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KNOWLEDGE FOR THE WORK WEEK

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MONDAY-READY·Featured courses

AI Literacy & Core Concepts

AI Foundations

AI for Complete Non-Technical Professionals

AI Foundations

02 · HOW IT WORKS

How it works

One studio for lessons, practice, review, and proof you can use Monday morning.

AI-Native Learning

Study smarter. Remember more.

One Platform

Every subject. Every skill.

Your personal learning studio.

Learn smarter

AI-powered study sessions that adapt to your pace and track what sticks.

CHAPTERS
01
02
03
04
05
Start with clarity.
FRAMEWORK
AI TUTOR
CHAPTER 3
3.1✓
3.2✓
3.3
3.4
3.5
3.6
Trade-offs in Practice
FRAMEWORK
1
2
3
AI TUTOR
explain me this concept in easier terms
Hands On Learning
PROJECT
Build-Along: Design a Component Library
Apply what you've learned by building a reusable, accessible component library.
1
2
3
4
MYTH VS REALITY
MYTH
REALITY
Beginner▾
Beginner
Intermediate
Advanced
CHAPTER 3
3.1✓
3.2✓
3.3
3.4
3.5
3.6
Master the Concepts
CHAPTER 3
3.1✓
3.2✓
3.3
3.4
3.5
3.6
Flashcards
Question
Answer
Daily Agenda
Review your Learnings
done
done
done
done

Every subject

Structured courses from fundamentals to mastery — in one focused screen.

AI Tutor • Daily Agenda • Course Studio • Quiz Engine • Flashcards • Interview Prep • Progress Tracking • Spaced Repetition • AI Tutor • Daily Agenda • Course Studio • Quiz Engine • Flashcards • Interview Prep • Progress Tracking • Spaced Repetition •

02 · CODE STUDIO — PRACTICE BEFORE IT COUNTS

Practice leaves the lesson with you.

Code examples live outside the chapter intro and chapter end. They are the hands-on lesson layer that turns the framework into a decision check.

WHAT RUNS NOW

Runtime ready

The script loads in the sandbox. Hit Run to score a real task against automation criteria.

automation_vs_augmentation_matrix.pyPython runtime readyExecuting linesRun completeRun
Step 01Score task risk
Step 02Invert high-consequence criteria
Step 03Choose augment or automate
Course 008 matrixExecution progress
1# Course 008: Automation vs. Augmentation matrix2scores = {"standard": 3, "error": 5, "volume": 4,3          "judgment": 4, "trust": 5}4invert = {"error", "judgment", "trust"}5adjusted = {k: (6 - v if k in invert else v) for k, v in scores.items()}6fit = round(sum(adjusted.values()) / 25 * 100)7rec = "AUTOMATE" if fit >= 65 else "AUGMENT"8print("Client Engagement Letter Review")9print(f"{fit}% automation suitability")10print(f"Recommendation: {rec}")

OUTPUT

Task: Client Engagement Letter Review

Automation Suitability: 44%

Adjusted legal-risk criteria lowered automation fit

Recommendation: AUGMENT

Course 008 resultMeridian should augment the partner review, not automate it.

REVIEW BEAT

Recall

Yesterday's framework resurfaces as a flashcard — spaced repetition without another lecture.

03 · DAILY AGENDA — COMPOUND FLUENCY

The next morning, it sticks.

Yesterday's frameworks become spaced flashcards and a short quiz — review that compounds without another lecture.

  • Flip cards from what you practiced yesterday — tap to reveal, rate your recall.
  • Answer one room-ready question with the explanation you'd give a colleague.
  • SM-2 scheduling brings weak concepts back before they fade.

REVIEW BEAT

Recall

Yesterday's framework resurfaces as a flashcard — spaced repetition without another lecture.

Daily AgendaSM-2 POWERED

AI Literacy Foundations · Ch.4 Fine-Tuning

CONCEPT · TOKEN ECONOMICS

What determines the cost of an LLM API call?

Cost = (input tokens + output tokens) × price per token. Input includes your prompt AND context.

Didn't knowKnew it ✓

Scroll to flip · then rate your recall

Your colleague proposes fine-tuning GPT-4 on internal docs "so it knows the company." What do you say?

AFine-tuning will make it a domain expert.
BRAG is better here — fine-tuning changes behavior, not knowledge.
CYou can only fine-tune open-source models.
WHY · THIS IS WHAT YOU SAY IN THE ROOM

Fine-tuning adjusts how a model responds. RAG grounds it in your documents at query time.

FlashcardsQuiz

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Join the beta.

Walk into Monday with sentences you have already pressure-tested.

Analysis

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Build, target, and measure video ad campaigns across connected TV and on-demand platforms

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Turn roadmaps into realistic delivery plans by matching scope, skills, and timelines

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Design scalable privacy controls that adapt to changing regulations without rebuilding your product

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Build defensible documentation architectures that prove responsible AI without exposing proprietary methods

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Now viewing Community-Driven Social Media Marketing for Product Teams, 7 chapters and 33 lessons

No card required · Beta opens June 30