Pick the right model for the job.

Plain comparisons of today's AI models, what the benchmarks mean, when to go local, and how to stop overpaying.

Guides
16
Reading
123 min
Library
108
models

LLM evaluation for beginners: test AI models on your own tasks

LLM evaluation for beginners: build a 20 to 50 case test set, grade it with code or a model judge, track results over time, and run a script today.

8 min read
models

How do LLMs work? Large language models explained without math

How do LLMs work? A plain-English tour of tokens, next-word prediction, training, attention and reasoning modes, and why models ace some tasks but flunk others.

8 min read
models

What are embeddings? How AI turns meaning into numbers

What are embeddings? Lists of numbers that place text on a map of meaning. How similarity search works, what embeddings power, and current models and prices.

7 min read
models

Fine-tuning vs RAG vs prompting: which one do you need?

Fine-tuning vs RAG vs prompting: what each changes, what each costs, when each one wins, and how million-token context windows changed the answer in 2026.

7 min read
models

Small language models: when a tiny model beats a giant one

Small language models run on a laptop or phone for a fraction of the cost. Which to try in 2026, what they do well, and how to test one against a big model.

7 min read
models

LLM API pricing compared (2026): what a million tokens really costs

LLM API pricing compared for September 2026: input, output, cached and batch prices for 13 models from five labs, plus the real cost of three everyday tasks.

8 min read
models

Voice AI in 2026: talking to computers, explained

Voice AI explained: how speech to text, text to speech and real-time speech models work, why speed matters, what voice agents can do now, and the privacy rules.

8 min read
models

AI benchmarks explained: what SWE-bench and friends measure

What SWE-bench Pro, Terminal-Bench, OSWorld and other AI benchmarks test in 2026, the current top scores, and how to read a leaderboard without being fooled.

8 min read
models

Which AI model should I use? How to choose by speed, smarts and cost

Which AI model should you use? Match the model to the task: fast, balanced or deep. The seven questions that decide it, a decision table and a quick test.

7 min read
models

What is a token in AI? Tokens and context windows, without math

A token is a chunk of text, about three quarters of an English word. See real examples, what a context window holds, and why output tokens cost more.

8 min read
models

How to run LLMs locally in 2026: tools, models and hardware

Run an LLM locally with Ollama, LM Studio, llama.cpp or MLX. Which open models to try in 2026, how much memory you need, and when local is not worth it.

8 min read
models

Open source vs closed AI models: what you gain and give up

Open source vs closed AI models in 2026: what open weights give you, the license catches to check, and how the two compare on quality, cost and privacy.

8 min read
models

Reasoning models explained: when thinking longer pays off

Reasoning models think before they answer. Learn when that extra thinking pays off, when it only adds cost and delay, and how to set the effort level.

7 min read
models

AI hallucinations: why models make things up, and 9 ways to catch them

AI hallucinations are confident answers that are false. Why models make things up, where the errors hide, and nine practical ways to catch them early.

8 min read
models

Reduce LLM costs: 8 ways to cut your AI bill without worse results

Reduce LLM costs with eight levers: smaller models, prompt caching, batch discounts, leaner prompts and output caps. Prices checked in September 2026.

8 min read
models

RAG explained: how AI answers from your own documents

What is RAG? Retrieval-augmented generation lets AI answer from your own documents. How it works step by step, when to use it, and where it breaks.

8 min read

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