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Claude Code Memory compaction — detailed walkthrough (based on rust/crates/runtime/src/compact.rs)

This gist explains, step by step, how the compaction logic in rust/crates/runtime/src/compact.rs works. It covers:

• When compaction is triggered
• How tokens are estimated
• How messages are summarized and merged
• How the compacted session is constructed
• Edge cases and tests that illustrate behavior

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Not heard enough about MCP yet?

Unlike traditional APIs that require rigid pre-defined integrations, MCP acts as a universal translator between large language models and enterprise systems (and other loosely defined "tools"), maintaining context across interactions and enabling real-time discovery of resources. You knew this part already. Let's answer some common questions that have come up (including whats in the title). Later in this post, we'll dissect an actual MCP implementation for Amazon Bedrock Knowledge Bases to understand how this protocol bridges the gap between human-like queries and machine-readable data.

The Question Everyone Asks: "But someone still needs to build the APIs, right?"

Let's address this confusion directly: Yes, developers still create interfaces to data and tools - but MCP fundamentally changes how, when, and by whom these interfaces are used.

The key difference is that MCP creates a standardized way for these interfaces to be connected at runtime by users rather

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• Key and Value Compression: The input for the $t$-th token at an attention layer is denoted as $\mathbf{h}_t \in \mathbb{R}^d$, where $d$ is the embedding dimension.

• A compressed latent vector for keys and values, $\mathbf{c}_t^{KV} \in \mathbb{R}^{d_c}$, is computed using a down-projection matrix$W^{DKV} \in \mathbb{R}^{d_c \times d}$ : $\mathbf{c}_t^{KV} = W^{DKV} \mathbf{h}_t$

• Here, $d_c (\ll d_h n_h)$ is the KV compression dimension, much smaller than the total dimension of keys and values.

• Keys ($\mathbf{k}_t^C$) and values ($\mathbf{v}_t^C$) are reconstructed from $\mathbf{c}_t^{KV}$ using up-projection matrices $W^{UK}, W^{UV} \in \mathbb{R}^{d_h n_h \times d_c}$.

• So $[\mathbf{k}{t,1}^C; \dots; \mathbf{k}{t,n_h}^C] = W^{UK} \mathbf{c}t^{KV}$, and $[\mathbf{v}{t,1}^C; \dots; \mathbf{v}_{t,n_h}^C] = W^{UV} \mathbf{c}_t^{KV}$

• Rotary Positional Embedding (RoPE): A decoupled key vector carrying positional information ($\mathbf{k}_t^R$) is generated using a separate projectio

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FROM continuumio/miniconda3:4.3.14

# Dumb init
RUN wget -O /usr/local/bin/dumb-init https://github.com/Yelp/dumb-init/releases/download/v1.2.0/dumb-init_1.2.0_amd64
RUN chmod +x /usr/local/bin/dumb-init

RUN conda update conda && conda install "conda=4.4.7"
RUN conda install --yes \
    python-blosc \
    cytoolz \

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#Creating a Twitter bot with Node.js Learn how you can create your own Twitter bot using Node.js and the new Twitter API. The bot will auto retweet in response to tweets with some particular hashtags. (https://goo.gl/4whEIt)

##Tools we need Here are the tools we’ll be using to create the bot — 

• Node.js installed in your machine
• A registered Twitter account

Create a Twitter application