3. Data, Science & AI

Analyzes skin photos using Gemini Vision API to deliver structured dermatological insights and recommendations.

Matches users by skin characteristics to recommend personalized skincare products within a community platform.

AI-powered tool for root cause analysis, retrieving incident context from databases, generating natural language explanations, and recommending remediation actions.

Guides integration of AI APIs (OpenAI, ChatGPT, ElevenLabs) for building applications requiring AI services.

Guides integration of AI APIs like OpenAI and ElevenLabs for application development and user response handling.

Optimizes SQL queries, analyzes indexes, reviews Alembic migrations, and identifies N+1 issues with execution plans for SQLAlchemy async.

Generates personalized product recommendations using user skin profiles and community data for targeted e-commerce or beauty applications.

Provides a library of recursive language model patterns for designing efficient analysis strategies on large documents.

AI agent using RAG and LangGraph to retrieve and answer questions from documents.

Downloads and analyzes YouTube video transcripts, enabling text data processing for insights and content analysis.

Optimizes AI prompts using OpenAI best practices, analyzing clarity, context, and output structure for improved model responses.

Creates interactive data visualizations using pyecharts, generating HTML reports with best practices for data presentation.

Scans cosmetic ingredient lists via OCR and uses AI to interpret ingredient efficacy and potential risks.

Generates images from text prompts using Google Gemini image generation API. Triggers on requests for creating, drawing, or illustrating visual content from descriptions.

Provides real-time access to US stock market data including price bars, news sentiment, and company details via the eng0 API.

Enables document-based Q&A by retrieving relevant information from a Supabase pgvector vector store using RAG techniques.

Generates data visualizations and dashboards by executing SQL queries written in markdown, powered by DuckDB.

Converts natural language queries to PostgreSQL SQL and generates PPT-style visualizations for data presentations.

Designs multi-agent architectures to solve complex tasks by decomposing them into subtasks and specializing agents for improved quality and scalability.

Enables natural language queries against PostgreSQL databases, converting plain English questions into executable SQL for data retrieval.

Analyzes SEC EDGAR filings for financial data, including 10-K, 10-Q, and insider trading information, using EdgarTools.

Diagnoses and mitigates context degradation in AI agent systems to maintain performance as context length increases.

Enables development of AI applications using OpenAI SDK, including GPT-5, chatbots, and semantic search via API integrations.

Enables semantic search, embeddings storage, and RAG pipelines using Weaviate vector database for AI applications.