker with AI Cover Letters

fullstackBeginner365d access
99onwards

Track job applications on a Kanban board. On each card, generate a tailored cover letter from the job description and your resume — streamed in real time.

  • Build a Kanban board with drag-and-drop using @dnd-kit
  • Store and retrieve user resume data for use in AI-generated content
  • Generate a tailored cover letter by combining job description and resume in a single prompt
  • Stream the AI cover letter into the UI in real time using Vercel AI SDK

Smart Notes App with AI Search

fullstackBeginner365d access
99onwards

A note-taking app where every note is embedded on save. The search bar uses semantic similarity to find relevant notes — not just keyword matching. RAG inside a real product.

  • Build a working notes app and then add AI as a layer on top — not the other way around
  • Generate and store vector embeddings for every document on save
  • Build a semantic search endpoint using FAISS similarity search
  • Demonstrate the difference between keyword search and semantic search to end users

Sales Forecasting with Time Series

data science & mlBeginner365d access
99onwards

Forecast product sales using Prophet and ARIMA. Build a Streamlit dashboard that visualizes predictions vs. actuals and explains seasonality and trend components.

  • Identify trend, seasonality, and stationarity in a real time series dataset
  • Train and evaluate both Prophet and ARIMA forecasting models
  • Compare model performance using MAE, RMSE, and MAPE
  • Visualize forecast results with confidence intervals in Streamlit

Customer Churn Predictor with XGBoost

data science & mlBeginner365d access
99onwards

Train an XGBoost classifier on a real customer dataset, tune hyperparameters with cross-validation, and deploy it as a FastAPI prediction endpoint.

  • Perform end-to-end ML: EDA → feature engineering → model training → evaluation → deployment
  • Train and tune an XGBoost classifier with cross-validation
  • Handle class imbalance using scale_pos_weight
  • Evaluate a classifier using precision, recall, F1, and ROC-AUC

Data Labeling Pipeline for AI Training

data science & mlBeginner365d access
99onwards

Build a pipeline that presents raw text data for human labeling via a clean UI and exports a structured JSONL dataset ready for LLM fine-tuning.

  • Design a practical data labeling schema for a real NLP classification task
  • Build a human-in-the-loop labeling tool using Streamlit
  • Export labeled data in the JSONL instruction-tuning format
  • Validate dataset quality: check for empty fields, label imbalance, and duplicates

Long Document Summarizer

genaiBeginner365d access
99onwards

Summarize documents that exceed the context window using chunking and map-reduce patterns. Handle PDFs, articles, and reports of any length intelligently.

  • Ingest documents from multiple input types: pasted text, .txt files, and PDFs
  • Implement map-reduce summarization to handle arbitrarily long documents
  • Control output structure using prompt instructions (bullets, Q&A, executive summary)
  • Estimate and display approximate API cost per operation

Prompt Engineering Lab

genaiBeginner365d access
99onwards

Run the same query through 3 different prompt templates and score outputs by quality. Build a systematic prompt testing and iteration tool — the skill every AI engineer needs.

  • Design and compare multiple prompt templates for the same task systematically
  • Use concurrent API calls to run prompt variants in parallel
  • Build a scoring system to quantitatively evaluate prompt quality
  • Identify which prompt patterns (chain-of-thought, few-shot, direct) work best for different query types

Chat with Your PDF

genaiBeginner365d access
99onwards

Upload a PDF, ask questions about it, get accurate answers. Build your first RAG pipeline end-to-end — chunking, embedding, vector storage, and retrieval.

  • Chunk documents into overlapping segments for effective retrieval
  • Generate and store vector embeddings using OpenAI's Embeddings API
  • Build and query a FAISS vector index for similarity search
  • Create a RetrievalQA chain that grounds answers in document content

Streaming Chat API Endpoint

backendBeginner365d access
99onwards

Build the server-side of a streaming chat — an SSE endpoint that proxies LLM chunks to the client in real time. Learn async generators, backpressure, and stream piping.

  • Understand the Server-Sent Events (SSE) protocol and its use cases
  • Build a streaming endpoint that proxies LLM response chunks in real time
  • Use Python async generators or Node.js streams for efficient chunk forwarding
  • Detect client disconnections and cancel upstream requests to avoid waste

LLM Proxy API

backendBeginner365d access
99onwards

Build an API wrapper around OpenAI — add request logging, API key auth, response caching, and basic error handling. Your first AI-aware backend service.

  • Build a reverse proxy API that wraps a third-party LLM service
  • Implement API key authentication with middleware
  • Log structured request data (latency, tokens, model) to a database
  • Cache identical LLM requests using SHA-256 hashed keys in Redis
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