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 production-ready cost dashboard for AI agents with real-time spend tracking, budget alerts, cost forecasting, and per-model breakdowns. Complete Python implementation with FastAPI and data aggregation.
Design and implement an AI agent gateway that provides centralized access control, structured logging, and intelligent rate limiting. Learn gateway architecture patterns, policy enforcement, and request routing for multi-agent environments.
Design a production-grade AI agent marketplace with catalog management, semantic search, automated provisioning, and usage-based billing. Learn the core data models and API patterns that power agent distribution at scale.
Five carefully chosen portfolio projects that showcase agentic AI skills employers actually look for, with guidance on documentation, deployment, and presenting your work on GitHub.
A practical guide for founders building AI agent startups. Covers market sizing, competitive positioning, go-to-market strategies, technical architecture decisions, fundraising dynamics, and the common pitfalls that sink agent-first companies.
Build an AI agent for tutoring centers that matches students with the right tutors based on subject, level, and learning style, schedules sessions, tracks progress, and communicates with parents.
Design and build a centralized webhook hub that receives events from multiple services, normalizes them into a common format, routes them to AI agent processors, and ensures reliable delivery with fan-out and retry logic.
Design and implement an AI copilot that understands your SaaS product context, proactively offers suggestions, and lets users maintain full control over all actions.
Create an AI-powered data import pipeline that detects file formats, maps columns to your schema automatically, cleans messy data, and validates records before insertion.
