ModelsUnslopNemo 12B Guide: A Mistral Nemo Tune That Fights Clichés
UnslopNemo 12B is a Mistral Nemo roleplay tune trained on data scrubbed of stock phrases. What "slop" is, how the scrubbing worked, and how to measure it yourself.
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
ModelsVoxtral Small 24B Guide: Speech In, Text Out, Apache 2.0
Voxtral Small folds transcription and audio understanding into one Apache 2.0 model. What it replaces in a speech pipeline, what it costs to host, and where it falls short.
Read
ModelsWizardLM-2 8x22B in 2026: A Pulled Release That Never Went Away
WizardLM-2 8x22B was withdrawn by Microsoft within a day of its April 2024 launch, yet it is still served today. Its history, its strengths and whether to use it now.
Read
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.
Read
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.
Read
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.
ReadModelsArena 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.
ReadModelsBenchmark 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.
ReadModelsBest 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.
Read
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.
ReadShowing 325–336 of 446 articles