Deep Architectural Understanding
Building from scratch forces you to understand the foundational math and data flow, which is often hidden away in high-level libraries.
Attention Mechanics
When you build a Transformer from the ground up, you directly implement the scaled dot-product attention formula. This provides a concrete understanding of how the Query, Key, and Value matrices interact and how the scaling factor `d_k` prevents vanishing gradients.
Attention(Q, K, V) = σ( (Q * Kᵀ) / √dₖ ) * VThe Importance of Tensor Shapes
The Transformer is an exercise in managing tensor shapes. A typical tensor shape for a batch of sequences is `[Batch Size, Sequence Length, Model Dimension]`.
Implementing the model yourself means you must meticulously track how these shapes are transformed as data flows through the embedding layer, multi-head attention blocks, and the final feed-forward network. This is a fundamental skill that the simulation steps in our application aim to visualize.
Separation of Concerns
Each part of the JAXforge application has a distinct role. Understanding this separation is key to grasping how the system works as a whole.
| Component | What it Understands | What it Doesn't Understand |
|---|---|---|
| Firebase (Firestore) | It understands data. Specifically, it knows you are saving and retrieving JSON objects that contain numbers (dModel, nHeads), strings (modelName, userId), and timestamps. | It doesn't care that dModel is an "Embedding Dimension" or that nHeads relates to "Attention Heads." It just sees numbers and text. |
| Gemini AI Configuration Generator | It understands the architecture. When you give it a task (e.g., "Translate short sentences"), it uses its knowledge of LLM design principles (d_model vs. latency, heads vs. complexity) to output the optimal parameters. | It doesn't know anything about the Firebase database structure or the specific HTML elements on your page. |
| JAX Simulation Logic (JavaScript) | It understands the mathematical relationships and data transformations (e.g., dModel / nHeads). It uses these parameters to simulate the tensor shapes in the forward pass. | It doesn't know why the AI chose those parameters, and it doesn't care about saving the configuration to Firebase. |