Topic
MODELS.
The AI models that read, reason, and generate — what they are, how they work, and how to choose between them.
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Terms in this topic:
GLOSSARY.
Large Language Model (LLM)
Token
Context Window
Fine-Tuning
Hallucination
Temperature
Inference
Multimodal
Transformer
Parameters
Training Data
Reasoning Model
Foundation Model
Image Generation
Code Generation
Cost Per Token
Latency
Attention
Pre-Training
Post-Training
Distillation
Quantization
Mixture of Experts (MoE)
Benchmark
Evaluation (Eval)
Streaming
Model Card
Frontier Model
Long-form on this topic:
ARTICLES.
- The Operator's Guide to Picking an AI Model in 2026Claude, GPT, Gemini, GLM, DeepSeek, local. Five questions that pick the right model for a real job, not a benchmark.
- Self-Hosting Your AI Stack: The 2026 SetupTailscale, Hetzner, Postgres, n8n, OpenClaw, and Ollama. The exact stack I run, what each piece does, and what it costs.
- Fine-Tuning a Small Model for One Specific JobOne task, 800 examples, a single 4090. A walkthrough of fine-tuning Qwen3 8B for structured extraction. What worked, what didn't, what it cost.