Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

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AI agents are great at working alone. However, they can also be effective when operating within networks of collaboration.

The real power of AI lies in how these agents communicate, coordinate, and complement each other’s abilities.

Orchestration patterns define this collaboration which turns isolated agents into intelligent, goal-driven systems capable of solving complex tasks together.

Here are 6 orchestration patterns that power the next generation of AI systems :

1.🔸Sequential Orchestration
Agents work one after another - each passing its result to the next.
Perfect for structured, step-by-step tasks like data pipelines or analysis chains.

2.🔸Concurrent Orchestration
Multiple agents work in parallel, handling different parts of a problem simultaneously.
This boosts speed, scalability, and overall task efficiency.

3.🔸Group Chat Orchestration
Agents communicate in a shared conversation, refining ideas together.
Useful for brainstorming, collaborative decision-making, or generating consensus.

4.🔸Maker–Checker Loops
One agent creates or proposes; another verifies, corrects, or approves.
Ideal for maintaining accuracy, compliance, and quality assurance in workflows.

5.🔸Handoff Orchestration
Agents transfer control dynamically based on context or expertise.
Best suited for adaptive workflows like support systems or multi-step automation.

6.🔸Magnet Orchestration
A central “manager” agent dynamically assigns tasks to the right specialists.
Enables fluid, context-aware collaboration across multiple intelligent agents.

AI Agents are evolving fast and orchestration is what makes them powerful.

Learning how these patterns can help you build smarter, more collaborative AI systems can be a huge advantage.

#AIAgent

AI Agent Orchestration Patterns

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Agentic AI vs Traditional AI: The Shift from Supervision to Autonomy

For years, Traditional AI has been like a skilled worker. It has been efficient, but dependent on human supervision.

Agentic AI, however, takes a different approach. It is a decision-maker that thinks, plans, and adapts without constant direction.

Let’s dive into what truly separates them 👇

Agentic AI: Smarter. Adaptive. Independent.

- Dynamic Learning: Perceives its environment, plans actions, and evaluates outcomes continuously.

🔸Goal-Oriented Thinking: Sets goals, creates sub-goals, and adjusts strategies like a human problem solver.

🔸Reasoning & Reflection: Learns from mistakes, refines decisions, and improves performance over time.

🔸Collaborative Agents: Multiple agents share context, delegate tasks, and synchronize for collective intelligence.

Traditional AI: Powerful but Limited.

🔹Static Learning Models: Train once, deploy, and manually retrain, with no real-time adaptability.

🔹Human-Dependent Execution: Waits for prompts or tasks, performs inference, and stops.

🔹Narrow Focus: Optimized for specific use cases without flexibility.

🔹No Self-Improvement: Requires human supervision for updates and tuning.

In Short:

- Traditional AI executes.

- Agentic AI thinks, plans, and evolves.

It’s the difference between automation and autonomy, a shift that’s redefining how AI systems operate, learn, and collaborate in the real world.

What other diffenreces have you noticed?

#AI

HemansAI

Data & AI Consulting, Development, AI Leadership Training.

Founder

85% of AI products and projects fail.

The two biggest challenges businesses face with AI:

1. Estimating and demonstrating AI value [VPs, CEOs, product managers, and decision-makers]
2. Lack of talent and skills [AI engineers and data professionals]

Most business leaders fall into the first category.

Even if you’re skilled at your job, your team might lack confidence that the AI solution will deliver the desired outcomes.

It’s frustrating, right? You see others succeeding with AI, but your team your team is stuck planning, fixing, or juggling disconnected tools.

AI is supposed to bring automation, sharper insights, and smarter decisions.

But when you try to implement it, you encounter chaos:

- Lack of clarity on ROI
- Endless troubleshooting instead of progress estimation
- Too many disconnected systems to demonstrate AI value

It’s easy to wonder if AI will ever deliver what it promises for your business and career.

Now, imagine a future where AI doesn’t just work—it works seamlessly in your organization.

Your data isn’t just stored—it powers real-time insights, precise forecasts, and simplified processes.

Picture an AI strategy that works without constant troubleshooting—a setup that scales and brings results.

I’m an AI consultant, here’s how I can help you:

- the AI leadership coaching

It’s about creating an AI path that’s right for your business or your career with real results, not just ideas.

Let’s connect. we’ll talk about AI strategies, products, and career opportunities.
connect@himanshuramchandani.co

Himanshu Ramchandani

You are building AI systems - but you're learning from the wrong places:

Most engineers follow tutorial repos with 10k stars

They copy code without understanding architecture

They build demos, not production systems

Here's what nobody tells you:

- best AI engineers don't learn from tutorials
- they learn from production codebases

I have reviewed 200+ AI repositories

These 5 will actually make you a better engineer:

1) vLLM - optimized LLM serving

↳ 62k+ stars
↳ used by Anthropic, Meta, NVIDIA

What you'll learn:

- how to serve LLMs at scale
- memory optimization techniques
- batch processing for inference
- the reality of production latency

Stop building toy apps.

Study how real systems handle 1000s of requests/second.

2) LangChain - but read the source

↳ 119k+ stars
↳ everyone uses it, nobody understands it

Don't use LangChain blindly:

- read the chain implementations
- understand prompt templating
- see how they handle context windows
- learn their error handling patterns

The code teaches you AI engineering patterns that tutorials skip.

3) Hugging Face Transformers

↳ 152k+ stars
↳ the library behind every AI product

Most engineers - from transformers import pipeline

Great engineers - 

- read the model implementations
- understand tokenization deeply
- see how attention masks work
- learn optimization tricks

Your shape errors?

Solved by reading their code.

4) Llama.cpp - inference optimization

↳ 89k+ stars
↳ runs Llama 3 on your MacBook

What they don't teach in bootcamps:

- quantization techniques (4-bit, 8-bit)
- CPU optimization strategies
- memory-efficient inference
- how to actually deploy models

This is how you get models running without burning $10k/month on GPUs

5) AutoGPT - Agent Architecture

↳ 180k+ stars
↳ flawed execution, brilliant concepts

Don't copy their code:

- study their agent loop design
- understand tool selection logic
- see how they chain LLM calls
- learn from their mistakes

They solved problems you'll face building agents.

Real Talk:

↳ spend 30 minutes/day reading these codebases
↳ start with files that confuse you
↳ trace how data flows through the system
↳ compare their approach to yours

In 3 months, you'll read AI papers and immediately know how to implement them.

Your Task:

- pick one repo from above
- find one file that scares you
- spend 1 hour understanding it
- you'll learn more than 10 tutorials combined.

Follow me Himanshu Ramchandani, for daily insights on AI engineering that actually matter.

P.S. - Join the Workshop on Building your first AI Agent in 90 min today 7:30 PM IST
and Join the Webinar on AI Agents for Business Leaders - ROI & Implementation Strategy tomorrow 9 AM IST [check comments]

AI Engineering & Leadership • Building an AI product studio and providing Development & Consulting services

ChatGPT can't do math. So why does your agent get calculations right?

You built an agent that "reasons"

Added ChatGPT + some tools

Called it intelligent

But here's what's actually happening:

↳ user asks a question
↳ LLM extracts parameters
↳ function gets called
↳ result comes back

That's it.

No magic. No intelligence. Just orchestration.

The real agent architecture:

Orchestration Layer → manages memory, state, and planning
Model → understands natural language intent
Tools → executes actual capabilities

When you ask "what's 65 × 85?"

The LLM doesn't calculate.

It CAN'T calculate.

It recognizes a math pattern, extracts numbers, calls calculator tool.

This is why your agent hallucinates prices but gets calculations right.

One uses training data (unreliable)

Other uses tools (deterministic)


Before: "my agent is smart"
After: "my agent routes to the right tool"

Stop building smarter prompts.

Start building better tool architectures.

Follow Himanshu Ramchandani for real AI engineering.


P.S. Building 7 production AI Agents in my bootcamp. Link in comments.
Join 75 Hard AI Engineering Challenge
Join 30 Day AI Agent Challenge [starting 2nd December]

Your transformer knowledge is worthless

- you read the "Attention is All You Need" paper
- you took a Hugging Face course
- you fine-tuned BERT

Congratulations - you are still useless

Here's the truth nobody tells you:

90% of AI engineers can't explain what happens in model.forward()

↳ they just stack layers and pray
↳ when training fails? chatGPT or stack overflow
↳ when memory explodes? smaller batch size
↳ when inference is slow? "just add more GPUs"

This is why you'll never build anything real

The engineers building GPT, Claude, and Llama?

They don't just use transformers

↳ they optimize every matrix multiplication
↳ they understand gradient flow at scale
↳ they know exactly why their model fails

Stop collecting certificates

Start understanding fundamentals

- matrix multiplication > another course
- calculus > another tutorial
- production constraints > another paper

You want to be valuable?

Build a transformer from scratch

Optimize it until it's 5x faster

Deploy it at scale

Then you're an AI engineer

Until then? You're just another API caller

Follow Himanshu Ramchandani for the brutal truths about AI engineering

P.S. - Get the deep dive in the FREE 75 Hard AI Engineering Challenge. AI Agents Bootcamp, ending early bird access today. (last 6 early bird seats left).

You're building AI agents. But you don't understand how they scale - Here's Why:

Every agent you deploy uses resources

Every API call costs money

Every response consumes compute

Most engineers build agents without understanding efficiency

Here's what changes everything:

- when you see this: agent.run()
- that innocent line is burning through your compute budget

Scaling laws predict exactly how your agent will perform:

↳ model size vs accuracy
↳ token usage vs cost
↳ response time vs user satisfaction

The truth about production agents:

Before understanding scaling:

- random model choices
- unpredictable costs
- can't forecast performance

After understanding scaling:

- calculate costs before deploying
- choose optimal model sizes
- predict agent behavior at scale

Chinchilla's insight: smaller models + more data = better results + lower costs

A 7B model trained right beats a 13B model trained poorly

This is Day 26 in the 75 Hard AI Engineering Challenge [join for free]

Real engineering.

Real results.


Follow me Himanshu Ramchandani for daily AI engineering insights

PS: FREE AI Agents Workshop this weekend. Join via newsletter 🚀 - AI Agent Bootcamp spots filling fast (check comments)

Comfort is why most AI engineers stay mediocre

Day 30 of the 75 Hard AI Engineering Challenge is done

Half the cohort is thriving

The other half? Drowning in content they have never seen before

"Himanshu, this is too hard"
"Can we slow down?"
"I don't understand derivatives"

My response?

(this dialogue from KungFuPanda movie)

The engineers building production AI systems aren't comfortable

- they are learning RAG at midnight
- debugging embeddings at 5 AM
- implementing attention from scratch on weekends

discomfort = growth

Here's what I'm seeing from those who push through:

↳ week 1: confused
↳ week 2: frustrated  
↳ week 3: connecting dots
↳ week 4: building confidently

The struggle isn't a bug <> It's the feature

Real AI engineering isn't copying tutorials

It's understanding

- why your training explodes
- why your embeddings cluster weird
- why your inference is slow

45 days to go. The best part?

You're already different than Day 1.

Still accepting latecomers who want the challenge

Follow Himanshu Ramchandani for real AI engineering

Here are some updates:
↳ 75 Hard AI Engineering Challenge ($0) 
↳ AI Agents Bootcamp (Start at $0) - add 7 deployed Agents in your portfolio
↳ 30 Day AI Agents Challenge ($0) starting 2nd December
↳ AI Builder's Bootcamp (Launching in January 2026)
↳ 4 Hands-On Workshop(for professionals)/Webinar(for leaders) in November on AI Agents 
↳ Product Launch - Knowledge Engine at Dextar
↳ AI Leadership Accelerator - The Elite - beta version (DM for pre booking)
↳ Long form AI Engineering deep dives on substack (sharing this week)
↳ Dropping new episode of Humanly Foolish podcast (Ask Anything AI)

- more updates will share later


P.S. - Free AI Agents Workshop this weekend. Link in comments.

You are spending $1,500 extra on API calls. Because you are still using JSON

Every API call you make:

- chatGPT charges per token
- claude charges per token
- gemini charges per token

That employee dataset? 126,860 tokens in JSON

Same data in TOON? 49,831 tokens

TOON (Token-Oriented Object Notation)

↳ 60% fewer tokens
↳ 60% lower costs
↳ same information

Here's what nobody tells you:

JSON wasn't built for LLMs.

It's verbose by design

JSON:

{
"users":[{"id":1,"name":"Alice"},
                {"id":2,"name":"Bob"}]
}
```

TOON speaks LLM's language:

users[2]{id,name}:
 1,Alice
 2,Bob


but Himanshu, why this matters?

When you send data to GPT-4:

↳ repeating field names = wasted tokens
↳ extra brackets = wasted tokens
↳ redundant syntax = wasted tokens

TOON declares structure once

Streams data as rows

Real impact at scale:

↳ 1M API calls with that employee data?

JSON: $2,537 in tokens
TOON: $996 in tokens

You just saved $1,541

What it means-

Before: blindly serialize everything as JSON
After: encode as TOON for LLM input, get 30-60% token reduction

Have you calculated your token costs? [repo link in comments]

Follow me Himanshu Ramchandani for practical AI engineering insights

P.S. - AI Agents bootcamp starting on 2nd December, 23 out of 60 are taken, today is last day of early bird access.

You are running local (on-premises) LLMs but don't know if they are actually good

- every AI engineer downloads Llama or Mistral
- runs them locally
- feels excited about - free inference

Then reality hits:

- is this output even correct?
- is GPT-4 actually better?
- am I just burning electricity for worse results?

Here's what nobody tells you:

Running local models without benchmarking is like driving blindfolded

You need three numbers:

↳ speed (tokens/second)
↳ quality (% correctness)
↳ cost (real cost per 1M tokens)

When I started the 75 Hard AI Engineering Challenge,
I thought local = always better

I was wrong

Real example from Day 32:

- mistral 7B locally: 35 tok/s, $1.50 per 1M tokens (estimated)
- gpt-4 API: 18 tok/s, $30,000 per 1M tokens (claimed)

For code generation?

- mistral: 67% correct
- gpt-4: 100% correct

The question is not "which is faster"
The question is "which solves MY problem"

For 100K tokens/month → use APIs
For 10M tokens/month → local wins
For sensitive data → local (on-premises) only

benchmarking:

Before:

- "this model feels slow"
- "the output seems wrong"
- "maybe I should try GPT-4"

After:

- "this model runs at 32 tok/s"
- "it's 89% accurate on my test set"
- "break-even is at 2M tokens/month"


Do this:

↳ pick 100 real examples from your work
↳ run through 3 different models
↳ measure speed, cost, quality
↳ choose based on data, not hype


Follow me Himanshu Ramchandani for daily AI engineering insights that actually matter

PS: Join the FREE 75 Hard AI Engineering Challenge [link in comments]

PPS: Learn AI Agents in the upcoming bootcamp, limited seats, FREE workshop on RAG  Agents today 7:30 PM IST

If you want to ship a production LLM app by 2025, you have less than 40 days so here's what you are going to do:

Stop calling OpenAI and praying it works

Your app breaks when their API goes down

You have zero control

Here's what production engineers actually do:

Build fallbacks

↳ openAI down? switch to anthropic
↳ both down? use your self-hosted model
↳ all automatic - users never notice

The math that changes everything:

- commercial API only:
$9,500/month at 50M requests

- hybrid approach (API + self-hosted):
$730/month at 50M requests

That's 92% savings

Companies like Notion and Shopify don't use one LLM

They use smart routing between multiple models

This is part of the Day 35 of the 75 Hard AI Engineering Challenge:

↳ multi-provider fallback system
↳ self-hosted model with vLLM
↳ cost optimization logic
↳ production deployment on AWS

This isn't theory

The difference between a demo and a product? 

reliability when things break

Build something that actually ships.


Follow Himanshu Ramchandani for real AI engineering.


PS: Build a fridge AI agent that will order groceries automatically, join the community and bootcamp waitlist.

Stop building smarter agents. Start building agent teams.

Everyone's obsessing over:

- better prompts
- bigger context windows
- smarter models

the real breakthrough is multi-agent architecture

Single Agent Problem:

You: optimize my resume for this role
Agent tries to:

- understand job requirements
- analyze your background
- rewrite experience
- create cover letter
- format everything

It's doing 5 jobs at once

Quality suffers

Multi-agent solution, same task, different approach:

Agent 1 → deep dives on company (research specialist)
Agent 2 → rewrites your resume (writing specialist)
Agent 3 → creates cover letter (personalization specialist)

Each agent is great at ONE thing.

- they share context
- collaborate
- roduce better outputs

Difference:

- single agent: Jack of all trades, master of none
- multi-agent: Specialists working together


Architecture Basics:

Orchestration layer → Routes tasks to right agents
Shared memory → Agents access same context
Sequential workflow → Output of one feeds into next
Parallel processing → Multiple agents work simultaneously

CrewAI, LangGraph, AutoGen already enable this.

Tonight at 7:30 PM IST, I'm showing you how.

We build a multi-agent system that:

→ researches companies
→ optimizes your resume
→ writes personalized cover letters

Live and step by step

Free workshop [Link in comments]

This is how production AI systems actually work

Follow Himanshu Ramchandani for real AI engineering

You built an AI agent from scratch yesterday

Most people think it's complex - It's not

Here's what I showed 87 engineers:

- AI agents aren't magic
- they are just 3 components working together
- anyone with basic Python can build one


We built an agent that:

↳ understands natural language
↳ uses external tools (calculator, web search)
↳ makes decisions autonomously
↳ handles real-time queries

Before: "AI agents are too complex for me"
After: "I just built one in 90 minutes"


Here's the truth:

- you don't need a PhD
- you don't need years of experience
- you need the right framework

We went from theory >> working code in one session

Most engineers overthink this


AI Agents Bootcamp starts Dec 2nd

↳ 7 production agents in 4 weeks
↳ Live builds, not tutorials
↳ Early bird closes in 24h

ROI - Build what companies pay $150K/year engineers to create

Thank you for joining the workshop. 7 more are coming in November.


Want the AI Agents Resource Bundle? Comment "AGENT" 👇

Follow Himanshu Ramchandani for practical AI engineering.

PS: Hosting 60 min FREE Webinar on - AI Agents for Business Leaders: ROI & Implementation Strategy, 9th November 9 AM IST [Register Comments]

Build Your First AI Agent in 90 min [Workshop]

Every time you manually run a task through ChatGPT, you're wasting time

Every workflow you could automate? Still manual

Every repetitive prompt you run? Could be an agent

Most people use AI like it's 2022

Here's what changed:

- AI doesn't just respond anymore, it acts
- This Friday, I'm showing you how to build your first AI Agent in 90 minutes
- Not theory! Not slides!

You'll build an agent that:

- plans multi-step tasks
- calls tools automatically
- executes without your input

The shift:

Before: You → Prompt → Copy → Paste → Repeat
After: You → Agent → Done

FREE Workshop. Friday. 90 minutes.

Time: 7:30 PM IST

By the end, you'll have a deployed agent and understand why companies are hiring AI engineers at $150k+

Register Here: https://lnkd.in/gj9-Ujxm

PS: I am hosting 8 workshops and webinars in November for FREE. This is the first one.

🎤 Speaker Spotlight
🚀 AI Unplugged: A Prelude to DevFest 2025
Get ready to learn from the best!
Join Himanshu Ramchandani, expert in AI Engineering & Leadership, as he shares insights on
💡 “How to Learn AI Fast with a Builder’s Mindset?”

Discover how to accelerate your AI journey by thinking like a creator, innovator, and problem-solver.

📅 Date: Saturday, 15th November 2025
🕙 Time: 10:00 AM onwards
📍 Venue: Devi Ahilya University Auditorium, Indore
🔗 Register here: https://lnkd.in/dufGG_4r

#SpeakerSpotlight #DevFest2025 #PreDevFest2025 #GDGIndore #AIUnplugged #GoogleDevelopers #LearnWithGDG #BuilderMindset #AI #TechCommunity #IndoreTech #DeveloperCommunity

How AI discovered a drug that makes dying lungs better:

Impossible, right?

Insilico Medicine just did it using AI in 18 months

Here's what shocked me:

- traditional drug discovery = 6 years, $400M, 90% failure rate
- insilico's AI approach = 18 months, $40M, and it WORKED

The results from 71 IPF patients:

↳ placebo group - lungs declined 62 mL (normal progression)
↳ AI-designed drug - lungs IMPROVED by 98 mL

No drug has ever reversed lung fibrosis before.

How they did it:

- AI read millions of research papers, found a target nobody tried
- generated a new molecule that didn't exist
- predicted it would work before spending millions on trials
- published in Nature Medicine (June 2025)

This isn't a research paper

Real patients got better

The platform now has 30+ drugs in development

When people ask, "what's AI actually good for?"
- this is it

Not generating tweets and texts.

Discovering medicines that save lives.

Follow Himanshu Ramchandani for AI systems that actually matter.

P.S. Full breakdown in my next newsletter edition this week [comments]

I've never shared this before

6 months ago, a senior engineer got rejected for promotion

"You use AI tools. We need someone who can architect AI systems."

That's the gap in 2025

Companies don't want LLM users

They want AI Agent Architects

I'm launching the AI Agents Bootcamp December 2nd.

What you build:

↳ 7 production-ready agents in 4 weeks
↳ ReAct patterns & planning systems
↳ LangGraph state machines
↳ Multi-agent orchestration
↳ Production deployment with guardrails

Real results:

Mamta (Amazon): "made my AI engineering transition"
Saravanan: "went from mechanical to AI engineer"
Meena (Amazon): "hands-on approach changed everything"

FREE workshops before launch:

Nov 19: RAG Agents Production
Nov 26: Deploy FastAPI + Docker

189 waiting. 60 seats only.

Two paths:

1 - Join FREE workshops (link in comments) and get FREE AI Agents Guides
2 - Secure bootcamp seat now

Not for quick hacks.

For engineers ready to architect systems companies fight over.

♻️ Repost if you know someone stuck at "just using ChatGPT"

Follow me Himanshu Ramchandani for AI engineering that matters

STOP using LLM APIs like toys

You're calling OpenAI's API like it's a REST endpoint

It's not

Every junior dev I meet:

- hardcodes API keys in Python files
- no retry logic
- no fallback providers
- no token management

Then their app crashes in production and they blame the API

Here's the truth:

↳ they'll fail on you
↳ llm APIs aren't reliable
↳ anthropic will have outages
↳ openAI will hit you with rate limits

Your $4k bill? That's your retry loop gone wrong

But nobody tells you this because they're too busy showing you toy examples from documentation

Production AI engineering isn't about making API calls

It's about:

↳ circuit breakers
↳ cost optimization
↳ exponential backoff
↳ multi-provider fallbacks

Your GPT-4 call doesn't need GPT-4

Use GPT-3.5 and save 80% on costs

Your system crashes?

You didn't classify your errors properly

Stop copying tutorials

Start building systems that don't break when the API sneezes

Follow me Himanshu Ramchandani for production AI that actually works

PS: This is from the Day 30 guide of 75 Hard AI Engineering Challenge, Join the AI Agents Bootcamp is Live.

[Live Workshop] RAG Agents - Build a production-ready document Q&A system


What we are going to cover:
- Retrieval-Augmented Generation
- Embeddings and Vector Representations
- Vector Databases
- RAG Pipeline
- Hands-On Project

Date: 19th November, 2025 (Today)
Time: 7:30 PM IST

→ why vector DBs aren't enough
→ how persistent memory changes everything
→ what enterprise RAG actually needs


Register by joining the workshop inner circle group [link in comments]

[Webinar] AI Agents for Business Leaders - ROI & Implementation Strategy

- roadmap
- case studies
- justifying team investment

Date: 10th November 2025
Time: 7 PM IST

PS: Register for the Webinar for FREE [check comments]

taplio

The Battle

Greg Coquillo

Himanshu Ramchandani

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taplio

The Battle

Greg Coquillo

Himanshu Ramchandani

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);}Greg Coquillo's LinkedIn Performance

Performance ScoreHimanshu Ramchandani's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreGreg Coquillo's LinkedIn Performance