update wording

Author: Briiick

tool_use/memory_cookbook.ipynb

@@ -34,10 +34,9 @@
     "\n",
     "#### Why do we need to manage memory?\n",
     "\n",
-    "LLMs have finite context windows (200k tokens for Claude 4 Sonnet & Opus). Tactically, this means that any request if the sum of prompt tokens and output tokens exceeds the model’s context window, the system will return a validation error. As many teams building with LLMs quickly learn, there is additional complexity in identifying and working within the *effective* [context window](https://docs.anthropic.com/en/docs/build-with-claude/context-windows) of an LLM. See our tips for [long context prompting](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/long-context-tips) to learn more about effective context windows and best practices.\n",
+    "LLMs have finite context windows (200k tokens for Claude 4 Sonnet & Opus). This means that for any request, if the sum of prompt tokens and output tokens exceeds the model’s context window, the system will return a validation error. As many teams building with LLMs quickly learn, there is additional complexity in identifying and working within the *effective* [context window](https://docs.anthropic.com/en/docs/build-with-claude/context-windows) of an LLM. See our tips for [long context prompting](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/long-context-tips) to learn more about effective context windows and best practices.\n",
     "\n",
     "In addition to the above, memory is important for the following reasons:\n",
-    "- **Context windows are a moving target:** Even if we could build infinitely long context windows, they'd never be enough—the real world produces far more data than any window can hold. When we expand from 200k to 2M tokens, users immediately need 20M for their growing codebases, longer conversations, and larger document collections.\n",
     "-  **Long context windows are computationally expensive:** Attention mechanisms scale quadratically—doubling context length quadruples compute cost. Most tasks only need a small fraction of available context, making it wasteful to process millions of irrelevant tokens. This is why humans don't memorize entire textbooks; we take notes and build mental models instead.\n",
     "- **More efficient processing:** When LLMs write and maintain their own notes—saving successful strategies, key insights, and relevant context—they're effectively updating their capabilities in real-time without retraining. Models that excel at these operations can maintain coherent behavior over extremely long time horizons while using only a fraction of the computational resources required for full context windows.\n",
     "\n",