Helping Techies Build Personal Brands for Career & Business Growth | AI-Assisted LinkedIn Strategist to CTOs, Directors, and Tech Founders & Coaches | 100K+ Insta | Mentored 1K+ Techies | Engineering Manager @ Jio

With over 9 years of industry experience, I help tech professionals achieve professional growth by crafting influential personal brands and leveraging platforms like LinkedIn.

As an Engineering Manager and Career Coach, I’ve mentored over 1000 professionals, helping them enhance their profiles, expand their networks, and implement proven growth strategies. My clients have successfully transitioned to better roles, expanded their influence, and built standout online presences.

What I Offer:
👉5-Step Career Branding Strategy: Tailored to job seekers, network builders, and those aiming to strengthen their LinkedIn presence.
👉Insights from 8+ Years in Tech: Experience across startups and giants like Byju’s and Jio, decoding how organizations operate in the digital era.

Recognized by: Byju’s, The Voice of Women, and Indian Express for my impact in career branding and coaching.

My Approach:
Speak like a leader. Work as an entrepreneur. Sell like Apple.

If you’re navigating a job transition, looking to boost your career, or want to become a recognized brand in tech, I’m here to help. Let’s connect and collaborate.

💡 Reach Me:
👉DM me on LinkedIn or Topmate.
👉Find me on other social media with @careerwithhina
👉For collaborations, email me at careerwithhina@gmail.com.

Let’s build a thriving community together. I’m just a message away!

AI vs ML vs DL vs GenAI vs LLM vs AI Agent - What’s the Difference?

Credit: Prem N.

In the evolving AI landscape, it’s essential to distinguish between foundational technologies and their specialised branches. This guide provides a clear breakdown:

𝟏. 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈):
A broad field focused on designing systems that replicate human intelligence — including reasoning, learning, and decision-making.

𝟐. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐌𝐋):
A subset of AI where systems learn from data patterns and improve over time, enabling predictions and automation without explicit programming.

𝟑. 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐃𝐋):
A specialized area within ML that leverages layered neural networks to process complex data, such as images or natural language.

𝟒. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 (𝐆𝐞𝐧𝐀𝐈):
An AI approach that creates original outputs - text, code, visuals - by learning from existing data structures and patterns.

𝟓. 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬):
Deep learning models trained on large text corporate, enabling machines to understand and generate human-like language at scale.

𝟔. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬:
Autonomous systems powered by LLMs or other models, capable of perceiving, planning, and executing tasks based on defined goals.

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Hina Arora

AI-Assisted Personal Branding for Tech Leaders | 9yrs+ in Tech | Helping Tech Leaders & Coaches Build Personal Brand for Professional Growth | Partnered with 20+ CTOs, Directors & Founders | Done-for-You Service

𝐓𝐡𝐞 𝐀𝐈 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐆𝐚𝐩 𝐢𝐬 𝐑𝐞𝐚𝐥. 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 𝐚 𝐌𝐚𝐩, 𝐍𝐨𝐭 𝐍𝐨𝐢𝐬𝐞. 
I just filtered the entire YouTube library for you.

Thanks, Sandipan Bhaumik 🌱, for curating this list.

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝟐𝟎 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐈 𝐬𝐢𝐦𝐩𝐥𝐞 & 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝:

1. 3Blue1Brown: Understand complex math behind AI visually and intuitively.
Link: https://lnkd.in/edegfEEv

2. Andrej Karpathy : Deep, practical AI lectures explained clearly.
Link: https://lnkd.in/eay7TU2a

3. Lex Fridman : Conversations with leading AI researchers and innovators.
Link: https://lnkd.in/ebbtpsww

4. StatQuest (Josh Starmer): Makes ML concepts fun with humor and clarity.
Link: https://lnkd.in/eqTeYjMT

5. Jeremy Howard : Practical deep learning with hands-on coding examples.
Link: https://lnkd.in/e_vHAu84

6. Two Minute Papers: Summaries of the latest AI papers in minutes.
Link: https://lnkd.in/eyBhZC9p

7. DeepLearning.AI: Structured AI learning from Andrew Ng.
Link: https://lnkd.in/e62uRF2g

8. Machine Learning Street Talk (MLST): Insightful debates and interviews.
Link: https://lnkd.in/eUwV47cn

9. freeCodeCamp: Free AI and ML tutorials with certification paths.
Link: https://lnkd.in/eUn2JUiM

10. Sentdex : Python-based machine learning and data projects.
Link: https://lnkd.in/e-dCBfas

11. Data School : Simple ML and data analysis concepts for beginners.
Link: https://lnkd.in/egtSHRy8

12. Codebasics: Real-world ML use cases and career-focused projects.
Link: https://lnkd.in/ez2NmfVd

13. Siraj Raval : Story-driven tutorials combining creativity and AI.
Link: https://lnkd.in/ehJf3jzR

14. Google Cloud Tech: Learn how to deploy and manage AI models.
Link: https://lnkd.in/euJTVeyM

15. Serrano Academy: Step-by-step tutorials on ML, DL, and AI tools.
Link: https://lnkd.in/eSzJJJWY

16. Tina Huang : Smart AI learning strategies and productivity tips.
Link: https://lnkd.in/exwv6q7i

17. Matt Wolfe : Quick updates on new AI tools and technologies.
Link: https://lnkd.in/eiVMeZj3

18. AI Explained: Deep dives into AI ethics, models, and progress.
Link: https://lnkd.in/etfCYhMq

19. The AI Advantage: Practical ways AI is transforming business productivity.
Link: https://lnkd.in/egyKfySP

20. Hamel Husain : Advanced insights into LLMs, RAG, and model fine-tuning.
Link: https://lnkd.in/eSgQMg_d

Whether you want to build models, deploy systems, or understand AI theory, this list covers every learning style.

𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐰𝐚𝐭𝐜𝐡 𝐫𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲 𝐚𝐧𝐝 𝐰𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐝𝐞𝐬𝐞𝐫𝐯𝐞𝐬 𝐦𝐨𝐫𝐞 𝐚𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧?

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(32) Notifications | LinkedIn

li_at

JSESSIONID

Tech Leader - Data & AI | Community Founder | Speaker | Podcast Host

(Former) Director of AI at Tesla, OpenAI, PhD @ Stanford. I don't use or check my LinkedIn, I only have an account for searching/hiring. Please use my X/email to contact.

Research Scientist, MIT

StatQuest

Deep learning @ Answer.AI, fast.ai and University of Queensland

DeepLearning.AI

Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of LandingAI

Machine Learning Street Talk (MLST)

freeCodeCamp

Data School

Codebasics

Freelance AI/ML Engineer | MLOps, Generative AI, Computer Vision | Helping businesses deploy AI faster & more reliably

Google Cloud

Serrano Academy

Youtuber & Entrepreneur

YouTube Creator (800k+) & Entrepreneur

AI Explained

ML Engineer with 20 years of experience helping companies with AI

Bharath kumar

I wake up at 6 AM.
I don’t touch my phone.
I drink warm water and vegetable juice on an empty stomach.
After freshening up, I do yoga for 50 minutes.
Shower.

Schedule my LinkedIn post.
Spend an hour connecting with people on LinkedIn.
Then leave for office.

By 9 AM, I’m in meetings.
I connect with my teammates,
Plan tasks for them.
Work, work, and work.
Lunch.
More work.

At 5 PM, I take a break — coffee in hand, mind a little lighter.
Wrap up by 6 PM and head home.

Healthy dinner with family at 7:30 PM.
Meditate for 20 minutes before bed.
Write down my thoughts for 10 minutes.

In bed by 10:00 PM.
Smiling.

That sounds like a perfect day.
And it happens… maybe once a qurter.
And those days, I live the version of me I aspire to be.

But...
Most days?

I wake up late.
Skip yoga.
Rush through shower and brushing my teeth.
Have breakfast in the car.

Schedule posts on weekends (and honestly, I love that about me).
Connect with people while traveling.
Work, work, and more work.
Skip lunch.
You guessed it, more work.

Evening cold coffee? Mandatory.
Leave for home and tell myself, “I’ll try harder tomorrow.”

And you know what?

I still go to bed smiling.

Because even on the “off” days, I showed up. 
I tried.

Every day doesn’t have to be perfect.
You don’t have to be your best self every day to be successful.

Progress isn’t linear.
Success is a marathon.

Give yourself grace.
You’re doing better than you think.

AI-Assisted Personal Branding for Tech Leaders | 9yrs+ in Tech | Helping Tech Leaders & Coaches Build Personal Brand for Professional Growth | Partnered with 21+ CTOs, Directors & Founders | Done-for-You Service

𝟏𝟐 𝐌𝐮𝐬𝐭-𝐅𝐨𝐥𝐥𝐨𝐰 𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐢𝐞𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬
If you’re learning AI or building your first production-grade system, GitHub can be your best teacher.

Thanks, Sivasankar, for curating this list...

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐳𝐞𝐝 𝐫𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐠𝐫𝐨𝐰 𝐟𝐫𝐨𝐦 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫 → 𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 → 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝:

𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫 𝐋𝐞𝐯𝐞𝐥 𝐁𝐮𝐢𝐥𝐝 𝐘𝐨𝐮𝐫 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧

1. AI-For-Beginners (Microsoft):
A complete 12-week curriculum covering AI fundamentals from classical AI to ethics.
Link: https://lnkd.in/ehVdWc5U

2. ML-For-Beginners (Microsoft):
End-to-end introduction to ML with projects, visual explanations, and Jupyter notebooks.
Link: https://lnkd.in/eTgeEGNa

3. Generative-AI-For-Beginners (Microsoft):
A modern guide to generative AI and LLM fundamentals your entry into real-world GenAI.
Link: https://lnkd.in/ekJ2jDgB

4. AI-Agents-For-Beginners (Microsoft):
Learn the principles of AI agents and how they work in real-world scenarios.
Link: https://lnkd.in/ewnSQzxc

𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐋𝐞𝐯𝐞𝐥 𝐇𝐚𝐧𝐝𝐬-𝐨𝐧 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 & 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠

5. Learn-AI-Engineering:
Curated roadmap covering AI/ML fundamentals, LLMs, RAG, and deployment practices.
Link: https://lnkd.in/e6KqP2qu

6. AI-Engineering-Hub:
Practical tutorials on RAG, vector databases, and AI agent architectures.
Link: https://lnkd.in/e6ysXemU

7. EdgeAI-For-Beginners (Microsoft):
Learn to deploy AI models on edge devices ideal for DevOps or IoT professionals.
Link: https://lnkd.in/e7SerUbs

8. 500+ AI/ML/DL/CV/NLP Projects (Ashish Patel 🇮🇳):
A massive collection of 500+ open-source projects to build and showcase your portfolio.
Link: https://lnkd.in/eYi9JP3E

𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐋𝐞𝐯𝐞𝐥 𝐃𝐞𝐞𝐩 𝐃𝐢𝐯𝐞 & 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐆𝐫𝐚𝐝𝐞 𝐒𝐤𝐢𝐥𝐥𝐬

9. NN-Zero-To-Hero:
Learn deep learning from scratch code neural networks manually to master the fundamentals.
Link: https://lnkd.in/eaJxZVJz

10. AIE-Book (Chip Huyen):
Practical guide to real-world AI engineering and building scalable AI systems.
Link: https://lnkd.in/eY_tUdeZ

11. Awesome-AI-Repositories:
Curated collection of advanced open-source AI engineering and infrastructure projects.
Link: https://lnkd.in/eBmgyTcj

12. Awesome-AI-Data-GitHub-Repos (Youssef Hosni):
Directory of top AI, ML, NLP, and CV repositories for research and exploration.
Link: https://lnkd.in/eSv7QaDK

Whether you’re just starting or scaling AI systems, these repos will help you learn, build, and deploy smarter.

💬 Which one are you exploring next? Share below, let’s grow together.

Technical Director | GenAI Practitioner | Azure Cloud Architect | Data & Analytics | Solutioning What’s Next

Microsoft

Sr Principal AI Architect at Oracle | Generative AI Expert & Strategist | xIBMer | Author: Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 15+ Yrs AI | IIMA | 100K+ Followers | 6x LinkedInTopVoice

Building something new | AI x storytelling x education

Applied Scientist | PhD & Generative AI Researcher @ Aalto University | Founder @ To Data & Beyond

nancy bou kamel

Picture a digital world where software works together like a team of tiny superheroes, each with its own superpower, to create amazing applications that run smoothly and efficiently. 

Welcome to the world of microservices, where small is mighty!

Microservices are small, independent pieces of software that work together to build an application. They communicate using clear rules, and each piece can be developed, deployed, and scaled on its own.

Some commonly used design patterns that can be beneficial when designing and implementing microservices:

✅ Gateway Pattern
✅ Service Registry Pattern
✅ Circuit Breaker Pattern
✅ SAGA Pattern
✅ CQRS (Command Query Responsibility Segregation) Pattern
✅ Bulkhead Pattern
✅ Sidecar Pattern
✅ API Composition Pattern
✅ Event-Driven Architecture Pattern
✅ Database per Service Pattern
✅ Retry Pattern
✅ Configuration Externalization Pattern
✅ Strangler Fig Pattern
✅ Leader Election Pattern

--------------------------------------------------------------
I help technical individuals create their career brands on LinkedIn.
👉https://lnkd.in/gAK7PqUd

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Microservice Design Patterns

❌ Don't only apply for the job but also ensure to establish a network with the recruiter for the future.

Interview Mail Tips-

You can achieve this by sending thoughtful emails.

✅ 𝗔𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝗷𝗼𝗯 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Application for [Job Title] - [Your Name]

Dear [Hiring Manager's Name],

I hope this message finds you well. I am writing to express my interest in the [Job Title] position at [Company Name] that I recently came across. I believe my skills and experience align well with the requirements of the role.

With a background in [Relevant Skills/Experience], I am excited about the opportunity to contribute to [Company Name]'s [specific project/department/goal], and I am confident in my ability to make a positive impact. I have attached my resume for your consideration.

I would appreciate the chance to discuss how my background and expertise could benefit your team. Please let me know if there is a convenient time for a call or a meeting.

Thank you for considering my application. I look forward to the opportunity to speak with you.

Best regards,
[Your Name]

✅ 𝗙𝗼𝗹𝗹𝗼𝘄-𝗨𝗽 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Follow-Up on My Interview

Hi [Hiring Manager's Name],

I hope you're doing well. I wanted to follow up on the interview we had for the [Job Title] position at [Company Name]. I'm really excited about the opportunity and would love to hear about the next steps in the process.

Looking forward to your response.

Best regards,
[Your Name]

✅ 𝗥𝗲𝗷𝗲𝗰𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Appreciation and Future Consideration

Hi [Hiring Manager's Name],

I hope this message finds you well. I wanted to express my gratitude for considering me for the [Job Title] position. Although I didn't make it to the next round, I'm thankful for the chance to learn about [Company Name]. I look forward to potentially crossing paths again in the future.

Thank you once again.

Best regards,
[Your Name]

✅ 𝗔𝗰𝗰𝗲𝗽𝘁𝗮𝗻𝗰𝗲 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Accepting the [Job Title] Position

Hello [Hiring Manager's Name],

I hope you're doing well. I wanted to formally accept the offer for the [Job Title] position at [Company Name]. I'm really excited about joining the team and contributing to [Company Name]'s success. Please let me know the next steps and any additional information you need from my end.

Thank you and looking forward to starting on [Start Date].

Best regards,
[Your Name]

✅ 𝗦𝗮𝗹𝗮𝗿𝘆 𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Salary Discussion for [Job Title] Position

Hello [Hiring Manager's Name],

I hope this message finds you well. I'm excited about the offer for the [Job Title] role at [Company Name]. I would like to discuss the compensation package to ensure that it aligns with my skills and experience. Could we set up a time to talk about this further?

Thank you and looking forward to your response.

Best regards,
[Your Name]




#emailtemplates

𝐓𝐨𝐨 𝐦𝐚𝐧𝐲 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬. 𝐂𝐚𝐧'𝐭 𝐝𝐞𝐜𝐢𝐝𝐞 𝐰𝐡𝐢𝐜𝐡 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧? 
Here are the 6 categories, understand these and tool selection becomes obvious:

𝟏. 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐩𝐮𝐫𝐩𝐨𝐬𝐞 𝐋𝐋𝐌 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐫𝐬
Models like Claude, OpenAI, Gemini, Llama, Mistral, Grok, DeepSeek, Cohere, and AI21Labs power everything from training to fine tuning to deployment.

𝟐. 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐚𝐧𝐝 𝐦𝐮𝐥𝐭𝐢 𝐚𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬
Tools like AutoGen, CrewAI, Superagent, CamelAI, MGX, Manus, AgentVerse, LangGraph, and AutoGPT help orchestrate agents, memory, planning, and complex workflows.

𝟑. 𝐋𝐋𝐌 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬
Platforms like LangChain, LlamaIndex, Haystack, OpenLLM, Kernel, DSPy, and Guardrails AI let you build end to end LLM applications with chains, memory, and tools.

𝟒. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐨𝐨𝐥𝐬
PromptLayer, Promptable, Langfuse, ChainForge, PromptPerfect, and PromptTools help you design, test, monitor, and optimize prompts at scale.

𝟓. 𝐕𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬
Systems like FAISS, Chroma, Pinecone, Weaviate, Redis, Qdrant, Milvus, and Relevance AI store embeddings for RAG pipelines and semantic search.

𝟔. 𝐍𝐨 𝐜𝐨𝐝𝐞 𝐚𝐧𝐝 𝐥𝐨𝐰 𝐜𝐨𝐝𝐞 𝐀𝐈 𝐛𝐮𝐢𝐥𝐝𝐞𝐫𝐬
FlowiseAI, Langflow, n8n, Replit, Cursor, Lovable, and Chatbase make it possible to build full LLM workflows without heavy coding.

Learning these six categories is the fastest way to understand how modern AI products are actually built.

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Which category should I break down into a deep dive next?

#LLM #AItools #GenAI #AIAgents

AI has asked a founder to layoff 30% of his workforce...

Founder asked AI : “How can we make the company more profitable?”
You can’t imagine what it answered…

A startup was not able to make their company profitable.

So, a founder asked an AI how to make their company profitable….

The model thought for a few seconds.
Then replied…

“Lay off 30% of employees”
“Automate repetitive tasks. Reduce office costs.”

It made perfect business sense.
But human sense? No…

When the team read the answer, the room went silent.

No one said it out loud, but everyone was thinking the same thing…

Is this what the future of decision-making looks like?

And a person who agree with this decision has been laid off…. (Not funny, but karma)

AI did what it was trained to do.
Analyze data.
Find efficiency.
Cut waste.

But it couldn’t see the people behind the payroll.
The single parent paying school fees.
The developer who stays late helping juniors.
The designer who brings fresh energy to every meeting.

It couldn’t understand loyalty, culture, or the quiet trust that holds teams together.

Profitability isn’t wrong to chase.
Every company needs it to survive.
But when it comes at the cost of the very people who built that company… 
that’s when logic stops being leadership.

AI gives answers.
Leaders create direction.

And maybe, the real question we should start asking isn’t…

“How can we be more profitable?”
but
“How can we grow without losing what makes us human?”


What are your thoughts on this ?

𝐘𝐨𝐮𝐫 𝐣𝐨𝐛 𝐭𝐢𝐭𝐥𝐞 𝐦𝐢𝐠𝐡𝐭 𝐧𝐨𝐭 𝐞𝐱𝐢𝐬𝐭 𝐢𝐧 𝟐 𝐲𝐞𝐚𝐫𝐬. 
Here are the 12 AI skills that will:

𝟏. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
Design precise and structured prompts for models like ChatGPT, Claude, and Gemini to boost accuracy and efficiency across every AI task.

𝟐. 𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧
Automate repetitive tasks using tools like Zapier, Make, Bardee, and n8n without writing heavy code.

𝟑. 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬
Build and deploy multi agent systems for research, reasoning, and workflow automation using CrewAI, LangGraph, and AutoGen.

𝟒. 𝐑𝐀𝐆
Improve LLM response quality by connecting models to private or enterprise data using Vectora, LangChain, or LlamaIndex.

𝟓. 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈
Use models like Grok, Gemini, Claude Sonnet, and GPT O3 to work across text, audio, images, and mixed modality tasks.

𝟔. 𝐅𝐢𝐧𝐞 𝐭𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐈 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬
Create specialized AI assistants or fine tuned models using OpenAI GPT Builder and Hugging Face.

𝟕. 𝐕𝐨𝐢𝐜𝐞 𝐀𝐈 𝐚𝐧𝐝 𝐚𝐯𝐚𝐭𝐚𝐫𝐬
Generate realistic avatars and voiceovers using HeyGen, Synthesia, ElevenLabs, or Vapi to enhance content.

𝟖. 𝐀𝐈 𝐭𝐨𝐨𝐥 𝐬𝐭𝐚𝐜𝐤𝐢𝐧𝐠
Integrate tools like Notion, Zapier, and ClickUp to automate entire workflows and boost productivity.

𝟗. 𝐀𝐈 𝐯𝐢𝐝𝐞𝐨 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧
Turn articles into videos using Runway, VEED, or OpusClip with built in editing and voice capabilities.

𝟏𝟎. 𝐒𝐚𝐚𝐒 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭
Build revenue generating AI products using APIs and platforms like Bubble, Cursor, and Softr.

𝟏𝟏. 𝐋𝐋𝐌 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭
Measure and optimize model performance, cost, and hallucinations using Helicone, Trulens, and PromptLayer.

𝟏𝟐. 𝐒𝐭𝐚𝐲𝐢𝐧𝐠 𝐮𝐩𝐝𝐚𝐭𝐞𝐝
Keep yourself sharp through newsletters, podcasts, and updates from platforms like Mindstream and OpenAI News.

These 12 skills will shape the AI professionals and builders of 2026.

♻️ Repost this to help your network get started
➕ Follow Hina for more

Which skill do you want a learning roadmap for next?

#AISkills #GenAI #AIEngineering #FutureSkills

Recruiters only spend 6 to 9 seconds reading each resume. 
Let's make the best one.

I have come up with some of the best resume tips for you. 

🚀 Resume name format
🚀 Resume colour
🚀 One-page myth
🚀 Technical skills
🚀 Priority first
🚀 Work Impact
🚀 Outside contribution
🚀 Innovation & creativity
🚀 Good header

Check the PDF to learn more about it.


LinkedIn
LinkedIn Guide to Creating
---------------------------------------
Follow Hina Arora for more such information.


Hashtags:

#resume #resumewriting #resumetips #hinaarora #cv #cvwriting

Resume

Ever Wondered How Netflix Works So Well? Here's the Secret! 

Netflix brings your favorite shows and movies right to your screen, but how does it do that?

Here’s a look at the cool tech behind it:

1. Microservices Architecture
Netflix uses something called "microservices," which means instead of one big computer doing everything, there are lots of small computers working together.
This helps Netflix run smoothly and fix problems quickly.

2. AWS Cloud
Netflix uses a service called AWS (Amazon Web Services) to store all its data and run everything online. It’s like Netflix's superpower that helps it work from anywhere in the world.

3. Open Connect
Netflix has its own special system, called Open Connect, to send shows and movies quickly to everyone, no matter where they are.

4. Programming Languages
Netflix uses different computer languages like Java, Python, and Node.js to build both the backend (stuff happening behind the scenes) and the front end (stuff you see on the screen).

5. Big Data & AI
Netflix uses tools like Apache Kafka and Apache Spark to understand what you like to watch and recommend new shows and movies just for you!

6. Security
Netflix makes sure your personal information is safe by using strong security systems, kind of like a digital lock for your data.

7. Chaos Engineering
Netflix even uses a fun-sounding thing called Chaos Monkey! It randomly tests Netflix to make sure it can keep working even if something goes wrong.

Netflix’s tech helps it stay fast, smart, and fun. It's like a magic trick behind the scenes that makes watching easy for us!

------------------------------------------------
👉 👉 Follow Hina Arora for more such content.

Don't miss this opportunity. 

A 5-Day AI Agents Intensive - a free course designed to help you build and deploy the next frontier of AI. (November 10 - November 15)

𝐖𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐠𝐞𝐭 𝐭𝐡𝐢𝐬 𝐭𝐢𝐦𝐞 𝐚𝐫𝐨𝐮𝐧𝐝:
➡️ Daily lessons from Google's engineers covering AI agents
➡️ Hands-on code labs you work through at your own speed
➡️ Live Q&A sessions with people who build this stuff 
➡️A capstone project to build something real (with prizes)

𝐖𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐥𝐞𝐚𝐫𝐧:
➡️ Day 1: What agents actually are and how they're different.
➡️ Day 2: How agents use tools to actually take action 
➡️ Day 3: Building memory so agents can handle complex, multi-turn tasks 
➡️ Day 4: Making sure your agents work reliably (logging, testing, metrics) 
➡️ Day 5: Taking agents from your laptop to production

𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫?
AI agents go beyond tools; they don’t just chat, they act. 
They complete tasks, use tools, remember context, and work autonomously. 

For anyone in dev, product, ops, or strategy, knowing how to build and deploy them is a real edge.

The course is free, with no prerequisites except curiosity and a willingness to try a bit of code.

Here's that registration link you've been waiting for...
https://lnkd.in/gZwZnTyf


#google #lifeatgoogle #ai #training #education #courses

Give yourself the next 40 days fully, and you can easily transition from PM to AI PM...I can give 100% guarantee...

3 hours daily for the next 40 days, and no one can beat you...

Here’s how to make these 40 days count 👇

𝐖𝐞𝐞𝐤 𝟏: 𝐁𝐮𝐢𝐥𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭:

Understand how AI is reshaping products and businesses.

* Learn what AI Product Managers actually do
* Read case studies of how companies use AI (Spotify, Notion, Canva, Microsoft Copilot)
* Watch: Google AI for Everyone (Free), Product Alliance – AI PM Basics

🎯 Output: Write a one-pager — “3 AI use cases that excite me as a PM.”

𝐖𝐞𝐞𝐤 𝟐: 𝐁𝐮𝐢𝐥𝐝 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠:
Learn how AI products are structured, without getting lost in technical details.

* Study LLMs, RAG, and fine-tuning (concept level)
* Understand AI safety, hallucinations, evaluation metrics
* Explore tools like ChatGPT, Claude, Perplexity, n8n, and Lovable

🎯 Output: Map one potential AI feature you could build in your current company or as a side project.

𝐖𝐞𝐞𝐤 𝟑: 𝐁𝐮𝐢𝐥𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 / 𝐏𝐫𝐨𝐝𝐮𝐜𝐭:
Move from theory → hands-on.

* Design your first AI-driven feature or mini product
* Use Figma, Lovable, or Bubble to create a working prototype
* Document your journey as a case study (problem → approach → outcome)
* Bonus: Share it publicly for feedback

🎯 Output: A working AI feature or prototype with a clear story behind it.

𝐖𝐞𝐞𝐤 𝟒–𝟓: 𝐁𝐮𝐢𝐥𝐝 𝐕𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲:
Show your transition.. because if people can’t see it, they can’t believe it.

* Update your LinkedIn headline and About section
* Post your project with insights you gained
* Engage with AI PMs and product leaders — join meetups, webinars, and communities

🎯 Output: Your LinkedIn now reflects your transition "AI PM ready."

Give yourself these 40 days fully.
You don’t need to switch jobs to level up, you just need to reposition yourself.

Because in today’s market, great PMs don’t just build products.
They build visibility.

If you’ve been applying, waiting, or hoping for the right call...
maybe it’s time to make your brand do the talking.

DM me ‘𝐁𝐫𝐚𝐧𝐝’, and let’s position you as the AI PM recruiters can’t ignore.

MM Hasan

𝐈𝐟 𝐲𝐨𝐮𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐥𝐨𝐨𝐤 𝐥𝐢𝐧𝐞𝐚𝐫... 𝐫𝐞𝐚𝐝 𝐭𝐡𝐢𝐬 𝐭𝐢𝐥𝐥 𝐞𝐧𝐝.

I wasn’t a Full-Stack Developer.
I was a 𝐉𝐚𝐜𝐤 𝐨𝐟 𝐚𝐥𝐥 𝐭𝐫𝐚𝐝𝐞𝐬, 𝐦𝐚𝐬𝐭𝐞𝐫 𝐨𝐟 𝐧𝐨𝐧𝐞.

And for years, I thought that was a flaw.

I started with 𝐂, 𝐂++,
explored 𝐀𝐧𝐝𝐫𝐨𝐢𝐝,
moved to 𝐏𝐇𝐏,
worked on 𝐟𝐫𝐨𝐧𝐭-𝐞𝐧𝐝,
even touched 𝐑𝐎𝐑,
and later found comfort in 𝐏𝐲𝐭𝐡𝐨𝐧.

It looked 𝐦𝐞𝐬𝐬𝐲.
But every switch was shaping me...
to 𝐥𝐞𝐚𝐫𝐧 𝐟𝐚𝐬𝐭𝐞𝐫,
𝐚𝐝𝐚𝐩𝐭 𝐪𝐮𝐢𝐜𝐤𝐞𝐫,
and connect dots others couldn’t see.

I went on to lead teams as an 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐌𝐚𝐧𝐚𝐠𝐞𝐫,
And today, I’m the 𝐅𝐨𝐮𝐧𝐝𝐞𝐫 𝐨𝐟 𝐚 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐁𝐫𝐚𝐧𝐝𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐜𝐲 𝐟𝐨𝐫 𝐓𝐞𝐜𝐡 𝐋𝐞𝐚𝐝𝐞𝐫𝐬.

From debugging backend code
to helping engineers, founders, and architects build their presence...
It all connects.

Being a 𝐣𝐚𝐜𝐤-𝐨𝐟-𝐚𝐥𝐥-𝐭𝐫𝐚𝐝𝐞𝐬 didn’t make me less focused.
It made me more 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 today.

If your career feels non-linear...
You’re not lost.
You’re just collecting dots to connect later.

“𝐈𝐟 𝐲𝐨𝐮 𝐰𝐞𝐫𝐞 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐨𝐨𝐝, 𝐲𝐨𝐮 𝐰𝐨𝐮𝐥𝐝𝐧’𝐭 𝐛𝐞 𝐥𝐚𝐢𝐝 𝐨𝐟𝐟.”

I have actually heard this line.
And honestly, it is one of the most damaging misconceptions in the job market.

A friend of mine, a highly respected CTO, was laid off earlier this year.
Not because he lacked vision.
Not because he was not delivering.
But because his company restructured and shut down an entire business unit overnight.

𝐆𝐮𝐞𝐬𝐬 𝐰𝐡𝐚𝐭? 𝐖𝐢𝐭𝐡𝐢𝐧 𝐰𝐞𝐞𝐤𝐬 𝐨𝐟 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐡𝐢𝐬 𝐣𝐨𝐛 𝐬𝐞𝐚𝐫𝐜𝐡, 𝐡𝐞 𝐤𝐞𝐩𝐭 𝐡𝐞𝐚𝐫𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐬𝐚𝐦𝐞 𝐛𝐢𝐚𝐬, 
“𝐈𝐟 𝐲𝐨𝐮 𝐰𝐞𝐫𝐞 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐨𝐨𝐝, 𝐰𝐡𝐲 𝐰𝐨𝐮𝐥𝐝 𝐭𝐡𝐞𝐲 𝐥𝐞𝐭 𝐲𝐨𝐮 𝐠𝐨?”

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐭𝐫𝐮𝐭𝐡 𝐈 𝐡𝐚𝐯𝐞 𝐬𝐞𝐞𝐧 𝐨𝐯𝐞𝐫 𝐚𝐧𝐝 𝐨𝐯𝐞𝐫 𝐚𝐠𝐚𝐢𝐧:
✔️ Layoffs are about business decisions, not talent.
✔️ Leadership skills do not vanish because a company restructures.
✔️ Some of the most resilient, driven professionals are the ones who have bounced back after layoffs.

If you are a hiring manager holding this mindset, you are not filtering out “𝐰𝐞𝐚𝐤” candidates.
You are filtering out leaders who could transform your team.

And if you are a tech leader who is been laid off, 
It does not define you. It repositions you.

𝐔𝐬𝐞 𝐭𝐡𝐢𝐬 𝐩𝐡𝐚𝐬𝐞 𝐭𝐨:
📌 Show up online and share your expertise.
📌 Build visibility around your leadership track record.
📌 Reframe your story, so people see your potential, not just your past.

Because in today’s market, skills and leadership get you in the door.
But personal branding makes people trust your story.

DM me ‘Brand’ if you want to reframe your career story after a layoff.

I wrote my first production code 9 years ago.
And like many of you, it didn’t go well.

I still remember the feeling.
Pushing the changes.
Refreshing the screen.
And watching everything break in real time.

No one prepares you for that moment.
You learn fast that production is a different world
and confidence doesn’t come from writing code
it comes from fixing the mess you created.

Over the years, I realised something important.
Good engineers aren’t the ones who never make mistakes.
They’re the ones who stay calm when things go wrong
own their decisions
and get better with every failure.

If your first production push didn’t go well
welcome to the club.
If your last one didn’t go well
you’re still learning.

And that’s the whole point of this journey.

Everyone is talking about AI skills right now.
But no one tells you the uncomfortable truth.

The real “AI skill” of 2025/206 isn’t prompt writing.
It isn’t fine-tuning.
It isn’t building agents.

It’s learning how to think with AI.

Because the people winning today aren’t the ones who know the most tools.
They’re the ones who know how to turn AI into an extension of their mind.

Here’s what I’m seeing everywhere:

People who treat AI like Google → stuck.
People who treat AI like a partner → unstoppable.

AI isn’t here to replace your skill.
It’s here to amplify it.

If you know how to break problems down
ask better questions
structure context
and guide the model’s reasoning —
you’ll be ahead of 90% of the industry.

The tools will change every 3 months.
Your ability to think with AI will stay.

So if you’re wondering what to learn next:
Learn how to work with AI the way leaders work with people —
with clarity, direction, and intention.

That’s the real skill that will define the next decade.

Say NO to these 20 corporate situations for a better work life balance.


Learning to say No isn’t about being uncooperative—it’s about protecting your time, energy, and career growth.

Here’s what you should stop saying Yes to:

❌ No to projects that don’t align with your goals.
❌ No to unrealistic deadlines.
❌ No to working late nights.
❌ No to working on weekends.
❌ No to unnecessary meetings.
❌ No to last-minute urgent tasks that aren’t your responsibility.
❌ No to taking blame for others’ mistakes.
❌ No to extra work without recognition or compensation.
❌ No to toxic work culture.
❌ No to office politics and gossip.
❌ No to overcommitment just to please others.
❌ No to being constantly available on calls/messages after work.
❌ No to being micromanaged.
❌ No to staying silent when something feels wrong.
❌ No to working without clear expectations.
❌ No to leadership roles if you don’t want them.
❌ No to sacrificing personal growth for job security.
❌ No to undervaluing yourself.
❌ No to compromising work-life balance.
❌ No to staying in a job that no longer serves you.

Your corporate journey is yours to control—set boundaries, stand firm, and thrive!


What’s the hardest No you’ve had to say at work?

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