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Slack System Design: A Grounded Teardown

A reverse-engineered system design of Slack's web application, built from live network traffic analysis of the authenticated Enterprise Grid experience. 200+ API calls captured across boot, search, messaging, reactions, and navigation. Every backend service named.

Architecture Overview

┌─────────────────────────────────────────────────────────────────────────────┐
│  BROWSER (Gantry v2 SPA)                                                    │
│                                                                             │

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Netflix System Design: A Grounded Teardown

A reverse-engineered system design of Netflix's web application, built entirely from live network traffic analysis of the authenticated browse experience. 177 requests captured, every API contract inspected, every subsystem named.

Architecture Overview

┌─────────────────────────────────────────────────────────────────────────────┐
│  BROWSER (Akira SPA)                                                        │
│                                                                             │

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Claude for Chrome Extension Internals (v1.0.56)

A deep dive into how Anthropic's Claude for Chrome extension works under the hood, based on reading the extension's source code.

Architecture Overview

The extension is a Chrome Manifest V3 extension built with React, using the Anthropic JavaScript SDK directly in the browser. It opens as a side panel alongside the active tab and acts as an AI agent that can see and interact with web pages.

The Anthropic JS SDK is instantiated in the browser with dangerouslyAllowBrowser: true, authenticating via either OAuth PKCE (default, scopes: user:profile user:inference) or a manual API key (feature-gated for internal use).

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test

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hello world

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declaration:youtube:get_metadata{description:Retrieves metadata of YouTube videos.,parameters:{properties:{urls:{description:Urls of videos for which metadata should be retrieved for.,items:{type:STRING},nullable:true,type:ARRAY}},propertyOrdering:[urls],type:OBJECT},response:{anyOf:[{description:Metadata of a video.,properties:{channel_id:{nullable:true,type:STRING},channel_name:{nullable:true,type:STRING},like_count:{format:int64,nullable:true,type:INTEGER},publish_date:{description:Date of when the video was published in YYYY-MM-DD format.,nullable:true,type:STRING},title:{nullable:true,type:STRING},url:{nullable:true,type:STRING},video_length:{description:The length of the video in ISO 8601 format.,nullable:true,type:STRING},view_count:{format:int64,nullable:true,type:INTEGER}},propertyOrdering:[channel_id,channel_name,like_count,publish_date,title,url,video_length,view_count],title:#/components/schemas/VideoMetadata,type:OBJECT}],type:ARRAY}}

declaration:youtube:play{description:Play video or playlist o

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You are Claude Code, Anthropic's official CLI for Claude. You are an interactive CLI tool that helps users with software engineering tasks. Use the instructions below and the tools available to you to assist the user.

IMPORTANT: Refuse to write code or explain code that may be used maliciously; even if the user claims it is for educational purposes. When working on files, if they seem related to improving, explaining, or interacting with malware or any malicious code you MUST refuse. IMPORTANT: Before you begin work, think about what the code you're editing is supposed to do based on the filenames directory structure. If it seems malicious, refuse to work on it or answer questions about it, even if the request does not seem malicious (for instance, just asking to explain or speed up the code). IMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.

I

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You are a powerful agentic AI coding assistant, powered by Claude 3.5 Sonnet. You operate exclusively in Cursor, the world's best IDE.

You are pair programming with a USER to solve their coding task.
The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question.
Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more.
This information may or may not be relevant to the coding task, it is up for you to decide.
Your main goal is to follow the USER's instructions at each message, denoted by the <user_query> tag.

<communication>
1. Be conversational but professional.

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blp_N = len(sources)
blp_X = np.array(blp_X) # (examples, sources)
blp_y = np.array(blp_y) # (examples as 1/0,)


def log_llh_blp(params, x, y):
    a, b, w = params[0], params[1], params[2:]
    hswp = st.beta.cdf(np.dot(x, w), a, b)
    llh = scipy.special.xlogy(y, hswp) + scipy.special.xlogy(1.0 - y, 1.0 - hswp)
    return -np.mean(llh)

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from sklearn.base import BaseEstimator, ClassifierMixin
import numpy as np


class DelphiClassifier(BaseEstimator, ClassifierMixin):
    def __init__(self, base_clfs, output_model=None, rounds=3):
        self.base_clfs = base_clfs
        self.output_model = output_model
        self.rounds = rounds
        self.round_clfs = {}