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.
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.
Models
ModelsBest 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.
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ModelsBest 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.
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ModelsBest 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.
ReadModelsBest Model for Frontend Code: Judgement Without a Compiler
Frontend work has no oracle that tells you the output is wrong. What that means for model choice, and how to close the loop with screenshots.
ReadModelsBest Model for Go: Easy to Write, Hard to Get Idiomatic
Go is the language models write most confidently, and confidence is the problem. Where generated Go compiles cleanly and still races under load.
ReadModelsBest Model for Large Repositories: Context Is Not Enough
A million-token window does not make a model good at a million-line codebase. What actually determines whether a model can work in a large repository.
ReadModelsBest Model for Legacy Code: Comprehension Beats Cleverness
Legacy work is mostly reading, not writing. Why the model that scores highest on code generation is often the wrong one for a twenty-year-old codebase.
ReadShowing 37–48 of 404 articles