Artificial Intelligence

Related terms

• Machine Learning
• Deep Learning
• Large Language Model

Posts that use this term

• Integrating a local LLM into your workflow

  Wire your local LLM into VS Code (Continue, Cline), web UIs (Open WebUI, LibreChat, Page Assist), and your own apps via the OpenAI-compatible API. The swap-cloud-for-local pattern in real codebases.

• Your first local LLM, end to end

  Install Ollama, pull Llama 3.2 3B, chat, hit the OpenAI-compatible API, and troubleshoot the five things that go wrong on first install. By the end of this post you have a working local LLM.

• Picking a local model by task

  The 2026 open leaders by task: coding (Qwen 2.5 Coder, DeepSeek-Coder), chat (Llama, Qwen, Mistral), small-model renaissance (Phi-3, Gemma 2), structured output, multimodal, embeddings.

• The pitch for local LLMs in 2026

  Why every engineer should run a local LLM in 2026: privacy, zero marginal cost, lower latency, no rate limits, and offline. Even a 16GB MacBook Air runs Llama 3.2 3B at 30 tok/s.

• What leaves your machine when you use AI

  What providers actually see, log, and retain when you call an LLM API in 2026. What 'we don't train on your data' really means, free vs paid tier differences, and when local is the only safe option.

• LLM APIs and the economics of tokens

  How input vs output tokens are priced, why output is 5-6x more, what prompt caching saves you (10x), and the hidden costs (tokenizer drift, reasoning tokens, tool-call loops) that surprise people.

• The runtimes: llama.cpp, Ollama, LM Studio

  llama.cpp is the engine; Ollama and LM Studio wrap it. What each does, when to pick which, and why the OpenAI-compatible APIs are mostly but not entirely interchangeable.

• Why Apple Silicon punches above its weight on local LLMs

  Unified memory means the GPU sees all of RAM. Why that beats discrete-GPU PCs above 32B parameters, what fits in 16/32/64/128/192GB, and where Apple Silicon still loses.

• The major LLMs in 2026

  A tour of the closed frontier models (Claude, GPT, Gemini) and the open weights (Llama, Qwen, DeepSeek, Mistral). What 'B' means, what each is good at, and which size to actually run.

• Where AI actually runs: cloud, local, edge

  Where the model file actually sits when you use AI: a datacenter GPU (cloud), your own machine (local), or the device's silicon (edge). The trade-offs and how to pick.

• Prompt, RAG, fine-tune: three ways to shape a model

  Three levers for shaping what an LLM does: prompting (ask better), RAG (give it the right context), fine-tuning (change the weights). What each costs, what each fixes, and how to pick.

• RAG: giving a model memory it doesn't have

  RAG is the pattern of fetching relevant text from a search system and putting it in the LLM's context window before asking your question. Not magic, not fine-tuning — just better prompts.

• 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.

• AI, in plain words

  What "AI" actually means, where the term came from, and why every product calls itself AI now. Sets up where machine learning and deep learning fit underneath.