The creator documents their experience training an open-source small language model called Nanoad using rented GPUs, highlighting the accessibility and potential for personalized AI tools despite the model’s limited capabilities. They reflect on the evolution of AI from their early studies to the present boom, encouraging viewers to embrace AI as a creative tool while continuing to develop their own skills.
In this video, the creator shares their experience with training a small language model (LLM) called Nanoad, developed by Andre, the “Vibe coding godfather.” Nanoad is an open-source project consisting of about 8,000 lines of Python code that effectively recreates a model similar to GPT-2. The creator followed the instructions to train the model, which took between 4 to 7 hours depending on the hardware configuration. After training, they were able to query the model through a web client, although the model’s capabilities were quite limited and somewhat humorous in its responses.
The creator highlights the accessibility of this project, emphasizing that anyone can clone the repository, rent GPUs, and train their own LLM with publicly available datasets. They mention using Lambda Labs to rent eight A100 GPUs, which cost around $14 per hour, making the process somewhat expensive but feasible. This accessibility excites the creator because it allows them to customize the model’s training data, potentially creating specialized tools like a Lua autocomplete or a Lua-based question-answering assistant tailored to their personal coding style.
Reflecting on their background, the creator recalls studying AI and machine learning in the early 2010s, working with simpler neural networks and functions during the AI winter. They contrast that era with the current boom in AI, where powerful models are readily available and easier to experiment with. This project rekindles their enthusiasm for hands-on AI work, allowing them to tinker and build something unique without needing deep expertise in model architecture or training from scratch.
Despite some frustrations with the model’s limited intelligence and occasional errors, the creator appreciates the opportunity to engage directly with AI technology rather than just building wrappers around existing models. They caution viewers not to fall into the trap of thinking AI will replace all skills or make mastery irrelevant. Instead, they encourage learning and competence, viewing AI as a tool to enhance creativity and problem-solving rather than a threat.
The video ends on a lighthearted note, with the creator inviting viewers to like and subscribe. They promise to build a personal webpage using React and TypeScript if they reach a million subscribers before the new year, humorously vowing to embrace the “soy dev” stereotype but avoiding Rust due to past memes. Overall, the video conveys excitement about the democratization of AI and the possibilities it opens for individual experimentation and learning.