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BRICS Summer School Learning Series | Skills for Future Leaders
๐๏ธ Tickets are running out. Secure your place and invest in your future today: https://t.co/Jel9eNbwtH
#BRICSSummerSchool2026 #LearningSeries #BRICSYouth #YouthLeadership #LeadershipDevelopment #SkillsForTheFuture
#LEARNINGSERIES
ABCD OF SHARE MARKET
๐ฐ CASH FLOW
There are 2 important terms every investor should know:
1๏ธโฃ Operating Cash Flow (OCF / CFO)
2๏ธโฃ Free Cash Flow (FCF)
1๏ธโฃ Operating Cash Flow (OCF / CFO)
Also called: Cash Flow from Operations (CFO)
๐ OCF = Cash generated from the company's core business operations.
Think of a grocery shop.
Sales received from customers = โน10,000
Cash paid to suppliers, employees, electricity, rent etc. = โน7,000
๐ต Remaining Cash = โน3,000
This โน3,000 is your Operating Cash Flow (OCF).
๐ While calculating OCF, buying a new machine or building is NOT included because that's an investing activity.
2๏ธโฃ Free Cash Flow (FCF)
๐ Free Cash Flow is the cash left after spending on CapEx (Capital Expenditure) required to maintain or expand the business.
Example:
Operating Cash Flow = โน3,000
CapEx (New machine + Renovation) = โน2,000
๐ต Free Cash Flow = โน1,000
Formula:
FCF = Operating Cash Flow โ Capital Expenditure
Why is Free Cash Flow Important?
This is the money a company can use to:
โ
Pay Dividends
โ
Reduce Debt
โ
Buy Back Shares
โ
Make Acquisitions
โ
Keep Cash on the Balance Sheet
๐ Easy Way to Remember
Operating Cash Flow = Cash generated by running the business.
Free Cash Flow = Cash left after investing to maintain or grow the business.
โ๏ธ SHARE PURANA TIP
Revenue is an opinion. Profit is an estimate. Cash is a fact.
That's why great investors always check the Cash Flow Statement, not just the P & L Statement.
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05/20/26
Creativity is stimulated by 3 factors
1. clear goals
2. Pressing problems
3. focused questions
Ask yourself constantly, what do you want.
It breaks down to outcomes, it navigates to process, and finally leads to questions you should answer.
Hallucination? Confabulations? Do you really trust your AI tool? Should you really trust your AI tool?
Are we more forgiving of human error?
Unpack this with @Aakrit, Founder of @ActivateSignal, in the latest episode of #LearningSeries.
#RPGGroup #FutureOfWork
When change outpaces adaptation, comfort isnโt an option.
@Aakrit, Founder of Activate, explains why this shift is unlike anything before.
Not the internet era. 100x bigger. Faster. Less forgiving.
Stay tuned.
#RPGGroup #LearningSeries #FutureOfWork #AIShift
CEX vs DEX
Difference between a Centralized Exchange and a Decentralized Exchange
#LearningSeries #GateAfrica #Gate #Cryptocurrency
#LearningSeries:
Across ๐, countries are strengthening responses to forced displacement & internal migration.
ย
What are the lessons emerging?
Join #Session5๏ธโฃ as we explore policy approaches and good practices.
Registerโคต๏ธ
https://t.co/NhJg4VJpJR
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๐ 18 March | โฐ 16hrs EAT
@DrNormanCSauce3 kicks off @DeKalbSchools #Access #Opportunity #LearningSeries @DrTWeaver brings #leadership & #leveragepoints for #systems change & insight into #scholars needs & #school #engagement @EpicOutler @COSDeKalbCounty @CherisseCamp
A routine slacklining session changed everything.
@SamarFarooqui shares how a police intervention during practice unexpectedly put his work in the national spotlight.
Stay tuned for more from his journey.
#RPGGroup #LearningSeries #HelloHappiness @SlackLifeInc
#Access & #Opportunity #LearningSeries @DeKalbSchools Doing the #work to #engage #students & create #pathways for all. For ALL. #bethechange #mentalmodels #systemimprovement @DrTWeaver @EpicOutler @DrNormanCSauce3 @COSDeKalbCounty @CherisseCamp
#Access & #Opportunity @DeKalbSchools #LearningSeries continues to do The Work of making pathways for each #student. Todayโs session centered systems work. Case studies. Hands on elevation of critical concepts. Incredible conversation & collaboration. @DrNormanCSauce3
Behind every smart decision is a simple, repeatable process.
At DataHer 101, weโre teaching women not just what to do, but why each stage matters.
So data stops feeling overwhelming and starts feeling empowering.
#DataHerAfrica #DataHer101 #LearningSeries #WomeninData
Follow along if youโre curious to see how 9 pages rewrote the future of money.
#Bitcoin #Blockchain #Crypto #Web3 #TechExplained #LearningSeries #Engineering #Fintech
๐ฌ Lesson 31 #GenAI #LearningSeries #AI
๐ง AGI โ When AI Starts Thinking Like Us (and Beyond) ๐
So far, weโve built narrow AIs โ great at one thing.
But AGI (Artificial General Intelligence) aims higher:
๐ Learn anything
๐ Adapt to any task
๐ Reason across domains โ like a human mind.
๐ก Itโs not just about smarter answers โ itโs about self-awareness, creativity, and problem-solving without human prompts.
๐ Todayโs LLMs + Agents + Multi-Agent Systems are the stepping stones toward AGI โ systems that can learn, plan, and innovate independently.
โ ๏ธ Weโre not there yet, but each leap in reasoning, memory, and collaboration takes us closer.
๐ฌ Laymanโs View:
Imagine teaching your computer once โ and it figures out the rest, just like you do. Thatโs AGI โ AI with a mind of its own. ๐งฉ
#AGI #GenAI #ArtificialIntelligence #AIAgents #MachineLearning #DeepLearning #AIRevolution #FutureOfAI #Tech #Innovation #AICommunity #AITrends #Automation
๐ฌ Lesson 30 #GenAI #LearningSeries #Agent
๐ค Multi-Agent Systems โ When AIs Start Teaming Up ๐
Weโve seen solo AI Agents think & act.
Now imagine a team of AIs collaborating like humans! ๐ง ๐ฅ
๐ก Multi-Agent Systems (MAS) =
Multiple agents communicating, sharing context & dividing work:
๐จโ๐ป Research Agent โ fetches data
๐งฎ Analysis Agent โ finds insights
โ๏ธ Writer Agent โ creates the report
โ๏ธ Supervisor Agent โ coordinates all
Together โ autonomous teams that plan, execute & refine results without human micromanagement. ๐
๐ Used in: trading systems ๐น, scientific research ๐ฌ, enterprise automation ๐ข & even gaming ๐ฎ.
๐ฌ Laymanโs View:
Think of it like an office full of AIs โ each expert at one job, all working in sync while you sip your coffee โ.
#GenAI #AI #ML #Automation #technology
๐ฌ Lesson 30 #GenAI #LearningSeries #Agent
๐ค Multi-Agent Systems โ When AIs Start Teaming Up ๐
Weโve seen solo AI Agents think & act.
Now imagine a team of AIs collaborating like humans! ๐ง ๐ฅ
๐ก Multi-Agent Systems (MAS) =
Multiple agents communicating, sharing context & dividing work:
๐จโ๐ป Research Agent โ fetches data
๐งฎ Analysis Agent โ finds insights
โ๏ธ Writer Agent โ creates the report
โ๏ธ Supervisor Agent โ coordinates all
Together โ autonomous teams that plan, execute & refine results without human micromanagement. ๐
๐ Used in: trading systems ๐น, scientific research ๐ฌ, enterprise automation ๐ข & even gaming ๐ฎ.
๐ฌ Laymanโs View:
Think of it like an office full of AIs โ each expert at one job, all working in sync while you sip your coffee โ.
#GenAI #AI #ML #Automation #technology
Thinking about selling your business? Donโt miss our November Learning Series.
Gain practical insights to help you plan with confidence while networking with your community.
#LearningSeries #ProfessionalDevelopment #Grow #Learn #Network
โก Lesson 27 #GenAI #LearningSeries #AI #LESSON
๐ง Building GraphRAG โ The Smart Stack Behind Smart AI
RAG connects facts.
GraphRAG connects meaning. ๐ธ๏ธ
Hereโs your lightning stack โก๐
๐งฉ Data Prep: LangChain | spaCy | Airflow
๐ธ๏ธ Graph DB: Neo4j | TigerGraph | ArangoDB
๐ฝ Vector DB: Weaviate | Pinecone | Qdrant
๐ค LLMs: GPT-4 | Claude | Llama 3
๐ Eval: RAGAS | TruLens | Grafana
๐ก Pro tip: Start simple (LangChain + Neo4j + GPT-4).
Scale later (Airflow + TigerGraph + Pinecone).
GraphRAG = Smarter retrieval. Deeper reasoning. Connected AI.
#Graph #RAG #GenAI #AI #Neo4j #LLMs #AI #ML #KnowledgeGraphs #TechNews #News #Innovation #Crypto
๐ฌ Lesson 26 #GenAI #LearningSeries #AI #RAG
๐๏ธ GraphRAG Architecture โ Turning Data into Knowledge Webs ๐
GraphRAG isnโt just smarter retrieval โ itโs structured reasoning in action. Letโs decode its architecture ๐
๐งฉ 1๏ธโฃ Data Ingestion Layer
Collects data from PDFs, APIs, documents, DBs
Extracts entities & relationships (people, places, causes, effects)
๐ง Uses NLP tools like spaCy, OpenAI NER, or LLMs for extraction.
๐ธ๏ธ 2๏ธโฃ Knowledge Graph Construction
Builds a Graph Database (Neo4j, ArangoDB, TigerGraph)
Nodes = Entities ๐งฉ
Edges = Relationships ๐
Embeddings are also stored for semantic reasoning.
๐ก Result: A connected web of structured knowledge.
๐ 3๏ธโฃ Retrieval Layer
Hybrid approach:
Vector search โ semantic relevance
Graph traversal โ relational depth
Finds not only whatโs related but also how.
๐ Example:
Instead of โFind Infosys founders,โ GraphRAG can answer โ
โShow people connected to Infosys through co-founding or leadership roles.โ
๐ง 4๏ธโฃ Reasoning & Generation Layer
LLM integrates retrieved context + relationships
Generates explainable, traceable answers with citations
Example:
โDrug A increases risk because it interacts with Protein B โ verified in Study C.โ
โ๏ธ 5๏ธโฃ Feedback & Continuous Learning
Human feedback + graph enrichment loops
Every new query improves graph qualities
โจ Takeaway:
GraphRAG = RAG + Reasoning.
It doesnโt just fetch โ it understands the fabric of data. ๐ธ๏ธ๐ก
#GraphRAG #RAG #GenAI #AI #LLM #MachineLearning #KnowledgeGraphs #ArtificialIntelligence #DataScience #Tech #FutureOfAI #Innovation #AICommunity #AITools
๐ฌ Lesson 26 #GenAI #LearningSeries #AI #RAG
๐๏ธ GraphRAG Architecture โ Turning Data into Knowledge Webs ๐
GraphRAG isnโt just smarter retrieval โ itโs structured reasoning in action. Letโs decode its architecture ๐
๐งฉ 1๏ธโฃ Data Ingestion Layer
Collects data from PDFs, APIs, documents, DBs
Extracts entities & relationships (people, places, causes, effects)
๐ง Uses NLP tools like spaCy, OpenAI NER, or LLMs for extraction.
๐ธ๏ธ 2๏ธโฃ Knowledge Graph Construction
Builds a Graph Database (Neo4j, ArangoDB, TigerGraph)
Nodes = Entities ๐งฉ
Edges = Relationships ๐
Embeddings are also stored for semantic reasoning.
๐ก Result: A connected web of structured knowledge.
๐ 3๏ธโฃ Retrieval Layer
Hybrid approach:
Vector search โ semantic relevance
Graph traversal โ relational depth
Finds not only whatโs related but also how.
๐ Example:
Instead of โFind Infosys founders,โ GraphRAG can answer โ
โShow people connected to Infosys through co-founding or leadership roles.โ
๐ง 4๏ธโฃ Reasoning & Generation Layer
LLM integrates retrieved context + relationships
Generates explainable, traceable answers with citations
Example:
โDrug A increases risk because it interacts with Protein B โ verified in Study C.โ
โ๏ธ 5๏ธโฃ Feedback & Continuous Learning
Human feedback + graph enrichment loops
Every new query improves graph qualities
โจ Takeaway:
GraphRAG = RAG + Reasoning.
It doesnโt just fetch โ it understands the fabric of data. ๐ธ๏ธ๐ก
#GraphRAG #RAG #GenAI #AI #LLM #MachineLearning #KnowledgeGraphs #ArtificialIntelligence #DataScience #Tech #FutureOfAI #Innovation #AICommunity #AITools
๐ฌ Lesson 25 #GenAI #LearningSeries #AI
๐ GraphRAG โ Beyond Retrieval
While RAG retrieves snippets of knowledge ๐, GraphRAG retrieves relationships between knowledge ๐ธ.
๐ What is GraphRAG?
GraphRAG = Graph Retrieval-Augmented Generation.
Instead of just storing data in a vector DB, it builds a Knowledge Graph:
๐ Nodes โ Entities (people, places, concepts)
๐ Edges โ Relationships (who works with whom, cause-effect, dependencies)
โ๏ธ How It Works
1๏ธโฃ Data Ingestion โ Extract entities + relations
2๏ธโฃ Graph Construction โ Build a knowledge graph
3๏ธโฃ Query Expansion โ Traverse the graph for connections
4๏ธโฃ Augmented Generation โ LLM uses structured context to answer
๐ญ Use Cases
Drug Discovery ๐ โ Link molecules, proteins, side effects
Fraud Detection ๐ต๏ธโโ๏ธ โ Trace hidden connections between accounts
Legal Research โ๏ธ โ Map case laws + precedents
Enterprise Search ๐ข โ Connect siloed knowledge across departments
โ๏ธ Why GraphRAG?
RAG โ What is relevant?
GraphRAG โ How are they connected?
โจ Takeaway:
GraphRAG makes LLMs not just smarter retrievers, but reasoners โ uncovering hidden links that plain RAG often misses. ๐
#GraphRAG #RAG #GenAI #AI #LLM #KnowledgeGraphs #ArtificialIntelligence #MachineLearning #DataScience #FutureOfAI #Innovation
@ajit2548
@ITGameTech
๐ฌ Lesson 25 #GenAI #LearningSeries #AI
๐ GraphRAG โ Beyond Retrieval
While RAG retrieves snippets of knowledge ๐, GraphRAG retrieves relationships between knowledge ๐ธ.
๐ What is GraphRAG?
GraphRAG = Graph Retrieval-Augmented Generation.
Instead of just storing data in a vector DB, it builds a Knowledge Graph:
๐ Nodes โ Entities (people, places, concepts)
๐ Edges โ Relationships (who works with whom, cause-effect, dependencies)
โ๏ธ How It Works
1๏ธโฃ Data Ingestion โ Extract entities + relations
2๏ธโฃ Graph Construction โ Build a knowledge graph
3๏ธโฃ Query Expansion โ Traverse the graph for connections
4๏ธโฃ Augmented Generation โ LLM uses structured context to answer
๐ญ Use Cases
Drug Discovery ๐ โ Link molecules, proteins, side effects
Fraud Detection ๐ต๏ธโโ๏ธ โ Trace hidden connections between accounts
Legal Research โ๏ธ โ Map case laws + precedents
Enterprise Search ๐ข โ Connect siloed knowledge across departments
โ๏ธ Why GraphRAG?
RAG โ What is relevant?
GraphRAG โ How are they connected?
โจ Takeaway:
GraphRAG makes LLMs not just smarter retrievers, but reasoners โ uncovering hidden links that plain RAG often misses. ๐
#GraphRAG #RAG #GenAI #AI #LLM #KnowledgeGraphs #ArtificialIntelligence #MachineLearning #DataScience #FutureOfAI #Innovation
@ajit2548
@ITGameTech
๐ฌ Lesson 24 #GenAI #AI #LearningSeries
๐ RAG in Action: Industry Use Cases
RAG = Retrieval ๐ + Generation ๐ค โ turning raw data into actionable insights.
๐ฅ Healthcare
Retrieve ๐งพ patient history + clinical guidelines
Generate ๐ doctor-assist reports & drug interactions
๐ Cuts errors, saves lives.
โ๏ธ Legal
Search ๐ case laws, precedents, contracts
Draft โ๏ธ summaries with citations
๐ Speeds up research, boosts accuracy.
๐ฐ Finance
Analyze ๐ earnings calls, filings, policies
Create ๐ก investor briefs, compliance reports
๐ From days of work โ minutes.
๐ E-Commerce
Retrieve ๐ product info + reviews
Generate ๐ค personalized customer answers
๐ Smarter search, higher sales.
๐ Education
Summarize ๐ textbooks, research papers
Generate ๐ custom study guides, quizzes
๐ Personalizes learning at scale.
๐ญ Manufacturing
Retrieve ๐ง equipment manuals + error logs
Generate ๐ troubleshooting steps
๐ Cuts downtime, improves efficiency.
๐ Customer Support
Search FAQs & past tickets ๐
Generate instant, contextual replies ๐ฌ
๐ Faster resolution, happier customers.
โจ Laymans view:=The knowledge assistant every industry needs.
From hospitals to courtrooms, trading floors to classrooms โ RAG is quietly powering the future of work. ๐
#RAG #GenAI #AI #LLM #ArtificialIntelligence #MachineLearning #DeepLearning #Tech #FutureOfAI #AICommunity #AITools #DataScience #Business #Innovation #FutureOfWork
๐ฌ Lesson 24 #GenAI #AI #LearningSeries
๐ RAG in Action: Industry Use Cases
RAG = Retrieval ๐ + Generation ๐ค โ turning raw data into actionable insights.
๐ฅ Healthcare
Retrieve ๐งพ patient history + clinical guidelines
Generate ๐ doctor-assist reports & drug interactions
๐ Cuts errors, saves lives.
โ๏ธ Legal
Search ๐ case laws, precedents, contracts
Draft โ๏ธ summaries with citations
๐ Speeds up research, boosts accuracy.
๐ฐ Finance
Analyze ๐ earnings calls, filings, policies
Create ๐ก investor briefs, compliance reports
๐ From days of work โ minutes.
๐ E-Commerce
Retrieve ๐ product info + reviews
Generate ๐ค personalized customer answers
๐ Smarter search, higher sales.
๐ Education
Summarize ๐ textbooks, research papers
Generate ๐ custom study guides, quizzes
๐ Personalizes learning at scale.
๐ญ Manufacturing
Retrieve ๐ง equipment manuals + error logs
Generate ๐ troubleshooting steps
๐ Cuts downtime, improves efficiency.
๐ Customer Support
Search FAQs & past tickets ๐
Generate instant, contextual replies ๐ฌ
๐ Faster resolution, happier customers.
โจ Laymans view:=The knowledge assistant every industry needs.
From hospitals to courtrooms, trading floors to classrooms โ RAG is quietly powering the future of work. ๐
#RAG #GenAI #AI #LLM #ArtificialIntelligence #MachineLearning #DeepLearning #Tech #FutureOfAI #AICommunity #AITools #DataScience #Business #Innovation #FutureOfWork
๐ฌ Lesson 23 #GenAI #AI #LearningSeries
๐ Hybrid RAG = Precision ๐ + Context ๐ง
Keyword โ exact matches (codes, case IDs)
Semantic โ meaning & fuzzy intent
๐ก Together = sharper, smarter, more human answers.
Itโs like a lawyer ๐ who cites the law โ๏ธ + interprets itโฆ
or a detective ๐ต๏ธโโ๏ธ using exact clues ๐ฌ + hidden patterns ๐ญ.
#RAG #AI #GenAI #ArtificialIntelligence #LLM #MachineLearning #DeepLearning #AICommunity #NLP #AITools #AITrends #FutureOfAI #DataScience #Tech
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