Blog
Analysis, explainers, and commentary on AI models, benchmarks, and pricing trends.
Release Coverage
Need the latest launch briefs before they become blog posts?
The new release desk tracks fresh model launches, gathers official sources and benchmark signal, and prepares editor-ready briefs before they are turned into public posts.
Open the model release deskWhat Is RAG? Retrieval-Augmented Generation Explained
A plain-English guide to RAG — the technique that lets AI models pull in real data instead of guessing. When to use it, when not to, and how it actually works.
Context Windows Explained: Why Bigger Is Not Always Better
What context windows actually are, why very large ones can degrade quality, and how to think about them when choosing a model.
Bench-Maxing and Why You Should Test AI Models on Your Actual Work
Formal benchmarks are increasingly gamed by AI labs. Here is the case for building a small personal test suite based on the work you actually do — and why community signal often beats published scores.
Temperature, Top-p, and the Inference Settings Nobody Explains
A practical guide to temperature, top-p, frequency penalty, and other generation settings — what they do, when to change them, and when to leave them alone.
AI Agents Explained: What They Are and How They Work
AI agents are models that can plan, use tools, and take actions autonomously. Here is what that actually means in practice — and which models are best at it.
How AI Benchmarks Work (And Why You Should Care)
A plain-English guide to MMLU, GPQA, HumanEval, SWE-bench, and Chatbot Arena — the tests that determine which AI models are actually good.
The AI Pricing Race to Zero
AI model pricing has dropped over 90% in two years. We tracked every price change and here is what the data shows about where pricing is heading.
Open Source vs Closed AI Models in 2026
DeepSeek, Llama, Mistral, and Qwen are closing the gap. We compare the latest open-source models against GPT, Claude, and Gemini on every benchmark.
What Is an LLM? A Non-Technical Guide
Large Language Models explained for everyone — what they are, how they work, and why they matter. No jargon, no PhD required.
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