Viktor Sokolov victory-sokolov

Project Requirements Document: The Urlist Website

The following table outlines the detailed functional requirements of The Urlist website.

Requirement ID	Description	User Story	Expected Behavior/Outcome
FR001	Creating a New URL List	As a user, I want to be able to start a new, empty list so I can begin adding URLs.	The system should provide a clear way for the user to initiate the creation of a new list, potentially presenting an empty list view or an "add new list" button.
FR002	A

Security Checklist (What can be done in CursorAI & software-level not infrastructure!)

Configuration Security

Detect secrets in code
Identify secrets committed to version control
Flag hardcoded credentials

Authentication & Authorization

Identify missing authentication checks
Detect improper authorization patterns

Install with npm i and run the server with npm start.

Depending on where you put your project, you might have to alter the path in the query params, but eventually you'll get the /etc/passwd file downloaded using the unsafe access:

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

Statements describing Python as a language

Interpreted: Python is an interpreted language, which means that the source code is translated and executed line-by-line at runtime, rather than being compiled to machine code beforehand.

Dynamic type system: Python uses a dynamic type system, which means that variable types are determined at runtime and can change during the execution of a program.

Garbage-collected: Python automatically manages memory allocation and deallocation using garbage collection, which frees developers from having to manually manage memory in their code.

Indentation-based syntax: Python uses indentation to define code blocks, making the code more readable and less cluttered with braces or other delimiters.

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

	{
	"always_run_in_app" : false,
	"icon" : {
	"color" : "orange",
	"glyph" : "chart-area"
	},
	"name" : "Grafana",
	"script" : "\/\/ Configuration\nconst url = \"http:\/\/grafana.example.com\";\nconst token = \"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\";\nconst orgId = 1;\nconst dashboardName = \"widgets\";\nconst dashboardId = \"xxxxxxxxxxxxxx\";\nconst tz = \"America\/Chicago\";\nconst darkMode = true;\nconst deviceSize = \"393x852\";\n\nparams = config.runsInWidget\n ? args.widgetParameter?.split(\",\") : []\n\nconst opts = {\n type: params[0] ? params[0] : \"md\",\n panel: params[1] ? params[1] : \"1\",\n from: params[2] ? params[2] : \"now-30m\",\n to: params[3] ? params[3] : \"now\",\n title: params[4] ? params[4] : \"Grafana\",\n align: params[5] ? params[5] : \"c\",\n};\n\n\/\/ Scriptable limits displayed image resolution to a max of 500x500\nconst imageSizes = {\n sm: new Size(474, 474),\n md: new Size(500, 210),\n lg: new Size(500, 500),\n re: new Size(480, 216),\n ci: ne

	# models.py

	from django.db import models

	class User(models.Model):
	# Old field (blue version)
	name = models.CharField(max_length=100)

	# New fields (green version)
	first_name = models.CharField(max_length=50, null=True, blank=True)

	import { AnyZodObject, z } from 'zod'
	import { Metadata, ResolvingMetadata } from 'next'

	type InferParams<Params> = Params extends readonly string[]
	? {
	[K in Params[number]]: string
	}
	: Params extends AnyZodObject
	? z.infer<Params>
	: unknown

	import { useMemo } from "react";
	import { useSearchParams } from "react-router-dom";

	type ParseConfig = Record<
	string,
	\| { type: "string"; defaultValue?: string }
	\| { type: "number"; defaultValue?: number }
	\| { parse: (value: URLSearchParams) => unknown }
	>;