Referred Link - https://www.linkedin.com/posts/business-infographics_strategic-storytelling-credit-to-eric-partaker-activity-7411375558619529216-hYFG

What’s the difference between a $10M company and a $100M company?

How they tell their story.

I’ve seen brilliant strategies fall flat.
Not because they were wrong.
Because the story didn’t land.

I’ve seen products with huge potential go nowhere.
Because no one could explain why they mattered.

I’ve seen great teams lose focus.
Because the vision sounded like buzzwords.

The companies that scale don’t just build better products.
They communicate better.
They lead with story.

Want people to:

Trust you
Follow you
Act on what you say?

Use these 6 storytelling frameworks:

1/ Simon Sinek’s Golden Circle

Start with what moves people.
→ WHY: The belief behind it all
→ HOW: Your unique approach
→ WHAT: The tangible result

Jobs didn’t return to Apple to sell computers.
He sold thinking differently. The rest followed.

2/ STARR Method

Turn wins into lessons others can run with.

→ Situation
→ Task
→ Action
→ Result
→ Reflection

Perfect for reviews, case studies, or proving ROI.

3/ SCQA Framework

Make complex ideas sound obvious.

→ Situation
→ Complication
→ Question
→ Answer

We used this at McKinsey.
Trusted by Fortune 500 CEOs.

4/ ABT (And, But, Therefore)

Persuade in 30 seconds.

→ AND: The shared context
→ BUT: The problem
→ THEREFORE: The solution

If it takes longer, you’ve lost them.

5/ 3-Act Structure

Make updates memorable.

→ Setup
→ Confrontation
→ Resolution

Turns your next all-hands into something people want to attend.

6/ Monroe’s Motivated Sequence

Move people from interest to action.

→ Attention
→ Need
→ Satisfaction
→ Visualization
→ Action

Works for pitches, keynotes, and team meetings.

When you use these frameworks:

Emails get answered.
Ideas get funded.
Teams align faster.

Because real leadership isn’t just vision.

It’s making your message impossible to ignore.

Tags:

 #Leadership, #StoryTelling, #SoftSkills 

Referred Link - https://www.linkedin.com/posts/sagarpandeysp_growth-activity-7410310016462778368-Rs4Q 

Most people use ChatGPT for answers.
Smart people use it for thinking.

That’s the difference between shallow research and work that actually stands out.

This visual breaks down 15 ChatGPT prompts that turn AI into a real research partner, not just a text generator.

Because good research isn’t about searching more.
It’s about asking better questions.

What this framework helps you do:

• Brainstorm stronger research topics
• Review literature faster (without missing gaps)
• Turn ideas into solid research questions
• Build timelines, hypotheses, and methods
• Validate sources and credibility
• Find gaps others overlook
• Predict trends before they go mainstream
• Connect ideas across disciplines
• Design experiments and ethics reviews
• Create summaries, quizzes, and research plans

Notice something important?

These aren’t “ChatGPT hacks”.

They’re thinking prompts.

The quality of your output will never exceed the quality of your prompts.

If you take ONE thing from this:

ChatGPT doesn’t replace researchers.
It amplifies the ones who think clearly.

Use it to structure thinking, not skip it. 

Tags:

#GenAI, #ChatGPT, #Productivity, 

 Referred Link - https://www.linkedin.com/posts/brijpandeyji_rag-is-no-longer-a-technique-its-a-whole-activity-7400390024598302721-YHZr

RAG is no longer a “technique.”

It’s a whole ecosystem — and a developer’s stack.

Over the past 18 months, Retrieval-Augmented Generation (RAG) has quietly evolved from a simple “search + LLM” pattern into a complete engineering discipline.

Today, building AI systems isn’t just about choosing a model.

It’s about architecting the entire pipeline — from extraction to embeddings, from vector databases to evaluation, from closed models to open-source innovators. 

Tags:

#ArtificialIntelligence, #AI, #RAG, #GenAI, 

Referred Link - https://www.linkedin.com/feed/update/urn:li:activity:7407459099518455808/ 

What is EBITDA, and what is your take on this metric?

→ EBITDA stands for:
• Earnings
• Before
• Interest
• Taxes
• Depreciation
• Amortization

It's a financial metric that shows how much money a company makes before taking into account non-operational expenses like interest and taxes and non-cash expenses like depreciation and amortization

→ Why is EBITDA important for Businesses?

EBITDA is important because it gives businesses an idea of how much money they're generating from their operations.

This is useful for investors and lenders who want to know how profitable a company is.

It’s like a scorecard to know how much money a company is making.


→ How is EBITDA calculated?

To calculate EBITDA, you need to start with a company's revenue and subtract its cost of goods sold.

Then, you subtract its operating expenses (like salaries and rent).

Another way to calculate it:

Net Income
+ Interest Expense
+ Taxes
+ Depreciation
+ Amortization

→ EBITDA vs. Net Income

EBITDA:
In EBITDA, you don’t take into consideration these expenses: Depreciation, Taxes, Interest.

Net Income:
But the net income is what remains as actual profit after depreciation, interest, taxes are taken in account. 

Tags:

#KidsSpecial, #TeachFinancetoKids, #Parenting  

Referred Link - https://www.linkedin.com/posts/rathanuday_agentic-ai-is-not-just-a-tech-upgrade-it-share-7395519699100303360-sks4

Agentic AI is not just a tech upgrade, it is transforming every major industry from the inside out.

If you understand where agents create impact, you can build solutions that companies will actually pay for.

Here are the industry-wise applications of Agentic AI you should know 👇

1. Real Estate
Automate lease creation, property search, document handling, and pricing predictions.

2. Technology & Software
Use agents for DevOps automation, QA/testing, documentation, and code review.

3. Healthcare
Enable triage agents, clinical decision support, medical documentation, and claim automation.

4. Finance & Banking
Automate KYC/AML, fraud detection, portfolio insights, and dynamic pricing.

5. Retail & E-Commerce
Power inventory automation, personalized shopping, and real-time pricing engines.

6. Manufacturing
Improve supply chain optimization, predictive maintenance, and quality inspection.

7. Education
Use tutoring agents, content creation, and automated student assessments.

8. Logistics & Transportation
Enhance fleet management, route optimization, shipment tracking, and customs workflows.

9. Customer Support
Handle ticket triage, multi-step resolutions, and automated conversation summaries.

10. HR & Recruitment
Automate resume screening, candidate ranking, onboarding flows, and policy checks.

11. Legal & Compliance
Deploy agents for contract analysis, risk alerts, and compliance monitoring.

12. Marketing & Advertising
Use agents for campaign creation, A/B testing, audience targeting, and scheduling.

Agentic AI is becoming the backbone of operations across every sector, and early adopters will dominate the next decade.

Tags:

 #AI #UseCases #AgenticAI

 Referred URL - https://community.spiceworks.com/t/m365-contacting-the-server-for-information-when-clicking-email/791658

Problem

Getting a ‘Contacting the server for information’ message when we clicks on emails.

Solution 

This can have 2 causes:

1. Outlook is having trouble downloading external images from the emails. You can solve this in the Trust Center:

Go to File Menu
Choose “Options”
Click on last menu “Trust Center” from left-hand side
Click on “Trust Center Settings” button
Check Off “Don’t download pictures automatically in HTML e-mail message or RSS items”

 2) Outlook is contacting ssl.gstatic.com, this sometimes happens when Gmail users send mail to Exchange mailboxes. Make sure this url is not blocked in your firewall. 

 Referred Link - https://www.linkedin.com/posts/howdangrashid_lowcode-powerplatform-copilot-share-7390283394837815296-3c-_

Low Code IS NOT DEAD. It's simply evolving.
Now let me explain how, so you don't let fear keep you behind in the Power Platform space.

After the Power Platform Community Conference in Las Vegas, I've had a lot of messages flood my inbox like:

“Is it still worth learning the Power Platform?”

“Is AI just going to build everything for us?”

"What skills should I learn now to prepare?"

And I get it.

With Microsoft pushing toward autonomous app and automation development, it does look like we’re heading for a “no-code” future.

But right now, and I believe for at least the next 5 years, Copilots will still need us.

They would need our direction and human touch that understands the technical processes behind what Copilots are 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 building in our Power Platform ecosystem.

We’re nowhere near the point where tools like the Plan Designer can create 𝙚𝙭𝙖𝙘𝙩𝙡𝙮 what we want.

They can speed things up (greatly), but they don't replace the technical expertise required to build great solutions with strong foundations.

So, Low Code isn’t dying, it’s just getting better (as it has been every year).

Just like copywriting didn’t die because of ChatGPT - it adapted.

AI can write a post, sure.

But it takes a copywriter who understands copywriting principles to differentiate good copy from bad copy and make it land.

If you missed it, I created a Cheat Sheet a few weeks back containing the skills which are future-proof in the Power Platform space, in my opinion.

There are certainly some skills worth learning more than others, and they are included in the sheet below.

I think these skills will be valuable in the next 5 years to come, at least.


#LowCode #PowerPlatform #Copilot #Automation #FutureOfWork #HumanInTheLoop #AIFuture #DigitalTransformation #Innovation #TechCommunity 

 Referred Link - https://www.linkedin.com/posts/the-gen-academy_genacademy-genai-rag-activity-7374314296928899072-m3Z5

𝗧𝗵𝗶𝗻𝗸 𝗼𝗳 𝗥𝗔𝗚 𝗮𝘀 𝗴𝗶𝘃𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗽𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝘁𝗼 “𝗼𝗽𝗲𝗻 𝗮 𝗯𝗼𝗼𝗸” 𝗯𝗲𝗳𝗼𝗿𝗲 𝗶𝘁 𝗮𝗻𝘀𝘄𝗲𝗿𝘀.

If you’ve bumped into Retrieval-Augmented Generation (RAG) and wondered what it really is (and when you actually need it), this mini-primer is for you.

𝗪𝗵𝗮𝘁 𝗥𝗔𝗚 𝗶𝘀 — 𝗶𝗻 𝗼𝗻𝗲 𝗯𝗿𝗲𝗮𝘁𝗵

RAG pairs a language model with an external knowledge source so answers are grounded in real, up-to-date information instead of just whatever the model remembers from training. That means fewer made-up facts and more verifiable responses.

𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗮𝗰𝗵 𝗳𝗼𝗿 𝗥𝗔𝗚
✅You want a domain-specific assistant (HR policy bot, clinical FAQ, internal IT helper).
✅You need current info beyond a model’s training cutoff.
✅You care about citations and traceability.

𝗧𝗵𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 (𝘀𝗶𝗺𝗽𝗹𝗲 𝘃𝗲𝗿𝘀𝗶𝗼𝗻)
✅𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 – Gather your sources (PDFs, sites, databases). Split long docs into smaller, meaningful “chunks,” turn each chunk into an embedding (a numeric vector), and store them in a vector database for fast similarity search.

✅𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 – Convert the user’s question into an embedding and fetch the closest chunks from the vector store.

✅𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 – Feed the question + retrieved chunks to the LLM to produce a grounded answer (and optionally add citations).

Why chunk? Models don’t magically use long context well; narrowing to the most relevant bits improves precision and keeps prompts lean.

𝗛𝗲𝗹𝗽𝗳𝘂𝗹 𝗮𝗱𝗱-𝗼𝗻𝘀 (𝘂𝘀𝗲 𝗮𝘀 𝗻𝗲𝗲𝗱𝗲𝗱)

✅𝗤𝘂𝗲𝗿𝘆 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 (𝗛𝘆𝗗𝗘, 𝗺𝘂𝗹𝘁𝗶-𝗾𝘂𝗲𝗿𝘆): Rewrite or expand the question so retrieval finds better matches. HyDE, for instance, has the model draft a hypothetical answer, embed it, and search with that to boost recall.

✅𝗥𝗼𝘂𝘁𝗶𝗻𝗴 & 𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻: If you have multiple stores (policies, product docs, web search), route the query to the best source and add filters (e.g., “last 90 days”).

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗹𝗼𝗰𝗸𝘀 (𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗵𝗲 𝗵𝗲𝗮𝗱𝗮𝗰𝗵𝗲)
✅ 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 (𝗰𝗵𝗮𝗶𝗻𝘀): Wire steps like “translate → retrieve → generate → parse” into a clear sequence you can swap and test.

✅𝗟𝗮𝗻𝗴𝗦𝗺𝗶𝘁𝗵 (𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆): Trace every run, see timings and inputs/outputs, and debug failures—super handy once you go beyond demos.

𝗦𝘂𝗺𝗺𝗮𝗿𝘆 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘁𝗮𝗸𝗲 𝘁𝗼 𝘄𝗼𝗿𝗸
✅Start simple: good chunking + a solid vector DB + a clear prompt template.
✅Measure what matters (accuracy on real tasks, not vibes).
✅Iterate: logs and traces will tell you where the bottleneck is.

Tags: 

#genai #rag 

 Referred Link - https://www.linkedin.com/posts/prem-natarajan-ai_%F0%9D%90%8F%F0%9D%90%AB%F0%9D%90%A8%F0%9D%90%A6%F0%9D%90%A9%F0%9D%90%AD%F0%9D%90%A2%F0%9D%90%A7%F0%9D%90%A0-%F0%9D%90%9A%F0%9D%90%AD%F0%9D%90%AD%F0%9D%90%9A%F0%9D%90%9C%F0%9D%90%A4%F0%9D%90%AC-%F0%9D%90%9A%F0%9D%90%AB%F0%9D%90%9E-activity-7377318581971165184-hL-R/

𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐚𝐭𝐭𝐚𝐜𝐤𝐬 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐭𝐲𝐩𝐞 𝐨𝐟 𝐀𝐈 𝐫𝐢𝐬𝐤𝐬.
While LLMs are powerful, they are also vulnerable to clever manipulations that bypass safeguards, expose sensitive data, or distort outputs.

Understanding these attacks is critical - not just for researchers, but also for businesses deploying AI. Here’s a breakdown of the major types of prompting attacks and how they operate:

🔑 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐀𝐭𝐭𝐚𝐜𝐤𝐬

1. Jailbreaks (Safety Bypass)
Trick the model into ignoring built-in rules or safety policies to return disallowed content.

2. Prompt Injection
Hide malicious commands inside external content so the model unknowingly executes them.

3. Instruction Overriding / Role Abuse
Convince the model to adopt roles or personas that override its safety checks.

4. Chained / Recursive Prompting
Break big restrictions into small prompts executed step-by-step until rules are bypassed.

5. Resource / Command Injection
Exploit API/tool access by forcing repeated costly, unauthorized, or harmful operations.

6. Prompt Leakage / Chain-of-Trust Attacks
Trick models into exposing hidden system prompts, policies, or internal instructions.

7. Adversarial Examples (Input Perturbation)
Slight text tweaks (like punctuation changes) that confuse parsing and produce wrong outputs.

8. Trojan / Backdoor Triggers
Hidden phrases or patterns that trigger abnormal model behavior when encountered.

9. Social-Engineering Prompts
Persuasive prompts that manipulate models into generating deceptive or fraudulent outputs.

10. Data Exfiltration via LLMs
Coax models into leaking private or sensitive data from prior context or training.

11. Model Inversion / Membership Inference
Probe models to infer whether specific records were part of their training set.

12. Covert Channels / Steganographic Prompts
Hide malicious instructions in harmless-looking text or encoded patterns.

Prompting attacks show that AI security is not just about model training - it is about prompt design, monitoring, and safeguards.
The more structured and layered your defences, the harder it becomes for attackers to exploit these vulnerabilities.

Tags:  

#ArtificialIntelligence, #AI, #ChatGPT, #LLM 

Referred Link -  

https://www.linkedin.com/posts/aditya-hicounselor_ai-sql-nosql-activity-7378057053979844608-z-AX

𝗔𝘀 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗲𝘃𝗼𝗹𝘃𝗲 𝘁𝗼𝘄𝗮𝗿𝗱 𝗔𝗜, 𝘀𝗰𝗮𝗹𝗲, 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹.

It’s not just SQL vs NoSQL anymore. It’s about choosing the right database for the data, the workload, and the business outcome.

𝗟𝗲𝘁’𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻 👇

1. 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 (SQL) Databases
Structured, reliable, and transaction-safe.
🔹 Banking | ERP | Order Management
Examples: MySQL | PostgreSQL | SQL Server

2. 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿 Databases
Built for analytics & fast aggregations.
🔹 Data Warehousing | BI | OLAP
Examples: Redshift | ClickHouse | HBase

3. 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 Databases
Optimized for metrics, events, and IoT data.
🔹 Monitoring | IoT | Real-time Analytics
Examples: InfluxDB | TimescaleDB | QuestDB

4. 𝗜𝗻-𝗠𝗲𝗺𝗼𝗿𝘆 Databases
Ultra-low latency & millisecond responses.
🔹 Fraud Detection | Real-time Apps | Caching
Examples: Redis | SAP HANA | Ignite

5. 𝗚𝗿𝗮𝗽𝗵 Databases
Focus on relationships & connections.
🔹 Social Networks | Recommendations | Knowledge Graphs
Examples: Neo4j | JanusGraph | Cosmos DB

6. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 Databases
Flexible schemas for semi-structured data.
🔹 CMS | User Profiles | Mobile Apps
Examples: MongoDB | Couchbase | Firestore

7. 𝗩𝗲𝗰𝘁𝗼𝗿 Databases
Powering AI & semantic search.
🔹 Generative AI | RAG | Similarity Search
Examples: Pinecone | Milvus | Weaviate

8. 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 Databases
Store data as objects aligned with code.
🔹 CAD/CAM | Complex Applications
Examples: db4o | ZODB

9. 𝗕𝗹𝗼𝗰𝗸𝗰𝗵𝗮𝗶𝗻 Databases
Immutable, secure, and decentralized.
🔹 Supply Chain | Provenance Tracking | Identity Verification
Examples: BigchainDB | CovenantSQL

Tags:
#SQL #NoSQL 

Referred Link - https://www.linkedin.com/posts/goli-satish_aws-azure-cloudcomputing-activity-7362664475293863937-okEy

Here’s a complete roadmap that takes you from cloud basics to advanced AWS services, step by step.

AWS offers over 200 services, and without a plan, it’s easy to feel lost. This roadmap helps you navigate the AWS ecosystem in the right learning sequence - starting with foundational concepts, moving through compute, networking, storage, databases, monitoring, and finishing with advanced deployment techniques.

By following this guide, you’ll not only understand AWS services but also learn how they fit together to create scalable, secure, and high-performing cloud solutions.

Whether you’re aiming for AWS certification or building production-ready applications, this path will keep you focused and efficient.

1. Introduction – Learn cloud basics, AWS infrastructure, and shared responsibility.

2. EC2 – Understand instance types, storage, keypairs, and deployment scripts.

3. VPC – Design secure networks with subnets, gateways, and security groups.

4. IAM – Manage access with users, roles, and policies.

5. S3 – Store and manage data with various storage classes.

6. SES – Send and manage emails securely at scale.

7. Auto Scaling – Automatically adjust resources for performance and cost.

8. Route53 – Manage DNS, routing policies, and health checks.

9. CloudWatch – Monitor, log, and alert on AWS resources.

10. RDS – Deploy and manage relational databases in the cloud.

11. DynamoDB – Use NoSQL databases for high-performance apps.

12. ElastiCache – Implement in-memory caching for faster response times.

13. ECR – Store and manage Docker container images.

14. ECS – Run and manage containerized applications.

15. EKS – Deploy Kubernetes clusters on AWS.

16. Lambda – Build serverless functions without managing servers.

17. CloudFront – Deliver content globally with caching and low latency.

 Tags:

#AWS, #Cloud, #LearningRoadmap

Referred Link - https://www.linkedin.com/posts/satya619_terraform-flow-in-cicd-terraform-devops-activity-7353265234523885568-fVSm 

1. Developer
- Role: The developer creates both the Terraform configuration files and the application code, ensuring that infrastructure and application requirements align seamlessly.

2. Source Control
- Process: After writing the code, the developer commits changes to a local Git repository. This is followed by pushing these commits to a remote repository, allowing for collaborative development and version control.

3. Static Code Analysis
- Purpose: Before initiating the CI/CD pipeline, a static code analysis tool, such as SonarQube, scans the code for potential security vulnerabilities and assesses overall code quality. This step helps catch issues early in the development process.

4. CI/CD Tool Trigger
- Action: The push to the remote repository automatically triggers the CI/CD pipeline configured in Jenkins, initiating the automated workflow.

5. CI/CD Tools
- Options: Various CI/CD tools are available, including CircleCI, GitHub Actions, and ArgoCD, providing flexibility based on project needs and team preferences.

6. Terraform Initialization
- Command: Jenkins executes the `terraform init` command to set up the Terraform working directory. This step involves downloading the necessary provider plugins to ensure proper configuration.

7. Infrastructure Planning
- Execution: The `terraform plan` command is run by Jenkins, generating an execution plan that outlines the actions Terraform will take to achieve the desired state specified in the configuration files.

8. Infrastructure Application
- Implementation: Jenkins then runs `terraform apply`, applying the planned changes to the infrastructure. This step implements actual modifications to the cloud resources as defined in the Terraform configuration.

9. Infrastructure Deployment
- Outcome: The infrastructure is deployed to the designated cloud provider, such as AWS, Azure, or GCP, ensuring that resources are correctly provisioned.

10. Infrastructure Ready for Use
- Result: The deployed resources, including virtual machines, networks, and storage, are now provisioned and available for immediate use, enabling further development and deployment of applications. 

Tags: 

#DevOps, #CICD, #Terraform

Referred Link - https://www.linkedin.com/posts/megha-kadur_heres-a-simplified-explanation-of-how-docker-activity-7352923738088099840-4qD4

Starting with Docker:
Applications are packaged into Docker Images, which include all the dependencies and runtime required to operate. These images are launched as Docker Containers, the running instances of the image.

Moving to Kubernetes:
The Docker Container is deployed into Kubernetes as part of a Pod, which is the smallest deployable unit in Kubernetes. A Pod can host one or more containers and serve as their operational environment.

Resource Utilization in Kubernetes:
ConfigMap: Provides configuration settings for Pods.
Secret: Stores sensitive data like passwords.
Persistent Volume: Manages storage requirements for Pods.

External Access:
A Service in Kubernetes provides network access to Pods. This Service is managed by a Deployment, which maintains the desired number of Pod replicas using a ReplicaSet for scalability and fault tolerance.

External Interaction:
End-users or other systems interact with the application through a Load Balancer, which routes requests to the appropriate Service within the Kubernetes cluster.

In Summary:
Docker containerizes the application, while Kubernetes orchestrates and scales it, ensuring reliability and efficient resource utilization. This system allows external users to seamlessly access the application while Kubernetes handles the underlying complexity.

Tags:

#Docker, #KuberNetes, @DevOps 

Source - https://www.linkedin.com/posts/saibysani18_you-cant-master-modern-analytics-without-activity-7354133324539269120-Lj1S

1. 𝐀𝐥𝐞𝐱 𝐓𝐡𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 – https://lnkd.in/eeEMPeG6
2. 𝐂𝐨𝐝𝐞𝐛𝐚𝐬𝐢𝐜𝐬 – https://lnkd.in/df4Eyb53
3. 𝐋𝐮𝐤𝐞 𝐁𝐚𝐫𝐨𝐮𝐬𝐬𝐞 – https://lnkd.in/dmbzK-zn
4. 𝐊𝐫𝐢𝐬𝐡 𝐍𝐚𝐢𝐤 – https://lnkd.in/eaqVxr57
5. 𝐟𝐫𝐞𝐞𝐂𝐨𝐝𝐞𝐂𝐚𝐦𝐩 – https://lnkd.in/d5Af6_39 

Tags:

 #DataAnalytics, #DataEngineering, #DataScience, #DataVisualization,

 Source - https://www.linkedin.com/posts/office-productivity-hacks_if-youre-only-using-kpis-youre-only-measuring-activity-7337418119889465344-YTfe

#PeopleManagement, #ProjectManagement, #Leadership, 

Source - https://www.linkedin.com/posts/wedreambigg_projectplaybook-projecttemplates-pmo-activity-7350698121594658816-HcvY

This ALL-IN-ONE Project Management Documentation Template Map is your ultimate Project Playbook in a graphic.

It’s how modern PMOs move from chaos to control, from hope to repeatable execution.

🔥 What's Inside This Playbook Wheel?
🟡 1. INITIATION — Business Case, Charter, Scope Docs
🟠 2. PLANNING — Gantt, Tracking Sheets, SWOT, Action Plans
🟢 3. EXECUTION — Test Cases, Requirements, SAP Templates
🟢 4. TRACKING — RACI, RCA, RAID Logs, Gap Analysis
🟢 5. CHANGE MGMT — ITIL, Change Logs, Impact Assessments
🟢 6. PROCUREMENT — Catalogues, Recovery, Performance Docs
🔵 7. RISK MGMT — Issue Logs, Registers, Cause/Effect
🔵 8. PM ESSENTIALS — Scope Trackers, Portfolio Dashboards
🔵 9. STAKEHOLDERS — Stakeholder Matrix, Communication Plans
🔵 10. PMO OFFICE — KPI Dashboards, Business Case, Action Plans
🔴 11. QUALITY MGMT — Quality Matrices, Control Charts
🔴 12. TIMELINE DOCS — Construction & Production Schedules

⚡ Why This Visual = Your Secret Weapon:
✅ No more duplicated effort
✅ Train new PMs in one glance
✅ Scale across teams and departments
✅ Instantly spot what’s missing from any project

💥 Save this.
🔁 Share it with every PM, Scrum Master, Product Owner, or team lead you know.

Tags:

#ProjectPlaybook #ProjectTemplates #PMO #Leadership

 

Source - https://www.linkedin.com/posts/business-infographics_4-meetings-every-leader-needs-credit-to-activity-7350905058659618819-FiGL

Tags:

#Leadership, #Productivity, #EffectiveCommunication, #ProjectManagement, 

 Referred Link - https://www.linkedin.com/posts/goyalshalini_ai-agents-vs-agentic-ai-whats-the-real-activity-7340626798772166656-Ceeg

Tags:

 #ArtificialIntelligence, #AgenticAI, #NewTechnology,

Referred Link - https://www.linkedin.com/feed/update/urn:li:activity:7340341186223607810

Artificial Intelligence is not a single technology—it's an entire ecosystem of interrelated domains working together to drive innovation across industries.

◾ The worldwide artificial intelligence industry size is projected to grow from USD 371.71 billion in 2025 to USD 2,407.02 billion by 2032, at a CAGR of 30.6%

𝗛𝗲𝗿𝗲'𝘀 𝗮 𝗹𝗼𝗼𝗸 𝗮𝘁 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗔𝗜 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲:

🔷 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 : Goal execution, task planning, agent protocols, and human-in-the-loop systems

🔶 𝗡𝗟𝗣 (𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴): From chatbots to text summarization and language modeling

🔵 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 : Supervised/unsupervised learning, transformers, CNNs, and optimization techniques

🟣 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 : Object detection, face recognition, scene understanding, and pose estimation

🟢𝗜𝗺𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 : Enhancing images through sharpening, filtering, edge detection, and more

𝗢𝗣𝗣𝗢𝗥𝗧𝗨𝗡𝗜𝗧𝗜𝗘𝗦

🟣 Advancements in AI-native infrastructure enhancing scalability and performance

🟣 Expansion of edge AI capabilities for real-time data processing and decision-making

🟣 Advancements in generative AI to open new avenues for AI-powered content creation

Tags:
#ArtificialIntelligence #MachineLearning #DeepLearning #NLP
#NaturalLanguageProcessing #ComputerVision #ImageProcessing
#AIInnovation #DataScience #FutureOfAI
#TechTrends #IntelligentAutomation #AITechnology #Li

Referred Link - https://www.linkedin.com/posts/vsadhwani_want-to-switch-from-software-engineer-to-activity-7339675261728083968-qaIF 

Study this roadmap:

You already know the SDLC processes – now dive into how to build, run and deploy cloud-native apps

3-Month Foundation :
• Learn core Linux (permissions, systemd, logging)
• Understand networking (VPC, DNS, NAT, firewalls)
• Pick a cloud provider (AWS / GCP / Azure / OCI)
↳ Focus on IAM, Compute, Storage, networking basics, billing fundamentals etc
• Learn Python for automation (already familiar? think how you can use with Boto3, SDKs, scripting)
• Learn Infrastructure as Code (Terraform / Pulumi/ AWS Cloudformation)
• Explore cloud-native patterns (microservices, managed services)
↳ Target: Junior Cloud Engineer roles (ave comp ~$100k)

You already know Git — now use it to:
• Version control your Terraform infrastructure
• Track changes to IaC and cloud automation scripts
• Set up GitOps-style deployment patterns

3-Month Advanced Track :
• Pick a specialty (Networking, Data, Databases, DevOps, Security, AI/ML etc)
If you picked DevOps:
• Work with containers (build/run with Docker)
• Deploy apps using Kubernetes (pods, services, configs)
• Set up CI/CD pipelines (using cloud-specific services)
• Implement cloud security concepts (IAM, roles, secrets management)
• Configure monitoring & logging (Cloud-Specific or Prometheus/Grafana)
• Automate end-to-end workflows(from build to deploy) using Python or Shell (SDKs, scripts, cron) - [this is very advanced]
↳ Target: Mid-level Cloud Engineer roles (ave comp ~$125k)

Leverage your SWE skills:
• Containerize existing apps
• Build pipelines for test → deploy
• Refactor into cloud-native services

Quick Wins:
- Complete Qwiklabs or provider-specific cloud labs
- Sign up for free tiers and experiment (watch your credit limits)
- Build and deploy 3 cloud-native projects
- Earn a practitioner or specialty certification (based on your tech exposure)

Free Resources to start, here's your one stop shop: https://lnkd.in/dF9xVE9X

Do this next: You've already built apps - now think which cloud-based services you'd need to run it on cloud (deploy it with Terraform + CI/CD)

Hands-on experience >> watching videos or reading blogs.

Remember, you're not starting from scratch. As a Software Engineer, you already think in systems.
Now think in cloud systems 

Tags:

#FreshersLearning, #LearningRoadmap, #CloudEngineer,  

 Referred Link - https://www.linkedin.com/posts/shraddha-sahu-5b99b8142_top-agile-tools-for-efficient-project-management-activity-7338146179228061698-u-tA

Here’s a quick guide to some of the best Agile tools out there:

IceScrum
▸ Agile management with Scrum and Kanban boards

Taiga
▸ Open-source support for Scrum and Kanban

Scrumpy
▸ Collaborative Planning Poker for sprint estimation

Hansoft
▸ Scalable Agile and Lean project management

YouTrack
▸ Issue tracking with Agile workflow support

Kanbanchi
▸ Kanban for Google Workspace users

ScrumDesk
▸ Scrum and Kanban with backlog management

Yodiz
▸ Agile issue tracking and project management

ScrumDo
▸ Customizable Agile management with analytics

Leankit
▸ Visual Lean workflow management tool

Pipefy
▸ Workflow automation and process optimization

VivifyScrum
▸ Simple Scrum and Kanban platform

KanbanTool
▸ Visual Kanban projects with analytics

Targetprocess
▸ Enterprise Agile planning and visualization

VersionOne Lifecycle
▸ Scalable enterprise Agile project management

Asana
▸ Task and project management software

Jira
▸ Widely used Agile software development tool

Businessmap
▸ Workflow analytics tool for process improvement

Agilefant
▸ Lightweight Agile and traditional project tool

Zoho Projects
▸ Flexible project management with Agile support

Wrike
▸ Customizable Agile project collaboration

ClickUp
▸ All-in-one productivity and management platform

Pivotal Tracker
▸ Story-based incremental software planning

Monday. com
▸ Customizable work management platform

Axosoft
▸ Scrum and bug-tracking for developers 

Tags:

#ProjectManagement, #ProductManagement, #Agile, #Scrum, #Tools, 

Referred Link - https://www.linkedin.com/posts/aishwarya-pani-63a476167_dataengineering-azuredataengineer-apachespark-activity-7337326842099470337-8reh

𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗙𝗿𝗲𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀:

1. 𝗘𝗻𝗱-𝗧𝗼-𝗘𝗻𝗱 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗔𝘇𝘂𝗿𝗲
https://lnkd.in/gykp5HNy

2. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 | 𝗔𝗽𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
https://lnkd.in/gBcDNUDK

3. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 | 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 & 𝗦𝗮𝗹𝗲𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
https://lnkd.in/gjZ8vuxA

4. 𝗢𝗹𝘆𝗺𝗽𝗶𝗰𝘀 𝗗𝗮𝘁𝗮 | 𝗔𝘇𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
https://lnkd.in/gb_gwT3R

5. 𝗜𝗥𝗖𝗧𝗖 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
https://lnkd.in/g34szQAV

6. 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗙𝘂𝗹𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
https://lnkd.in/gsPWybWw

7. 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 | 𝗥𝗲𝗮𝗹 𝗖𝗿𝗶𝗰𝗸𝗲𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
https://lnkd.in/gE-qnnrD

8. 𝗔𝘇𝘂𝗿𝗲 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
https://lnkd.in/gMM2MDcz 

Tags: 

#DataEngineering hashtag#AzureDataEngineer hashtag#ApacheSpark hashtag# 

 Referred Link - https://www.linkedin.com/posts/shraddha-sahu-5b99b8142_they-say-the-secret-to-scrum-success-lies-activity-7335870422132961280-NkD9

• Scrum Board (Tools: Jira, Trello, Azure DevOps): This is your visual roadmap. It tracks tasks, progress, and bottlenecks. It needs to be clear, concise, and updated frequently.

• Product Backlog (Tools: Jira, Trello, Excel):  This is the "what." It lists all features and improvements needed. Prioritize wisely; this list fuels your sprints. It’s the master list of everything you need to do.

• Sprint Backlog (Tools: Jira, Trello, Scrumwise, ClickUp): This is the "how." It breaks down the Product Backlog into manageable chunks for a sprint.

• Burndown Chart (Tools: Jira, Scrumwise, Excel, Google Sheets): This chart visualizes progress against time. It shows whether you are on track to meet your sprint goal. A must-have for tracking velocity and identifying potential issues.

• Velocity Chart (Tools: Jira, VersionOne, ScrumDo):  Track your team’s pace. It helps predict future sprint capacity. Consistent velocity is a sign of a well-oiled machine.

• Impediment Log (Tools: Jira, Confluence, physical whiteboard): Don't let roadblocks derail you.  This log tracks and solves problems quickly. Transparency is key here.

• Definition of Done (Tools: Jira, Confluence, physical whiteboard):  Know when a task is truly complete.  Everyone needs to be on the same page. This avoids ambiguity and rework.

• Retrospective Tools (Tools: FunRetro, Miro, MURAL): Continuous improvement is key. These tools facilitate efficient and engaging retrospective meetings. Reflection builds a stronger team.

• User Story Mapping (Tools: Miro, Lucidchart, StoriesOnBoard): Visualize the user journey and prioritize features. This keeps the user at the center of your development process.  Understanding the 'why' behind the 'what' is key.

• Timeboxing Tools (Tools: TimeTimer, Focus Booster, Google Timer): Focus is power. These tools help maintain concentration and prevent task creep.

Tags:

  #ScrumTools, #EssentialTools

 Referred Link - https://www.linkedin.com/posts/sachin-sharma-product-career-coach_careercoach-sachinsharma-productmanagers-activity-7334559311441862656-mvau

But how to start effectively as Product Manager

🟰 Create a Product Vision

🟰 Dive into Market Research

🟰 Define your User Personas

🟰 List out Requirements

🟰 Map out a simple Roadmap

Tags:

#ProductManagementKickOffDocs, #SoftwareProductManagement, #ProjectManagement,