zackradisic

// First, let's look at the traditional visitor pattern
// This is how we might implement a simple expression evaluator

// Traditional Visitor Pattern
namespace Traditional {
  // Abstract base class for expressions
  abstract class Expr {
    abstract accept<T>(visitor: ExprVisitor<T>): T;
  }

Houtamelo

Introduction

This lesson aims to teach the reader about the inner-workings of Rust's *(deref operator) and trait Deref, as well as how to take advantage of those features.

Requirements

This lesson assumes that:

• You have amateurish understanding of Rust's type system. Most importantly, understand that references are types, meaning &T, &mut T and T are all different types.

Terminology

nymous

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

LukeMathWalker

name: Security audit
on:
  schedule:
    - cron: '0 0 * * *'
  push:
    paths:
      - '**/Cargo.toml'
      - '**/Cargo.lock'
jobs:
  security_audit:

yogthos

Introductory resources

• High level overview https://yogthos.github.io/ClojureDistilled.html
• An Animated Introduction to Clojure https://markm208.github.io/cljbook/
• Interactive tutorial in a browser https://tryclojure.org/
• Interactive exercises http://clojurescriptkoans.com/
• Clerk notebooks with introductory examples https://github.clerk.garden/anthonygalea/notes-on-clojure
• More interactive exercises https://4clojure.oxal.org/
• Lambda Island tutorials https://lambdaisland.com/
• Functional Programming with Clojure resources https://practicalli.github.io/

ksaldana1

Domain Modeling with Tagged Unions in GraphQL, ReasonML, and TypeScript

GraphQL has exploded in popularity since its open-source announcement in 2015. For developers who had spent a lot of time managing data transformations from their back-end infrastructure to match front-end product needs, GraphQL felt like a tremendous step forwards. Gone were the days of hand-writing BFFs to manage problems of over-fetching.

A lot of value proposition arguments around GraphQL have been about over/under fetching, getting the data shape you ask for, etc. But I think GraphQL provides us more than that—it gives us an opportunity to raise the level of abstraction of our domain, and by doing so allow us to write more robust applications that accurately model the problems we face in the real world (changing requirements, one-off issues).

An underappreciated feature of GraphQL is its type system, and in particular features like [union types](https:

swlaschin

Architecture

• Use Onion architecture

  • Dependencies go inwards. That is, the Core domain doesn't know about outside layers

• Use pipeline model to implement workflows/use-cases/stories

  • Business logic makes decisions
  • IO does storage with minimal logic
  • Keep Business logic and IO separate

• Keep IO at edges

fhoering

import json
import subprocess
import sys

from pex.fetcher import PyPIFetcher
from pex.pex_builder import PEXBuilder
from pex.resolvable import Resolvable
from pex.resolver import resolve_multi, Unsatisfiable, Untranslateable
from pex.resolver_options import ResolverOptionsBuilder

cscalfani

How to think about Monads

UPDATE 2021: I wrote this long before I wrote my book Functional Programming Made Easier: A Step-by-step Guide. For a much more in depth discussion on Monads see Chapter 18.

Initially, Monads are the biggest, scariest thing about Functional Programming and especially Haskell. I've used monads for quite some time now, but I didn't have a very good model for what they really are. I read Philip Wadler's paper Monads for functional programming and I still didnt quite see the pattern.

It wasn't until I read the blog post You Could Have Invented Monads! (And Maybe You Already Have.) that I started to see things more clearly.

This is a distillation of those works and most likely an oversimplification in an attempt to make things easier to understand. Nuance can come later. What we need when first le

snoby

kube2iam your security partner

• Introduction
• Setup kube2iam in your cluster
• Setting up the Role in AWS
  • The Role
• Trust Relationship