Tested · Open Source · Updated May 2026
Reviews, comparisons
& builds for AI systems.
Hands-on reviews of AI agent frameworks, head-to-head comparisons of LLM coding tools, and build tutorials with working code. Plus a live leaderboard of fifty models.
01 — Reviews
Latest framework and tool reviews.
Head-to-head comparisons of agent frameworks and AI coding tools — based on real testing, not vendor docs.
CrewAI vs LangGraph vs AutoGen
We built the same 3-agent research assistant in all three. Code, cost, and verdict per use case.
read →Claude Code vs Cursor vs Codex
Three real-world tasks, three AI coding tools. Success rate, latency, and real billing data.
read →All Reviews
Head-to-head comparisons of agent frameworks, AI coding tools, and observability platforms.
read →02 — Best Of
Curated, opinionated shortlists.
Verdict matrix, methodology, and the tools we ruled out — laid out so you can scan or read deep.
AI Agent Frameworks 2026
Ten frameworks tested. Verdict matrix for prototyping, production, Python, TypeScript, and cost-sensitive teams.
read →AI Engineer Certifications 2026
Coursera, AWS, GCP, Azure, DeepLearning.AI — does any of it actually move salary? Our take, with data.
read →LLM Benchmark Comparison 2026
Long-form analysis behind our interactive leaderboard. Which benchmarks matter, which to ignore.
read →03 — Build
Step-by-step builds with working code.
Architecture diagrams, real cost numbers, and full repositories on GitHub. Copy what works.
Research Agent with CrewAI
End-to-end build of a 3-agent research assistant. Working code, ~$0.05 per run, full repo on GitHub.
read →AI Models Leaderboard
Interactive comparison of fifty models — pricing, benchmarks, context, and a cost calculator.
read →All Tutorials
Hands-on, runnable tutorials for agents, MCP servers, observability, and deployment.
read →04 — Learn
Choose a learning path.
A curriculum if you want one, a reference if you don't. Pages are short by design and cross-linked.
Set up Python, understand what an agent actually is, write your first ReAct loop in under fifty lines.
Recurring agent shapes, the constraints token budgets impose, and how to specify behavior in a version-controlled way.
Multi-agent coordination, the protocol layer, and the trade-offs that matter at scale across the major frameworks.
05 — About
A reference, not a course.
This is a developer-focused reference for building with large language models. It covers AI agents, agentic workflows, the Model Context Protocol, and the Python frameworks people actually reach for in 2026 — LangChain, CrewAI, and AutoGen. Every concept is paired with runnable code, every framework page explains when to pick it and when to skip it, and every architectural pattern is grounded in a specific failure mode it solves.
The goal is to give you a working mental model fast, then hand you the code to try it yourself. There is no signup, no paywall, and no gated chapters — the entire guide is open and indexable. Pages sit in the 1,000 to 1,700 word range and cross-link aggressively, so following a link rarely sends you down a long detour. APIs change, so check the framework's own docs if something looks off; the site is updated as the ecosystem moves.
If you are new to all of this, start at Getting Started — it sets up the prerequisites and walks through your first agent in under fifty lines of Python.