April 8, 2026
Agent wars, assemble!
Ask HN: Learning resources for building AI agents?
Dev food fight: Keep it simple, love the toolkits, or measure everything
TLDR: A learner asked for the best ways to build AI agents, and the thread split fast: hands-on minimalists, toolkit believers, and benchmarking fans traded jabs. The consensus twist—build something small, measure it with tools like Calibra, and even ask other AIs for help—matters because this fast-moving field rewards quick, tested learning.
A simple “what should I learn?” post about building AI agents (apps that use chatbots to do tasks) turned into a spicy showdown. One builder bragged they’d already stitched together Google Analytics with a home‑grown agent using a popular toolkit, but sighed that it didn’t feel truly autonomous—like a self-driving car that still asks for directions. Cue the counterpunch: another commenter rolled in with “just start building” energy, saying skip the heavy toolkits and go minimalist. Think: fewer moving parts, fewer headaches, fewer “install 19 plugins before hello world” vibes.
Then the scorekeepers showed up. A link to Calibra promised hard numbers to judge prototypes, turning the thread into a gym for agents—no more vibes, only reps. Meanwhile, a pragmatic voice chimed in: ask Claude or ChatGPT for guidance, and peek at open‑source agent blueprints. Basically, don’t reinvent the robot.
The strongest opinions? Minimalism vs. machinery: build it bare-bones or embrace the big toolkits. The drama? Whether “autonomous agents” today are more babysitter than butler. The memes? Jokes about frameworks being IKEA furniture with 300 screws, and agents that need a chaperone. Underneath the snark, the vibe is clear: learn by doing, test with numbers, and let the robots tutor you.
Key Points
- •The poster completed Antonio Gulli’s “AI Agentic Design Patterns.”
- •They studied Sam Bhagwat’s “Principles of Building AI Agents” and “Patterns for Building AI Agents.”
- •They took courses from LangGraph Academy.
- •They reviewed related content on DataCamp.
- •They seek recommendations for useful resources (courses, papers, hands-on approaches) for building AI agents in a rapidly evolving field.