Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

If you're feeling overwhelmed with how fast AI is evolving, you're not alone.

Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind.

But the good news is that you don’t need to learn everything all at once. What you need is structure.

So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable.

💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition.

If you're moving faster or slower based on time or experience, that’s okay too.
And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here.

👇 Here’s the roadmap:
🔖 Level 1: GenAI & Transformer Foundations
 Tokens, embeddings, transformers, decoding, and inference with open-weight models.

🔖 Level 2: Prompting & Language Model Behavior
 Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting.

🔖 Level 3: Retrieval-Augmented Generation (RAG)
 Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation.

🔖 Level 4: LLMOps & Tools
 LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data.

🔖 Level 5: Agents & Agent Frameworks
 Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation.

🔖 Level 6: Memory, State & Orchestration
 Vector and symbolic memory, episodic vs persistent state, memory compression.

🔖 Level 7: Multi-Agent Systems
 Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams.

🔖 Level 8: Evaluation & Reinforcement Learning
 LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops.

🔖 Level 9: Protocols & Safety
 MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates.

🔖 Level 10: Build & Deploy
 FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens.

📌 Bookmark this.
🛠️ Build something after every level.

And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will  teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 

💻 Pro-tip: Start with cookbooks!

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Hundreds of AI minds gathered in Central Park on a chilly Saturday morning, it was incredible :)

The past weekend in New York felt like one of those moments where community completely surprises you in the best way.

I was traveling to the city for a work event, and my friend Pranjal and I had a thought- What if we host a small AI meetup on Saturday morning in Central Park? Nothing fancy, just a space for people to connect.

So we put up a quick Luma link and shared it on LinkedIn. We expected maybe a handful of people. Instead, the sign ups crossed 450+. 

We put a small table with coffee, muffins, cookies and some cool swag- thanks to Teachable. We weren’t sure how it would go. We just hoped people would show up.

What happened next honestly moved me.

We reached the park at 9.20am to set up for a 10am meetup, and people were already there. In the cold. On a Saturday morning. People had traveled from Boston, Maryland, upstate New York, New Jersey, even Philadelphia. I kept talking to folks thinking, you really came all this way… for this?

The meetup was meant to last two hours. People stayed until 3pm! Almost six hours of conversations about AI, careers, ideas, and everything in between. No microphones. No agenda. Just humans connecting because they care about the same things.

This experience reminded me of something I tend to forget- community doesn’t need perfect planning. It needs intention. It needs genuine people. And it needs a space where everyone feels welcome.

I’m grateful to every single person who showed up. You made this morning unforgettable, and you reminded me why I love building in this space.

Shout-out to Aditya Suresh and Pranam Daga for helping us with putting this together ❤️

There is a quiet moment every November when life slows down just enough for reflection. I never grew up celebrating Thanksgiving. Back home in India, I didn’t even know what it meant. But after moving to the US, watching my friends celebrate, watching sitcoms where families gathered around food and gratitude, I started understanding the deeper meaning behind it.

A moment to pause.
A moment to look around.
A moment to look back.

This year, gratitude feels a little different for me. A little heavier in the best possible way.

I feel grateful for a life where I get to wake up every day and do work I genuinely love. AI is chaotic and intense and fast. Yet it gives me energy. It gives me purpose. And I feel lucky to be part of this ecosystem during a time when everything is shifting.

I feel grateful for the people who hold me steady.
A mom who pushed me when the world expected her to play it safe. A mom who defied every norm to carve space for me to choose ambition without apology.

A husband who is involved in my life in the most beautiful way supportive is honestly an understatement. He is a true partner in every single thing I do. He listens to every idea, every frustration, every new plan I dream up and he backs me with a kind of belief that strengthens my own. Chinnu amma and Chinnu appa who patiently listen to my ten year entrepreneurial plans and cheer for me with the kind of warmth only family can give.

Mentors and colleagues who let me thrive creatively who challenge me in the right ways who make every room I walk into feel lighter and more possible.

I feel grateful for health. For a body I get to take care of. For the time I carve out to move, to eat well, to nurture myself. It took years to realize that taking care of your body is also taking care of your dreams.

And I feel grateful for the little girl I used to be the one who sat with a notebook and wrote her goals on every page the one who kept believing she would get there someday. If I could talk to her today, I would tell her that she is doing great and that her stubborn belief in herself was worth it.

Thanksgiving may not be something I grew up with, but it has become a reminder to pause and appreciate the rarest gift of all the good people around you. People who are genuinely supportive, genuinely positive, and genuinely rooting for you.

I am grateful for all of it.
And grateful for everyone who has been part of my journey so far.

If you are reading this, I hope you take a moment to celebrate the smallest wins, the unexpected joys, and the people who make your life feel expansive.

li_at

JSESSIONID

What an electrifying afternoon! Absolutely delighted to have joined the Mayfield AI List honorary lunch at the beautiful Golden Gate Club in San Francisco.

The room was filled with the brightest minds in the AI VC world. it was a privilege to meet and listen to inspiring leaders like Navin Chaddha, Aparna Chennapragada and Garry Tan. 

I had the opportunity to host a focused roundtable, diving deep into the current state of the industry. We tackled the crucial discussion around Perception vs. Reality of AI, analyzing current hype cycles, and zeroing in on investment opportunities that hold real long-term potential.

It’s moments and small-group settings like these, where leaders can candidly discuss where the real value lies beyond the buzz, that drive the industry forward.

Huge thanks to the Mayfield and Startup Grind team (special shoutout to James Gee and Fadia Frangie Doumat) for hosting and for the award ♥️

No alternative text description for this image

Wrapped up AI Dev 25 hosted by DeepLearning.AI

More than 1400 AI developers gathered in one space, and the energy was something you could feel instantly. Meeting so many builders and hearing what you’re creating reminded me why this community moves the needle the way it does.

Hearing Andrew Ng talk about the future of work was a standout moment. The way he encourages everyone on his team to code, regardless of background, is exactly the kind of mindset shift this era needs. AI literacy is becoming a baseline skill, and he articulated that perfectly.

Our panel with Michael Kearns, Hatice Ozen, Kay Zhu, and Josh Gordon covered how the ecosystem is evolving across the stack, from Groq , Google, and Amazon Web Services (AWS)  to Fireworks AI and how teams like Genspark are using RFT on Fireworks AI in production workflows. We also touched on multi-agent systems, practical best practices, and where developer tooling still needs to mature.

I also loved my conversation with Michael Kearns on responsible AI and how that thinking has evolved from classic ML days to the generative era. His perspective at the intersection of academia and industry always leaves me thinking differently.

Grateful for the conversations and connections today. If we met, thank you for stopping by. If we didn’t, I’m always happy to keep the discussion going. There’s a lot to build, and we’re still early.

Thank you DeepLearning.AI team for having me ❤️

When you’re an AI engineer, the worst bottleneck isn’t writing code. It’s navigating everything around the code.

Last week I tried out Rovo Dev from Atlassian and it really changed how I approach dev work, plus how I see AI agents changing the landscape of software engineering.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗼𝘃𝗼 𝗗𝗲𝘃?
RovoDev is an AI agent built to live inside your dev workflow, including your code, your issues (let’s say in Jira), your docs (let’s say in Confluence), your pull requests. 

It helps you move from idea → code → deployment with fewer context switches.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝗮𝗳𝘁𝗲𝗿 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁:
→ I asked in plain English: “What does this module do? Where are its dependencies?” and got a clear breakdown, no digging through 3 dozen files.
→ I linked my GitHub/Bitbucket + Jira and was able to surface relevant issues, test failures and comments in one context instead of switching tools.
→ The CLI experience (yes, in my terminal) allowed me to trigger agents to draft tests, summarise PRs, or suggest refactoring ideas, without leaving my code. 

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀?
What I realized after using Rovo Dev is that the real time-sink in engineering isn’t writing code, it’s in fact more about re-orienting yourself.

Jumping between Jira, Confluence, and your repo just to understand context.
Trying to figure out why something was built the way it was.
And then losing your train of thought before you can even start solving the problem.

Well, that's where Rovo Dev actually helps close that gap.

If you’ve ever joined a new project and spent hours just piecing together what’s going on, or if you constantly find yourself switching between tools to get one small thing done, you should try this out: fandf.co/42MTjr8 

Thank you Atlassian for partnering with me on this post, and giving me access to Rovo Dev to test it out :)

#AtlassianPartner

This is one of the best resources for aspiring AI engineers, that covers all the basics - advanced in classroom teaching: https://lnkd.in/dgr5zEve

Highly recommend saving this so you can come back to it later.

Goldmine for aspiring AI Engineers

Lately, I’ve noticed something interesting in the AI startup world.

The ones growing the fastest aren’t building the next big model.

They’re building agentic systems.
AI that doesn’t just talk, it does.
Copilots that summarize meetings.

Agents that predict churn or handle internal ops automatically.

These aren’t demos anymore. They’re real products driving revenue.

What separates the ones that scale from the ones that stall comes down to one thing : Data.
Every great agent lives and dies by how it manages memory, retrieval, and reasoning speed.
And that’s exactly why so many founders I work with build on MongoDB Atlas.

It’s not just a database.
It’s the data layer that brings structured, unstructured, and vector data together so your AI can actually reason in real time.

If you’re a startup building AI that acts, not just talks, go check out the MongoDB Startup Program: https://fandf.co/493VsD1

#MongoDBPartner

🚨Context Engineering for GenAI Workshop happening tomorrow!

In this session, we’ll dive deep into:
→ What context really means in LLMs and how it shapes performance

→ The toolkit that helps you engineer it better: Summarization, Pruning, Query Rewrite, RAG, Context Offloading, Isolation, and Memory

→ How to optimize for accuracy, cost, and reliability across use cases

→ A live demo showing how these ideas come together in real time

This workshop is for every engineer who wants to move beyond prompt tuning and start thinking like a system designer.

🧩 Context Engineering
📅 November 8
🕐 1.5 hours
🎙️ with Arvind Narayanamurthy & me

🎟️ Save your spot: https://lnkd.in/dvFmMiRr

If you’re an AI engineer today, you should absolutely get familiar with Model Context Protocol (MCP) and Responses API. These two are becoming the backbone of modern agentic systems.

Let’s unpack why 👇

1️⃣ MCP: The Connector Layer

Model Context Protocol (MCP) standardizes how models connect to external tools, APIs, and live data.

Think of MCP as the wiring system that allows your LLM to talk to the outside world, like tools, databases, vector stores, or your company APIs.

When you build or host your own MCP server, you’re giving your model new abilities to reason, act, and fetch context beyond what’s in its prompt.
This is what turns a plain LLM into an agent.

If you’re working on production-grade AI systems, you’ll often need to:
→ Integrate custom tools or data sources
→ Maintain context across multiple steps
→ Ensure reliable tool invocation and streaming

MCP makes all that possible.

2️⃣ Fireworks AI Responses API

Once your MCP setup is ready, the Responses API is how you connect it to the model.

It’s a next-generation SDK that powers conversational AI. It supports:
→ Stateful conversations (previous_response_id)
→ Real-time streaming for interactive apps
→ Tool calling and safe execution

The Fireworks Responses API follows the same schema and format as OpenAI’s Responses endpoint.

So if you’ve been using OpenAI models, you can switch to open-source models on Fireworks AI with almost zero code change.

You’ll unlock faster inference, lower latency, and broader model choice, including open models like Qwen, Mistral, Llama, and Gemma hosted natively on Fireworks AI.

You can even mix and match:
✅ Use MCP to extend model capabilities
✅ Use Responses API to orchestrate reasoning, tool use, and streaming

That’s the magic combo powering many of today’s most advanced agentic systems.

3️⃣ Why This Matters

Agentic systems aren’t built with just a model call.
They’re built with context, memory, and actions. MCP and the Responses API make that orchestration reliable, stateful, and production-ready.

If you’re serious about building enterprise-grade AI apps, start experimenting with:
→ MCP Examples (connecting to MCP servers and invoking tools)
→ Conversation Chaining (using previous_response_id to continue dialogue)
→ Streaming Output (for responsive real-time interfaces)
→ Ephemeral Mode (for privacy-friendly, non-persistent interactions)

Happy Building 🚀
〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re an AI engineer or an AI leader looking to level up your skills in LangChain and LangGraph, this one is for you.

I’m teaming up with Arvind Narayanamurthy  for a beginner–intermediate, 1.5 hour hands-on workshop where you’ll learn how to move from simple prompts to real AI applications and agentic workflows.

Why this matters
- Most engineers know how to prompt. Few know how to orchestrate.
- LangChain and LangGraph are becoming the backbone of production AI systems.
- If you want to build or lead AI engineering teams, you need these mental models.

What you’ll learn
- Foundations of LLM orchestration and where LangChain fits
- Essential building blocks: chat models, prompting patterns, structured outputs, LCEL
- Tool calling with MCP adapters
- RAG basics end to end
- LangGraph workflows and agentic architectures
- Two live labs: a micro-app and a simple agent
- Quick dive into LangSmith for debugging

The workshop is recorded, and everyone gets the replay.

If you want practical, real-world skills to build modern AI systems, this is the one to sign up for.

Grab your seats: https://lnkd.in/eW2WZdcf

Are you wondering where the AI industry is actually headed? 
Every week brings a new framework, a new toolkit, a new acronym. But beneath all the noise, one truth is becoming clearer: the real moat isn’t the model, it’s the combination of data and AI. 
 
AI is the toolkit. 
Data is the substance. 
AI gives us reasoning power. 
Data gives us understanding. 

That connection came to life at Snowflake BUILD 2025, where the  focus wasn’t on hype, it was on grounding AI in reality. 
 
We saw how Snowflake Intelligence lets teams ask complex questions in natural language and get trusted answers from governed data. How the Cortex Agents API enables developers to build domain-specific agents that reason and collaborate. And how the Managed MCP Server securely bridges external AI systems to enterprise data, turning “agentic AI” from an idea into something production-ready. 
 
Even unstructured data, the 80 % of information hidden in PDFs, chats, and documents, is now first-class and accessible through simple AISQL queries. 
 
This feels like a turning point. 
The next era of AI won’t be defined by bigger models, but by deeper integration between data and reasoning. 
 
AI without data is just potential. 
Data without AI is just storage. 
Together, they become intelligence. 
 
#SnowflakePartner #SnowflakeBUILD

If you’re an AI engineer trying to understand agentic systems, keep reading 👇

A single-agent system relies on one model to do everything: reasoning, planning, execution, and validation. It’s efficient for simple, sequential tasks but quickly breaks down as workflows grow more complex or interdependent.

A multi-agent system, on the other hand, distributes intelligence across specialized agents that collaborate. Each has its own role, memory, & context - and together, they solve problems that a single model can’t handle alone.

But here’s what most people miss: there’s no standard architecture.

A multi-agent system is like Lego blocks, you design it for your use case.

You can build:
→ a supervisor agent managing sub-agents
→ a hierarchical setup with layered control
→ or a hybrid model combining centralized planning and peer coordination
The challenge isn’t in building agents, it's in managing how they talk to each other.

Three reasons multi-agent systems often fail:

❌Memory fragments across agents.
Each agent keeps its own context. When one agent hands work to another, subtle details get lost or duplicated. Without a shared memory layer or context synchronization, agents start “forgetting” what the others already know.

❌Operational costs multiply.
What costs $0.10 for a single agent might cost $1 when ten agents coordinate. Every message, validation step, or context handoff adds latency, token cost, and complexity. At scale, coordination overhead often outweighs the benefit of specialization.

❌ Write conflicts cascade.
If multiple agents write to the same source of truth: a shared database, knowledge base, or document, their changes can collide. One agent updates a schema, another updates logic on top of the old version, and suddenly the system’s state fragments.

TL;DR: Not every workflow needs a multi-agent setup.

When tasks are independent, read-heavy, and parallelizable, multi-agent systems shine. But for sequential logic and tight dependencies, a single-agent pipeline is still faster, cheaper, and easier to maintain.

As always, Galileo has published a new phenomenal free handbook- Mastering Multi-Agent Systems.
 
If you want to go deeper into how these systems work, why they fail, how to evaluate them, explore different architectures, and even build LLMOps around them, this is one of the best reads out there: https://lnkd.in/diptdEC2

#GalileoPartner

Multi-Agent AI System Cheatsheet 

I learned Design Thinking on my first day as a data scientist at IBM, and it quietly became one of the most useful mindsets in my AI career. It taught me to slow down, understand the real problem, and build solutions that actually land with users.

Here’s how I still use it today:

→ Solve the right problem
Most AI teams jump into modeling too early. Design Thinking forces you to reframe, question assumptions, and check if the problem is even defined correctly.  

→ Diverge before you converge
Explore many possibilities, then narrow down. This prevents tunnel vision and leads to better architectures, better experiments, and better product decisions

→ Use the 2x2 grid
Am I exploring or deciding?
Am I thinking about the problem or the solution?
This simple check saves me from going down the wrong path.  

→ Observe behavior, not opinions
The real insights come from watching users, not asking them. The same applies when evaluating agents, prototypes, or internal tools.  

→ Prototype fast, assign ownership
Ideas die when nobody owns them. Execution is where Design Thinking turns into impact.  

This mindset shaped how I build AI systems then, and it still shapes how I build them now.

♻️Share this with your network

Design Thinking Framework

Last week I came home to a little surprise from OpenAI filled with their new books, 100 Chats for Studying, Career, and College Life. 

I flipped through them right away and loved how visual and genuinely interesting they’ve made each section. It feels approachable, practical, and fun in a way most tech books don’t even try for.

If you want to explore the prompts yourself, you can check them out at this link: https://lnkd.in/ddycaDAg

It’s a pretty cool resource to keep on your desk as you build, study, or just experiment with new ideas.

Here is my absolute fav one which I wish I could have used as a student 👇  
I want to understand [concept]. Explain it with an analogy to real-world items.

Let me know in the comments below which ones is your favorite?

If you’re getting started as an AI engineer, this one’s for you 👇

Here are 12 things you need to learn if you want to become a real AI engineer (or level up as one):

1️⃣ LoRA & qLoRA fine-tuning
Learn low-rank adaptation to fine-tune massive models while keeping GPU and memory usage in check.
https://lnkd.in/dxAfNJjP
https://lnkd.in/dcfhk4YB

2️⃣ Reinforcement Learning from Human Feedback (RLHF)
Understand how to align model behavior with human expectations using curated reward signals.
https://lnkd.in/da4qf5JV
https://lnkd.in/drEbQ4x2

3️⃣ Reinforcement Learning from Verifiable Rewards (RLVR)
Use simulated or programmatic reward models when human feedback is too expensive.
https://lnkd.in/dFBgDAyd

4️⃣ RAG Fundamentals
Learn how to build Retrieval-Augmented Generation systems- ingest documents, create embeddings, stitch context, and prompt LLMs.
https://lnkd.in/dFkgD6RQ
https://lnkd.in/d7v6DzEG

5️⃣ Advanced RAG Architectures
Master multi-hop retrieval, hybrid sparse-dense search, and complex reranking for multi-document use cases.
https://lnkd.in/dUrAz2D3

6️⃣ Embedding Models & Vector Databases
Benchmark sentence encoders, tune indexes (FAISS/Pinecone), and understand latency vs recall trade-offs.
https://lnkd.in/d8zeWbmn
https://learn.pinecone.io/

7️⃣ Prompt Engineering & Chain-of-Thought
Design prompts that guide reasoning step-by-step, manage templates, and dynamically inject context.
https://lnkd.in/djbJD578
https://lnkd.in/d9M75H_i

8️⃣ MCP & Agent-to-Agent Integration
Use Model Context Protocol (MCP) to coordinate LLMs with APIs and other agents for seamless A2A handoffs.
https://lnkd.in/dfjvxhvP
https://lnkd.in/dJ5NWuaK

9️⃣ Long-Context Window Strategies
Handle 100k+ token sequences using smart chunking, hierarchical retrieval, and memory-augmented architectures.
https://lnkd.in/djgkXGaZ

🔟 Model Efficiency: Quantization & Distillation
Shrink models with post-training quantization (4/8-bit), pruning, and knowledge distillation for faster, cheaper inference.
https://lnkd.in/d2iurEyv

1️⃣1️⃣ LLMOps & Model Monitoring
Build CI/CD pipelines for LLM deployment, track drift, monitor hallucinations, and set up alerting for cost or performance issues.
https://lnkd.in/dxik45e6

1️⃣2️⃣ Evaluation & Benchmarking
Measure with perplexity, ROUGE, F1, and human evaluations. Automate benchmarks to compare models at scale.
https://lnkd.in/dP_NJXg9

〰️〰️〰️〰️
♻️ Share this with your network 
🔔 Follow me (Aishwarya Srinivasan) for data & AI insights, and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re getting started with building AI agents, this Agentic AI Toolkit Cheatsheet will come in handy 👇

You’ll quickly realize it’s not just about choosing a model. You need orchestration, memory, retrieval, evaluation, and a solid deployment stack.

And if you’re not careful, your architecture turns into a mess of disconnected tools.

That’s why I put together this Agentic AI Toolkit Cheatsheet.

It organizes the full stack of what goes into building production-grade AI agents- from model hosting to feedback loops, so you can stop guessing what’s missing in your pipeline and start shipping actual systems.

Here’s what it covers:

🔧 Foundation Models & Hosting
🎛️ Model Customization (SFT, RFT, RLHF)
🤖 Agent Frameworks
🧩 Orchestration & Workflow
📥 Data Ingestion & Preprocessing
🧠 Embedding & Retrieval
🧷 Memory & Knowledge Storage
🔌 Tool Integration
🧪 Evaluation & Monitoring
🖥️ User Interfaces
🚀 Deployment & Hosting


💡 A few tips if you’re working on your first agentic stack:

→ Choose one tool per layer to start, depth over breadth.
→ Build iteratively and introduce evaluation as early as possible.
→ Use event-driven workflows to manage complexity.
→ Think about memory and feedback as first-class components, not afterthoughts.


📌 Bookmark this.
📚 Use it to audit your stack.
🛠️ Build with it.
📤 Share it with a teammate who’s lost in the sea of GenAI tooling.

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

This Week of AI is here⚡️
Another packed week in the world of AI- here’s your quick TL;DR of everything that happened across the AI ecosystem this week 👇

- Apple Forges Billion-Dollar AI Alliance with Google for Siri Overhaul
- Anthropic Foils First Documented Large-Scale Autonomous AI Cyberattack
- World Labs Unveils Marble, A Multimodal World Model for Spatial Intelligence
- OpenAI Rolls Out GPT-5.1 with Adaptive Reasoning and Dual Personalities
- Microsoft Creates MAI Superintelligence Team Led by Mustafa Suleyman
- Google Launches ‘Agentic Checkout’ and AI Shopping Tools for Holiday Season
- IBM Joins OpenSearch Software Foundation as Premier Member

♻️ Share this with your network
🔔 Follow me (Aishwarya Srinivasan) for no BS data and AI updates and insights

This week, the AI landscape was defined by blockbuster partnerships, a major infrastructure upgrade, and a chilling preview of autonomous cyber warfar

Open article: This Week in AI: Week of 16th November 2025 by Aishwarya Srinivasan • 3 min read

This Week in AI: Week of 16th November 2025

Thanksgiving week, yet a lot happening in this week in AI!
Catch everything you need to know here 👇

Vision-Language Models connect what AI sees with what it reads and reasons.

They’re the foundation of AI systems that can interpret charts, medical images, retail shelves, or product catalogs.

But a generic VLM doesn’t understand your domain’s visual language.
That’s where fine-tuning becomes essential.

𝐖𝐡𝐲 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐚 𝐕𝐋𝐌 𝐦𝐚𝐭𝐭𝐞𝐫𝐬

A pretrained VLM already knows the basics of visual-text reasoning.
Fine-tuning helps it specialize for your domain.

→ In healthcare, it learns to detect anomalies in MRIs and X-rays.
→ In retail, it interprets shelf images and product layouts.
→ In enterprise, it extracts structured data from invoices and reports.

You’re not rebuilding intelligence, you’re refining perception to fit your use case.

𝐇𝐨𝐰 𝐋𝐨𝐑𝐀 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭

Full model fine-tuning is expensive and compute-heavy.
Low-Rank Adaptation (LoRA) keeps the base model frozen and trains only small adapter layers.

That means:
→ Faster training cycles
→ Smaller memory footprint
→ Lower compute costs
→ Domain adapters that are easy to swap in and out

You can maintain one base model and multiple lightweight adapters for each use case such as invoices, medical forms, or retail analytics.

𝐈𝐧𝐬𝐢𝐝𝐞 𝐚 𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥

A VLM has three main components:
→ 𝐕𝐢𝐬𝐢𝐨𝐧 𝐄𝐧𝐜𝐨𝐝𝐞𝐫 converts pixels into visual tokens.
→ 𝐅𝐮𝐬𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 combines visual and text context for reasoning.
→ 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐃𝐞𝐜𝐨𝐝𝐞𝐫 generates captions, summaries, or structured responses.

Each layer introduces potential failure modes like poor resolution, misaligned regions, or verbose hallucinations. Fine-tuning improves alignment and reliability across these components.

𝐓𝐡𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞

→ 𝐃𝐚𝐭𝐚 𝐝𝐞𝐬𝐢𝐠𝐧: Collect diverse, high-quality, clearly labeled visuals.
→ 𝐓𝐚𝐬𝐤 𝐝𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 : Choose the right setup: captioning, VQA, extraction, or localization.
→ 𝐋𝐨𝐑𝐀 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Train adapters for each domain efficiently.
→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Use both quantitative metrics and human review for grounding and accuracy.

Always evaluate across different slices such as document type, lighting, and template to surface hidden biases.

𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐫𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲

Each domain adapter should have its own dataset lineage, version, and evaluation score.
Reliability requires attention to fairness, privacy, consistency, and uncertainty handling.
Fine-tuning doesn’t just improve accuracy, it strengthens governance and ethical alignment.

LoRA fine-tuning makes VLMs faster to adapt, cheaper to deploy, and more aligned with your real-world data. 

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

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!

Hina Arora

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.

Follow Hina for more such content

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

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.

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

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

#LLM #AItools #GenAI #AIAgents

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

Follow Hina Arora to learn more

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

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 ?

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

Everyone wants to work in AI, but 80% have no idea which role to choose.
And that’s where their journey breaks before it even starts.

In the last few months, I’ve spoken to engineers, analysts, PMs, and even designers who want to move into AI but feel completely lost.
Not because of lack of interest — but because the AI ecosystem is huge and evolving fast.

To make it easier, I created this detailed map of the Top Careers in AI.

It covers roles across engineering, product, research, strategy, and even education — because AI today needs more than just coding. It needs problem-solvers, system thinkers, researchers, communicators, and leaders.

Some interesting patterns I’ve noticed while helping tech professionals pick the right path:

• Many engineers naturally fit into ML Engineer, Data Engineer, or Algorithm roles — but hesitate because they think they need a PhD. Most don’t.

• Product and strategy-oriented folks shine in AI Product Strategist and AI Consultant roles, because they understand impact and business context.

• People who enjoy teaching, mentoring, or explaining complex topics often find a great fit as AI Educators or AI Trainers.

• Designers with curiosity about AI can transition into Voice Interaction roles, especially with strong UX skills.

• Research-heavy roles like AI Researcher or NLP Specialist need deeper math/ML foundations — but are incredible for those who love experimentation and problem-solving.

Every role has its own skill stack, learning path, and growth curve.
But once you understand the landscape, choosing becomes much easier.

If you’re planning a transition into AI in 2025, start with clarity.
Know your strengths.
Match them with the right direction.
Then build skills step by step.

Which AI role are you exploring for your career? Let’s discuss.

AI-Assisted Personal Branding for Tech Leaders | 9yrs+ in Tech | Helping 20+ CTO / Founders / Directors / Coaches / Consultant Build Personal Brand for Professional Growth | Keynote Speaker | Done-for-You Service

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

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?

taplio

The Battle

Aishwarya Srinivasan

Hina Arora

Followers

Posts

Engagements

Likes

Performance ScoreAishwarya Srinivasan's LinkedIn Performance

Performance ScoreHina Arora's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Aishwarya Srinivasan

Hina Arora

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Aishwarya Srinivasan's LinkedIn Performance

Performance ScoreHina Arora's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreAishwarya Srinivasan's LinkedIn Performance