News & guides
Needle-in-a-Haystack Tests: What They Prove and What They Miss
A perfect needle-in-a-haystack chart says a model can find one planted sentence. It says very little about whether it can reason over your documents.
· 9 min read
Paged Attention: Virtual Memory for the KV Cache
Paged attention stores the KV cache in fixed-size blocks instead of one contiguous slab, which is what lets a server hold far more concurrent sessions.
· 9 min read
Planning Storage for Self-Hosted Model Weights
How to size disk for open-weight models: what drives checkpoint size, why quantisations and versions multiply it, and why storage is rarely the real constraint.
· 9 min readPositional Encoding: How a Transformer Knows Token Order
Attention is order-blind by default. How position gets injected, why the method decides how far context can stretch, and what you see when it fails.
· 9 min read
Pretraining vs Post-Training: Where a Model Gets Its Behaviour
Pretraining decides what a model knows. Post-training decides how it behaves. Knowing which stage owns a problem tells you whether prompting can fix it.
· 9 min readRepetition Penalties: Why They Break Code Generation
Frequency and presence penalties suppress loops by punishing tokens the model already used. On code that punishes syntax, and the loop was rarely the real bug.
· 9 min read
Residual Connections: Why Deep Models Train at All
The one-line change that made hundred-layer networks possible, why the residual stream behaves like a shared bus, and what it explains about pruning.
· 8 min readRLHF Explained: Training on Comparisons Instead of Answers
Reinforcement learning from human feedback shapes the qualities nobody can write down. The three-stage pipeline, and the failure modes you see as a user.
· 9 min read
Rotary Embeddings (RoPE) Explained With Clock Hands
RoPE encodes position by rotating vectors rather than adding to them. Why that gives relative distance for free, and how it made 1M context possible.
· 9 min readScaling Laws Explained: What Predicts a Model Getting Better
Scaling laws describe how loss falls as compute, data and parameters grow. What they actually claim, where they stopped applying, and why it matters to you.
· 9 min read
Self-Consistency Decoding: Ask Five Times, Take the Majority
Sampling several answers and voting beats a single answer on some tasks and is pure waste on others. The mechanism, the cost, and when to reach for it.
· 8 min readSparse vs Dense Activation: What Runs on Every Token
Sparse activation means most of a model sits idle for any given token. That single fact explains model pricing, memory bills and misleading spec sheets.
· 9 min readReady to try it?
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