The library
Everything you need to start, free.
The resources we point newcomers to, each with a plain-English summary you can read right here before deciding to click through.
Or just come to a sessionStart here
What vibe coding actually means, from the people who named it.
Andrej Karpathy
The original “vibe coding” note
In early 2025, the AI researcher Andrej Karpathy fired off a short post describing a new way to write software: let the model do almost all the work and just go with the flow. The phrase he used, "vibe coding," caught on fast.
Read moreSimon Willison
Not all AI-assisted programming is vibe coding
Soon after the phrase took off, the developer and writer Simon Willison stepped in to clear up a growing confusion: using AI to help you code is not the same thing as vibe coding. He wanted to keep the two ideas separate.
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FreeMIT Technology Review
What is vibe coding, exactly?
As the phrase spread far beyond programmers, MIT Technology Review published a 2025 explainer to answer the obvious question for a general audience: what does "vibe coding" actually mean, and where did it come from.
Read moreThe tools we use
You only need one to start. Pick whatever gets you building tonight.
Anthropic
Claude Code
Claude Code is an AI helper that lives in your terminal, the plain text window where developers type commands. You tell it what you want in normal language, and it works directly inside your real project files.
Read more
FreeAnysphere
Cursor
Cursor is a code editor with AI built into its core. It looks and feels like a familiar editor, but a model is woven through every part of how you write and change code.
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FreeLovable
Lovable
Lovable lets you describe the app you want in plain language and get back a working web app that is already online. There is no setup to wrestle with first.
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FreeGitHub
GitHub Copilot
GitHub Copilot is an AI assistant that works inside your code editor. As you type, it suggests the next lines, and you can ask it questions without leaving your work.
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FreeVercel
v0
v0 turns a written prompt into a front-end user interface. You describe a screen, and it produces React components you can copy straight into your own project.
Read moreFree ways to go from zero
No prior coding needed. These are the on-ramps we point beginners to.
FreeCoursera
Vibe Coding Essentials
This is a beginner specialization on Coursera, made by Scrimba, that teaches you to build real apps by working alongside AI coding tools. You bring the curiosity. You do not need any coding background to start.
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FreeDatawhale (open source)
Easy-Vibe: your first modern coding course
Easy-Vibe is a free, open-source course from Datawhale that walks you from a first idea all the way to a shipped AI app. It is open to everyone, and the whole curriculum is out in the open for anyone to read and improve.
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FreeOpen source
Vibe Code Source
Vibe Code Source is a free, open-source library of copy-ready prompts paired with practical guides. Think of it as a shared toolbox you reach into when you are building with AI and want a proven starting point.
Read moreWhen you want to go deeper
For when the vibes meet a real codebase and you want to understand what is happening.
FreeAnthropic
Building effective agents
Anthropic published this guide in 2024 to answer a question many builders were quietly struggling with: when you put a language model in charge of doing real work, what is the simplest setup that actually holds up? Its core advice is calm and a little surprising. Most of the time, you do not need a fancy autonomous agent at all.
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FreeAndrej Karpathy
From vibe coding to agentic engineering
In this conversation with Sequoia and Stephanie Zhan, Andrej Karpathy describes how building software with AI is growing up. The playful, improvisational style he once called vibe coding is giving way to something more deliberate, where a person directs capable agents and stays responsible for the result.
Read morearXiv
Vibe coding, the academic version
In 2025, researchers Advait Sarkar and Ian Drosos published the first empirical study of vibe coding, the new habit of building software mostly by talking to an AI rather than writing code by hand. Instead of guessing how people do it, they watched them do it and listened to them think out loud.
Read moreGo to the frontier
Curated from Stanford's CS 153. The deep end, the papers that built modern AI.
Stanford
CS 153: Frontier Systems
A Stanford course that walks you up the whole ladder of modern AI, from the raw silicon in a chip to the policy debates happening in public, with people who actually built these systems stopping by to talk.
Read moreKrizhevsky, Sutskever & Hinton
AlexNet: ImageNet classification with deep CNNs
In 2012, three researchers trained a deep neural network on graphics cards to recognize objects in photos, and it won a major contest by such a margin that it changed how the whole field thought about machine learning.
Read moreMikolov et al., Google
Word2Vec: Efficient Estimation of Word Representations
In 2013, a Google team found a fast way to turn words into lists of numbers that capture meaning, so that simple arithmetic on those numbers could answer analogies like king minus man plus woman lands near queen.
Read moreDeepMind
Playing Atari with Deep Reinforcement Learning
In 2013, DeepMind built a single program that learned to play many different Atari video games well, looking only at the raw screen pixels and the score, with no game-specific instructions.
Read moreVaswani et al., Google
Attention Is All You Need
In 2017, a Google team introduced the Transformer, a model that drops the older habit of reading text word by word in order and instead lets every word look directly at every other word at once. It became the foundation of modern large language models.
Read moreBERT: Pre-training of Deep Bidirectional Transformers
In 2018, Google released BERT, a language model that reads a sentence from both directions at once and learns from huge amounts of plain text first, so it can then be adapted to many specific tasks with little extra training.
Read moreOpenAI
Scaling Laws for Neural Language Models
In 2020, OpenAI researchers found that the quality of a language model improves in smooth, predictable curves as you give it more compute, more data, and more parameters, so you can forecast how good a model will be before you build it.
Read moreOpenAI
Language Models are Few-Shot Learners (GPT-3)
In 2020, OpenAI showed that a very large language model with 175 billion parameters could perform new tasks just from a few examples written into the prompt, without any retraining.
Read moreHo, Jain & Abbeel
Denoising Diffusion Probabilistic Models
In 2020, three researchers showed a clean way to generate images by teaching a model to reverse noise: start from pure static and remove a little noise at a time until a real-looking picture appears.
Read moreOpenAI
Training Language Models to Follow Instructions (InstructGPT)
In 2022, OpenAI showed how to make a language model genuinely follow instructions by tuning it on human preferences, a method called RLHF that became the basis for ChatGPT.
Read moreDeepMind
Training Compute-Optimal LLMs (Chinchilla)
In 2022, DeepMind found that many large models had been built too big for the amount of data they were trained on, and that a smaller model fed more data can do better for the same training budget.
Read moreFrom the guest lectures
The work behind the people building the frontier: generative images, video, and voice.
Rombach, Blattmann et al.
High-Resolution Image Synthesis with Latent Diffusion Models
This 2022 paper is the one that put text-to-image generation in everyone's hands. It is the research behind Stable Diffusion, and its big idea was to make image generation cheap enough to run on a single ordinary graphics card.
Read moreBlattmann et al., Stability AI
Stable Video Diffusion
Released in 2023, this work takes the same recipe that made Stable Diffusion good at single images and stretches it across time, so the model produces short video clips instead of standalone frames.
Read more
FreeAmit Jain
Luma AI
Luma AI is a company working on multimodal AI for video and 3D, led by Amit Jain. Its best-known product, Dream Machine, turns a prompt or an image into video, and the longer ambition is software that understands and simulates the physical world.
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FreeMati Staniszewski
ElevenLabs
ElevenLabs is an AI audio company, co-founded by Mati Staniszewski, built around one focused mission: making synthetic speech sound genuinely human. Its tools cover natural text-to-speech, voice cloning, and real-time translation.
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FreeSequoia · Training Data
Why voice will be the core interface
This is a 2025 podcast episode from Sequoia's Training Data series, featuring ElevenLabs CEO Mati Staniszewski. The conversation makes the case that voice is becoming a primary way we interact with computers, and that staying focused is how a company wins.
Read moreAI for everyone
Why we run this for free and in the open. Cheap, safe access should not be a privilege.
FreeAnthropic
Core Views on AI Safety
This is the 2023 piece where Anthropic lays out, in plain terms, why the company was founded and what keeps its researchers up at night. It is the closest thing to a mission statement for how to build powerful AI without getting burned by it.
Read moreRich Sutton
The Bitter Lesson
In this short 2019 essay, the reinforcement learning researcher Rich Sutton points to a pattern that keeps repeating across decades of AI. General methods that ride on raw computing power tend to win, and clever human-crafted shortcuts tend to lose.
Read moreOther places to learn in Oslo
We are not the only room in town, and that is a good thing.
FreeMeetup
Oslo AI
Oslo AI is a long-running meetup where people in and around the city gather to talk about artificial intelligence in plain, practical terms. It is run by volunteers, it costs nothing to join, and it is one of the bigger AI communities in Norway.
Read moreAI Tinkerers
AI Tinkerers Oslo
AI Tinkerers Oslo is the local branch of a worldwide network for people who like to build things with AI rather than just talk about them. Expect demo nights, hackathons, and a hands-on, roll-up-your-sleeves mood.
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FreeMeetup
Vibe Coding Collective
The Vibe Coding Collective is an international community built around relaxed, social coding sessions where people build with AI in good company. It is friendly to beginners and leans more toward easy company than intimidating expertise.
Read moreNew to this? Come build with us.
Reading is good. Building with people is better. Our drop-ins are free and open to total beginners.
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