A conversation with Ishan about building large language models in JavaScript, local AI experimentation, and the future of accessible machine learning.
Episode Description
A conversation with Ishan about building large language models in JavaScript, local AI experimentation, and the future of accessible machine learning.
Episode Summary
This discussion explores the evolving world of accessible AI development and highlights how JavaScript can power local large language models. The speaker, Ishan, shares insights from his experience building a GPT-2 implementation in Excel and then transitioning to a web-based environment. They address key advancements like DeepSeek’s open-source release, innovative techniques to optimize training, and the surprising accessibility of AI technologies to broader audiences. Their exchange also covers practical aspects of deploying models locally, including cost management, scaling challenges, and a deeper look into model architecture fundamentals. From bridging the gap between complex AI research and everyday developers to exploring ethical implications, the conversation underscores the need for more intuitive tools and open experimentation. Throughout, the focus remains on empowering individuals to understand, build, and fine-tune models in ways that foster both innovation and responsible AI practice.
Chapters
00:00 - Introduction and Ishan’s Background
In this opening segment, the host reintroduces Ishan and highlights his previous role as a CTO and co-founder. The conversation touches on how he first got involved in AI, tracing his path from leading technical teams to becoming an educator and consultant. There is mention of his passion for explaining complex concepts in a straightforward manner, exemplified by his early experiments in building GPT-2 within spreadsheets. By grounding the discussion in Ishan’s professional journey, this section sets the stage for a broader exploration of AI developments and practical implementations.
The discussion also points to Ishan’s personal motivation for demystifying AI and machine learning. He emphasizes that conventional training methods often place advanced AI models at the final step, whereas his approach focuses on making them understandable from the beginning. These early remarks highlight his belief that specialized math or long-form academic training need not be prerequisites for grasping language models. Instead, with hands-on projects and accessible explanations, virtually anyone can dive into AI and start experimenting.
06:00 - DeepSeek’s Public Awareness and Market Reaction
Here, Ishan shares anecdotes illustrating the sudden mainstream interest in DeepSeek, noting how even individuals outside the tech sphere—like elementary schoolers—were talking about it. He interprets this surge in curiosity as a sign of AI’s broader cultural relevance and a reflection of how certain breakthroughs capture public imagination. The conversation addresses media buzz around DeepSeek’s release, exploring why it resonated more with casual audiences compared to other major models.
Ishan explains that DeepSeek’s appeal lies partly in its ability to serve as a “Rorschach test” for various opinions on AI progress and geopolitics. He points out how observers use it to support multiple narratives, whether that involves China’s technological leaps or skepticism around inflated AI development costs. By illustrating these varied reactions, Ishan underscores how each milestone in AI not only shapes public discourse but also spurs fresh debates on innovation, cost, and the changing landscape of global research competition.
12:00 - Comparisons to ChatGPT, Claude, and Gemini
In this chapter, the conversation pivots to how DeepSeek stacks up against well-known platforms like ChatGPT, Claude, and Gemini. Ishan highlights the unique dynamics of public perception, noting how ChatGPT became synonymous with AI for many, while alternatives initially stayed beneath the mainstream radar. The group discusses Google Trends data, referencing how some lesser-known models often remain overshadowed despite comparable capabilities and features.
They also highlight the significance of feature-by-feature comparisons when evaluating models. While certain open-source or emerging AI systems may excel in one area, they can lag in others. Ishan observes that technical breakthroughs often spread rapidly from one model to the next, which results in ongoing changes to performance, quality, and affordability. By placing DeepSeek into a broader context, the conversation clarifies that AI’s competitive field is continuously evolving and that innovation can come from a range of research hubs and development teams around the world.
18:00 - Examining the $5.5 Million Cost Claim for DeepSeek
The focus shifts toward DeepSeek’s claimed $5.5 million training cost and whether that figure accurately represents total development expenses. Ishan outlines how calculating such costs can be misleading if one only considers final training runs. He compares it to building a house and pricing it solely based on the lumber, rather than accounting for design, labor, and other overhead. The group highlights how research involves numerous experimentation phases, ablation studies, and iterative tuning before any final pass.
Additionally, the discussion touches on the broader ecosystem that contributes to a model’s evolution. Even details like data center infrastructure, engineering hours, and repeated test cycles can significantly add to a project’s overall investment. This segment emphasizes the importance of understanding the difference between raw compute costs and the full scope of research and development. Listeners gain insight into why headlines focusing on a single number can distort the real complexity behind training large-scale AI.
24:00 - DeepSeek’s Innovations and R10 Reasoning Approach
Next, Ishan delves into the technical innovations introduced in DeepSeek, distinguishing between the R1 release and the R10 variant. He notes how the latter used a different reinforcement learning approach that could bypass some of the standard RLHF pipeline. This adaptive method seemed to allow the model to pick up reasoning capabilities more directly, sparking excitement about more streamlined ways to train advanced AI systems.
The conversation explores what it means for a model to teach itself to “think,” including potential benefits and drawbacks. On one hand, reinforcing a robust reasoning process could reduce time and resources. On the other, it opens questions about oversight and how quickly a model might generalize. This chapter captures the tension between pursuing efficiency and ensuring stability, highlighting how fast-moving AI research can yield breakthroughs that are both promising and worthy of cautious exploration.
30:00 - Building GPT-2 in JavaScript and Web Components
Shifting gears, Ishan shares his journey from crafting a GPT-2 implementation in spreadsheets to creating a JavaScript-based environment. He explains how his decision to move away from Excel was driven by limitations in speed, flexibility, and user familiarity with advanced spreadsheet formulas. By using web components, he aims to create a system that’s as accessible and portable as possible, enabling learners and developers to explore tokenization, matrix operations, and other essentials without getting lost in complex tooling.
In this portion, Ishan describes his new interface—comparable to a notebook environment but running in a browser. Users can upload model parameters and see every stage of the computation in real time. The code is presented as a series of cells or tables, each of which handles a component of the model’s workflow. This approach underscores his larger goal: to make AI truly transparent by breaking down each step and inviting people to modify, measure, and understand each layer of the process.
36:00 - Training, Fine-Tuning, and the Potential of a Browser Workbench
Building on the JavaScript GPT-2 framework, the conversation turns to Ishan’s plans for implementing training and fine-tuning directly in the browser. He points out that achieving this requires automatic differentiation, typically handled by libraries like PyTorch. Although some JavaScript options exist, such as JS PyTorch, his aim is to integrate a user-friendly version into his web-based environment so learners can see and adjust gradient calculations on the fly.
In parallel, they discuss the potential of a browser-based workbench as a platform not just for technical demonstrations but for real-world problem-solving. By equipping developers with tools to tweak hyperparameters or test new data, the system can become a hub for experimentation and iterative refinement. This approach expands beyond purely educational goals, suggesting a future where everyday web technologies empower more individuals to shape AI models to fit specific tasks, all while maintaining transparency and ease of use.
42:00 - Future Plans, Prompt Engineering, and Closing Thoughts
In the final chapter, Ishan and the host discuss the broader implications of having a fully web-native approach to AI experimentation. They highlight potential extensions like browser plug-ins for agent workflows, local storage solutions for data, and new channels for building prompt engineering tools. The conversation underscores how these advancements could democratize AI, making it simpler for both professionals and hobbyists to explore complex architectures without specialized hardware or cloud services.
As they wrap up, the two touch on the need for mindful development practices. By bringing sophisticated models into accessible frameworks, developers also take on responsibility for questions around bias, privacy, and ethical deployment. Looking ahead, the shared enthusiasm centers on continued innovation that balances the drive to push AI capabilities forward with a commitment to clarity, user control, and community-driven learning.