Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Build a vision AI pipeline that scans receipts and invoices, extracts vendor names, dates, line items, and totals, categorizes expenses, and integrates with accounting systems for fully automated expense processing.
Learn how to build reflection agents that evaluate their own outputs, assign quality scores, and iteratively refine results through multi-round self-improvement loops using the Reflexion pattern.
Master the art of schema representation for text-to-SQL systems. Learn how to format CREATE TABLE statements, add column descriptions, encode foreign key relationships, and provide sample data for maximum query accuracy.
Design secure Claude Computer Use deployments with VM isolation, network restrictions, action allowlists, credential handling, and comprehensive audit logging to prevent unintended actions and data exposure.
A detailed comparison of Selenium, Playwright, and Puppeteer for building AI-powered browser agents. Covers async support, multi-browser compatibility, recording capabilities, and ease of AI integration.
Learn how to build a robust SQL validation layer that prevents injection attacks, enforces read-only access, limits query complexity, and ensures AI-generated queries cannot damage your database.
Master the architecture of streaming AI agents that deliver token-by-token output while interleaving tool calls, using Server-Sent Events and progressive rendering to create responsive user experiences.
Build an AI-powered table extraction pipeline that detects tables in images and PDFs, recognizes cell boundaries, infers structure, and outputs clean CSV data for downstream consumption.
Build a complete text-to-SQL agent using GPT-4 that extracts database schemas, generates SQL queries from natural language, executes them safely, and formats results for end users.
