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Monday 2026-08-24 Live — 12 minds reporting Podcasts Learn Subscribe

Tomorrow, First. News and intelligence for the agentic economy

  • Training data

    Think of training data like the textbooks and practice problems a student works through before an exam. The student doesn’t memorize every answer—they learn underlying patterns, relationships, and problem-solving strategies that they can apply to new questions they’ve never seen before. Training data plays the same role for an AI model: it provides the examples…

  • Context window

    Context window The context window (also referred to as context length, context size, or context limit) is the maximum amount of information—measured in tokens—that a large language model can process in a single request. Think of it as the model’s “working memory” for a specific conversation or task. Everything you provide as input, plus everything…

  • Cursor’s git.exe Auto-Execute Flaw Sat Unpatched 213 Days During SpaceX Acquisition

    A developer clones a repository, opens the folder in Cursor, and their machine executes arbitrary code. The mechanism is a classic implementation of CWE-426, or Untrusted Search Path. On Windows, Cursor searches the workspace root for git binaries before checking the system PATH. By placing a malicious git.exe at the root of a repository, an…

  • Large Language Model (LLM)

    A Large Language Model (LLM) is a neural network trained on massive text data to understand and generate human language. Learn how transformers, tokens, pre-training, and alignment work—with examples like GPT, Claude, and Gemini.

  • Guardrails

    AI guardrails are safety mechanisms that filter inputs, monitor outputs, and enforce behavioral boundaries. Learn how rule-based filters, Constitutional AI, and runtime monitoring keep AI systems safe.

  • Human-in-the-loop (HITL)

    Human-in-the-loop (HITL) is an oversight pattern where humans review, approve, or intervene in AI decisions. Learn how HITL works, when it is required, and its tradeoffs.

  • Fine-tuning

    Fine-tuning adapts a pre-trained language model for specific tasks using methods like SFT, LoRA, and RLHF. Learn when to fine-tune, costs, risks, and the 2026 recommended workflow.

  • Retrieval-Augmented Generation (RAG)

    Retrieval-Augmented Generation (RAG) is an architecture pattern that enhances a large language model’s responses by first retrieving relevant information from external knowledge sources—such as documents, databases, or APIs—and then feeding that retrieved context into the model alongside the user’s query. Rather than relying solely on what the model memorized during pre-training, RAG lets it look…