The Employee Factory - Invidious

The Employee Factory

The Employee Factory is described as the world’s first integrated career counseling and learning platform designed to help individuals plan their professional journey and target their "dream companies" and "passive online careers"

The Purpose and Mission
The core purpose of the platform is to bridge the gap between raw talent and the specific requirements of the modern job market. Its mission is defined by several key goals:
• Preparing "Floor Ready" Talent: The platform aims to provide candidates who are immediately ready to handle specific project needs within a corporate environment.
• Solving Indecisiveness: Recognizing that many job seekers feel lost, the platform provides the "right guidance" and professional training needed to boost a career path.
• Saving Time for Recruiters: For "Employee Seekers," the platform offers an untapped pool of professionals, saving them valuable time by vetting and training candidates beforehand.

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6:39

Vector DB vs Graph DB: The Ultimate LLM Data Battle

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Is Your AI Actually Good? How to Test LLMs (Part 17)

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Fine-Tuning LLMs: The Complete 2025 Guide (Part 13)

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Multi-Agent Systems: How to Build AI Teams (Part 9)

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LlamaIndex: The Ultimate Data Framework for AI (Part 11)

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Master LangChain: Build Pro-Level AI Apps (Part 10)

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How Hackers Trick AI (And How to Defend It)

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8:03

Anthropic MCP: The “USB Port” for AI Explained (Part 8)

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6:57

Vector Databases: AI’s Long-Term Memory (Part 5)

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Why Knowledge Graphs are Replacing Vector Search

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5:55

Stop Using Vector DBs: MCP is the Future

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RAG Explained: Connect AI to Your Own Data (Part 4)

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Shared 2 weeks ago

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How AI Thinks: Tokens & Embeddings Explained

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Stop Using RAG. MCP Changes Everything.

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8:31

Stop Using Vector DBs for Everything

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9:17

Stop Using Vector DBs: Is GraphRAG the Future?

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8:47

Stop Using Vector DBs for Everything (The Truth)

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9:17

Stop Using RAG! Why MCP Is The New AI Standard

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77 views

6:44

Stop Using RAG: The Better Way to Build AI

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29 views

8:36

Build a Full AI System: The Ultimate Project

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9:13

Fix Slow AI Agents: Production Latency Guide

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7:35

Building a 24/7 Autonomous AI Startup: The ITGYANI OS v3 Blueprint 🚀

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Shared 3 months ago

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7:59

ITGYANI OS 24x7 AI Startup - Autonomous Mode

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Shared 3 months ago

37 views

6:54

AI: Salvation or Humanity’s Final Invention?

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Shared 6 months ago

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6:16

AI Gets to Work

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Shared 6 months ago

35 views

6:56

Automated Wealth: Build Passive Income Using AI Systems

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Shared 7 months ago

102 views

5:34

4.9 - Lab: Extracting (Unload) Data from Snowflake to S3 📤🧪

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Shared 1 year ago

537 views

2:42

4.8 - Snowpipe: Billing Estimation & Key Considerations for Data Ingestion 💰📊

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Shared 1 year ago

51 views

11:08

4.7 - Lab: Create and Implement Snowpipe in Snowflake 🧪📤

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Shared 1 year ago

73 views

2:02

4.6 - Introduction to Continuous Data Ingestion in Snowflake 🔄🚀

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Shared 1 year ago

36 views

11:06

4.5 - Lab: Ingest JSON from S3 to Snowflake 🧪📥

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Shared 1 year ago

51 views

8:28

004 - Lab: Ingest CSV from S3 to Snowflake 🧪🗂️

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Shared 1 year ago

35 views

7:56

003 - Lab: Create an Integration Object to Connect Snowflake with AWS S3

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Shared 1 year ago

115 views

0:45

001 - Section Overview: Snowflake - Data Loading, Ingestion, and Extraction

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Shared 1 year ago

140 views

4:01

5.2 - Data Ingestion Section Overview: Snowflake - Data Loading, Ingestion, and Extraction

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Shared 1 year ago

224 views

10:58

4.9 - Snowflake Search Optimization Feature: Improve Query Performance

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Shared 1 year ago

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6:09

4.6 - Lab: Selecting Your Clustering Keys in Snowflake

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Shared 1 year ago

235 views

7:44

4.8 - Lab: Query Processing and Caching in Snowflake

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Shared 1 year ago

156 views

0:45

5.1 - Data Ingestion Section Overview: Snowflake - Data Loading, Ingestion, and Extraction

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Shared 1 year ago

50 views

17:32

4.4 - Lab: Micro-partitions and Clustering Keys in Snowflake

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Shared 1 year ago

171 views

4:37

4.7 - Lab: Check Query Profile and History in Snowflake

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Shared 1 year ago

681 views

9:04

4.5 - Understanding Clustering Depth and Cluster Overlap in Snowflake

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Shared 1 year ago

750 views

5:58

4.3 - Benefits of Micro-partitions and Clustering in Snowflake 🌐

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Shared 1 year ago

157 views

8:40

3.2 - Lab: Create Tables in Snowflake

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Shared 1 year ago

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4:01

3.3 - Snowflake: Views, Materialized Views, and Secure Views

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4:35

3.5 - Lab: Create Secure Views in Snowflake

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Shared 1 year ago

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5:28

3.4 - Lab: Create Views in Snowflake

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Shared 1 year ago

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4:16

3.1 - Introduction: Different Types of Tables in Snowflake

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3:06

3.6 - More About Views in Snowflake

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7:51

4.2 - Introduction to Partitions and Clustering Keys in Snowflake

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Shared 1 year ago

161 views

2:13

4.1 - Section Overview: Data Warehousing and Engineering with Snowflake

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Shared 1 year ago

57 views

4:35

2.9 - Snowflake: Resource Monitors

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Shared 1 year ago

131 views

5:24

2.8 - Snowflake: Track Your Consumption

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Shared 1 year ago

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10:00

2.7 - Snowflake: Understanding Different Billing Components

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Shared 1 year ago

86 views

9:49

2.6 - Snowflake Virtual Warehouses: Powering Scalable Compute

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Shared 1 year ago

36 views

4:51

2.5 - Snowflake Object Hierarchy: Understanding the Structure

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213 views

4:02

2.4 - Snowflake Architecture: A Comprehensive Overview

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Shared 1 year ago

52 views

2:21

2.3 - Lab: Setup Snowflake Trial Account

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Shared 1 year ago

59 views

2:26

2.2 - Two Aspects of a Data Ecosystem

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Shared 1 year ago

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3:24

2.1 - What is a data warehouse

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Shared 1 year ago

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