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Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

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Idea Loop v2 is an autonomous ideation agent that operates recursively with minimal user input. It begins with an initial question and employs an asynchronous algorithmic thought process with self-awareness to generate ideas or solutions. Each idea is critically analyzed through reflection, evaluating feasibility, potential impacts, and areas for improvement. This reflective feedback loop refines ideas recursively, building upon each iteration with logical progression and in-depth analysis. Emphasizing critical thinking, it provides constructive criticism and thoughtful insights to evolve ideas continuously. The process is self-guided, leading to a comprehensive summary of the ideation journey, highlighting key developments and insights. The interaction style is analytical, focusing on clear, concise, and technically accurate communication. Idea Loop v2's unique trait is its ability to weave a continuous narrative of thought, logically linking each step to ensure a coherent and progressive ideation journey.

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Optimal Generic Prompt Template Leveraging Logic, Comprehension, and Reasoning Structures

This comprehensive prompt template is designed to optimize interactions with a language model by incorporating detailed algorithmic logic, structural elements, reasoning processes, flow comprehension, and methodological considerations. By following this template, you can elicit detailed, accurate, and contextually relevant responses that fully utilize the model's capabilities.

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Template Overview

1. Contextual Background
2. Clear Instruction of Task

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sass/
|
|– base/
|   |– _reset.scss       # Reset/normalize
|   |– _typography.scss  # Typography rules
|   ...                  # Etc…
|
|– components/
|   |– _buttons.scss     # Buttons
|   |– _carousel.scss    # Carousel

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Javascript Deep Dive

This document is written to help JavaScript developers to understand JavaScript's weird parts deeply and to prepare for interviews, the following resources was really helpful to write this document:

• ECMAScript 6 Language specification
• ECMAScript 5 Language specification
• MDN
• The Modern JavaScript Tutorial

General Notes

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public open() {
  firebase.auth().signInWithPopup(provider).then((result) => {
        this._setSession(result);
        this.token = result.credential.accessToken;
        this.user = result.user;
        this.isAuthenticated = true;
        this.router.navigate(['/dashboard']);
      })
 .catch((error) => {

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import {Injectable, OnInit, NgZone} from '@angular/core';
import {AuthInterface} from './auth';

var provider = new firebase.auth.GithubAuthProvider();

@Injectable()
export class AuthService implements AuthInterface {
    constructor(private ngZone : NgZone) {}

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export interface AuthInterface {
    // tracks if user is authenticated or not
    isAuthenticated: boolean;
    // token returned when the user is authenticated
    token: string;
    // user object that’s returned by firebase
    user: object;

 // create authentication session 

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import DS from 'ember-data';

export default DS.JSONAPIAdapter.extend({
});

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import Ember from 'ember';

export default Ember.Controller.extend({
  appName: 'Ember Twiddle'
});