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 ♥️

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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

Yudi J

I’m the founder and face behind Yudi J, a content-first education brand helping international students and immigrants succeed in the U.S.

We create actionable content, community support, and career-focused programs to help students navigate the complex journey of studying and working in America — from choosing the right university to landing full-time jobs and understanding immigration pathways.

What I do:
• Lead a content team of 10 across YouTube, Instagram, LinkedIn, and newsletters
• Run accelerator programs focused on job hunting, profile building, and career strategy
• Manage a thriving community of 2,500+ international students and job seekers
• Build partnerships with universities, creators, and edtech companies
• Handle brand operations, course design, sales, and business growth

The mission is simple: make the international student journey easier, less lonely, and more successful — one video, session, and program at a time.

Founder

Auto Approve

Senior Product Manager

I drive strategic and cross-functional initiatives. 
I drive all aspects of program management (planning incorporating dependencies, key milestones and deliverables, data gathering, analysis, communication, risk management and mitigation, status tracking and reporting, implementation, monitoring, execution, and delivery of the initiative/program.
I influence and collaborate across organizations including engineering, product, security, legal, compliance, finance, go-to-market, and more to effectively build commitment with delivery teams, negotiate competing requirements, ensure alignment and program progress, and improve processes. 
At Auto Approve we build solutions that innovates and simplify the auto refinance process using the modern technology suite (Microservices, React, Springboot, GCP, Sonar Cloud, Testrail, GitLab, etc)
- Work with Engineering and Product to develop and manage the work intake process for new requests
- Develop and manage a detailed project schedule and work plan
- Provide project updates on a consistent basis to various stakeholders about strategy, adjustments, and progress
- Manage contracts with vendors and suppliers by assigning tasks and communicating expected deliverables

Senior Technical Program Manager

scale.jobs

I provide 
- Strategic guidance to scale.jobs leadership team on product development, go-to-market strategies, and industry best practices
- Expertise in digital marketing, with a proven track record of successful campaigns across multiple platforms, including LinkedIn, YouTube, Instagram, and email newsletters. My approach integrates creative content strategies with data-driven analysis to enhance brand visibility and engagement.
- Leadership in social media marketing initiatives, designing and executing strategies that amplify our digital presence and drive meaningful interactions with our target audience.
- Insights from my engagements with job seekers, recruiters, career coaches, and industry thought leaders
- Feedback and guidance on long-term roadmap, overarching strategy, exploring potential expansions into adjacent verticals and emerging markets

Marketing Advisor and Angel Investor

EXL Health

Led an onshore team of 6+ software developers to develop our core product and support new and existing clients using technologies like .net, java, node, angular, microservices, service fabric, etc.
Reduced $25000 cost by leading a project of moving the client facing team of 50+ members from old time tracking system to existing project management tool - JIRA.
Improved the overall release quality and delivery time by 40% by introducing and influencing code review and code freeze process which also eliminated last minute scope creeps.
Managed the project with a scrum team of 7+ engineers to build a mobile application which increased client’s users efficiency by 60% as it lets you connect offline when they are on the field.
Designed and executed the beta release process with our clients to increase the quality by over 50% and saving 500+ hours by reducing the number of releases.
Executed product manager’s vision by transforming and leading 5+ cross functional teams from waterfall to agile teams using SCRUM framework.
Led 5 scrum teams for the entire product development lifecycle from the discovery of new features to releasing and maintaining them.
Administered the Atlassian products like JIRA, Confluence and Bitbucket for the 500+ users by designing the workflows, configurations, automating a 15+ processes by using scriptrunner plugin, etc.
Executed product manager’s vision by leading an offshore team of 23+ engineers who delivers monthly maintenance releases  and quarterly feature releases.

Software Development Manager

Managing and leading team both onshore in Chico (8 team members) and in India (25 team members).
- Provided Agile/Scrum/Kanban training to several departments and offshore team which improved the software product
development process by 40%
- Managed multiple product implementation project with the budget of $700,000+
- Created release management tool saving 2000+ hours labor annually.
- Served as JIRA administrator - upgrade JIRA/Confluence softwares, create process workflows, resolving in-house bugs.
- Led the agile implementation and training.

Scrum Master and Software Development Team Lead

- Served as Scrum Master to facilitate scrum ceremonies (sprint planning, retrospectives, daily stand-ups, demos and other Scrum-related meetings).
- Provided Agile/Scrum/Kanban training to several departments and offshore team which improved the software product development process by 40%
- Managed multiple product implementation project with the budget of $700,000+ 
- Created release management tool saving 2000+ hours labor annually.
- Led the UI Automation project to develop automated test cases which reduced the manual regression testing by 250+ hours at the end of every release.
- Served as JIRA administrator - upgrade JIRA/Confluence softwares, create process workflows, resolving in-house bugs.
- Led the agile implementation and training.
- Managed the JIRA in-house implementation project.
- Delivered key milestone for major initiative 3 weeks early.
- Facilitated the implementation of 1st product Beta release with client involvement to enhance the quality
- Standardized several project management artifacts – defect and issue tracking tool, project charter, RACI matrix, and communication plan.

Technical Project Manager

- Served as Scrum Master to facilitate scrum ceremonies (sprint planning, retrospectives, daily stand-ups, demos and other Scrum-related meetings).
- Delivered key milestone for major initiative 3 weeks early.
- Created release management tool saving 2000 hours labor annually.
- Standardized several project management artifacts – defect and issue tracking tool, project charter, RACI matrix, and communication plan.

Project Management Intern

California State University, Chico

OrgSync Subject Matter Expert:
- Managed, trained and advised use for the web portal, OrgSync (16000+ users, 250 organizations).
- Leveraged marketing initiative management skills to increase user base by 28%.
- Consulted various departments for customization of the tool to eliminate the manual processes.
- Successfully created and facilitated more than 20 leadership and soft skills training workshops.

Paraprofessional (OrgSync)

Capgemini

- Improved productivity and maximized performance by developing and optimizing the web-based application to track payroll management system for the offshore bank.
- Applied Agile methodology to deliver products through performing meticulous unit testing, resulting in 13% contribution to offshore development center.

Software Engineer

Freelancer Software & Web Developer

- Worked with multiple clients to help them set up their e-commerce business resulting in 30-40% of revenue growth.
- Provided excellent customer service resulting in 100% of customer retention.
-Gathered Product/Business requirement, Designed, developed the web application.

Web Application Developer

Intellika Technologies Pvt Ltd - Dynamics 365 Services

- Served as Scrum Master and delivered the in-house project before a month.
- Delivered the product demo which contributes 20% of overall revenue.
- Automated testing scripts saving 1000 hours.

Software Developer Intern

Result-oriented technical program manager and senior scrum master with experience in leading large and small agile teams to deliver and build complex enterprise systems. I help the teams to do their best work and know when to “drive” versus “coach”. I take initiatives to identify and resolve issues that can improve overall efficiency and effectiveness of complex programs. I have built and scaled multiple products and teams by implementing high throughput processes and managing entire SDLC for various parts of the company. 

I oversee end-to-end program execution across teams / groups, including risk management, communication, issue/conflict resolution and dependency management. I collaborate with responsible partners (onshore teams and offshore teams) to proactively identify, communicate, manage and mitigate program risks. I organize and lead cadence-based stakeholders / leadership meetings, tracking actions, ensuring timely communication of program status, schedule, risks, and blockers. I drive process improvements and leverage metrics to improve transparency, overall team performance and efficiency around key cross-org programs.

I partner with cross-functional teams to deliver business value in a timely manner while maintaining a high level of quality and transparency. I work with customer facing teams and product management to support product enhancement requests and voice of the customer initiatives. 

I have led programs by achieving alignment on program scope, release planning, as well as aligning one or more teams on vision, business goals, creating program structure, and securing cross-functional alignment. I partner with cross-functional teammates to drive workgroup alignment meetings to address tactical release issues, dependencies, team status, production issues, metrics review, milestones, etc. (cross functional - Product Owners, Scrum Masters, TechOps, Functional Managers, Release Management).

My areas of expertise include:
- Program Management - End to End
- Project Management (Agile, Scrums, Kanban)
- JIRA, Confluence, Bitbucket, Slack, Trello
- Software Development Life Cycle (SDLC)

Pritesh Jagani

Most people use AI.
Few people become AI-powered.

Last year, I was juggling 12 tools a day - docs, slides, edits, emails and still burning out.
I thought working harder was the solution.
Turns out, working smarter with AI was the real game-changer.

The moment I automated half my workflow with AI tools, everything shifted - faster results, more creativity, zero burnout.

1. Create Like a Pro, Not a Robot
Use tools like Jasper, Gamma, and Kittl to turn ideas into polished content that looks and reads like it was built by a team, not one tired human.

2. Automate the Boring Stuff
Make and Fireflies handle the repetitive chaos. AI isn’t just about creativity, it’s about reclaiming your time.

3. Learn & Grow at Light Speed
Platforms like StudyPal and Perplexity make learning frictionless. No fluff. Just the insights that actually matter.

4. Collaborate Smarter, Not Harder
With Taskade and Chatbase, teams can brainstorm, plan, and support customers - all without drowning in meetings or tickets.

5. Build an AI-Driven Career Stack
From Kickresume to Akkio, every part of your professional journey can now be powered by AI, if you know where to look.

The future doesn’t belong to those who fear AI.
It belongs to those who partner with it.

You don’t get replaced by AI, you get replaced by someone who knows how to use it.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

Sr. Product Manager | I help international students to Study Abroad (USA), land their dream job, and navigate their immigration journey

Remote work is not going anywhere, it is expanding.
And if you know where to look, you can find thousands of high-paying remote jobs across tech, marketing, design, operations, support, and more.

Here are the Top 40 Websites for Remote Jobs you should be using -

- Mainstream & High-Quality Remote Job Boards

FlexJobs, Jobgether, WeWorkRemotely, Otta, RemoteOK, JS Remotely, Dynamite Jobs, Remotive, Working Nomads, LetsWorkRemotely, NoCommute, People-First Jobs, Jobspresso, Remote.com, FindAsync
Best for: curated, reliable remote listings across multiple industries.

- Global & Visa-Free Remote Job Platforms

Remote Rebellion, World Teams, Himalayas, RemoteSource, No Visa Jobs
Best for: candidates looking for global roles without relocation or visa barriers.

- Remote-Focused Work Communities & Niche Boards

RatRaceRebellion, Remoters.me, NoDesk, remote4me, Remotists, BeeFrii, Smooth Remote, DailyRemote, Remote Work Junkie, Citizen Remote, Remote Leaf, RemoteHub, remote.io, Flexa, Remote.co
Best for: remote-only companies, startup jobs, and specialized remote careers.

- Flexible, Async, and Startup-Friendly Remote Sites

Remotewx, Growmotely, SkipTheDrive, JustRemote, Pangian
Best for: async work, flexible schedules, and early-stage companies.

Finding remote jobs becomes 10× easier when you know where to search — and these 40 platforms cover the entire market, from entry-level to senior roles across the world.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator, where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

“Applying Endlessly" to every job you see online won't get you a Job

Landing great roles follow a structured system - learning the right skills, building proof of work, networking intentionally, and using AI to speed up every step.

Here’s a step-by-step ladder showing the exact path to go from zero clarity → real skills → strong projects → confident interviews → a job in 2026.

Follow the ladder one step at a time, stay consistent, and let AI become your accelerator - not your replacement.

Here’s the breakdown : 

1. Start With Awareness – Understand 2026 job trends and what companies actually want.
2. Align With Goals – Pick your direction, map your skills, and set learning goals.
3. Research What Companies Hire For – Study job listings, patterns, and real expectations.
4. Learn the Skills (Smartly) – Follow a structured roadmap instead of random tutorials.
5. Build Projects That Prove Your Skills – Solve real problems and show end-to-end work.
6. Create a Strong Portfolio – Upload polished projects with clear documentation.
7. Write a Job-Winning Resume – Keep it clean, quantified, and ATS-friendly.
8. Apply Strategically (Not Randomly) – Target companies and track applications.
9. Prepare for Interviews – Practice behavioral + technical challenges intentionally.
10. Build Visibility & Network – Share learnings, engage publicly, connect with recruiters.
11. Boost Consistency – Review weekly, fix weaknesses, build discipline.
12. Avoid Pitfalls – Don’t overuse AI or chase 10 skills at once.
13. Recharge & Reflect – Prevent burnout and adjust your strategy.
14. Power Your Career With AI – Use AI for learning faster, interview prep, workflow, and project building.
15. Publish & Share – Teach what you learn, attract opportunities, and stand out.

If you follow this ladder with patience, clarity, and consistency, getting a job in 2026 becomes a predictable process - not a guessing game.

Start small, scale your efforts with AI, share your progress, and trust the journey. Your next opportunity is built one step at a time.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

The 7 Hills Every Job Seeker Must Climb to Become Irreplaceable in 2026

AI won’t replace you, but someone who knows how to use AI absolutely will.
The future belongs to job seekers who master these seven core areas.

1️⃣ Awareness & Market Understanding
Know how industries are shifting and what companies will value next so you can stay ahead of the curve.

2️⃣ Skill Mastery & Deep Learning
Go beyond theory and develop real technical depth through consistent practice and project-based learning.

3️⃣ Proof-of-Work Portfolio
Show what you can do through strong, practical projects that demonstrate real impact and problem-solving ability.

4️⃣ Communication & Personal Branding
Build a visible, credible identity online by telling your story clearly and sharing your expertise confidently.

5️⃣ Resume, Applications & Positioning
Apply strategically with tailored resumes and positioning that make your fit obvious within seconds.

6️⃣ Interview Performance & Confidence
Improve your clarity, presence, and confidence through mock interviews and repeatable storytelling frameworks.

7️⃣ Adaptability, AI Fluency & Consistency
Embrace new tools quickly, use AI to boost your productivity, and deliver consistent results over time.

Climb these seven hills, and no AI will ever replace you.
You’ll be the one using AI to lead, build, and thrive.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

When I was in college, I used to spend hours switching between tabs, juggling notes, and racing deadlines.

Research felt endless. Writing essays was painful. And group projects? Pure chaos.

But students today have something we didn’t - AI tools that make studying smarter, not harder.

Here are the Top 30 AI Tools Every Student Should Know in 2025:

Research & Learning
• ChatGPT – Simplify complex topics and generate explanations.
• Elicit AI – Find academic papers and summarize them.
• ChatPDF – Chat with your PDFs instead of reading 40 pages.
• Consensus AI – Get evidence-based answers from scientific papers.
• Perplexity AI – Ask questions and get cited, well-researched results.

Writing & Essays
• Grammarly – Fix grammar, tone, and clarity instantly.
• Jasper AI – Create essays and articles with natural flow.
• Copyai – Generate ideas and write drafts faster.
• QuillBot – Paraphrase or summarize long text with ease.
• Wordtune – Rewrite sentences for smoother academic writing.

Productivity & Organization
• Notion AI – Manage notes, projects, and tasks in one place.
• Todoist AI – Organize study plans and to-do lists effortlessly.
• Motion – Automate your calendar and schedule study sessions.
• Readwise – Save and recall key highlights from research.
• Rowscom – Automate spreadsheets and simplify analysis.

Presentations & Creativity
• SlidesAI – Turn notes into professional slides in minutes.
• Gamma App – Design modern presentations without PowerPoint.
• Canva Magic Write – Create visuals and text with AI help.
• Framer AI – Build websites for projects visually and quickly.
• Beautifulai – Design aesthetic presentations automatically.

Study Assistance & Exam Prep
• Quizlet AI – Create flashcards and quizzes automatically.
• Socratic by Google – Scan questions and get instant explanations.
• TutorAI – Learn any topic interactively with AI tutors.
• Explainpaper – Break down complex research papers.
• Upword – Summarize long documents into clear notes.

Communication & Collaboration
• Otterai – Record and transcribe lectures or discussions.
• Firefliesai – Summarize meetings or group calls.
• Descript – Edit video/audio projects like a document.
• HeyGen – Create AI videos for projects or assignments.
• Claude AI – Collaborate with a context-aware writing assistant.

AI is not replacing learning - it’s enhancing how we learn.

The students who use these tools wisely will graduate with more time, more skills, and less stress.

Save this post and start building your AI-powered student toolkit today.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

 Best 30 AI Tools For Students

The AI Stack You Need for 2025–26

The professionals dominating in 2025 are not working harder, they are working smarter with AI.

From coding to content, here are 30 game-changing AI tools that will power your entire workflow 👇

1. All-in-One Assistants
ChatGPT / Claude / Gemini / Grok / Perplexity
→ Smarter reasoning, faster writing, and real-time conversations for research, brainstorming, and productivity.

2. Developers & Coders
GitHub Copilot • Replit Ghostwriter • Cursor AI • Windsurf AI • Codeium
→ Write, debug, and deploy code with AI copilots that understand your stack, spot bugs, and autocomplete your logic.

3. Productivity & Workflow
Notion AI • Taskade AI • Gamma App • Otter.ai • Superhuman
→ Manage projects, summarize meetings, design slides, and automate your daily to-dos seamlessly.

4. Creativity & Design
Leonardo AI • Runway ML • Pika Labs • Midjourney • Canva
→ Generate cinematic visuals, design slides, or animate text into videos - creativity has never been this fast.

5. Learning & Research
NotebookLM • Consensus AI • SciSpace • ChatPDF • Elicit.org
→ Extract key insights, summarize academic papers, and turn PDFs into Q&A engines.

6. Content Creation & Marketing
OpusClip • HeyGen • Copy.ai • Descript • Persona AI
→ Auto-create short clips, avatars, ad copy, and podcasts - everything you need for high-impact digital content.

AI is your career accelerator.
Learn how to integrate these tools into your workflow and become unstoppable.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

If you want to stand out in crowded markets, land more interviews, and grow faster…
you need to master the tools that top performers are already using.

Here are 60 AI tools every job seeker should know -

1. Productivity & Workflow
Tools that help you automate tasks, organize your work, and stay consistent every single day.

2. Personal Branding
AI that boosts your online presence, sharpens your writing, and helps you publish high-quality content.

3. Learning & Upskilling
Smart platforms that explain concepts, generate study plans, and accelerate your technical growth.

4. Interview Preparation
AI mock interview partners that help you practice answers, improve clarity, and build real confidence.

5. Resume & Portfolio
Tools that create ATS-friendly resumes and build portfolios that instantly prove your skills.

6. Job Search & Applications
AI systems that find relevant roles, tailor your applications, and dramatically increase interview callbacks.

AI won’t magically get you hired, but job seekers who use these tools will always move faster, learn faster, and get noticed faster than those who don’t.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

Stop using the wrong AI for your job search

Most job seekers are leaving opportunities on the table because they're using one AI tool for everything.

The truth? Each AI model has unique strengths. ChatGPT excels at personalized content. Perplexity delivers research-backed insights. Grok catches real-time trends. Gemini powers through documentation.

Using the wrong tool is like bringing a hammer to every job - sometimes you need a screwdriver.

Here's the framework that's helping smart candidates land offers faster:

- ChatGPT = Your Personal Career Assistant → Tailored resumes for each role → Cover letters that actually get read → Mock interview prep that feels real → Emails to recruiters that get responses

- Perplexity = Your Research Engine → Real salary data for negotiations → Expert insights on breaking into roles → Company research with actual sources → Skills analysis of top candidates

- Grok = Your Trend Detector → Emerging roles before they explode → What skills are trending in your industry → Hidden hiring signals on social media → Early adopter job search strategies

- Gemini = Your Productivity Powerhouse → Job descriptions → organized checklists → Messy resumes → perfect formatting → Email chaos → actionable task lists → Portfolio creation at scale

The difference?
❌ Using ChatGPT for everything = generic results 
✅ Using the right tool for each task = competitive advantage

Most candidates are still manually doing what these tools can automate in seconds.
While they're spending hours on one application, you're customizing 10.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

linkedin.com/in/tarun-c-isaac-

linkedin.com/in/franklin-mangwaira/

Sally Sultan

Most people only use ChatGPT… and miss out on tools that are 10x better for the exact things they need.

Because here’s the real plot twist:

Some AI tools outperform ChatGPT so brutally in specific tasks that using ChatGPT instead actually slows you down.

These tools are not generalists -
They're specialists built for speed, precision, and insane output quality in one domain.
Here is the full list of those tools.

1. Writing & Editing Tools (Jasper, Copyai, Grammarly)
Sharper, faster writing output with stronger clarity, tone, and consistency.

2. Research & Analysis Tools (Perplexity, Elicit, Consensus)
Delivers verified, citation-backed insights with deeper academic-level understanding.

3. Design & Creative Tools (Canva AI, Midjourney, Adobe Firefly)
Generates polished visuals and creative concepts far beyond text-only models.

4. Video & Audio Tools (Descript, HeyGen, ElevenLabs)
Produces studio-quality edits, voiceovers, and videos with unmatched precision.

5. Productivity & Automation Tools (Notion AI, Zapier, Motion)
Automates tasks and organizes workflows so you get more done with less effort.

6. Coding & Debugging Tools (GitHub Copilot, Replit AI, Cursor)
Generates cleaner code, fixes bugs faster, and accelerates development workflows.

7. Marketing & SEO Tools (Surfer SEO, Frase, Semrush AI)
Improves rankings with smarter keyword insights, optimized content, and data-driven briefs.

8. Business & Operations Tools (Gamma, Airtable AI, ClickUp AI)
Streamlines internal processes and enhances team execution with smarter automations.

9. Learning & Study Tools (Quizlet, TutorAI, ChatPDF)
Explains tough concepts simply and helps students learn faster and retain more.

10. Visual, 3D & Creative Media Tools (Runway, Kaiber, Luma AI)
Creates cinematic visuals, animations, and 3D assets with professional-grade quality.

These tools show that ChatGPT is just one piece of the AI ecosystem. Explore them, test what fits your workflow, and keep upgrading the way you work.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

 64 AI Tools that are actually than chatgpt

❌ Think AI Agents are just hype? This post will open your eyes.

AI Agents are no longer experimental, they’re reshaping every tech career, from Product Managers to Data Scientists. They automate repetitive tasks, boost decision-making, and free teams to focus on strategy and innovation.

From QA testing to data cleaning, cybersecurity monitoring to DevOps automation, AI Agents are becoming the invisible teammates every professional needs.

Here are some real-world use cases across tech roles:
1. Product Managers → Automate roadmap insights, competitor analysis, and release notes.
2. Software Engineers → Speed up testing, bug detection, and code generation.
3. Data Scientists → Optimize model training, experiment tracking, and bias detection.
4. Cybersecurity Specialists → Run phishing filters, anomaly detection, and auto-remediation.
5. DevOps Engineers → Automate deployments, error tracking, and resource scaling.
6. UI/UX Designers → Generate wireframe suggestions and usability reports.
7. AI/ML Engineers → Handle model deployment, drift detection, and explainable AI.
8. Data Engineers → Manage ETL automation, schema mapping, and data validation.

The takeaway? AI Agents are not replacing jobs - they’re evolving roles and helping tech professionals level up faster than ever.

Ready to future-proof your career? Learn how to integrate AI Agents into your workflow today.

If you found this useful, then please:
♻️ Repost to help others in your network
💭 Tag someone you know or Comment your thoughts below

I hope this helps, All the best!

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

You do not need to know coding to build a stunning website anymore.
AI can now design, write, and launch entire websites faster than most web agencies.

Here are 12 of the best AI tools for website creation that every entrepreneur, freelancer, and developer should know 👇

1. Cursor
An AI-powered code editor that helps you design, write, and debug websites effortlessly. Perfect for developers building custom sites.

2. Lovable
Instantly turns your idea into a beautiful, responsive website with smart branding and automated styling.

3. Durable
Generates a complete website with copy, images, and contact forms in seconds. Ideal for freelancers or startups.

4. Mixo
Launch startup websites instantly with AI-generated landing pages and email capture forms, no tech skills required.

5. LeiaPix
Builds stylish personal or business websites using simple natural language commands.

6. TeleportHQ
Creates responsive websites visually with real-time editing and AI-powered code generation.

7. Framer AI
Turns text prompts into modern, animated websites in minutes — perfect for product launches or portfolios.

8. Wix ADI
Wix’s AI design assistant that automatically creates personalized, ready-to-publish websites for any niche.

9. Unbounce Smart Builder
Uses AI to design high-converting landing pages optimized for your goals and conversions.

10. AppyPie Website Builder
A no-code AI builder that transforms simple ideas into mobile-friendly websites instantly.

11. 10Web
Converts your text prompts into WordPress websites optimized for SEO, speed, and performance.

12. Bookmark (AIDA)
AI-driven website builder that designs and customizes your site automatically in minutes.

Whether you are building a personal portfolio, startup site, or e-commerce store
AI can handle the design, copywriting, layout, and optimization for you.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

Creating a polished and visually engaging presentation used to take hours, but with AI tools, you can turn ideas into beautiful slides in minutes. 

Whether you’re preparing for a client meeting, pitch deck, classroom session, or team presentation, these tools help you save time and level up your design quality without needing expert skills.

Here are 10 powerful AI tools that make presentation building faster, smarter, and more creative:

1. Gamma
Creates visually appealing presentations instantly, helping you turn rough ideas into clean decks with minimal effort.

2. Pitch
Drafts full presentation decks in seconds using AI-powered layouts, content suggestions, and branded themes.

3. PresentationsAI
Transforms simple text or ideas into professional slide decks with automated design and brand-aware formatting.

4. Jasperai
Helps write slide content, outlines, headlines, and speaker notes—perfect for fast, ready-to-present scripts.

5. Beautifulai
Builds clean, polished decks using smart layouts that automatically adjust as you add or modify content.

6. Slidesgo
Offers AI-ready templates that can be customized easily and exported into Google Slides or PowerPoint.

7. Canva (AI Presentation Maker)
Converts your topic into a complete slide deck with matching visuals, icons, fonts, and auto-generated design suggestions.

8. Prezi (Prezi AI)
Creates dynamic, non-linear, zoom-style presentations that stand out and enhance storytelling.

9. Adobe Express
Enhances PowerPoint imports using AI-driven design recommendations, visuals, animations, and slide styling.

10. Venngage (AI Pitch Deck Generator)
Turns your ideas into pitch-ready decks with structured slides, icons, charts, and visually balanced layouts.

AI does not just save time, it makes your presentations look like they were built by a design team.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

Most job seekers use ChatGPT incorrectly....
But the top 1% use frameworks that turn AI into a career advantage.

If you want ChatGPT to write stronger resumes, better interview answers, and powerful career stories…
you need to stop giving vague prompts and start using proven structures.

Here are the Top 15 ChatGPT Frameworks every job seeker must know: 

1. STAR - Situation, Task, Action, Result
Turn messy experiences into clear, impact-driven stories that recruiters love.

2. CAR - Challenge, Action, Result
Highlight how you overcame obstacles and delivered meaningful results.

3. PAR - Problem, Action, Result
Show your problem-solving ability in a simple, powerful way.

4. CFR - Context, Fit, Results
Write personalised cover letters that explain why you are the perfect fit.

5. ATS-FIT - Keywords, Title, Skills, Fit
Optimize your resume using job-specific keywords and tailored alignment.

6. MAP - Match, Adapt, Personalize
Adjust your experience to match the company’s language and expectations.

7. MIRROR - Match Intent, Reflect Role, Optimize Resume
Rewrite your resume to mirror employer expectations with clarity.

8. KDA - Keywords, Duties, Accomplishments
Convert basic duties into measurable, achievement-focused bullet points.

9. SCAN - Skills, Competencies, Achievements, Needs
Craft strong professional summaries that match employer priorities.

10. CLEAR - Context, Leadership, Example, Achievement, Result
Showcase leadership experiences with structure and credibility.

11. PEAR - Project, Execution, Achievement, Relevance
Rewrite projects to highlight execution and job-aligned results.

12. ACE - Action, Contribution, Evidence
Demonstrate impact with clear actions and quantifiable proof.

13. STEP - Situation, Task, Execution, Proof
Turn raw experience into polished, value-driven achievements.

14. BRAG - Break Down Role, Responsibility, Achievement, Gain
Explain your role clearly while emphasizing standout accomplishments.

15. SMART - Specific, Measurable, Achievable, Relevant, Time-bound
Rewrite your goals or achievements with clarity and measurable depth.

ChatGPT becomes 10× more powerful when you give it structured frameworks,
because frameworks turn your experience into a story, and stories get you hired.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

 Top 15 ChatGPT Frameworks For Job seekers

If you’re preparing for job interviews in 2025, ChatGPT + a few smart AI tools can give you a huge advantage.

Here's a guide that breaks down the exact prompts you can use to prepare for any interview - from domain knowledge and behavioral questions to technical deep dives and resume-based prep.

Here’s are all the ChatGPT Prompts listed:

📌 Domain-Specific Prep
Prompt:
"I am preparing for an interview in the [Industry/Domain] (e.g., FinTech, Healthcare, EdTech). What are domain-specific questions and trends I should be aware of?"

📌 Resume-Based Interview Questions
Prompt:
"Based on my resume below, generate 10 likely interview questions and the best possible answers. My resume: [Paste your resume]"

📌 Role-Specific Technical Interview Prep
Prompt:
"I'm applying for a [Job Role] role at [Company Name]. Provide me with technical interview questions and sample answers based on the required skills: [List skills like Python, SQL, etc.]"

📌 Behavioral Interview Mastery
Prompt:
"Give me 10 common behavioral interview questions for [Job Role], and help me answer them using the STAR method. I have experience in [Your Experience/Projects]."

📌 Tailored Interview Question Generator
Prompt:
"Based on this job description: [Paste Job Description], what are the top 15 questions I might be asked in an interview? Include ideal answers based on industry best practices."

📌 Technical Deep Dive Prompts
Prompt:
"Explain [Topic] in simple terms for an interview. Then ask me 3 follow-up questions and answers an interviewer might ask to test deep understanding."
Example topics:
• How REST APIs work
• Big-O Notation
• Kubernetes
• Architecture
• ETL Pipelines

📌 Portfolio/Project Questions
Prompt:
"Here is a project I worked on: [Describe your project]. What questions might I be asked about this in a technical interview? Help me frame confident and clear answers."

📌 Mock Interview Roleplay Prompt
Prompt:
"Act like a hiring manager for a [Job Role] position at [Company Name]. Ask me 5 rounds of interview questions. After each of my answers, give me feedback and suggest improvements."

With the right prompts and supporting AI tools, interview prep becomes faster, clearer, and far more effective - helping you walk into every interview confident and ready.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator, where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

Most candidates send 100+ job applications hoping someone notices.

But the smartest job seekers in 2025?

They are using ChatGPT as a job-hunting assistant - to automate, personalize, and accelerate every step.

Here are 7 AI Prompts that can actually help you land a job 👇

1. Daily Job Hunter
Ask ChatGPT to scan job boards every morning for roles that match your title, location, and pay range.

2. Resume Rewriter
Paste your resume + job description, and let ChatGPT tailor your resume to match it perfectly.

3. Cover Letter Builder
Create personalized, persuasive cover letters that align with your experience and tone.

4. Interview Prep Coach
Simulate real interviews and get structured answers using the STAR method.

5. Salary Negotiation Script
Draft confident emails for salary discussions backed by data and skills.

6. Skill Gap Filler
Close gaps fast by turning weaknesses into a 14-day learning plan.

7. Company Insider Report
Get a quick 1-page summary before every interview - company news, culture, and competitors.

AI won’t just get you a job, it’ll help you apply smarter, faster, and with more confidence.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

7 Prompts To Actually Get Hired

The job market is changing faster than ever and by 2026, the roles that dominate hiring will look very different.

If you want to stay relevant, competitive, and future-proof, you must understand where the industry is heading.

Here is the 2026 Job Market Map, the roles every job seeker should know 👇

1. Software Development Domain
From Front-End, Back-End, Full-Stack, Mobile, QA, to DevOps - these roles power the apps, systems, and products we use daily.

2. Data Domain
Data Analysts, Engineers, BI Developers, DBAs, Data Stewards, ETL Developers - all focused on interpreting, managing, and transforming data for better decisions.

3. Cloud & Infrastructure Domain
Cloud Architects, SREs, Platform Engineers, Network Admins, Cloud Security Engineers, Infrastructure Engineers - the backbone of modern digital systems.

4. Cybersecurity Domain
Cybersecurity Analysts, Pen Testers, SOC Analysts, Security Architects, Incident Response Leads, GRC Specialists - protecting organizations from rising threats.

5. AI & Machine Learning Domain
ML Engineers, AI Researchers, MLOps Engineers, NLP Engineers, GenAI Engineers, Computer Vision Engineers - building the future of intelligent systems.

6. Product & Design Domain
Product Managers, UX/UI Designers, Product Marketers, Product Owners, Design Researchers, Business Analysts - shaping user experience and business outcomes.

Whether you’re switching careers or leveling up, understanding these domains helps you choose the right skills, the right roles, and the right opportunities for 2026 and beyond.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

Your phone can now replace half the tools you use, if you know the right AI apps.

Most people still use AI only on their laptops…
but the smartest job seekers, students, and creators use AI on the go with these free mobile apps.

Here are 14 Free AI Mobile Apps you should be using 👇

1. Character AI
Chat with AI assistants, companions, and custom personalities.

2. Pi
A warm, conversational AI helper for everyday guidance.

3. Perplexity
Ask anything and get fast, cited answers powered by AI.

4. ChatGPT
The most popular AI chat tool, now fully mobile and even smarter.

5. ELSA Speak
Improve English pronunciation with instant feedback.

6. AI Writer
Write essays, emails, and posts with minimal effort.

7. SwiftKey AI Keyboard
Type faster with predictive AI suggestions and auto-writing.

8. Socratic
AI-powered homework help with step-by-step explanations.

9. Aivah.ai
Create interactive AI avatars directly from your phone.

10. CoPilot
A strong ChatGPT alternative for fast, accurate responses.

11. DaVinci
Generate creative photos using AI.

12. VideoLeap
Edit videos with AI tools for effects, cuts, and enhancements.

13. Dolby On
Record studio-quality audio right from your phone.

14. Pixa Motion
Turn images into dynamic, moving photos with ease.

AI on your phone means you can learn, create, edit, write, and work anytime, anywhere.
The future belongs to those who use these tools daily, not occasionally.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator, where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

Don’t learn AI, If you want to lose your job !!

On the other hand Developers who master AI will not just save their job but they will also lead teams.

The smartest engineers are not writing every line of code,
They are leveraging AI to review, build, secure, and ship faster than ever.

Here are the top AI tools every modern developer should know :

1. Code Reviews - qodoai
Your personal AI QA teammate.
Automatically writes test cases, detects bugs early, and reviews pull requests like a senior engineer.

2. Frontend UI - Locofyai
Turns Figma designs into React or Next.js code in minutes.
Pixel-perfect, production-ready, and saves hours of front-end development.

3. Smart Docs - Mintlify
Writes clean, auto-generated documentation directly from your codebase.
Finally, docs that developers actually want to read.

4. Security - DeepCode (by Snyk)
Your AI security guard for code.
Scans repos, detects vulnerabilities, and suggests fixes in real time.

5. Prototyping - Lovable.dev
Build and deploy full-stack apps with AI that codes MVPs in hours, not weeks.
Describe your idea - Lovable writes, tests, and launches it for you.

AI is not replacing developers, it is upgrading them.
The ones who build with AI will always stay ahead of those who build without it.

And if you want to turn recruiter visibility into real job offers, that’s exactly what we teach inside the Job Hunting Accelerator,
where Big Tech recruiters train international students to win in today’s market: https://lnkd.in/gxt5X9BC

 Forget ChatGPT These AI Tools Are Built For Developers

14 job hunting lessons that helped this international student (ex-IBM, Google, Microsoft, now Fireworks AI) complete 11 internships and land multiple tech offers:

I recently had Aishwarya Srinivasan on my podcast and what an incredible inspiration she is for all international students out there. 

She moved from India to the U.S. for her master’s at Columbia, broke into AI with no prior U.S. work experience, and went on to land roles at IBM, Google, Microsoft, and now leads at Fireworks AI. Not to mention, a whopping 11 internships on her resume. 

1. During her undergrad in India, Aishwarya did 11 internships because she found the curriculum outdated and wanted hands-on experience. She picked up new languages like R on the job when needed. She didn’t wait for skills, she learned as she went.

2. Instead of waiting for opportunities, she approached startups and offered to work on projects, sometimes as a volunteer. She reached out directly, showing eagerness to contribute, not just asking for jobs. This gave her exposure to real-world tools early.

3. She built her LinkedIn as a portfolio, documenting every project, conference talk, and internship. Her posts were about sharing her work, not trying to go viral or fit an influencer mold. Recruiters and researchers could see her growth at a glance.

4. Networking wasn’t limited to job fairs, she joined university clubs, attended every event, and made a habit of talking to people from other teams, even during coffee breaks. She wanted to hear what others were working on and learn from their experience. This ripple effect led to more invitations and connections.

5. Aishwarya never planned her career path in advance. She made each next move based on what new skills or exposure a role could give her. This openness helped her pivot between IBM, Google, Microsoft, and startups.

6. She didn’t worry about building the perfect resume before starting; instead, she focused on doing the work. Every project or talk became a line on her LinkedIn. Over time, this built a dense, keyword-rich profile that attracted recruiters.

7. When applying for roles, she targeted a handful of companies that matched her interests, not hundreds at random. She focused her learning and networking towards what those specific teams cared about. That’s how her skills aligned closely with hiring needs.

P.S: Every week, I share free insights like this in my job hunting community with over 4100+ people. If you want to get hired in the next 3 months, join Yudi J academy: https://lnkd.in/gWWZJDEf

We’ve got modules on everything:
- Networking, resume, job strategy
- focus sessions with the community
- live sessions with Hiring Managers
- guest sessions with recruiters and immigration lawyers

With 100s of stories to back it all up. 

Continued in comments….

If I were an international student in 2025 and wanted to break into tech, I wouldn't waste my time chasing a 4.0 GPA.

I've talked to hundreds of people who work in tech, from mid-level companies to Big Tech.

And here's what I learned: No one cares about your GPA as long as you keep it above 3.0.

Why? Because proof of work beats academic performance every single time.

Let me show you what I mean.

Student A has practical skills with a 3.2 GPA. They've built 3 side projects, contributed to open source, and completed 2 internships.

Student B has a 4.0 GPA but zero proof of work. No projects. No internships. Just perfect grades.

Who gets the job? Student A. Every time.

Companies want to see what you can build, not what you can memorize.
They want proof that you can solve problems, ship code, and work on a team.

Your portfolio matters more than your transcript.
Your ability to explain your projects in an interview matters more than your calculus grade.

So, focus on this instead:
- Network and land opportunities that help you learn practical skills
- Build projects, contribute to open source, join a non-profit/volunteer
- Grab internships, even if they pay less at first, you’ll learn a lot working

Let me conclude the post with this: imagine you were running a team and you had to onboard a developer, who would you choose?

The one who has been building since their 1st year in college, or the one who hasn’t worked on any major project in all of their student life? 

Create proof of work that speaks louder than any GPA ever could.

P.S: Every week, I share free insights like this for international students and job seekers. Join 45,000+ readers who are turning their U.S. journey into a success story worth telling.
Link: https://lnkd.in/gce2ipwc

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Pritesh Jagani

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The Battle

Aishwarya Srinivasan

Pritesh Jagani

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

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Performance ScoreAishwarya Srinivasan's LinkedIn Performance