GPT

Posts that use this term

• Local agents and tool use

  Function calling on open models in 2026. Which ones actually work, why local agents break when they break, and the scaffolding that keeps them upright.

• Picking a local model by task

  The 2026 open leaders, sorted by what you actually want to do: coding, chat, the small-model crowd, structured output, vision, embeddings, and audio.

• The pitch for local LLMs in 2026

  The case for running an LLM on the machine you already own. Privacy, no per-call cost, faster first token, no rate limits, and it works on a flight.

• What leaves your machine when you use AI

  What providers actually see, log, and keep when you call an LLM API in 2026. What "we don't train on your data" really means, how free and paid tiers differ, and when local is the only safe choice.

• LLM API bills, and why a token costs what it costs

  How input and output tokens get priced, why output runs 5-6x more, and how prompt caching cuts the input bill by 10x. Plus the hidden costs that ambush people.

• The major LLMs in 2026

  A field guide to the closed frontier models and the open weights you can actually run. What the "B" numbers mean, and which size fits your machine.

• The context window, and why models hallucinate

  An LLM only sees a fixed-size slice of text at a time. When it doesn't know something, it predicts anyway. That's a hallucination, not a bug.

• From models to LLMs

  An LLM is one kind of ML model, trained on text, predicts the next token. That single trick at scale gets you ChatGPT, and also explains where it breaks.

• How a model learns: training and inference

  Training is the expensive one-time event where a model's numbers get tuned. Inference is the cheap repeated use afterwards. The gap in cost is enormous, and it shapes the whole industry.

• What makes a model: data and algorithm

  A model is a file of learned numbers, produced by running an algorithm over data. Both ingredients matter, but bad data beats a good algorithm every time.

• Inside AI: machine learning and deep learning

  Open the AI umbrella. Machine learning is the part that learns from data. Deep learning is ML done with neural networks, and that's where today's models live.