The journal

How the context layer is built.

Benchmarks, retrieval architecture and the engineering behind indexing a codebase once and serving it to every tool that asks.

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21–30 of 66 · page 3 of 7

Mar 31, 2026 context window AI basics LLM fundamentals

What Is a Context Window? The Simple Explanation Everyone Gets Wrong

The context window is your AI's short-term memory. It's not storage — it's a fixed-size desk. Everything the AI is thinking about right now must fit on this desk.

Mar 31, 2026 training data context window parametric memory

Context Window vs. Training Data: Two Completely Different Things

Training data is everything the model learned. The context window is what it's thinking about right now. Here's why the distinction matters.

Mar 31, 2026 Flash Attention GPU optimization SRAM

Flash Attention: How One Paper Made Long Context Possible

Flash Attention never materializes the full n×n attention matrix. Instead, it computes in tiles using fast GPU SRAM. Here's how it works and why it's 2-4× faster.

Mar 31, 2026 future of AI state space models Mamba

The Future: Will Context Windows Grow Forever? (Ring Attention, SSMs, Retrieval-Augmented Everything)

Three competing paradigms: grow context via hardware, replace attention with O(n) alternatives like Mamba, or build external memory systems. Which will win?

Mar 31, 2026 GPU memory memory wall VRAM requirements

The Quadratic Memory Wall: A Precise Analysis of GPU Memory Requirements per Context Length

Total GPU memory = model weights + KV cache + activations + workspace. Here's the exact formula to compute maximum context length for any GPU configuration.

Mar 31, 2026 Infini-Attention compressive memory infinite context

Infini-Attention and Compressive Memory: Unbounded Context with Bounded Memory

Google's Infini-Attention combines standard attention with a compressive memory that persists across segments — enabling theoretically infinite context at O(1) memory.

Mar 31, 2026 information theory Shannon channel capacity

The Information-Theoretic Limits of Context Windows

There are fundamental limits on how much information a fixed-width attention mechanism can extract from n tokens. Here's the math from Shannon's channel capacity to attention bounds.

Mar 31, 2026 KV cache VRAM calculation GPU memory

KV Cache Memory Math: Calculating Exactly How Much VRAM You Need

The exact formula for KV cache memory and worked examples for every major model architecture. Calculate your GPU requirements precisely.

Mar 31, 2026 KV cache GPU memory VRAM

The KV Cache: Why Your AI Needs So Much GPU Memory

During inference, the model stores Key and Value vectors for every token. This KV cache is often the biggest memory consumer. Here's the math behind it.

Mar 31, 2026 lost in the middle attention distribution prompt engineering

The 'Lost in the Middle' Problem: Why Your AI Ignores Information in the Center of Long Prompts

Research proved that LLMs attend strongly to the beginning and end of context but poorly to the middle. This is a mathematical property, not a bug.