Transformer Architecture
215 episodes · Page 5 of 9
#2406: Why Million-Token Context Windows Can't Handle 3 Reasoning Steps
Needle-in-a-haystack is dead. Here's what actually measures whether models can think across long documents.
#2374: How Granular Can MoE Experts Get?
Exploring the limits of expert granularity in Mixture of Experts models—how narrow can segmentation go before efficiency or accuracy suffers?
#2366: Why LLMs Forget the Middle of Long Conversations
Why do large language models struggle with the middle of long conversations? Explore the science behind attention dilution and practical fixes.
#2357: Microsoft's Phi: When Data Quality Beats Model Size
Explore Microsoft AI's Phi family of small language models, designed for edge deployment and high efficiency.
#2355: Why Open-Weight Models Are Winning
Discover how Cogito v2.1 leverages process supervision and MoE architecture to redefine reasoning efficiency in open-weight AI models.
#2350: NVIDIA's Strategic Pivot: From Chipmaker to Model Builder
Dive into NVIDIA’s Nemotron 3 Super, a hybrid MoE model combining Mamba, Transformers, and multi-token prediction for cutting-edge efficiency.
#2349: The 30-Person Lab Outpacing AI Giants
Discover how Arcee AI’s Trinity Large Thinking delivers cutting-edge reasoning at a fraction of the cost, all from a team of just 30.
#2348: Diffusion Models Take on Text Generation
Explore Inception Labs’ Mercury 2, a groundbreaking diffusion-based language model that rethinks text generation and reasoning.
#2314: One Model or Three? Inside Claude's Architecture
What makes Claude’s Haiku, Sonnet, and Opus different? Discover how architecture shapes their unique strengths and weaknesses.
#2312: When Bigger Context Windows Aren't Better
Exploring the real-world impact of massive context windows in AI models, from academic research to codebase analysis.
#2261: The Gap Between AI Output and Art
We assess if AI can truly invent a Tolkien-level language, write a coherent novel, or author an original screenplay—and where the real gaps in crea...
#2188: Is Emergence Real or Just Bad Metrics?
The debate over whether AI models exhibit genuine emergent abilities or just appear to because of how we measure them—and why it matters for safety...
#2164: Why Bigger Context Windows Don't Fix Attention
Frontier models have million-token context windows, but attention degrades well before you hit the limit. New research reveals why bigger isn't bet...
#2076: Is Pure NLP Dead? The Hidden Scaffolding of AI
Modern AI didn't appear from nowhere. Discover how decades of linguistic rules and statistical models built the foundation for today's LLMs.
#2067: MoE vs. Dense: The VRAM Nightmare
MoE models promise giant brains on a budget, but why are engineers fleeing back to dense transformers? The answer is memory.
#2066: The Transformer Trinity: Why Three Architectures Rule AI
Why did decoder-only models like GPT dominate AI, while encoders and encoder-decoders still hold critical niches?
#2065: Why Run One AI When You Can Run Two?
Speculative decoding makes LLMs 2-3x faster with zero quality loss by using a small draft model to guess tokens that a large model verifies in para...
#2064: Why GPT-5 Is Stuck: The Data Wall Explained
The "bigger is better" era of AI is over. Here's why the industry hit a data wall and shifted to a new scaling law.
#2062: How Transformers Learn Word Order: From Sine Waves to RoPE
Transformers can’t see word order by default. Here’s how positional encoding fixes that—from sine waves to RoPE and massive context windows.
#2061: The Memory Bottleneck That Drives Attention Design
Attention is the engine of modern AI, but it’s also a memory hog. Here’s how MQA, GQA, and MLA evolved to fix it.
#2060: The Tokenizer's Hidden Tax on Non-English Text
Why does a simple greeting in Mandarin cost more to process than in English? It's the tokenizer's hidden inefficiency.
#2056: Music as Language: The Architecture Behind AI Song Generation
A look at how AI music models use audio tokens, transformers, and diffusion to turn text into songs.
#2040: The Rebellion Against Big Tech's AI Lock-In
Why run LLMs locally? We break down Ollama, llama.cpp, vLLM, and llamafile—and when to use each.
#2032: Jerusalem's Skyscrapers Are Just Holograms
A producer claims Jerusalem's new towers aren't built—just light projected onto scaffolding to fool investors.