Transformer Architectures and the Future of LLMs
Like Duolingo, but for Transformer Architectures and the Future of LLMs. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
For the part of you with thirty open tabs that never became anything.
64 levels across 9 sections, about 128 minutes end to end, roughly 26 days at five minutes a day. It moves through The Transformer Core: Advanced Mechanics, Efficiency and the Memory Wall, Beyond Quadratic Complexity, Dynamic Computation and Sparsity, The Long Context Frontier, Reasoning and System 2 Thinking, Multimodality and World Models, Post-Transformer Frontiers, and The Future of AI Development.
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Key ideas in Transformer Architectures and the Future of LLMs
- The residual stream is an additive vector space
- Early layer features remain accessible via identity path
- The stream acts as an asynchronous bulletin board
- Attention heads move existing features
- QK is decoupled from Value
- Information movement handles sequence-level context
- The residual stream's role as a persistent, additive communication channel
- The specific mechanism of information transport handled by attention heads
- MLPs operate on tokens in isolation (position-wise), meaning they cannot move information across the sequence
- Additive residual updates cause the variance of the stream to grow with depth, potentially leading to activation saturation
- MLPs function as key-value memories where the first linear layer 'keys' into specific patterns and the second 'values' provides the update
- The residual stream's role as a persistent, additive communication channel rather than a sequential processor
- LayerNorm acts as a gain control that re-scales the 'volume' of the signal to a fixed range for the next layer's circuitry
- The MLP's role is to refine or expand the internal representation of a token based on its current features
- Normalization ensures that the model can remain sensitive to small updates even after hundreds of previous additions
- The Logit Lens applies the final unembedding matrix to intermediate residual states
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Transformer Architectures and the Future of LLMs the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
How do features from the very first layer manage to survive all the way to the end of a deep model?
Get it right to open this lesson and 63 more in the app.
Where Transformer Architectures and the Future of LLMs takes you
Master the intricate mechanics of modern large language models and explore the frontier of post-transformer architectures, from State Space Models to neuro-symbolic reasoning.
- 1
The Transformer Core: Advanced Mechanics
- The Residual Stream as a Communication Channel
- Scaling Laws and Chinchilla Optimality
- 2
Efficiency and the Memory Wall
- KV Cache Management and PagedAttention
- IO-Awareness with FlashAttention
- Model Distillation and Quantization
- 3
Beyond Quadratic Complexity
- Linear Transformers and Kernel Tricks
- State Space Models (SSMs) and Mamba
- RWKV and Receptance-Weighted RNNs
- 4
Dynamic Computation and Sparsity
- Mixture of Experts (MoE) Architectures
- Conditional Computation and Early Exiting
- 5
The Long Context Frontier
- Positional Encoding Evolution
- Retrieval-Augmented Generation (RAG) at Scale
- 6
Reasoning and System 2 Thinking
- Chain of Thought and Self-Correction
- Search-Based Inference (Q* and Beyond)
- 7
Multimodality and World Models
- Native Multimodality vs. Adapters
- JEPA and Predictive World Models
- 8
Post-Transformer Frontiers
- Liquid Neural Networks
- Neuro-symbolic Integration
- Energy-Based Models and Spiking Neural Networks
- 9
The Future of AI Development
- Hardware-Software Co-design
- The Path to AGI: Agentic Workflows
9 sections · 21 units · 64 levels. Built to play, not to enroll.
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