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

I teach AI Agents and Lead Gen | Lead Gen Man(than) | 100K+ students

For brand collaboration: manthan@leadgenman.com

Hi, I'm Manthan. 

For my TL;DR people:
I help entrepreneurs, business owners, and students transform their businesses with my content around AI agents and automation into profitable ventures for the future. I post stories, actionable tips, life lessons, failures, and everything I experience along the way. It's a journey you will want to be part of!

Here's the extended version, lol:
I run my AI Automation Agency and create content about AI. Here, I share everything I know when it comes to automation, agency monetization, marketing, and GTM strategies. I'm real with everything I do online; nothing is sugar-coated.

- I've a bachelor's degree in computer science and technology.
- I've built a following of over 200,000+ students across all of my course platforms.
- I've worked with YC-backed companies like Retell AI, Vectorshift AI and Persana AI just to name a few.
- I've been a panel speaker at multiple international events.

You can find my courses here: courses.leadgenman.com
Want us to build AI-Workflows for your business: leadgenman.com
Want us to build Lead Gen Outbound Systems: leadgenman.com/leadgen

Learn from me; grow with me.

Manthan Patel

Build your first AI Voice Agent with Retell AI: https://lnkd.in/dgzuQrH5 

Here's the link to free course (10+ hours): https://lnkd.in/duU4_gN3

CouponCode = NONOVEMBER [expires in 4 days]

11 hours of lectures. 17 real AI voice projects.
I spent months building this.

Every single thing I've learned from building and deploying AI voice automation to handle 10,000+ calls

This isn't any other breakdown.
It's a full AI voice agent masterclass.

I built it for people who are done wasting time on manual calls.
People who want to go from "figuring out voice AI"
to building call systems & charge $5k+ for deployments.

Here's what's inside:
→ The fundamentals of AI voice agents and how it works.
→ Build multi-agent workflows and design conversational flows that scale without falling apart.
→ Real call systems built with Retell AI, Make, n8n, and Go HighLevel.
→ 13+ AI voice agents - from appointment booking to outbound call automation.

This is the kind of resource I wished when I started.
Now it is and it's completely free.

If you're serious about AI voice agents & building actual call systems

Creative teams have massively improved CLIENT PRESENTATION with this here!
 
This Nano Banana + Gamma + n8n WORKFLOW creates personalized pitch decks, proposals, creative briefs & presentations on autopilot getting enterprise-quality results in minutes.
 
Here's what it does:
 
→ Takes client data from a simple web form
→ AI expands your brief into detailed creative concepts
→ Powered by Nano Banana pro, Gamma API generates beautiful pitch decks/proposals/one-pagers instantly
→ Add personalized elements (mood boards, case studies, pricing) automatically
→ Bulk generate 50+ customized presentations from your client database
 
No copywriting skills. No design bottlenecks. No outsourcing fees.
 
Just 24/7 automated presentation creation that turns your client data into pitch-ready materials while you focus on creative work with Gamma and Nano Banana Pro.
 
Perfect for creative directors pitching new concepts, design teams needing instant client decks, or anyone tired of fighting with PowerPoint.
 
Want the workflow + how I set it up (step-by-step)?

Try it here: https://lnkd.in/d5X_2F_Y

Here’s the link to the resource: https://lnkd.in/gJAT9eKH
 
*P.S. marketing & creative firm clients saved 10+ hours per week with this automation btw.

#gammapartner

AI Voice Agent (Explained).
 
Most people think voice AI is just text-to-speech with extra steps. They're missing the entire orchestration layer that makes it actually work.
 
After shipping 30+ voice agent projects over the last 1.5 years, here's what's really happening under the hood when your AI picks up the phone:
 
𝗧𝗵𝗲 𝗩𝗼𝗶𝗰𝗲 𝗔𝗜 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
At its core, it's an orchestration layer over three modules:
 
1️⃣ 𝗧𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗯𝗲𝗿 (𝗦𝗽𝗲𝗲𝗰𝗵-𝘁𝗼-𝗧𝗲𝘅𝘁)
Listens to raw audio → Converts to text with Whisper/Deepgram
Handles accents, background noise, interruptions
 
2️⃣ 𝗠𝗼𝗱𝗲𝗹 (𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗟𝗟𝗠)
Processes context → Generates responses
Uses GPT-4, Claude 3, custom prompts
 
3️⃣ 𝗩𝗼𝗶𝗰𝗲 (𝗧𝗲𝘅𝘁-𝘁𝗼-𝗦𝗽𝗲𝗲𝗰𝗵)
Converts text → Natural speech
ElevenLabs, PlayHT voices with emotion
 
Platforms like Retell AI AI take these three modules, optimize the latency, manage the scaling & streaming, and orchestrate the conversation flow to make it sound human.
 
𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗥𝗲𝘁𝗲𝗹𝗹 𝗔𝗜 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁:
 
→ Natural, Human-Like Conversations (no robotic pauses)
→ High Scalability and 24/7 Availability
→ Deep Customization and Control
→ Seamless Integration and Monitoring
→ Enterprise-Grade Security and Compliance
 
𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗯𝘂𝗶𝗹𝘁:
 
• Customer calls in to check order status → AI handles 24/7
• Patient calls after hours to schedule → Books appointment automatically
• Lead fills out website form → AI calls within 30 seconds to qualify
 
𝗧𝗵𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗹𝗼𝗰𝗸𝘀 𝗮𝗿𝗲 𝘀𝗶𝗺𝗽𝗹𝗲𝗿 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸:
 
𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹𝗶𝗻𝗴
→ Book appointments, check inventory, process payments
→ Update CRM records in real-time
 
𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄
→ Guided pathways, fallback handling
→ Multi-turn conversations, intent routing
 
𝗪𝗮𝗿𝗺 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿
→ Seamlessly hands off to humans
→ Passes complete context and conversation history
 
𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴
→ Track performance metrics
→ Sentiment analysis, call outcomes
→ Latency tracking, speaker rates
 
𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.
 
• Call data flows instantly to Make 
• Connect voice conversations to 1,500+ apps
• No code required - just webhooks
 
Retell sends data → Make catches it → Magic happens:
 
𝗡𝗼 𝗰𝗼𝗱𝗶𝗻𝗴 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 (𝗯𝘂𝘁 𝗱𝗲𝘃𝘀 𝗰𝗮𝗻 𝗴𝗼 𝗱𝗲𝗲𝗽):
 
→ Drag-and-drop conversation flows
→ Pre-built voice & LLM providers ready
→ Deploy to phone number in seconds
→ Full API access for developers who want control
→ WebSocket streaming, custom webhooks, BYOC support
 
The setup that used to take weeks now takes minutes. The infrastructure that cost thousands now costs pennies per call.
 
AI Voice Agents are automating repetitive calls so humans can focus on complex problems.
 
Build Your First AI Voice Agent Today with Retell AI.
 
Over to you: What's the first process you'd automate with a voice agent?

AI Voice Agent

LLMs are AI models, but not all AI models are LLMs.

Building upon traditional approaches, these eight specialized models advances AI's ability to understand, reason, and generate across different domains and modalities.

Here's architectures of these 8 state-of-the-art models:

1️⃣ LLMs (Large Language Models)
These foundational models process text token-by-token, enabling everything from creative writing to complex reasoning.

2️⃣ LCMs (Large Concept Models)
Meta's newer approach encodes entire sentences as "concepts" in SONAR embedding space, transcending word-level processing.

3️⃣ VLMs (Vision-Language Models)
These multimodal combine visual and textual understanding to interpret images and generate text about them.

4️⃣ SLMs (Small Language Models)
Compact yet powerful models optimized for edge devices with tight energy and latency constraints.

5️⃣ MoE (Mixture of Experts)
These models activate only relevant expert networks per query, dramatically improving efficiency while maintaining performance.

6️⃣ MLMs (Masked Language Models)
The OG bidirectional models that look at both left and right context to understand meaning in text.

7️⃣ LAMs (Large Action Models)
Emerging models that bridge understanding with action, executing tasks through system-level operations.

8️⃣ SAMs (Segment Anything Models)
Foundation models for universal visual segmentation with pixel-level precision.

Here's how these specialized architectures differ from traditional approaches:

Traditional AI:
- One model architecture applied to many tasks
- Often excels in one area but underperforms in others
- Requires significant compute and data for general capabilities

Specialized Architectures:
- Purpose-built for specific modalities and tasks
- Optimized for particular constraints (speed, size, precision)
- Open up new capabilities like concept-level understanding, visual segmentation, and action execution

Understanding these distinctions is essential for selecting the appropriate model architecture for specific applications, making more effective and contextually appropriate AI interactions.

These specialized models aren't alternative approaches; they're redefining technologies.

✅ Process information in ways that match specific tasks and domains
✅ Optimize for different constraints like size, speed, accuracy, and multimodality
✅ Generate more reliable, contextual, and useful outputs for targeted applications

Matching the right architecture to the right task is essential. It saves time, boosts productivity, and creates a more natural flow in AI-human interactions.

Over to you: What specialized AI architecture do you think would benefit your work the most?

AI voice agents are now running entire cold call campaigns (without human intervention)

Here's how businesses are automating their outbound sales process:

The Setup:
Retell AI enables batch calling campaigns with personalized variables pulled directly from Google Sheets. No CRM required.

How it works:

Step 1: Create Your Agent
→ Define the system prompt with your pitch
→ Add dynamic variables like {{first_name}} and {{company_name}}
→ Select voice model and language preferences

Step 2: Personalization at Scale
→ Upload CSV with prospect data
→ Agent pulls name and company for each call
→ Moves through the list automatically after each conversation

Step 3: Qualification & Booking
→ Agent identifies interested prospects
→ Collects email addresses on the spot
→ Books meetings through Cal.com, Inc. integration
→ Sends calendar invites automatically

What makes this different:
• Each call uses personalized data (not generic scripts)
• Prospects hear their name and company mentioned naturally
• Zero manual dialing or follow-up required
• Scales to hundreds of calls simultaneously

The technical stack:
- Retell AI for voice infrastructure
- Google Sheets for contact management
- Calendar for appointment scheduling
- No coding or CRM setup needed

This technology is upscaling how outbound sales teams operate. Instead of 50 dials per day per rep, companies are running 500+ personalized conversations daily.

Here’s here breakdown of AI Voice Agent: https://lnkd.in/d78zta6D

Build your first AI Voice Agent here: https://lnkd.in/dMmTWra7 

Over to you: What industry would benefit most from this approach?

In 30 minutes, I built a complete AI voice agent that books appointments, qualifies leads, and handles calls 24/7.
(free video guide)

The full training just dropped to teach you how to do the same.

It's designed for anyone who wants to automate their calling workflows using Retell AI.

You will learn:

- The 30-minute setup to build end-to-end AI voice agents with zero coding
- How to connect Retell AI, n8n, Make, HighLevel, and your CRM
- Why voice agents are the missing link between manual calling and full automation
- Real examples: AI booking appointments, qualifying leads, following up automatically

Imagine a voice agent that can:
- Greet callers by name from your database
- Handle objections and answer questions in real-time
- Capture emails and preferred time slots
- Book calendar appointments without human involvement
- Send instant confirmations to prospects

If you recognize the power of AI voice agents but don't know how to get started, this video is for you.

No more missed calls. No more manual scheduling. It just works.

Build your first AI Voice Agent: https://lnkd.in/dfzFDRYC

Here's the full video guide series on AI Voice Agent: https://lnkd.in/djTqFk-v

𝗜𝗳 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗔𝗜 𝘃𝗼𝗶𝗰𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, 𝘆𝗼𝘂 𝗡𝗘𝗘𝗗 𝗧𝗢 𝗞𝗡𝗢𝗪 𝘁𝗵𝗶𝘀 𝘀𝗶𝘅-𝗹𝗮𝘆𝗲𝗿 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸! 🛠️

AI voice agents are evolving fast, opening up many possibilities for a new paradigm of customer interaction. In today's world, businesses still use scripted IVR menus and static call flows that frustrate customers and waste time. With AI voice agents, we can create natural conversations that adapt in real-time, handling thousands of concurrent calls with low latency.

There are many tools and possibilities for AI voice agents today, creating both exciting opportunities and a lot of noise. To cut through the confusion, here's a framework of six key tech stack layers you can leverage to build powerful, production-ready voice automation:

Let's break it down: ⬇️

1. 𝗩𝗼𝗶𝗰𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺  
Start with Retell AI as your foundation.  
No-code builder + developer API, 30+ languages, 99.99% uptime.  
→ Orchestrates STT, LLM, and TTS with sub-800ms latency for human-like conversations.

2. 𝗖𝗵𝗼𝗼𝘀𝗲 𝗬𝗼𝘂𝗿 𝗟𝗟𝗠 𝗕𝗿𝗮𝗶𝗻:  
Connect GPT-5 for complex reasoning, Gemini for long context, or custom models.  
The AI decides what to say, how to respond, and when to take action.  
→ Think: the intelligence that powers every decision your agent makes.

3. 𝗔𝗱𝗱 𝗩𝗼𝗶𝗰𝗲 & 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘁𝘆:  
Select TTS providers like ElevenLabs or Cartesia for natural voices.  
Clone your voice or choose from libraries, control speed, emotion, and tone.  
→ This is what makes your agent sound human, not robotic.

4. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗧𝗼𝗼𝗹𝘀 & 𝗗𝗮𝘁𝗮:  
Connect calendars, CRMs and databases.  
Book appointments automatically, pull customer data during calls, update records in real-time.  
→ Like giving your agent hands to actually do things, not just talk.

5. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀:  
Use n8n, Make, or Zapier to connect agents to existing systems.  
Trigger actions during or after calls, send emails, create tickets, build complex automations.  
→ Turns voice agents into full business process automation.

6. 𝗔𝗱𝗱 𝗧𝗲𝗹𝗲𝗽𝗵𝗼𝗻𝘆 & 𝗦𝗰𝗮𝗹𝗲:  
Connect phone numbers via Twilio, Telnyx, or Retell's built-in telephony.  
Handle inbound and outbound calls, manage routing, scale to hundreds of concurrent calls.  
→ Most voice agents fail here — this is production deployment, not demos.

Understanding this tech stack can improve deployment speed, reliability, and customer satisfaction, leading to more sophisticated and scalable AI voice automation.

[𝗡𝗼𝘁𝗲 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲𝘀𝗲 𝗹𝗮𝘆𝗲𝗿𝘀 𝘄𝗼𝗿𝗸 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿, 𝗻𝗼𝘁 𝗶𝗻 𝗶𝘀𝗼𝗹𝗮𝘁𝗶𝗼𝗻.] 🛠️

This tech stack is adapted from Retell AI's production deployment framework for building AI voice agents that actually work at scale.

Save 💾 ➞ React 👍 ➞ Share ♻️

Build your first AI Voice Agent with Retell AI: https://lnkd.in/dgzuQrH5

Building AI Voice Agents

💡 Gamma + n8n = 💸

I just automated 50 client proposals in 10 minutes using Gamma's API + n8n
No design skills. No manual work. Just data in → polished decks out.

With Gamma API, creating automated presentations, websites, documents & carousels is now as easy as filling a form - watch AI generate stunning visuals in seconds!

Drop "GAMMA" in the comments and I'll DM you everything you need:
✅ n8n Gamma workflow template
✅ Video Setup tutorial [5 minutes]
✅ 15 Gamma Prompts

Happy Presenting! 🎨✨

#gammapartner

I spent 2 months trying to hire a developer in Poland for my AI automation agency.
 
$1,300 in legal fees.
47 emails with lawyers.
Zero progress.
 
Then I found out 35,000+ companies (including Shopify, Nike, and Reddit) solve this in hours using Deel.
 
Here's what you should know about global hiring in 2025:
 
The Old Way (What I Did):
→ Research local labor laws for days
→ Set up legal entities ($50K+ per country)
→ Find local payroll providers
→ Navigate compliance nightmares
→ Wait 2+ months to hire ONE person
→ Pay lawyers more than the actual employee
 
The Deel Way:
→ Click hire
→ Deel handles compliance in 150+ countries
→ Employee onboarded in 48 hours
→ Done.
 
They own 250+ legal entities worldwide. That means they can directly hire in Paris, Singapore, or São Paulo without middlemen.
 
When you're building AI agents and automation systems, your best talent is global. Ukrainian ML engineers. Brazilian developers. Indian AI researchers.
 
Deel processes $22B in payroll annually because they solved the one problem every growing company faces: hiring the best people, regardless of location.
 
They just raised $300M. While everyone chased AI unicorns, Deel quietly built a $17.3B empire solving payroll. Not because hiring is sexy. Because removing friction from global work is the future.
 
By 2030, NOT being able to hire globally will feel as outdated as dial-up internet.
 
The Real Innovation:
 
Deel just launched Anytime Pay. Your team members can access their earned wages instantly, multiple times per month. No more waiting for payday. No more late fees. No predatory payday loans at 40% interest.
 
78% of workers would struggle if their paycheck was delayed just one week. Deel spent 4 years building the infrastructure to end that.
 
Over to you: What's stopping you from hiring globally right now?
 
Drop a comment "DEEL" - I'll share the exact process I use to build remote AI teams across 6 countries.

The AI Agent Tech Stack Everyone's Using in 2025 (but nobody's talking about)

While everyone's debating which LLM is best, the real builders are quietly assembling this exact stack:

The Orchestration Layer
↳ LangGraph 
↳ Built for stateful, long-running agents
↳ Used by teams shipping to production

The Local Brain
↳ Ollama 
↳ Run GPT/Llama/Mistral on your machine
↳ Zero API costs, full control

The Memory Layer
↳ ChromaDB for vector embeddings
↳ SQLite for conversation history
↳ Agents that actually remember context

The Interface Stack
↳ Streamlit → Web apps in minutes
↳ FastAPI → Production-ready APIs
↳ Docker → Deploy anywhere

Here's what most people miss:

You don't need a CS degree.
You don't need expensive infrastructure.
You don't need to wait for "the perfect model."

This stack handles:
• Multi-step workflows
• Persistent agent state
• Human-in-the-loop decisions
• Complex task orchestration

The companies winning with AI agents aren't using magic.

They're using this stack.

Start with one component.
Master it.
Add the next.

That's how you build AI agents that actually work.

Save this for when you're ready to build.

Over to you: What's your tech stack with AI agents?

AI Agent Starter Pack

I just built an AI voice agent.

And it sounds more human than most customer service reps.

How to build your first AI voice agent with Retell AI:

Step 1 → Create your agent
↳ Go to Retell AI, hit "Voice Agent"
↳ Retell handles the entire voice stack for you

Step 2 → Configure your agent
↳ Pick your LLM (GPT-5, Claude, or others)
↳ Choose a voice model (ElevenLabs, Cartesia)
↳ Select your language (30+ supported)

Step 3 → Give it a job
↳ Write a simple prompt about how it should speak
↳ Define your use case (support, sales, scheduling)
↳ Set conversation goals and personality

Step 4 → Test your agent
↳ Hit "Call" and have a real conversation
↳ Tweak the tone until it feels right
↳ Test edge cases with the built-in simulator

Step 5 → Automate it
↳ Generate your API key in Retell
↳ Connect to your CRM, calendar, or workflow tools
↳ Deploy in production with one click

Step 6 → Extract insights
↳ Every call creates a full transcript
↳ Track call quality, sentiment, and completion rates
↳ Turn conversations into actionable data

Retell AI achieves ~800ms latency.

That's close to human response time.

- It handles interruptions naturally (barge-in detection)
- Books appointments through Cal(.)com or Calendly
- Updates your CRM in real-time with automation tools like Make or n8n
- Remembers context across the entire conversation

And it works in 30+ languages.

Same agent. Different language.

Supercharge your Call Operations with Voice AI Agents.

The future doesn't just talk back.

It schedules, qualifies, and converts.

Build your first agent → https://lnkd.in/dgzuQrH5

Save this for later. Repost ♻️ for others.

Create your First AI Voice Agent

You're 10 minutes away from making this kind of presentation with AI.
 
This week, I used Gamma Agent to rebuilt an entire pitch deck ecosystem, from a single prompt to research-backed slides with citations, executive summaries, and multi-audience versions.
 
To do the same, all you need is Gamma and a basic prompt. No complex setup, just tell Agent what you need like you're talking to a teammate.
 
Here's what it actually does:
 
➡️ Drop a URL: Agent pulls the article and turns it into slides
➡️ Ask for research: It searches the web and adds competitor insights directly
➡️ Request fact-checking: Agent verifies claims and adds citations automatically
➡️ Change audience: Tell it "make this for executives" and watch it reframe perfectly
➡️ Generate summaries: 20-slide deck becomes a 2-page brief instantly
 
Now imagine this:
 
Hours of manual research → Done in seconds
Hiring a designer for updates → Agent reshapes content on demand
 
But the real unlock isn't speed here, it's having an AI that actively improves your content instead of just formatting it.

Try Gamma today: https://lnkd.in/g5VGP8Sz 
 
I compiled 15 Gamma prompts that work for every niche. Want them?
 
Like this post + comment "GAMMA" and I'll DM you all 15 prompts (need to be connected).
 
#gammapartner

I just built a YouTube Thumbnail A/B Testing app in 5 minutes (𝘃𝗶𝗯𝗲 𝗰𝗼𝗱𝗲𝗱 𝗶𝘁)
 
The gold rush is happening right now. Millions of people are building apps with AI, and it's completely removing that line between technical and non-technical people.
 
I'm seeing this everywhere:
→ Product managers building internal tools
→ Designers shipping their own apps
→ Marketing teams creating automation workflows
→ Salespeople building their own CRM features
 
Today I built a YouTube Thumbnail Editor with Replit to split test different versions.
 
𝗧𝗛𝗘 𝗘𝗡𝗧𝗜𝗥𝗘 𝗣𝗥𝗢𝗖𝗘𝗦𝗦:
1. Typed what I wanted in plain English
2. Described the features (thumbnail preview, presets, export)
3. Replit handled EVERYTHING else
 
No setup. No package management. No database config. No authentication headaches.
 
𝗪𝗛𝗔𝗧 𝗜𝗧 𝗔𝗖𝗧𝗨𝗔𝗟𝗟𝗬 𝗗𝗢𝗘𝗦:
• Upload your base thumbnail
• Add different elements (sunglasses, caps, lighting effects)
• Preview changes instantly
• Export different versions for A/B testing
• All powered by AI image generation
 
The craziest part is it integrated Gemini API automatically. I didn't even touch API keys.
 
𝗛𝗘𝗥𝗘'𝗦 𝗪𝗛𝗬 𝗧𝗛𝗜𝗦 𝗠𝗔𝗧𝗧𝗘𝗥𝗦:
 
We're entering an era where everyone gets to build. Your ideas don't have to die in a Google Doc anymore. You can literally vibe code your way to a working app.
 
From idea → working app → deployed → live domain in minutes.
 
Not hours. Not days. MINUTES.
 
And once you build it, Replit handles:
✓ Database
✓ Deployment
✓ Hosting
✓ Monitoring
✓ Everything for production
 
Plus you can build on your phone. Yes, there's a mobile app where you can code from anywhere.
 
The future isn't about knowing how to code. It's about knowing what to build.
 
Don't miss out on this gold rush. Start building your ideas today.
 
Try it yourself: https://lnkd.in/drwiagQi
 
Want my full guide on building and monetizing apps with AI?
Comment "APP" and I'll send it straight to your DMs.
 
Over to you: What app would you build if you knew it would only take 30 minutes?

98% of your website visitors disappear without a trace.
 
Every marketer knows this pain:
 
You spend thousands on ads. Traffic floods your site. Forms get ignored. Visitors bounce. And your best leads? Gone forever.
 
That's exactly what Apollo Inbound just solved.
 
𝗧𝗛𝗘 𝗧𝗥𝗨𝗧𝗛 𝗔𝗕𝗢𝗨𝗧 𝗜𝗡𝗕𝗢𝗨𝗡𝗗:
• Long forms kill conversion (who wants to fill 15 fields?)
• Anonymous visitors leave without identifying themselves
• Slow follow-up means lost momentum
• You need 5+ tools just to manage the flow
 
I've been using Apollo's new Inbound solution and it's future of inbound closing.
 
𝗛𝗘𝗥𝗘'𝗦 𝗪𝗛𝗔𝗧 𝗜𝗧 𝗔𝗖𝗧𝗨𝗔𝗟𝗟𝗬 𝗗𝗢𝗘𝗦:
 
→ 𝗥𝗲𝘃𝗲𝗮𝗹𝘀 𝗮𝗻𝗼𝗻𝘆𝗺𝗼𝘂𝘀 𝘃𝗶𝘀𝗶𝘁𝗼𝗿𝘀: See company name, team size, and key decision makers instantly (even if they never fill a form)
 
→ 𝗦𝗵𝗼𝗿𝘁𝗲𝗻𝘀 𝗳𝗼𝗿𝗺𝘀 𝘁𝗼 𝗢𝗡𝗘 𝗳𝗶𝗲𝗹𝗱: Visitors just enter email. Apollo enriches everything else automatically - name, company, role, location, intent
 
→ 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 & 𝗯𝗼𝗼𝗸𝗶𝗻𝗴: AI scores every lead in real-time and routes to the right rep. Qualified buyers can book meetings directly on your site
 
→ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗻𝘂𝗿𝘁𝘂𝗿𝗲: If they don't convert immediately, Apollo keeps them warm with smart follow-ups
 
Everything happens in ONE platform. No more juggling forms, routers, schedulers, enrichment tools, and follow-up sequences.
 
From anonymous visitor → identified lead → qualified opportunity → booked meeting.
 
Just a faster path from website traffic to pipeline.
 
𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲:
✓ More qualified leads (you capture the 98% who usually ghost)
✓ Faster speed-to-lead (instant routing, not manual sorting)
✓ Less tool fatigue (one platform vs five)
 
Finally… inbound that actually works.
 
Want to see it in action?
 
Sign up for a demo here: https://lnkd.in/dz7W_QGJ
 
Over to you: What's your biggest frustration with inbound right now?
 
#ApolloInbound #ApolloPartner

Apollo Inbound

Most people don't know how to set up Google Sheets credentials for n8n.
 
Let me show you how in under 5 minutes.
 
The problem is Google Cloud Console looks scary. 5,000 menu options. Technical jargon everywhere. Most people click around randomly, get frustrated, and give up.
 
But you only need 5 clicks.
 
Here's the exact process:
 
𝗦𝘁𝗲𝗽 𝟭: 𝗢𝗽𝗲𝗻 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗻𝘀𝗼𝗹𝗲 Click "New Project" Name it example: "n8n Automation"
 
𝗦𝘁𝗲𝗽 𝟮: 𝗘𝗻𝗮𝗯𝗹𝗲 𝘁𝗵𝗲 𝗔𝗣𝗜 Search "Google Sheets API" in the top bar Click "ENABLE" This gives n8n permission to talk to your sheets.
 
𝗦𝘁𝗲𝗽 𝟯: 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 Credentials → Create Credentials → Service Account Name it "n8n-sheets-bot" Click "Create and Continue" → "Done"
 
𝗦𝘁𝗲𝗽 𝟰: 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗞𝗲𝘆 Click your service account → Keys → Add Key → Create New Key Select JSON → Create File downloads automatically.
 
𝗦𝘁𝗲𝗽 𝟱: 𝗦𝗵𝗮𝗿𝗲 𝗬𝗼𝘂𝗿 𝗦𝗵𝗲𝗲𝘁 (𝗖𝗥𝗜𝗧𝗜𝗖𝗔𝗟) Open the JSON file Find "client_email" (looks like: projectiamgserviceaccount) Copy that email Open your Google Sheet → Share → Paste email → Give Editor access
 
Paste the JSON content into n8n credentials. Done.
 
What you can build: 
✅ Auto-log form responses to sheets 
✅ Generate daily reports at 6 AM 
✅ Send alerts when data changes 
✅ Update CRM from sheet triggers 
✅ Sync inventory in real-time
 
I made a full video guide for every step. 

Comment "SHEETS" and I'll send it to you.

Over to you: What's the first thing you'd automate with Google Sheets + n8n?

Google Sheets + n8n setup!

Your competitors just automated 40 hours of work into 2 hours with browser agents.
You're still doing it manually.
 
Airtop's browser agents launched in September.
And they’re already changing the game.

Here's what I'm building for clients with Airtop:
- Sales: 6-hour lead lists → 15 minutes automated
- Growth: 2-day enrichment → Real-time ICP scoring
- Marketing: Monthly competitor reports → Live Slack updates
- RevOps: Login-blocked data → Seamless extraction

The gap is widening. Fast.
 
Most teams are still wrestling with Captchas and login walls.
Smart teams automated past all of it just by chatting with AI.
 
I built the complete guide: 8 proven browser automations.
 
The exact system to:
→ Build powerful lead lists with ICP scoring automatically
→ Monitor competitor activity and deliver insights to Slack
→ Extract data from sites that require OAuth, 2FA, or Captcha
→ Automate web interactions using natural language prompts
→ Support use cases that were previously impossible
 
This is your automation advantage with Airtop.
 
Teams deploying browser agents today will be months ahead by Q2.
 
Effortless automation isn't an edge anymore. It's the requirement.
 
I'm giving away the complete automation package for free.
 
To claim it:
- Comment "AIRTOP"
- Like this post
- Connect with me for the DM
 
You get: 8 ready-to-use template automation links + 1 month FREE Airtop Premium access.
 
Your competitors are already automating.
 
Repost this so your network doesn't get left behind ♻️

Automate the Web with Airtop!

Every AI you've used makes you adapt to it.

Learn its prompts. Figure out its quirks. Start every conversation from scratch.

Tavus PALs reverse that.

They adapt to you. They learn you. They remember you.

AI you can actually connect with.

Here's what that actually means:

𝗦𝗲𝗲 𝘆𝗼𝘂: Full video interface with facial recognition. Actual face-to-face interaction where it reads your expressions.

𝗛𝗲𝗮𝗿 𝘆𝗼𝘂: Voice interaction with tone and emotion detection. Understands not just what you're saying, but how you're saying it.

𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿 𝘆𝗼𝘂: Perfect recall of every conversation, ever. No more repeating yourself or rebuilding context.

𝗔𝗱𝗮𝗽𝘁 𝘁𝗼 𝘆𝗼𝘂: Learns your communication style, preferences, and patterns automatically.

𝗘𝘃𝗼𝗹𝘃𝗲 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂: Your PAL on Day 100 is unrecognizable from Day 1. It anticipates what you need before you ask.

This is the difference:

- Most AI feels like typing commands into a machine.
- PALs feel like talking to someone who actually knows you.

You're not crafting perfect prompts anymore. You're not losing context between sessions. The AI handles the context automatically.

Multimodal AI (text, voice, video). Persistent memory across all interactions. Continuous learning and adaptation. 24/7 availability.

For the first time, computing feels human.

Check it out: tavus.io/pals

2025 has been the year of AI Agents, and here's how you can start learning about them.
 
You don't have to spend a single penny to learn about AI Agents -
 
Generative AI for everyone: https://lnkd.in/d8kGVA29
 
Getting started with LLM: https://lnkd.in/dJ-dJY2q
 
Prompt engineering for VLMS: https://lnkd.in/dZ3_4Dkb
 
Build AI Apps with GPT Wrappers: https://lnkd.in/dUf7gj7z
 
Agentic RAG with LlamaIndex: https://lnkd.in/dvY3nzNx
 
Agent memory: https://lnkd.in/dfm_YPWa
 
Fundamentals of AI Agents Using RAG and LangChain: https://lnkd.in/dTvBKJGJ
 
AI Agentic Design Patterns with AutoGen: https://lnkd.in/dU8dxuJp
 
Multi AI Agent Systems with crewAI: https://lnkd.in/dQiQajaC
 
AI Agents in LangGraph: https://lnkd.in/dyeWMa72
 
AI Python for beginners: https://lnkd.in/dFpn5j7U

Stop paying for Manychat . I use BooSend to send auto DMs and voice messages whenever someone comments, then let an AI agent book calls and sell for me.

In 2 days, one DM automation flow added 7,000+ followers and 20+ qualified leads.
Unlimited contacts, unlimited DMs, 100% Meta approved, easy CRM, n8n, voice ai and email integrations.

Plus knowledge base so the agent keeps your voice and brand tone.

Try it here: https://lnkd.in/d7Ww_Ci3

taplio

The Battle

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The Battle

Aishwarya Srinivasan

Manthan Patel

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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