Tendril
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A curated walkthrough of the library — ordered lessons, a 15-question quiz between each, and 3 next-steps so you stay in flow. Earn XP, badges, and a streak as you go.
Lessons · 6434 available · College+ view
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Lessons handpicked for the College+ shelf.
The agent market matured fast. Here's the field map — frontier labs, frameworks, browsers, local stacks, benchmarks — so you can pick the right tool without shopping by hype.
Underneath every agent framework is the same primitive — the model returns a structured tool call, you execute it, you feed the result back. Master this loop and every framework looks familiar.
Model Context Protocol is the most important open standard in agents. One protocol, 1,200+ servers, and your agent can plug into almost any system. Here's how it actually works.
One smart agent is fine. Two agents checking each other's work is better. Master the canonical orchestration patterns: planner/executor, judge/worker, debate, and swarm.
Fresh College+ lessons added to the library.
Different AI vendors tune refusal behavior differently — affecting your application's UX.
Understand the AI pricing landscape across input, output, cached, batch, and reserved tiers.
How to read AI model leaderboards critically — and when to trust your own evals instead.
How to architect AI applications that survive provider rate limits gracefully.
Subject tracks
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The agent market matured fast. Here's the field map — frontier labs, frameworks, browsers, local stacks, benchmarks — so you can pick the right tool without shopping by hype.
Underneath every agent framework is the same primitive — the model returns a structured tool call, you execute it, you feed the result back. Master this loop and every framework looks familiar.
Model Context Protocol is the most important open standard in agents. One protocol, 1,200+ servers, and your agent can plug into almost any system. Here's how it actually works.
One smart agent is fine. Two agents checking each other's work is better. Master the canonical orchestration patterns: planner/executor, judge/worker, debate, and swarm.
LangGraph became the production favorite in 2026 for good reasons — explicit state, checkpointing, first-class MCP. Build a real agent end-to-end and learn why.
Computer Use lets Claude see your screen and use it — mouse, keyboard, apps. The capability is real, the gotchas are real. A hands-on look at what works in 2026.
Browser agents — Operator, Atlas, Browser Use, MultiOn — are the most visible agent category. The capability is genuine, the failure modes are specific. Build with eyes open.
Numbers on leaderboards are seductive and often wrong. Learn the big benchmarks, their leaderboard positions, their recently-exposed cheats, and how to run your own evals.
A prototype agent and a production agent have the same LLM. What's different is everything around it — durable state, retries, idempotency, observability. The real engineering.
An agent is a new attack surface. Prompt injection, privilege escalation, data exfiltration — these are no longer theoretical. Learn the attacks and the defenses.
Everything comes together. Design, code, test, secure, and ship a production-quality agent with open-source code you can fork today.
Skills let you package a prompt, tools, files, and configuration into a named capability Claude can invoke on demand.
ChatGPT's agent mode can browse, click, file taxes, book meetings, write code across multiple apps.
Codex cloud can work in the background and in parallel. Learn how to split tasks so multiple agents do not trample the same files.
Models get more useful when they can act through tools. Learn the difference between hosted tools, your own functions, and MCP-connected capabilities.
Build an AI study agent that tracks what you've learned, plans your week, and adapts when you fall behind. Beyond chatbot prompting, into actual agentic study.
Move past chatbots and build a workflow where AI takes multi-step actions on your behalf. Here's the safe-by-default beginner pattern.
Turn the local Hermes Agent ecosystem into a product map students can reason about before they build their own agent system.