January 3, 2026
Repost or renaissance?
Neural Networks: Zero to Hero
Zero to Hero drops again: fans cheer, skeptics yell “old news”
TLDR: Karpathy’s popular AI course teaches you to build neural networks and small chatbot-style models step by step. The community is split between celebrating its clarity and grumbling that it’s a 2022 rerun, with self-promo links and calls for date labels fueling the drama—still a must-see for learners.
Andrej Karpathy’s “Zero to Hero” is the step-by-step, build-it-yourself AI class teaching folks how to create neural networks in code, all the way to mini versions of chatbots like GPT. The lineup runs from a gentle intro to gradients to a swaggering “Backprop Ninja,” and even a WaveNet-style detour. It’s approachable: basic Python and high school math. The main binge starts with the intro video and spills into a lively community on Discord.
Cue the drama: the top question was basically, “Is this new?” with one commenter asking outright and another demanding it “should have a (2022) label.” That sparked a familiar internet tussle—fresh drop vs classic repost—while others swooped in with their own tutorials and blog write-ups, including a self-aware “shameless plug.” One user traced it back to an HN comment, turning the whole thread into a game of link tag.
Of course, the memes showed up: jokes about “Zero to Hero to Repost,” and riffs that “Backprop Ninja” sounds like a Netflix show. Between cries of repost fatigue and fans praising the clarity, the vibe is pure study-group energy colliding with calendar-police gatekeeping. The takeaway: whether it’s a rerun or renaissance, people still want hands-on AI explained in plain English.
Key Points
- •Andrej Karpathy offers a free course that builds neural networks from scratch and progresses to modern models like GPT, with a focus on language modeling.
- •Prerequisites include solid Python skills and introductory math; a Discord channel supports collaborative learning.
- •The syllabus covers implementing backprop from first principles via micrograd and building a bigram character-level model (makemore) using PyTorch tensors.
- •Subsequent videos develop an MLP language model, introduce ML best practices, analyze activations/gradients, and apply Batch Normalization; residual connections and Adam are noted for future coverage.
- •Further sessions manually backpropagate without autograd and construct a WaveNet-like hierarchical CNN, introducing torch.nn and practical deep learning development workflows.