Matthew Blasa

LinkedIn

Contractor

Aspire360

Lead Data Scientist (Consultant)

Data Scientist & ML Engineer Consultant 

The Home Depot

Senior Data Scientist

Brinks Home Security

Solutions Architect- Analytics Center of Excellence 

edison.ai

Ruffalo Noel Levitz

Senior Data Strategist

Data Strategist 

Carrefour

Lead Analytics Engineer

University of Southern California

Senior Data Analyst

J.P. Morgan Private Bank

Data Analyst

Bridging the gap between data scientists and the business.

Learn more: 
AI Product and Strategy Substack:  https://datalife360.substack.com/
Pragmatic Data Scientist: https://pragmaticdatascientist.substack.com/
Linkedin Learning: https://shorturl.at/hg0Bc

Strategy Consultant

Interested in a discovery session?
email: info@aspire360.ai or DM me for more information.

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

Most AI engineers can't explain why their agents fail in production

I reviewed 43 agent systems this quarter

- only 3 had proper error handling
- only 1 had memory that actually worked
- zero had correct guardrails implementation

Here's the production-ready architecture most engineers skip:

PHASE 1 - FOUNDATIONS

- stop calling APIs
- start building cognitive loops

↳ reAct (Thought → Action → Observation)
↳ structured outputs with Pydantic validation
↳ tool execution layers that don't hallucinate
↳ repos - langchain-ai/langgraph

PHASE 2 - COORDINATION

- single agents hit walls fast
- teams scale infinitely

↳ langgraph state machines for memory
↳ multi-agent orchestration patterns
↳ RAG that cites sources correctly
↳ frameworks: /crewAI, microsoft/autogen

PHASE 3 - PRODUCTION REALITY

- this is where 90% fail

↳ guardrails: guardrails-ai/guardrails, NVIDIA/NeMo-Guardrails
↳ human-in-the-loop for critical decisions
↳ state rollback when execution breaks
↳ cost monitoring and model switching

PHASE 4 - THE FRONTIER

- self-improving systems
- autonomous exploration

↳ tree-of-Thought reasoning: princeton-nlp/tree-of-thought-llm
↳ multi-agent debates for validation
↳ systems that learn from failures: OpenBMB/ChatDev

The complete roadmap with 8 production projects → see comments

Engineers shipping Phase 3 systems - $180k-$250k
Engineers stuck in tutorial hell - $80k-$120k

The architecture gap is a $100k salary gap

Which phase are you currently building?

Follow Himanshu Ramchandani for AI engineering that goes beyond tutorials


P.S. - This roadmap took 200+ hours - Now it's free. The implementation is where you prove mastery. Check the roadmap below. 

PPS: Last day for AI Agent Architect Bootcamp Registration, last cohort for this year.

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

AI Agent Architect Roadmap

YOU copying RAG tutorials from YouTube

But 90% fail in production

I spent 4 hours analyzing GitHub's top RAG repositories

Here's what actually works:

1) RAGFlow - 68.7k stars
production-ready engine with deep document parsing

2) RAG_Techniques - 23k stars
30+ advanced techniques with runnable code

3) LangChain/LlamaIndex
Still relevant, but basics only

Bonus one

4) Awesome-RAG
list of Retrieval-Augmented Generation (RAG) applications in GenAI

What separates winners from copypasters:

↳ they chunk semantically, not by character count

→ 150-word fixed chunks = context loss
→ semantic boundaries = 42% better retrieval

↳ they don't just embed and pray

→ add query rewriting
→ implement hybrid search (keyword + vector)
→ use re-ranking (cross-encoders, not just cosine)

↳ they test on YOUR data

→ generic benchmarks lie
→ test retrieval time <200ms
→ measure answer accuracy, not vibes

The brutal truth:

simple RAG = 5-10 likes on your demo
production RAG = actual users who don't complain


Clone the repos, test and break things

Then build what actually scales

Your chatbot shouldn't hallucinate more than your CEO (don't beat me for this 💀 )

Follow Himanshu Ramchandani for daily AI insights that ship.

P.S. Building RAG systems? Start with evaluation metrics first, then code. Join the FREE AI Agents production workshop today at 7:30 PM IST

Those who were asking what to build in AI. Here are 37 AI projects that'll actually get you hired:

1. Build your own sub-4-bit model quantization library
2. Build your own LLM serving layer with dynamic KV cache
3. Build your own intelligent dynamic batching system for inference
4. Build your own high-performance CUDA kernel library for sparse models
5. Build your own distributed checkpoint optimized file system
6. Build your own stateful AI agent framework with reflective memory
7. Build your own next-generation vector database indexer
8. Build your own RAG orchestrator with self-healing retrieval logic
9. Build your own LLM safety and alignment evaluation framework
10. Build your own universal lossless tokenizer converter tool
11. Build your own serverless, real-time feature store
12. Build your own domain-adaptive synthetic data generator
13. Build your own real-time prediction drift detection system
14. Build your own data version control integrated with feature lineage
15. Build your own adversarial attack defense and mitigation toolkit
16. Build your own automated fairness and bias auditing toolkit
17. Build your own GPU-accelerated explainability engine (XAI)
18. Build your own hardware-aware ML performance profiler
19. Build your own modular reinforcement learning environment
20. Build your own universal model format translator with optimization
21. Build your own multimodal vector database supporting text, image, and audio
22. Build your own vision transformer pre-training library optimized for video
23. Build your own 3D object reconstruction pipeline using neural radiance fields
24. Build your own code generation model from natural language commands
25. Build your own differentiable physics engine for robotics simulation
26. Build your own high-fidelity audio generation library using diffusion models
27. Build your own knowledge graph construction engine from LLM output
28. Build your own data leakage detection system for distributed training
29. Build your own secure model inference sandbox with confidential computing
30. Build your own watermarking system for AI-generated content
31. Build your own federated learning orchestrator with differential privacy
32. Build your own zero-trust model access control layer
33. Build your own Model Card automation tool for governance
34. Build your own system for quantifying model uncertainty in predictions
35. Build your own adaptive GPU scheduler based on convergence speed
36. Build your own serverless platform for rapid fine-tuning jobs
37. Build your own model compiler for specialized commodity hardware

PS: Created a repo: Build Your Own AI Everything
Anyone can contribute to it. By building any project.

You are not stuck. You are just comfortable being mediocre AI professional:

quit tutorial hell. stop copying code. read the paper. implement from scratch. understand backprop. actually understand it. maintain a learning log. write what you built today. deploy something broken. fix it in production. read Attention Is All You Need twice. train a model on your own data. your actual data. not MNIST. feel the GPU burn. watch loss curves at 3am. turn off autopilot. code without stackoverflow. think before you import. eat without scrolling. debug without chatgpt first. plan your architecture. draw it on paper. then on excalidraw. then build. organize your experiments. track every hyperparameter. take AI safety seriously. read the original transformer paper. act on ideas fast. find your niche. build in public. write technical blogs. save someone's training run. learn systems design. read research daily. create your own datasets. stay skeptical of benchmarks. refine your prompts. optimize for latency. act with rigor. help junior engineers. be humble. stop using tools you don't understand. follow your curiosity. craft your portfolio. learn MLOps properly. systemize your experiments. write code. write docs. write posts. write papers. iterate on feedback. leave Discord open less. talk to researchers. contribute to open source. visit AI labs. look for unsolved problems. experience production failures. then learn. sit with your confusion. build something real. embody first principles. what do you actually want to build? turn off notifications. experiment. break things. fine-tune models. fall in love with the problem. notice inefficiencies in pipelines. visualize attention maps. talk to domain experts with humility. don't trash other frameworks. be technical. be precise. become an engineer. cherry-pick your tools. discard the hype. failed experiments aren't failures. invite hard problems. accept when simpler works. read Karpathy. be contrarian. choose depth over breadth. do work that compounds. let problems obsess you. lose yourself in implementation. value compute time. experience the full stack.

what did I miss?

Follow me Himanshu Ramchandani for insights on AI that actually compound

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).

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 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

2026 AI Engineering predictions

1) most AI agents fail in production because we have no way to see why they fail and engineers spend most time manually checking outputs

2) RAG systems work great for 3 months then quietly get worse as embeddings drift and data goes stale

3) traditional monitoring tools show system healthy while the AI gives wrong answers because we can track uptime but not reasoning quality

4) bootcamp graduates with shipped projects are getting hired over CS graduates with perfect GPAs because portfolio beats pedigree when the field moves this fast

5) companies need people who can debug production AI systems not people who can explain transformer architecture

6) job posts now ask for agent orchestration and context architecture but nobody agrees what these terms mean yet

7) generic chatbots are becoming commodities while real money is in narrow deep solutions like AI for dental clinics or solar installers

8) small teams building with AI leverage are shipping faster than large traditional teams because less coordination overhead

9) fine tuning is coming back because RAG alone works for simple cases but companies wanting competitive advantage are training their own models

10) multi agent systems sound great in demos but coordination is brutal in production as every agent you add multiplies the failure points

11) context design matters more than I expected because how you structure knowledge and retrieve information determines reliability more than which model you use

12) EU AI Act compliance deadline is August 2026 for high risk systems and if you serve EU customers in healthcare finance or hiring this is real

some questions: 

13) will voice interfaces actually replace typing for coding because it seems unlikely

14) one person with AI agents really compete with 50 person teams maybe for simple products but skeptical for complex B2B systems

15) are degrees becoming worthless or are we in a temporary bootcamp hype cycle because probably somewhere in between (edtech 2.0)

16) I thought prompt engineering would be the key skill but turns out production reliability and system design matter way more

17) I underestimated how much time goes into data pipeline work because it is not glamorous but it is 60% of the actual job

18) I overestimated how fast companies would adopt agents because most are still figuring out basic RAG implementations

19) learn to build systems that work when you are not watching them because production AI needs different skills than prototype AI

20) understanding failure modes matters more than understanding architectures so learn what breaks and why

21) get comfortable with uncertainty because best practices do not exist yet and you will figure things out as you go

What do you see differently? Where do you think I am wrong?

Follow Himanshu Ramchandani for honest takes on AI engineering, not just hype

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.

You're watching everyone else build AI agents while you're still watching tutorials

You told yourself "next year"

But next year never comes

Right now? AI Agents are your unfair advantage

The difference between engineers who build and engineers who watch:

→ builders have 7 production agents in their portfolio
→ watchers have 47 bookmarked tutorials they never finished

Here's what hurts:

↳ that customer support role paying $140K? going to someone with agent experience
↳ that promotion you wanted? given to the engineer who automated incident management
↳ that freelance client paying $15K/project? hired someone who already built RAG systems

233 people are waiting against 60 seats - [4 days left]

In 4 weeks, you'll have:

↳ Customer support agent (cuts costs 30%)
↳ Financial analysis agent (10x faster research)  
↳ RAG Q&A system (the 2025 skill everyone's hiring for)
↳ Code reviewer (48 hours → 30 seconds)
↳ Sales qualification agent (4x team productivity)
↳ Incident management agent ($500K+ ROI)
↳ Content generation agent (5x output)

Not theory. Production-ready systems you can show employers tomorrow

Starts December 2nd, 7:30 PM IST

The engineers crushing it in 2025 are building their portfolios right now

Will you be one of them?

Secure your seat here: https://lnkd.in/gtHqDG4c

P.S. Free access to bootcamp orientation + 30-day AI Agents guide + recordings on Nov 30, 9 AM IST
Production Ready AI Agent workshop Satuday 9 AM IST FREE

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]

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