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

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]

Himanshu Ramchandani

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]

You're using matrix multiplication every day. But do you know what it actually does?

Every time you use ChatGPT, a billion matrix multiplications happen

Every image you process through a model? Matrix operations

Every embedding you create? Matrix lookups

Most AI engineers use these operations without understanding them

Here's what nobody tells you:

- linear algebra isn't just math
- it's the language AI speaks

When you see this in PyTorch:
`output = layer(input)`

That innocent line? It's doing matrix multiplication

Your input is a matrix, the layer weights are a matrix

Multiply them. Get your output.


Vector = ordered list of numbers

That word embedding in GPT? A vector with 768 numbers.

That image patch in a vision model? A vector with thousands of numbers

Everything in AI is vectors.

Matrix = vectors stacked together

↳ your batch of data? Matrix
↳ your neural network weights? Matrix
↳ your entire model? Just matrices multiplying matrices

Why dimensions matter:

- you have 32 sentences
- each has 20 words
- each word is 768 dimensions

Shape: (32, 20, 768)

You pass it through a layer that projects to 512 dimensions.

Weight matrix shape: (768, 512)

Output shape: (32, 20, 512)

The 768 matched up and got consumed.

This is why you get shape errors.

The inner dimensions must match or the multiplication fails.

What GPT actually does:

1) converts words to vectors (matrix multiplication)
2) computes queries, keys, values (3 matrix multiplications)
3) calculates attention scores (dot products)
4) projects output (another matrix multiplication)

Everything you think is magic, is just matrix operations at scale.

The shift in thinking:

Before:

- copy code from tutorials
- get shape errors constantly
- don't know why training is slow
- can't debug model issues

After:

- know what every layer does
- fix dimension errors instantly
- optimize computation
- read papers fluently

Real example:

Training explodes?
- You understand it's repeated matrix multiplications amplifying gradients

Model won't fit in memory?
- You calculate the matrix sizes and know why

Inference is slow?
- You identify the bottleneck matrix operations


Do this:

↳ implement vector addition yourself, not with NumPy, pure Python
↳ implement matrix multiplication, feel how slow it is
↳ then use NumPy, feel the difference

Now you know why GPUs matter.

Now you know why transformers are compute-heavy.

You and I are using Linear Algebra every day through our phones. Can you tell me where?

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

P.P.S. - AI Engineer HQ Bootcamp starts in 24 Hours. 11/25 seats already gone. If you want to build production AI systems 🚀

Your model isn't broken - You just don't understand derivatives

You spent 3 hours training your model

It hit 95% accuracy

You closed your laptop, satisfied

The next day, you ran model.fit() again

Waited another 3 hours

Got 93% accuracy

↳ why is it different?
↳ why isn't it learning faster?
↳ why do you even need to retrain?

Here's what they don't tell you:

Every time you call model.fit(), you're not running magic

You're running calculus.

- derivatives
- gradients
- optimization

That's it.

The truth about AI training:

↳ your model has millions of weights
↳ the loss function tells you how wrong those weights are
↳ derivatives tell you which direction to adjust each weight
↳ gradient descent is just following those derivatives downhill

When your loss explodes → your derivatives are too big

When training stalls → your derivatives are too small

When your model won't converge → your optimization is broken

What most engineers miss:

They treat optimizers like black boxes

- Adam?
- SGD?
- RMSprop?

Just plug and play, right?

Wrong!

When you understand the calculus, you understand:

- why your learning rate matters
- why mini-batches beat full batches
- why momentum accelerates training
- why adaptive optimizers work better for transformers

Okay engineers -
"my model is not converging - let me try a different optimizer"

Great engineers -
"my gradients are vanishing - I need to adjust my architecture and learning rate schedule"

Here's the reality:

↳ you don't need to prove theorems
↳ you don't need a math PhD

But you absolutely need to understand:

↳ what a derivative means
↳ how gradients flow through networks
↳ why your loss function matters
↳ what your optimizer is actually doing

Because when (not if) your training fails, that understanding is what saves you

Companies training GPT, Claude, and Gemini?

They're not just running scripts

They're deeply optimizing every aspect of gradient flow

Because at scale, small improvements in optimization = millions saved

This is part of Day 12 in the FREE 75 Hard AI Engineering Challenge [Check comments]

Real engineering fundamentals

The math that actually matters

Follow me Himanshu Ramchandani, for more such guides.

PS: Hosting a FREE AI Agents Workshop this weekend. Join the workshop by subscribing to the newsletter, the resources will be delivered to your email inbox.

Calculus

Your $10,000 ML model just evaporated because you forgot one 3-line code block

You trained for 3 hours.

Hit 95% accuracy.

Closed your laptop, feeling like a genius.

Next morning? Gone. All of it.

Welcome to every AI engineer's first nightmare.

Here's what nobody tells you:

- your trained model lives in RAM
- when Python exits, RAM clears
- your model vanishes like it never existed

Most tutorials teach you TO TRAIN models

Almost none teach you to SAVE them.

That's the gap between tutorial hell and production.

The solution?

Serialization

Without it, you can't deploy a single model.

Think of it like this:

→ fresh food spoils in hours (model in RAM)  
→ frozen food lasts months (serialized model)  
→ use when you need it (load and predict)

Here's the shift:

- Beginner thinking
"I'll just retrain the model when I need it"

- Professional thinking
"Train once. Save everything. Load in 2 seconds. Use until the model becomes outdated."

The critical mistake:

- you save just the model
- but your model was trained on SCALED data

Without the scaler? Your predictions are garbage.


You need to save:

- model
- scaler  
- encoder
- feature names
- metadata

Everything that touches your data during training.

Why this matters RIGHT NOW

Without serialization:

↳ no model deployment  
↳ no production APIs  
↳ no team collaboration  
↳ no real-world ML

With serialization:

↳ train once, deploy everywhere  
↳ API responds in milliseconds  
↳ share models with your team  
↳ build actual systems people use

The reality check:

Every single production ML system uses this.

Tesla's autopilot? Serialized models.  
Netflix recommendations? Serialized models.  
Your bank's fraud detection? Serialized models.

Zero exceptions.

One security warning:

↳ Never load model files from strangers
↳ Pickle files can execute code when loaded
↳ Treat them like .exe files from the internet
↳ Would you run random executables? No

Same rule for model files.

Have you ever lost a trained model and had to start over?

Drop a 💾 below.

What's your biggest deployment challenge right now?

Follow Himanshu Ramchandani for more posts like this.

P.S. - Would you like the complete technical guide with code examples? I wrote a 10,000-word deep dive. Join the 75 Hard AI Engineering Challenge for FREE to access it.

You're reading research papers wrong - Here's the 3-pass method that works:

Most researchers waste hours reading papers they should not finish

I used to do the same

Read every paper start to finish

Took notes on everything

Felt productive

Then I'd realize - this paper was not even relevant.

All that time? Gone.

The problem with how you read papers:

↳ you treat every paper equally
↳ you read linearly from start to finish
↳ you commit before we know if it's worth it

Result?

- burnout
- wasted time
- information overload

Enter the Three-Pass Method -

↳ created by S. Keshav, this approach filters papers systematically
↳ you build knowledge incrementally.
↳ you invest time only where it matters.

[Pass 1] Quick Scan (5-10 minutes)

What you read:

- Title, abstract, introduction
- Section headings (ignore the body)
- Conclusion
- References (tick off what you recognize)

What you answer (Five C's):

1) Category - what type of paper is this?
2) Context - which papers is it related to?
3) Correctness - do the assumptions seem valid?
4) Contributions - what's actually new here?
5) Clarity - is it well written?

Decision point:

Can't answer these?

- the paper is poorly written or irrelevant
- stop reading
- move on

This saves you from reading 70% of the papers you find.

[Pass 2] Grasp the Content (up to 1 hour)

Now you're reading for real.

But still not every word

What you do:

↳ read with a pen - annotate margins
↳ study the figures carefully
↳ check if graphs have proper error bars (rushed work often skips this)
↳ mark unfamiliar references for later

Output?

- you should be able to summarize the main thesis to someone else
- you understand the content even outside your specialty

Most papers? Pass 2 is enough.

Still confused?

[Pass 3] Achieve Mastery (1-5 hours)

Only do this if you're:

- reviewing the paper
- building on this work
- trying to replicate it

Attempt to recreate the work from their assumptions

Challenge every statement

Compare your mental model with their actual approach

Mindset shift -

↳ reading a paper completely is not the goal
↳ understanding what you need to know is the goal
↳ not every paper deserves your full attention

How many papers have you abandoned halfway through?

What's your biggest challenge with research papers?

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

P.S. - This is based on S. Keshav's paper "How to Read a Paper." Full breakdown in my newsletter edition 👇 

P.P.S. - AI Engineer HQ Bootcamp starts in 3 days. 11/25 seats already gone. If you want to build production AI systems, this is it. DM me "BOOTCAMP" 🚀

p-values are killing your product - and your stats professor won't tell you why

I see this pattern everywhere:

- data scientist runs A/B test
- gets p-value of 0.06
- panics - "Not significant! Kill the feature"

Meanwhile, that feature could've generated $500K in revenue

All because they didn't understand what p-value actually means

Here's what nobody tells you about p-values:

p-value = 0.06 doesn't mean your result is bad

It means:
"If this effect was pure luck, you'd see results this extreme 6% of the time"

That's all it means

The 3-step system I use (and you should too):

[Step 1] State Your Hypothesis

Null Hypothesis: Nothing changed. The feature has zero effect.
Alternative Hypothesis: Something DID change. The feature moves the metric.

Your job? Prove the null hypothesis wrong.

[Step 2] Pick Your Threshold (Before Testing)

Most teams use 5% (p < 0.05) as their significance level.
This means: "I'm okay with being wrong 5% of the time."
Conservative teams use 1%.
Startups sometimes use 10%.
The critical part: Choose BEFORE you see results. Not after.

[Step 3] Accept or Reject

p-value < 0.05 → Reject null hypothesis → Your feature likely works
p-value > 0.05 → Accept null hypothesis → Not enough evidence yet

But here's the trap most people fall into.

What a p-value does NOT tell you:

❌ how big the effect is (that's effect size)
❌ whether the result matters to your business (that's practical significance)
❌ the probability your hypothesis is true (that's Bayesian thinking)

The reality check:

p-value = 0.04 with a 0.1% conversion lift?
Statistically significant. Practically useless.

p-value = 0.06 with a 10% conversion lift?
Not significant. But you'd be crazy to ignore it.

Smart teams look at both.

- statistical significance (p-value)
- practical significance (effect size)
- business context (is the cost worth it?)

The real question isn't - Is it significant?

The real question is - Given this evidence, what's the smartest decision for our business?

Follow me Himanshu Ramchandani, for more.


PS: This is part of the 75 Day Guide at the 75 Hard AI Engineering Challenge.

PPS: 4 Workshops and 4 Webinars are coming up this November on AI Agents, be updated through the AI newsletter.

I am starting this challenge on 18th October 2025 to the end of this year.

75 consecutive days.

No breaks.

No excuses.

The Framework we are going to follow:

- learn
- build
- deploy
- market
- support

Why am I doing this?

Because:

- I'm tired of people saying, "I'll learn AI next year"
- courses don't create builders – consistency does
- the AI revolution isn't waiting for anyone to feel "ready"

The challenge:
- free to join
- daily roadmap provided
- live support & community
- all resources included
- discord study groups
- weekly workshops & office hours

By December 31st, you'll have:

- a portfolio of 10+ deployed AI projects
- 75 days of documented learning
- production-ready AI engineering skills
- a network of fellow builders
- proof that you can do hard things

Registration opens tomorrow (October 17th) - Day 0


P.S. - Drop your questions if you have any.

[FREE] The registration is live: 75 HARD AI Engineering Challenge

October 18 → December 31, 2025

Register here: https://lnkd.in/dA56EjcA

Here's what you're signing up for:

- 75 consecutive days of building AI systems
- No breaks
- No excuses
- No shortcuts

Every single day:

→ Learn AI engineering (Python to Production)
→ Build real projects (not tutorials)
→ Deploy to the cloud (proof of work)
→ Document publicly (build your brand)

Miss a day? You restart from Day 1.


What's included:
→ complete daily roadmap & resources
→ live workshops every week
→ discord community (study groups, debugging help)
→ office hours with me
→ code reviews & portfolio building
→ career guidance for AI engineering roles

This is for you if:

- you're tired of just watching tutorials
- you want a real portfolio that stands out
- you're willing to show up every single day
- you can handle being uncomfortable

This is NOT for you if:

- you're looking for easy
- you quit when things get hard
- you're not ready to commit publicly

By December 31st:

- you'll have 7+ deployed AI projects
- you'll have proven you can do hard things
- you'll have a portfolio that opens doors

Or you'll learn you're not ready yet.

Either way, you'll know.

👉 Register now: https://lnkd.in/dA56EjcA


#75HardAIEngineering

I almost canceled the 75 Hard AI Engineering Challenge on Day 3.

Let me explain.

Day 1 (Saturday): 167 people registered, energy was insane
Day 2 (Sunday): first "I quit" message, panic
Day 3 (Monday): the git problem was too hard
Day 4 (Tuesday): fixed it, people loved the struggle
Day 5 (Wednesday): 97% still here, I'm in shock

Here's what's really happening behind the scenes:

- every morning at 5am, I wake up terrified
- did I make today's problem too easy? Too hard?
- will this finally be the day everyone quits?

Then the messages start rolling in.

"digital root chain broke my brain in the best way"
"I have been using terminal wrong for YEARS"
"used git rebase at work, felt like a wizard"

These aren't beginners.

These are working engineers discovering they have been half-assing the fundamentals.

For the first 7 Days, the level will be easy.

The topics look basic:

- Python Basics
- Command-line mastery
- Git and GitHub
- API Mastery  
- Serialization & ML Models


Last Sunday, people joined the live session:

"Jam with the AI Squad" - terrible name, I know. (Ep - 1)

But they showed up.

No judgment. Just engineers helping engineers.

That's when I realized what's actually happening:

- we are not doing a challenge
- we are building a soldier's mindset


An army of engineers who won't panic when:

- the terminal is their only option
- git history is corrupted
- apis fail mysteriously
- models need deployment
- documentation doesn't exist

5 days down. 70 to go.

The ones who survive these 75 days won't just know AI engineering.

They'll be unfireable.

Because while everyone else is prompting ChatGPT,
You will actually understand what's happening underneath

Real question: What seemingly "basic" skill are you pretending to know?

(I will go first - I googled "git rebase" every time for 2 years)

The rule is simple - just show up.

I quit motivation

I started this challenge when I was not motivated

Motivation has long gone from my thinking process.

I don't do things when I get motivated anymore.

If something is important, I focus on execution.

Let me teach you something about motivation:

↳ It will make you think that you are going to win
↳ It will disappear exactly when you need it most (usually around day 3)
↳ It works best at 2 AM when you're supposed to be sleeping
↳ It's basically a friend in emotional form when the weather is good
↳ It treats Sunday night and Monday morning very differently

You know that feeling when you watch a motivational video and suddenly want to run a marathon, start 3 businesses, and learn AI... all before breakfast?

Yeah, that lasted until you actually had to put on running shoes or open your laptop.

I have seen systems work better in this case.

Replace motivation with:

- systems (boring, but they work)
- discipline (your 6 AM self will hate your 11 PM self)
- identity (I'm someone who does X vs I want to do X)

To build this, I started the 75 Hard AI Engineering Challenge [comments]

I stopped waiting for motivation when I realized my bills weren't waiting for me to feel inspired. LOL

PS: Are you in the loop?

ChatGPT gave you 3 different answers to the same question at temperature=0

Here's why that shouldn't be possible:

You blamed floating-point arithmetic

You were wrong

Everyone thinks LLM nondeterminism comes from:

concurrency + floating point

That GPUs running parallel threads finish in random order, causing different results

Here's what actually happens:

↳ your output changes based on how many OTHER users are hitting the API
↳ not because of race conditions
↳ because batch size affects the reduction order in matrix operations

Math:

↳ user A queries alone → batch size 1
↳ user B queries with 50 others → batch size 51
↳ same prompt, different batch sizes
↳ different floating-point reduction orders
↳ different outputs

The system is deterministic

But YOU can't control the batch size

So from your perspective?

Nondeterministic

The real problem isn't your GPU

It's that kernels lack "batch invariance"

This is why ChatGPT gives you different answers to the same question

This is why your experiments aren't reproducible

This is why RL training silently becomes off-policy

The "concurrency + floating point" hypothesis misses the entire picture

This is what is explained in this blog by thinking machines [check comments]

Follow Himanshu Ramchandani for more.

P.S. - I am hosting 8 AI workshops and webinars this month for free. Join the email list below.

Last 5 Days [AI Engineer HQ Bootcamp]

Enrollment closes in 5 Days OR when full.
(whichever comes first)

👉 Grab your seat: https://lnkd.in/ddGQqhtD
📋 Full curriculum: https://lnkd.in/dtQyhEBT

11 out of 25 seats are taken already.

Questions? DM me directly: https://lnkd.in/dex6W4Re

This is the LAST cohort of 2025.

Don't be the person asking "what if" in January.

Application Form for October Bootcamp: https://tally.so/r/wAozBk

⏳Seats filling fast! 

Join waitlist group for updates: https://lnkd.in/dzivf2yr
(You'll get first access every time)

AI Engineer Bootcamp 2025

Companies don't pay you to train models - they pay you to build systems that work:

You spent weeks training GPT-4 to be perfect

Tested it locally with sample data

Everything worked

Then you deployed it

↳ users can't access it
↳ data gets lost randomly  
↳ costs spiral out of control
↳ you have no idea what's breaking

Here's what nobody tells you:

- building models is 20% of the job
- building the infrastructure around them is 80%


The brutal truth about production AI:

- your model needs data to train on → that data comes from somewhere
- your model makes predictions → those predictions go somewhere
- your model needs to retrain → fresh data must flow continuously

None of this happens by magic.

It needs a PIPELINE.

What most AI engineers miss:

They think production means - put model in API

Wrong.

Production means:

↳ data flowing 24/7 without loss
↳ services that restart when they crash
↳ scaling when traffic spikes
↳ logs you can actually debug
↳ costs that don't bankrupt you

When your training job fails at 3 AM, you need to know why

When users report wrong predictions, you need to trace the data flow

When your CEO asks why AWS costs doubled, you need answers

The shift in thinking:

Before:

- deploy model to Flask API
- hope it works
- panic when it breaks
- can't debug production issues

After:

- data flows through message queues
- services recover from failures automatically
- everything is logged and monitored
- you scale without thinking about it

Real production pipeline:

- data ingestion → validates and queues data
- message queue → prevents data loss during crashes
- processor → transforms data safely
- database → stores everything persistently
- API → serves data to users
- monitor → tells you what's breaking

All running in AWS. All containerized. All scalable.

This is what companies actually run

This is what they pay you to build

This is what separates engineers from researchers


Companies need systems that work at 3 AM when you're asleep

Once you learn this, you can build anything

- recommendation engines
- chatbot backends
- model serving platforms
- real-time prediction systems

Same patterns. Different use cases.


Building real systems. Not just models.


Follow me Himanshu Ramchandani, for daily insights on production AI.

P.S. - Want to learn how to build this exact pipeline from scratch? Step-by-step guide with full code in the 75 Hard AI Engineering Guide.

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.

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]

3 Long-form Resources to Start Your AI Engineering Journey

1) Python
2) Command Line Mastery
3) Git and GitHub Beginners Guide

You can access all by joining the 75 Hard AI Engineering Challenge.

It will be delivered to your email inbox: https://lnkd.in/dA56EjcA

Your smart fridge just canceled your dairy subscription - You didn't ask it to:

↳ noticed you're browsing vegan recipes
↳ checked your health calendar
↳ ordered almond milk instead of dairy
↳ canceled dairy subscription
↳ updated your meal plan

You didn't ask it to.

Scary or helpful?

This is happening now.

AI agents are moving from experiments to production.

Real agents I've deployed:

1) Customer Support Agent

- reads tickets
- searches knowledge base
- drafts responses
- escalates complex issues
- follows up automatically

Running 24/7, handles 100 tickets daily

2) Meeting Scheduler Agent:

- reads emails
- checks calendars
- proposes times
- books meetings
- sends confirmations

saved me 10 hours/week

How it helps the business:

- not replacing humans
- handling the repetitive work humans hate

What engineers need to learn:

↳ orchestration frameworks (LangChain, CrewAI)
↳ tool integration patterns
↳ reasoning frameworks
↳ error handling
↳ memory management

That's what I am going to cover in the upcoming workshops.

Follow me Himanshu Ramchandani for more.

PS: I am hosting 8 workshop and webinar FREE events in November, get the updates through the AI newsletter [check comments]

🎤 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

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