ModelsA/B Testing Two Models Without Fooling Yourself
Comparing two models on live traffic sounds simple and usually is not. Sample sizes, paired designs, and the metrics that actually settle the question.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Picking between models — and testing them on your own work.
Models
ModelsActive vs Total Parameters: The Number Spec Sheets Hide
A 2.8T model and a 27B model can be two-to-one apart on the figure that governs thinking. How to read parameter counts across the 2026 field.
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ModelsAgentic Coding Models Compared: Who Survives the Loop
Chat benchmarks say little about a model driven in a loop for forty turns. What agentic performance actually measures, and how the 2026 field ranks on it.
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ModelsArena Elo Explained: What a Preference Rating Measures
Arena ratings come from blind pairwise votes, not from tests. What the number means, why gaps under fifty points are noise, and where it misleads.
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ModelsBenchmark Contamination Explained, and What to Do About It
Contamination inflates scores when test data leaks into training. How it happens, why it is over-diagnosed, and the practical defences that actually work.
ReadModelsBest Model for Agentic Workflows: Reliability Compounds
An agent that is 95 percent reliable per step fails most thirty-step tasks. Why compounding error, not peak capability, decides which model to run in a loop.
ReadModelsBest Model for Backend Code: Where Benchmarks Transfer Best
Backend work is the one domain where SWE-bench scores roughly mean what you want them to mean — and the places where they still mislead you.
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ModelsBest Model for Batch Jobs: Throughput, Not Intelligence
When nobody is waiting for the answer, latency stops mattering and unit cost dominates. How to pick and operate a model for offline high-volume work.
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ModelsBest Model for Data Science: Correct Code, Wrong Analysis
The code runs, the chart renders, and the conclusion is unsound. Why data science needs a different evaluation than general code generation.
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ModelsBest Model for DevOps: Blast Radius Changes the Calculus
Infrastructure code fails differently: rarely, and expensively. Why terminal benchmarks are the relevant signal and what to gate before applying.
ReadModelsBest Model for Documentation: Accuracy Is the Only Metric
Documentation is the task where fluent output is most dangerous, because readers cannot verify it. How to pick and constrain a model for docs that stay true.
ReadModelsBest Model for Enterprises: Capability Is the Easy Part
At enterprise scale the constraints are licensing, data residency, auditability and version stability. How those narrow the field before capability matters.
ReadShowing 1–12 of 119 articles