mycotics

1. Create a route that will create a random token, store it in the session as sign_up_token, and encode it as a JWT that expires in 10 seconds

def generate
    # Generate a random token
    random_token = SecureRandom.hex(16)

    # Save it in the session
    session[:sign_up_token] = random_token

mycotics

async function generateProof(targetDifficulty) {
  const encoder = new TextEncoder();
  let nonce = Math.floor(Math.random() * 1e6); // Start with a random nonce for uniqueness

  while (true) {
    // Combine nonce and timestamp for added variability
    const message = `captcha-${nonce}-${Date.now()}`;
    
    // Compute the SHA-256 hash of the message
    const hashBuffer = await crypto.subtle.digest('SHA-256', encoder.encode(message));

mycotics

  flowchart LR;
      A[CI MULTI CHAPTCHA]-->B{Select captcha service by developer?};
      classDef green color:#022e1f,fill:#00f500;
      classDef red color:#022e1f,fill:#f11111;
      classDef white color:#022e1f,fill:#fff;
      classDef black color:#fff,fill:#000;
      B--YES-->C[How to use?]:::green;
      
      C-->U[I choose recaptcha.]:::green;

mycotics

dbt Best Practices and Conventions at Mixbook

This document outlines the design of a dbt project aimed at delivering an outstanding business user experience at Mixbook, catering specifically to analysts and stakeholders who are proficient Looker users.

Introduction

The ultimate output of the dbt transformations will be well-structured data marts (dimensions, facts and wide tables) and insightful reports (dashboard-ready tables).

Business users should't filter out non-customer users or determine which order item shouldn’t be counted. These complexities should be handled within the dbt pipelines.

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Blueshift

Source	Destination	Last Import	Status
Currency Rates API	public.federal_reserve_raw_currency_rates	2025-01-13	🔵 Active
Delighted API	public.delighted_raw_mixbook_nps_surveys	2025-01-12	🟢 Probably Active
SurveyMonkey API	public.delighted_raw_mixbook_nps_surveys	2024-11-14	🟠 Probably Not Active
Iterable API	public.delighted_raw_mixbook_nps_surveys	2025-01-13	🟢 Probably Active
Attentive (S3)	public.raw_iterable_campaigns	2025-01-13	🟢 Probably Active
Shippo	public.shippo_raw_trackings	2024-11-14	🟠 Probably Not Active

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Filtering Before Joins

Predicate pushdown (also called filter pushdown) is an optimization technique where filtering conditions (WHERE clauses) are applied as early as possible in a query's execution plan. This reduces the amount of data read and processed, improving performance.

In Amazon Redshift, pushing down filters before joins or aggregations minimizes the amount of data that needs to be loaded into memory or shuffled across nodes, leading to significant performance gains.

Not Great:

SELECT 
 o.order_id, 

mycotics

class EventBusMiddleware
  def initialize(app)
    @app = app
  end

  def call(env)
    status, headers, response = @app.call(env)
    headers['Access-Control-Expose-Headers'] = 'X-Event-Bus'
    headers['Access-Control-Allow-Headers'] = 'X-Event-Bus'
    if Thread.current[:event_bus]