No BS AI/ML Content | ML Engineer with a Plot Twist 🥷30M+ Views 📝

Done-for you Audience Building & Content Writing for AI/ML Founders that frees up 100% of your time, so you can focus entirely on building your company  
  
Is your work too important to ignore? 💌 https://tally.so/r/wk69VJ  (or shoot me a DM) 👉
  
It's 2025 and we're at the peak of the attention economy  
  
Where Authenticity is king, visibility is currency and people wanna buy from people

Not faceless dehumanized brands  
  
So, they told you LinkedIn was "free marketing" you slapped some posts and called it strategy  
  
You've poured hours crafting the 'perfect' post, only to watch it sink without a trace..  
  
Then it dawned on you, your technical brilliance isn't enough to draw a crowd  
  
Hell, maybe you've even tried mimicking influencers, scrambling for any traction..  
  
Your post got 500 f*cking views... from your friends...  
  
Being ignored is soul-crushing  
  
It's not your fault tough  
  
When you started your company, you didn't sign-up for a degree in an Audience Building, Copywriting and LinkedIn strategy 

And here you are, sacrificing yet another late night for your vision  

"Am I just sabotaging my efforts?"  
  
"Is my life-work meaningless?"  
  
Getting traction feels impossible  
  
You hoped hiring some marketer would save your weekends but..  
  
Tech is a code they can't crack  
  
They can only churn out the lame, soul-sucking crap we all ignore on sight  
  
With armies of founders flooding feeds, your voice dissolves into meaningless white noise  
  
But in the process, you've wasted more of your precious time explaining 101 tech concept and rewriting low-quality drafts 

Done being ignored?  
  
Let's make the world listen 
  
I can help you with:  

✒️ No BS AI/ML Content  
⭐ LinkedIn Profile Revamp
🎯 Website Copy Optimization  

I've done it before:

↳ For founders backed by YC, a16z, Sequoia, and Techstars
↳ For bootstrappers in LLMs, ML, Vector DBs, Data Analytics, and Engineering
↳ For AI and Machine Learning leaders at Google, Microsoft, Airbnb, and YouTube

If they could rise above the noise, so can you

Yeah, even if you're an introvert with a "boring" niche

Still with me?  
  
Amazing—You're amazing!  
  
Cheers,  
Paolo

My booking link: 💌 https://tally.so/r/wLW0oz (or shoot me a DM) 👉

Paolo Perrone

NVIDIA just made their best AI models free.

99% of developers have no idea.

While you burn cash on OpenAI credits...
NVIDIA open-sourced Nemotron.
Their entire agentic AI family 🤯

Free Models That Actually Ship:
• Nano: Runs on your laptop
• Super: Single GPU beast
• Ultra: Multi-GPU monster

What They Do:
→ Graduate-level reasoning
→ Math without hallucinations
→ Code that compiles
→ Visual reasoning that works
→ Tool calling. No loops.

The genius part:

They published EVERYTHING.
Models. Datasets. Techniques.
Actually open source.

Game Changers:
• 6x faster on new hardware
• No API limits
• Runs on a Raspberry Pi
• Commercialize it. No strings.

The uncomfortable truth:

You're waiting for GPT-5.
Smart teams ship with Nemotron.

Who's using it:
- Accenture
- CrowdStrike  
- ServiceNow
- Deloitte

The kicker:

Download from Hugging Face. Now.
Deploy tomorrow.
Zero license fees.

Forever.

They built enterprise NIM too.
(That costs money. Models don't.)

Still paying for API credits? 

♻️ Repost before OpenAI charges per thought

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷50M+ Views 📝

I just finished Google's biggest AI Agents course ever.

250+ pages of production insights. 100% free.

Here's everything they released:

DAY 1️⃣ INTRODUCTION TO AI AGENTS (50 pages)

Most agent demos fail in production.
This covers the 5 levels of AI agents, from basic reasoning to self-evolving systems, plus security and architecture patterns that scale.
🔗 https://lnkd.in/gN9JaXgR

DAY 2️⃣ AGENT TOOLS AND MCP (50 pages)

MCP servers can add new tools without your approval.
Your poetry bot suddenly gains purchasing power overnight.
This breaks down tool design, the Model Context Protocol, and hidden security risks.
🔗 https://lnkd.in/gRWDSjBr

DAY 3️⃣ CONTEXT ENGINEERING AND MEMORY (70 pages)

Agents with zero long-term memory treat every session like meeting you for the first time.
Learn how to build memory systems that actively curate context, not just save conversations.
🔗 https://lnkd.in/gbgWJ4kH

DAY 4️⃣ AGENT QUALITY (50 pages)

Traditional QA doesn't work for agents. You can't unit test judgment.
This covers the 4 pillars of agent quality (effectiveness, efficiency, robustness, safety) and how to evaluate reasoning.
🔗 https://lnkd.in/g6DHcZDx

DAY 5️⃣ PROTOTYPE TO PRODUCTION (40 pages)

Building an agent takes 2 weeks.
Trusting it in production takes 6 months.
Learn evaluation gates, circuit breakers, and evolution loops that close the gap.
🔗 https://lnkd.in/gYTakhAK

Huge props to Antonio Gulli and Shubham Saboo for making this happen, and make it 100% free

250+ pages. Zero cost. All production-focused.

♻️ Repost to save a fellow AI engineer 6 months of trial and error

4 math concepts you need to understand LLMs in 10 minutes.

No PhD required.

I spent 6 months confused by AI papers.
Then I found this breakdown by Giles Thomas.
Everything clicked.

Here's the cheat sheet:

1️⃣ Vectors = Direction + Distance in Space

Think GPS coordinates, but in 50,000 dimensions.
Each position represents a different meaning.
"Cat" lives near "dog" in this space.
"Airplane" lives far away.

That's it. That's embeddings.

2️⃣ Matrices = Space Transformers

Remember rotating shapes in geometry class?
Same thing, but with meaning.

A matrix takes your 50,257-dimensional vocab
Projects it down to 768 dimensions
Then back up again.

It's compression without losing the important stuff.

3️⃣ Dot Product = Similarity Score

Multiply matching positions. Add them up.
Big number = similar vectors.
Small number = different vectors.

This is how LLMs know "king - man + woman = queen"

4️⃣ Neural Network Layers = Just Matrix Math

Remove the activation function and what's left?
Matrix multiplication.
A projection from one space to another.

Add activation back = now it can learn patterns.

The GPT-2 Example That Makes It Real:

Input: "The cat sat on the"
1. Convert each word to a one-hot vector (50,257 dimensions)
2. Project down to embeddings (768 dimensions)  
3. Let attention mechanism figure out relationships
4. Project back up to vocab space
5. Pick highest probability: "mat"

That's literally how ChatGPT works.
Just bigger.

Why This Matters:

Everyone's scared of AI math.
But it's high school algebra in a trench coat.

Once you see through the intimidation,
you can actually build stuff.

♻️ Repost to demystify AI for someone else

Sebastian Raschka, PhD just exposed the biggest secret in AI.

After dissecting 9 flagship LLMs, he found something nobody wants to admit:

We haven't invented a new architecture in 7 years.

The evidence is damning:
• DeepSeek V3: 671B parameters, still a transformer
• Kimi K2: 1 TRILLION parameters, still a transformer
• GPT-OSS: OpenAI's latest, still a transformer
• Llama 4: Meta's flagship, still a transformer

Every "breakthrough" model?
Same skeleton. Different makeup.

Here's what's actually happening:

DeepSeek V3/R1: Compressed attention (MLA) + expert routing (MoE)
Gemma 3: Sliding windows to fake longer context
SmolLM3: Removed position embeddings entirely (!)
Kimi K2: Just made DeepSeek bigger

The brutal reality:

• No new architectures since "Attention is All You Need" (2017)
• Every innovation = efficiency hack
• We're polishing, not inventing

671 billion parameters.
1 trillion parameters.
Same. Damn. Architecture.

While everyone's chasing parameter counts, Sebastian mapped the actual mutations:
• MoE: Use 37B of your 671B parameters
• Sliding attention: Pretend you have infinite context
• MLA: Compress your KV cache and pray

This isn't innovation.
It's optimization theater.

The most honest technical analysis you'll read this year.

Read it. Then ask yourself:
If we can't innovate past transformers, what happens when we hit the parameter wall?

♻️ Repost if you appreciate actual technical depth over hype

The best AI engineers master PyTorch. The rest just import it.

Here's how to join the first group:

1️⃣ Master the Foundation
🔸 NumPy arrays → PyTorch tensors: https://lnkd.in/grmapDsK
🔸 Tensor operations that matter: https://lnkd.in/gjk3ErXu

2️⃣ Learn PyTorch Basics (Skip the Fluff)
🔸 60-minute blitz (actually 60 mins): https://lnkd.in/gU86ZGqt
🔸 What torch.nn really does: https://lnkd.in/gEmCGg6R
🔸 Daniel Bourke's 1-day crash course: https://lnkd.in/gCdBG_jc

3️⃣ Understand Autograd (The Magic Behind It All)
🔸 Karpathy's micrograd repo: https://lnkd.in/g_hTUmji
🔸 Karpathy's micrograd video: https://lnkd.in/gxM7h9E9
🔸 See gradients flow in real-time: https://lnkd.in/gKdwgAzz

4️⃣ Build Real Networks
🔸 Aladdin's playlist (underrated gem): https://lnkd.in/gP3k_rNx
🔸 NLP from scratch (no shortcuts): https://lnkd.in/g77inpPa
🔸 Adversarial examples (break things): https://lnkd.in/gFDzkep8

5️⃣ Production PyTorch (Where 90% Fail)
🔸 Distributed training without tears: https://lnkd.in/gdPZDnxB
🔸 Profile before you scale: https://lnkd.in/gfs7JQ6R
🔸 Mixed precision (2x speedup): https://lnkd.in/gikyETYK
🔸 Performance tuning secrets: https://lnkd.in/g8BE7J28

The uncomfortable truth:

Most "PyTorch experts" can't debug a CUDA out-of-memory error.
They copy tutorials. They call high-level APIs.

But when production breaks? Crickets.

Start with micrograd. Build intuition.
Then scale up. One tensor at a time.

What PyTorch concept finally clicked for you?

♻️ Repost to help someone skip the 3am debugging sessions

The AI bubble is 75% of US GDP growth.

Read that again.

I spent the weekend analyzing Deutsche Bank's latest report.
What I found made me cancel my AI startup plans.

The dot-com bubble was 15.6% of GDP when it popped.
AI is already 5x bigger. And growing.

Here's the brutal reality nobody wants to admit:

1️⃣ Every AI Company Is Bleeding Cash
• OpenAI: Losing money on every $200/month subscription
• Break-even price: $2,000/month (10x current)
• 2026 projected loss: $14 billion
• 2029 projected loss: "Hundreds of billions"

2️⃣ AI Infrastructure Is a Money Pit
Data centers need $2 trillion annual revenue to break even.
Actual AI industry revenue: $390 billion.
That's a $1.6 trillion gap. Every. Single. Year.

3️⃣ AI Adoption Is Dead in the Water
• 95% of AI pilots fail (MIT study)
• AI coding tools make devs SLOWER (METR report)
• 77% of workers say AI increased their workload
• Adoption rates missed predictions 2 years running

The uncomfortable truth:

This isn't 1999. When the dot-com bubble popped, e-commerce was real.
It bounced back.

AI profitability is mathematically impossible.
The efficient compute frontier guarantees it.
Floridi's Conjecture proves agentic AI can't exist.

When this pops, there's no bouncing back.

What's keeping it alive? Your 401k.
70% of US venture capital is flowing into AI.
Half the S&P 500's growth is just tech companies building data centers.

Remove AI spending and US growth is already at zero.
We're IN the recession. AI is just hiding it.

♻️ Repost before your retirement fund discovers math

AI agent bootcamps charge $2,000 for this. You're getting it for free.

1️⃣ Design (6+ hours)
→ Langflow docs: docs.langflow.org
→ Travel planning agent template: https://lnkd.in/gmbWX5Mg
→ Agentic design patterns by Andrew Ng: 

2️⃣ Build (22+ hours)
→ LangGraph course: https://lnkd.in/g7raxjpC
→ LangSmith (modules 1-3): smith.langchain.com
→ Evaluation: https://lnkd.in/ghE-8EwA
→ Prompt engineering: 4 Methods of Prompt Engineering

3️⃣ Deploy (35+ hours)
→ LangChain Academy (modules 1-6): academy.langchain.com
→ MLOps specialization: https://lnkd.in/gmeb53UP
→ System design fundamentals: YouTube playlist
→ Distilabel for synthetic data: https://lnkd.in/gd_jJaJE

63 hours. $0.
The $2,000 bootcamp teaches the same stack with better marketing.

💾 Save this. It's the curriculum.
♻️ Repost to save someone $2,000.

Wanna build AI agents? Stop watching random tutorials.

Here's the actual learning path:

1️⃣ Design (6+ hours)
→ Langflow intro: langflow.org
→ Multi-agent workflow patterns
→ Build: Travel planning multi-agent system

2️⃣ Build (22+ hours)
→ LangGraph course: https://lnkd.in/g7raxjpC
→ LangSmith for tracing: smith.langchain.com
→ Evaluation metrics and prompt engineering

3️⃣ Deploy (35+ hours)
→ LangChain Academy: academy.langchain.com
→ MLOps fundamentals
→ LangGraph Cloud deployment

Total: 63 hours. If you follow the order.
6 months if you don't.

💾 Save this.  Start at Phase 1.
♻️ Repost if you've ever jumped straight to deployment and regretted it.

Your AI agent has dementia. LangChain just dropped the cure: 

DeepAgents CLI with actual persistent memory.

This project solves the most infuriating problem in AI development, agents that forget everything the moment you close your terminal. 

It gives your AI assistant an actual memory that persists across sessions. 

Key Features:

✅ True persistent memory
Your agent writes markdown notes to ~/.deepagents/memories/ and reads them before every response. Like a human taking notes.

✅ File operations that work: 
Read, write, and edit files in your project with diff previews and human approval. No more "I can't access your filesystem."

✅ Shell command execution
Run tests, install packages, deploy code - all with your permission. Because we're not insane.

✅ Multi-agent personalities
Create specialized agents (backend-dev, frontend-wizard, devops-guru) each with their own memory and expertise.

Getting started is stupidly simple. 
Just install and run: pip install deepagents-cli && deepagents

Harrison said they built this because copy-pasting context 47 times a day is insanity (we agree).

Check out the code, see the full toolset, and contribute on GitHub!

♻️ Repost if you're done being your AI's memory bank

90,000 AI papers published on Arxiv.

Ilya Sutskever says you only need 27 to understand 90% of what matters.

I just went through his list. Here's what shocked me:

What's NOT on the list:
❌ No RAG papers (everyone's obsessed with retrieval)
❌ No prompt engineering papers (despite the hype)
❌ No LangChain or agent frameworks
❌ Nothing about fine-tuning optimization
❌ Zero papers on "emergent abilities"

What IS on the list:
✅ "Attention is All You Need" (obviously)
✅ "The Unreasonable Effectiveness of RNNs" (2015, still matters)
✅ "Scaling Laws for Neural Language Models" (this is the one)
✅ "Kolmogorov Complexity and Algorithmic Randomness" (wait, what?)

The pattern I noticed:

1️⃣ Ilya's not telling you to read papers about using AI.
He's telling you to read papers about how intelligence works.
Three categories dominate his list:
Fundamentals (the architecture breakthroughs)
Attention mechanisms
Residual connections
LSTMs and sequence modeling

2️⃣ Scaling (why bigger actually works)
Scaling laws
Pipeline parallelism
Training dynamics

3️⃣ Compression (the theory underneath)
Minimum description length
Kolmogorov complexity
Information theory

Here's the controversial take:
If you're only reading papers from 2023-2024, you're studying tactics.

Ilya's list is strategy:

"Attention is All You Need" is from 2017.
"Understanding LSTM Networks" is from 2015.
"Deep Residual Learning" is from 2015.
The foundations haven't changed.
We're just scaling them.

The most surprising paper on the list:

"The First Law of Complexodynamics"
It's not even about neural networks.
It's about how complex systems evolve.

This is what separates engineers who use AI from engineers who understand AI.

If you only read 5:

1) Attention is All You Need (transformers)
2) Scaling Laws for Neural Language Models (why GPT works)
3) Understanding LSTM Networks (sequence modeling)
4) Deep Residual Learning (why deep networks train)
5) Kolmogorov Complexity (the theory of intelligence)

♻️ Repost to save someone from FOMO-reading 90,000 papers

There's a special hell in AI/ML engineering.

It hits around Year 2.
I call it the "valley of almost useful."

You're past "Hello World" tutorials.
Not good enough for real jobs.
Stuck building demos nobody uses.

The daily torture:

Morning: "Today I'll implement that paper"
Noon: Your embeddings are somehow 7-dimensional
Evening: Stack Overflow has zero answers
Night: "Maybe I should go back to web dev"

The worst part?

You know just enough to be dangerous.
Just not enough to be valuable.

🔥 The 3 Levels of "Almost Useful"

Level 1: The Overengineering Phase
Every problem needs a neural network.
CSV parsing? Better use GPT-4.
Your hello world has 17 dependencies.

Level 2: The Imposter Syndrome Pit
Everyone else seems to ship AI products daily.
Your Twitter feed is pure pain.
"Just deployed my 5th AI startup this week!"
Meanwhile, you can't get embeddings to work.

Level 3: The Clarity Moment
You realize 90% of "AI Engineers" are just prompt engineers.
Most production AI is boring Python scripts.
The magic isn't in the model - it's in the problem.

Here's what got me through the valley:

1️⃣ Ship broken things
My first production model had 67% accuracy.
Still saved the company $200K/year.
Perfect is the enemy of deployed.

2️⃣ Join a startup, not FAANG
Startups let you touch everything.
Break things. Learn fast. Ship faster.
Big tech will teach you one tiny slice.

3️⃣ Build boring solutions
Stop chasing cutting-edge.
Most businesses need basic classification.
Not your 12-layer transformer architecture.

4️⃣ Document everything
Future you will thank present you.
Especially when debugging at 3 AM.
"Why did I set embedding_dim=7?"

5️⃣ Find your valley friends
The loneliest part is thinking you're alone.
You're not. We all went through this.
Some of us just hide it better.

The uncomfortable truth:

The valley of almost useful is necessary.
It's where you learn that AI isn't magic.
It's engineering.

And engineering is mostly debugging.
And crying.
But mostly debugging.

Still in the valley?
Good. That's where real engineers are made.

Not in bootcamps.
Not in courses.
In the trenches, shipping broken code that somehow works.

Welcome to the club.

P.S. That senior engineer who seems to know everything? They googled it 5 minutes before the meeting. We're all just better at pretending now.

♻️ Repost to help someone realize they're not alone in the valley

Just watched an ML engineer at a fintech startup build a production fraud detection model in 2 hours.

From raw data to deployed.
No feature engineering. No pipeline code.
She just typed what she needed in plain English.

I was skeptical. Had to see it myself.

She opened Plexe, typed "fraud detection for card transactions" and pointed it at their Postgres db.
That's it.

2 hours later: deployed model with 0.89 AUC on 3M transactions.
Here's what actually happens: 

1️⃣ Describe your problem in plain English
"Product embedding model for search"
"Customer lifetime value predictor"
"Fraud detection system"
No templates. No configuration files.

2️⃣ Point it at your data
Postgres, Snowflake, S3, whatever you're using
It analyzes your schema automatically
No manual feature mapping

3️⃣ It handles what AutoML skips
• Engineers features from raw data (AutoML just uses columns as-is)
• Generates comprehensive evaluation reports with failure analysis
• Shows exactly where and why your model breaks
• Gives prioritized improvement recommendations
• Deploys with monitoring

The critical difference:
❌ AutoML: "Here's a model with 0.87 accuracy. Good luck."
✅ Plexe: "Here's why it works on these patterns, why it fails on those edge cases, and the three changes that will improve it most."

This is what changes:
You stop being a data plumber.
You start being a problem solver again.

Best part? 

Open-source core (Apache 2.0): https://lnkd.in/gEx42Q9b
Try it with $20 free credits: https://console.plexe.ai
Use code: LAUNCHDAY20

What ML model have you been putting off because wrestling with pandas makes you want to scream?

♻️ Repost to save someone 3 months of pipeline work

I pushed every agent framework until it broke. Most broke fast.

CrewAI vs LangGraph vs Agno vs PydanticAI vs Atomic Agents...
Plus SmolAgents, Mastra, and AutoGen.

The brutal truth:

Most devs hate the popular ones.

CrewAI and AutoGen? "Tricky to debug"
LangGraph? "Overly complicated abstractions"
SmolAgents' CodeAgent? "Cool idea, doesn't always work"

What actually matters:

🎯 Control vs Agency
Some frameworks let LLMs run wild (AutoGen)
Others keep them on a tight leash (Atomic Agents)
Guess which ones actually ship to production?

🛠️ Abstraction Level
High abstraction = Quick start, debugging nightmare
Low abstraction = More code, but you know what broke
Pick your poison.

💀 The Learning Curve
LangGraph: Steep as hell, but scales
CrewAI: Easy start, hits a wall
PydanticAI: Write everything yourself

The uncomfortable truth:
"AI agent frameworks are some of the worst forms of abstraction"

But here's what actually works:
• Agno for developer experience
• LangGraph for multi-agent at scale
• PydanticAI when you need control
• CrewAI if you're just prototyping

Half these frameworks won't exist in 2 years.
The winners? Whoever makes debugging possible.

Saved you 2 weeks of testing. You're welcome.
♻️ Repost if you've been burned by agent framework promises

a16z just published their AI coding report.

The math blew my mind:

AI coding costs $10k/year per developer.
But creates $100k+ in value.
That's $3 trillion in new GDP.

The entire economy of France. From a few Silicon Valley startups.

Here's what nobody's talking about:

Software development just shifted from pure people cost to people + compute cost.

Your junior developer in Bangalore?
Now competing with Claude at $10k/year.

The uncomfortable truth:

We're watching the end of cost-based outsourcing.

Not because AI replaces developers.
But because location doesn't matter when compute costs dominate.

I tested the numbers myself:

• 100k context window filled
• Claude Opus 4.1 reasoning mode
• 10k output tokens
• 3 queries/hour, 7 hours/day

= $10,500 annually

That's more than many offshore developers.

But here's the kicker:

The companies winning aren't cutting developers.
They're hiring MORE.

Why?

Because when productivity doubles, the math changes:
• 1 developer + AI = 2x output
• Cost per feature drops 50%
• Suddenly every project has positive ROI

The report shows clear winners already:

Cursor: $500M ARR in 15 months
Windsurf: $2.4B Google acquisition
Claude Code: Started a war with its own distribution channel

The pattern is obvious:

Lower barriers → More projects → More complexity → More developers needed

AWS didn't kill sysadmins.
WordPress didn't kill web developers.
AI won't kill software engineers.

It just changes what they do.

University CS programs?
Already obsolete.

Teaching algorithms when AI writes them faster?
Teaching syntax when AI handles implementation?
Teaching debugging when AI prevents most bugs?

That's like teaching calligraphy in the age of keyboards.

The real skill now:

Knowing when the AI is hallucinating.
Dragging it out of architectural holes.
Translating business needs into prompts.

The $3 trillion opportunity isn't in replacing developers.

It's in making every developer 10x more valuable.

While universities update their curriculums,
smart developers are already adapting.

Are you?

♻️ Repost if you think CS education needs a complete overhaul

Plexe is a Python library that turns "I need to predict customer churn" into a working ML model.

No feature engineering. No pipeline code. Just natural language intent and your data.

It integrates with OpenAI, Anthropic, and Ray, making it easier to build sentiment analysis, fraud detection, and forecasting models in minutes instead of weeks.

Plexe doesn't just build the model - it generates a thorough evaluation report showing exactly where and why it performs well (or doesn't).

A powerful tool for anyone working at the intersection of product development and machine learning.

♻️ Repost if you've ever deployed a model and just hoped it worked.

🎁 Launch special: Get $20 free credits → https://console.plexe.ai
Code: LAUNCHDAY20

The most underrated AI agents video on YouTube has 155K views.

It should have 15 millions.

Dexter breaks down why coding agents actually fail in production.
Not theory. What broke when they shipped real agent systems to real software teams.

The line that changed how I think about agents:
"Everything is context engineering."

He shows:

→ Why naive back-and-forth prompting doesn't scale (and what does)
→ Spec-first development that keeps agents on track
→ Context compaction strategies for long-running workflows
→ How subagents handle complexity without exploding

No demo theater. Just hard-won lessons from production.

If you're building agents that need to work beyond the prototype, this is required watching.

Watch it: https://lnkd.in/e99smz5g

♻️ Repost if you've hit the "context limit exceeded" wall

I just watched Sebastian Raschka build the entire attention mechanism from scratch in 2 hours.

No abstractions. No "just use torch.nn.MultiHeadAttention."

Line-by-line Python until it clicks.

This is 's breakdown of how LLMs actually work.

Step 1️⃣ Basic self-attention (no weights) 
Shows you the raw math: how a model decides which words matter.
You see why "bank" in "river bank" gets different attention than "bank" in "savings bank."

Step 2️⃣ Add learnable parameters 
Now the model can learn what to pay attention to.
This is where it stops being a simple lookup and becomes intelligence.

Step 3️⃣ Causal masking 
The trick that makes GPT work: tokens can only attend to previous tokens.
Without this, the model cheats during training.

Step 4️⃣ Multi-head attention 
The breakthrough: run 8 attention mechanisms in parallel.
One head learns syntax. Another learns semantics. Another learns long-range dependencies.
This is why transformers work.

👉 What makes this different:

Most tutorials either:
Show you the math (incomprehensible)
Show you the PyTorch API (you learn nothing)

Sebastian does both.

You see the math implemented in code, step by step.

By the end, you understand why attention_weights = softmax(Q @ K.T / sqrt(d_k)) isn't magic.

It's just weighted averaging with learnable weights.

💡The moment it clicked for me:

Attention isn't complicated.
It's just "which words should influence this word's representation?"
Then you do that for every word.
Then you stack it 96 times (GPT-4).

That's it.

The Lovable for Data Analysis Just Dropped

Upload a file → Ask a question → Get your answer.

No Python, no SQL, no notebooks required.

Compare that to what you’re doing now: 

❎ Write SQL queries or Python scripts 
❎ Debug when they break
❎ Switch between notebooks, terminals, dashboards 
❎ Wait for a data scientist to help you

This agent actually understands your data:

✅ Sees your structure, knows how to query it 
✅ Maintains context across your workflow 
✅ Fixes its own errors
✅ Generates insights, not code you need to interpret

This is a big deal for anyone doing real data analysis work. 

You get the power of a data science team without needing to become one.

The best part?

I've got $15 in free credits for you to test it (no expiration date).

♻️ Repost + comment "GIVE ME MY WEEKENDS BACK 😭" and I'll send you the link

Traditional RAG: retrieve → generate → pray

Agentic RAG: retrieve → validate → retry → succeed

One is a pipeline. The other is a feedback loop.

I just built this Agentic RAG system with LangGraph + Milvus

It knows when it's wrong, and fixes itself

Here’s how:

Router: "Can I answer without retrieval?" 
✅ Yes: Skip to generation 
❌ No: Retrieve from Milvus

Grader: "Are these chunks actually relevant?" 
✅ Good: Generate answer 
❌ Bad: Rewrite query, loop back

Validator: "Does this answer match the sources?" 
 ✅ Yes: Return to user 
❌ No: Retry with different approach

It's a graph with feedback loops, not a one-shot pipeline.

The difference:
Traditional RAG commits to one path. No second chances.
Agentic RAG loops back, validates, catches mistakes, and retries until it's right.

You get a system that debugs itself before you see the output.

Why this stack:
LangGraph = control flow primitives (routing, looping, branching) 
Milvus, created by Zilliz = production-grade vector search at scale
Together: RAG that thinks instead of guesses.

Full implementation 👇

This is the RAG architecture pattern that will become standard.
♻️ Repost to save a fellow engineer from another week of RAG debugging

BFCM isn't won by the brand with the best products.

It's won by the brand that sees what's breaking first.

Most operators spend BFCM like this: 

9 AM: Campaign's crushing it, scale to $10K/day
11 AM: ROAS drops 40%, nobody knows why
2 PM: Still diagnosing
5 PM: $6K burned on a broken campaign

The strategy wasn't wrong. 

You just couldn't see the problem fast enough. 

Diagnosing "why is ROAS dropping?" right now: 

→ Open Meta Ads Manager
→ Check Shopify analytics
→ Pull GA4
→ Cross-reference Klaviyo
→ Export to spreadsheet
→ Try to connect the dots

20 minutes later you've burned $3K.

Triple Whale + Moby give you real-time answers from your complete data stack. 

👉 https://lnkd.in/gtgB8GJw

Moby has real-time access to all of it. 

Ask Moby: "Why did ROAS drop?"

Get: "iOS campaign CTR crashed 67% after creative refresh. Top SKU went out of stock an hour ago. Traffic's converting to lower-margin backup products."

Not a guess. The actual answer.

One brand caught this: 

The campaign was showing 187% ROAS. 
Moby a flagged 41% return rate. 
Actual ROAS after was returns: 73%. 

They were about to scale into a loss.

During BFCM, 20 minutes to diagnose = thousands in wasted spend. 

The brands that win can see problems in seconds, not hours. 

And yes, I am a Triple Whale partner.

👉 https://lnkd.in/gtgB8GJw

♻️ Repost if you've burned budget figuring out what went wrong

Paul Iusztin

Stealth AI Startup

Founding Al Engineer

Decoding ML

Content to help you design, code, and deploy production-grade AI, GenAI and information retrieval systems.

Founder

Remote Work

💼 Senior machine learning engineer, contractor, consultant, and freelancer with 𝟔+ 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 offering scalable and modular machine learning solutions for businesses worldwide.

➞ I can design, implement, train, evaluate, and operate end-to-end ML pipelines for your business. I can scale up your ML systems by leveraging the latest MLOps technologies.

➞ I have spent 7+ years writing code in Python, but I also enjoy using Node.js for web development and C/C++ for optimizing various applications.

➞ My go-to machine learning frameworks are PyTorch, Keras, and Sklearn. Also, my favorite MLOps tech stack is AWS, W&B/MLFlow, and DVC.

🔥The world of AI is constantly changing, new technologies are born almost daily, and therefore, I am always open to new challenges.

💬 𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐦𝐞, and let's see how I can help your business.

Senior Machine Learning Engineer • Freelancer • Contractor

Metaphysic.ai

🟡 Computer vision & deep learning workflow engine: orchestrate & execute

The client, a leading GenAI platform, faced difficulties scaling and breaking down its complex ML jobs. Their manual processes and CLI-based tooling added significant friction in serving their clients. Thus, the client required a more intuitive and scalable tool that could be used across the company to train and serve its ML models.

→ 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿𝗲𝗱 the ML worker architecture to follow DDD principles, making it more 𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲, 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, and 𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝗯𝗹𝗲.

→ 𝗗𝗲𝗰𝗿𝗲𝗮𝘀𝗲𝗱 the 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 of the data curation DL models 𝗯𝘆 𝟴𝟮.𝟳%

→ 𝗘𝘅𝘁𝗲𝗻𝗱𝗲𝗱 their clustering algorithm to accurately run on 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 >𝟭𝟬𝗸 𝗶𝗺𝗮𝗴𝗲𝘀 and 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗱𝗲𝘁𝗲𝗰𝘁 the number of 𝗰𝗹𝘂𝘀𝘁𝗲𝗿𝘀 and 𝗼𝘂𝘁𝗹𝗶𝗲𝗿𝘀.

Senior Machine Learning Engineer

⚫️ Scalable ML batch pipeline for continuous training & inference

The client, a leading GenAI platform, struggled with manual training and calling their ML models. As a tedious and unscalable process, they needed to automate their ML processes completely and integrate them with their current web platform.

→ 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗱 and 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 a 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗽𝗮𝗰𝗸𝗮𝗴𝗲 that orchestrates the ML steps into 3 fully automated batch pipelines: feature, training, and inference, 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗮 𝘁𝗲𝗱𝗶𝗼𝘂𝘀 𝟮-𝗵𝗼𝘂𝗿 𝗺𝗮𝗻𝘂𝗮𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀/𝘃𝗶𝗱𝗲𝗼.

→ 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 the 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝘁𝗶𝗺𝗲 of the batch ML pipeline 𝗯𝘆 𝟲𝟮.𝟱% while preserving the same accuracy and quality.

→ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗠𝗟𝗢𝗽𝘀 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 such as IaC, CI/CD, monitoring, experiment tracking, and model registries, making the system reproducible, testable and trackable.

→ 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 a scalable & cost-effective 𝗮𝘀𝘆𝗻𝗰 𝗯𝗮𝘁𝗰𝗵 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 on top of AWS ECS & SQS that scales in and out based on the number of jobs, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 their 𝗔𝗪𝗦 𝗰𝗼𝘀𝘁𝘀 𝗯𝘆 𝟱𝟮%.

⚫️ Big data ingestion & archiving pipeline

The client wanted to expand its platform features by securely ingesting, preprocessing, and storing TB of data into their platform.

→ 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 to the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of a 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐧𝐠 >𝟓 𝐓𝐁 of data in 𝐨𝐧𝐞 𝐠𝐨 in a reliable, fast, and secure way.

→ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 a scalable 𝗱𝗮𝘁𝗮 𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 that supports multiple media types (images, videos, audio, 3D objects), archiving the original files on an affordable storage solution, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗰𝗼𝘀𝘁𝘀 𝗯𝘆 𝘂𝗽 𝘁𝗼 𝟴𝟭%.

Towards Data Science

⚫️ ML and MLOps engineering blog

Helping machine learning engineers level up through hands-on practical articles and tutorials on designing and productionizing ML systems.

➞ Published 𝐓𝐡𝐞 𝐅𝐮𝐥𝐥 𝐒𝐭𝐚𝐜𝐤 𝟕-𝐒𝐭𝐞𝐩𝐬 𝐌𝐋𝐎𝐩𝐬 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 with 90k+ views on Medium and 600+ stars on GitHub. An MLE & MLOps course that teaches you how to design, build, and deploy an end-to-end ML batch system: https://github.com/iusztinpaul/energy-forecasting

Machine Learning Engineer / Technical Writer

CoreAI

⚫️ Real-time social media recommender system using machine learning & Kafka

The client, a social media start-up from Israel, needed a real-time dashboard and recommender system on top of their Kafka infrastructure using modern ML and MLOps solutions.

➞ 𝐋𝐞𝐝 the architecture system design of a modular real-time social media recommender system built on top of the client's Kafka nervous system that leverages streaming and ML technologies such as Kafka Streams, Kotlin, PyTorch, Python, and Docker.

➞ Quickly 𝐩𝐥𝐚𝐧𝐧𝐞𝐝 and 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐝 a scalable recommender system based on heuristics, picked through A/B testing, using Kafka Streams and Kotlin. It served as a vital entry point and baseline for future algorithms.

➞ 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a dashboard that computes KPIs in 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐟𝐨𝐫 𝟏𝟎𝟎𝐤+ 𝐮𝐬𝐞𝐫𝐬 on the client's Kafka infrastructure using ksqlDB, SQL, Elasticsearch, and Apache Superset.

Machine Learning Tech Lead

⚫️ coreControl - end-to-end MLOps infrastructure

The client, a machine learning service provider, required an end-to-end ML/MLOps infrastructure to scale their operations and save development resources across multiple AI projects.

➞ 𝐋𝐞𝐝 the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of coreControl by integrating the software with the client's products and collaborating with other ML engineers, developers, and graphic designers.

➞ 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 the 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐢𝐦𝐞 across the client's machine learning projects 𝐛𝐲 𝟑𝟎% using coreControl.

➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 the data backbone of coreControl that syncs the data between multiple MLOps tools and exposes them to a UI through a RESTful API using Python, FastAPI, PostgreSQL, Docker, and AWS.

➞ 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐝 a Python package that wraps various MLOps tools that standardizes the data scientists' work and centralizes all the ML artifacts to easily be analyzed in a single place using Python, DVC, and ClearML.

⚫️ Prototyping AI solutions for Demand Forecasting, Churn Prediction, and Customer Segmentation

The client, a machine learning start-up, wanted to expand their services portfolio by prototyping and gaining knowledge in various AI fields that leverage structured retail data.

➞ 𝐋𝐞𝐝 𝐚 𝐭𝐞𝐚𝐦 𝐨𝐟 𝟐 to develop an ML clustering solution that aggregates data from multiple sources and finds customer segments and insights for new potential clients.

➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a prototype for churn and demand forecasting used as a proof of concept to close deals with various clients.

➞ 𝐒𝐚𝐯𝐞𝐝 𝟒𝟎% of 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 by building a reusable Python ML framework that can quickly be tailored to custom problems based on structured data.

Politehnica University of Timisoara

⚫️ Teaching the foundations of 𝐚𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 laboratory.

➞ Supervised and unsupervised learning
➞ Linear regression, SVMs, Decision trees
➞ Clustering, Dimensionality reduction
➞ Dense neural networks
➞ Convolutional neural networks
➞ Recurrent neural networks
➞ Transformers

Teaching Assistant

Everseen

🟡 Autonomous supply chain system using computer vision & deep learning

The client, a well-known player in the retail sector, needed to expand its deep learning and computer vision solutions by researching and building autonomous systems for its operations in the retail industry.

➞ 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 a Mask-RCNN model for 2D object detection and segmentation on 100k proprietary retail images, with an 𝐦𝐀𝐏 𝐨𝐟 𝟖𝟓%.

➞ 𝐒𝐩𝐞𝐞𝐝 𝐮𝐩 the 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐭𝐢𝐦𝐞 of the Mask-RCNN model 𝐛𝐲 𝟓𝟎% by levering Docker, ONNX, and TensorRT to containerize and accelerate the models.

➞ 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 a scalable classifier using an image retrieval model that can dynamically handle new classes without additional training.

➞ 𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐝 the 𝐫𝐞𝐩𝐫𝐨𝐝𝐮𝐜𝐢𝐛𝐢𝐥𝐢𝐭𝐲 of the 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 by implementing MLOps solutions, such as DVC & MLFlow, that included data & model versioning, registries, and experiment tracking features.

Continental

🟡 Autonomous driving system using computer vision & deep learning

The client, a well-known player in the automotive sector, needed to create an autonomous driving car prototype that could accurately detect traffic participants by leveraging DL and CV solutions.

➞ 𝐋𝐞𝐝 the 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 of a prototype for real-time traffic participants detection by deploying a CenterNet model for 3D object detection on a test car using an Nvidia Drive AGX C++ platform.

➞ 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 the reproducibility of the experimentation process by implementing MLOps solutions (DVC) that version unstructured big data.

➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a replacement to the Hungarian algorithm that 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐝 the 𝐬𝐩𝐞𝐞𝐝 by 𝟑𝟎%, making the system 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞.

➞ 𝐒𝐚𝐯𝐞𝐝 𝟐 𝐝𝐚𝐲𝐬, in 𝐞𝐯𝐞𝐫𝐲 𝐦𝐨𝐧𝐭𝐡, of 𝐫𝐞𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 work by automating the steps for the creation of the 𝐦𝐨𝐧𝐭𝐡𝐥𝐲 demo.

Mid-Level Machine Learning Researcher

⚫️ Autonomous driving research using computer vision & deep learning

The client, a well-known player in the automotive sector, needed to expand its research into the autonomous driving field using novel deep learning and computer vision methods.

➞ 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 in 3D object detection, depth estimation & completion, tracking, and sensor fusion algorithms using various sensors such as images, radar & LiDAR point clouds.

➞ 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐛𝐲 𝟒𝟎% the 𝐦𝐞𝐦𝐨𝐫𝐲 𝐮𝐬𝐚𝐠𝐞 of the 3D object detection code by introducing the Dynamic Voxelization algorithm into the preprocessing pipeline.

➞ 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 state-of-the-art models to generate LiDAR-like dense point clouds from sparse Radar point clouds on various autonomous driving datasets, such as KITTI, nuScenes, DDAD, and A2D2.

Junior Machine Learning Researcher

Elementum Technologies

⚫️ Dorel - An Uber for handymen

𝐃𝐨𝐫𝐞𝐥 is an app that connects handypersons with customers. It contains a review and portfolio system where any handyperson can build his profile. Based on that, a user can determine whether he fits the job. The app intends to bring, once again, trust in handypersons.

➞ 𝐏𝐢𝐭𝐜𝐡𝐞𝐝 the 𝐢𝐝𝐞𝐚 to various start-up incubators.

➞ 𝐌𝐚𝐧𝐚𝐠𝐞𝐝 the backend components of the software solution of Dorel using an Agile methodology.

➞ 𝐋𝐞𝐝 the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of the Django RESTful API server and the notifications microservice.

➞ 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 and 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐝 the Redux layer of the React Native mobile app.

Co-Founder

🤖 Senior Machine Learning Engineer • MLOps • Contractor ~ Helping startups engineer production-ready ML/AI systems.

I am a senior AI engineer and contractor with 8+ years of experience. I design and implement modular, scalable, and production-ready ML systems for startups worldwide.

My central mission is to build an AI academy and data-intensive AI/ML products that serve the world.

Since training my first neural network in 2017,  I have 2 passions that fuel my mission:

→ Designing and implementing production AI/ML systems using software engineering and MLOps best practices.

→ Teaching people about the process.

.

I currently develop vertical AI agent solutions for a US startup. In the past, I built AI, ML and MLOps products for Metaphysic, CoreAI, Everseen, and Continental.

I am the Founder of Decoding ML, a channel for battle-tested content on learning how to design, code, and deploy production-grade AI systems.

As a content creator, I co-authored the LLM Engineer’s Handbook, an Amazon bestseller and industry standard on LLM, RAG, and LLMOps solutions, which sold over 15k+ copies.

.

If you want to learn how to build end-to-end production-ready AI systems with software engineering and MLOps best practices, subscribe to my Substack:

🔗 https://decodingml.substack.com

.

💬 If you need AI solutions for your business, let’s discuss!

🌎 Only open to full remote positions as a contractor.

.

Contact:

✉️ Email: p.b.iusztin@gmail.com

💻 Decoding ML: https://decodingml.substack.com

🟡 Scalable ML batch infrastructure for continuous training & inference

The client, a generative AI leader, struggled with manual training and calling their ML models. As a tedious and unscalable process, they needed to automate their ML processes completely and integrate them with their current web platform.

➞ 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a modular 𝐏𝐲𝐭𝐡𝐨𝐧 𝐩𝐚𝐜𝐤𝐚𝐠𝐞 that composes the ML model required steps into 3 main batch pipelines that can easily be automated: feature, training, and inference, 𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐢𝐧𝐠 a 𝐭𝐞𝐝𝐢𝐨𝐮𝐬 𝟐-𝐡𝐨𝐮𝐫 𝐦𝐚𝐧𝐮𝐚𝐥 𝐩𝐫𝐨𝐜𝐞𝐬𝐬/𝐯𝐢𝐝𝐞𝐨.
➞ 𝐃𝐞𝐩𝐥𝐨𝐲𝐞𝐝 a scalable & cost-effective 𝐛𝐚𝐭𝐜𝐡 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 and 𝐂𝐈/𝐂𝐃 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 on top of AWS ECS & SQS that scales in and out based on the number of jobs, 𝐬𝐚𝐯𝐢𝐧𝐠 𝐭𝐡𝐨𝐮𝐬𝐚𝐧𝐝𝐬 𝐨𝐟 𝐝𝐨𝐥𝐥𝐚𝐫𝐬/𝐦𝐨𝐧𝐭𝐡.
➞ 𝐒𝐚𝐯𝐞𝐝 𝐦𝐨𝐧𝐭𝐡𝐬 𝐨𝐟 𝐟𝐮𝐭𝐮𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 by using good software practices to make the Python module & batch cloud infrastructure flexible and composable.
➞ 𝐁𝐮𝐢𝐥𝐭 a 𝐯𝐨𝐢𝐜𝐞 𝐬𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐬 𝐏𝐨𝐂 in ~1 week required to 𝐜𝐥𝐨𝐬𝐞 the 𝐝𝐞𝐚𝐥 with one of the 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐜𝐥𝐢𝐞𝐧𝐭𝐬.

⚫️ Big data ingestion & archiving pipeline

The client wanted to expand its platform features by securely and safely ingesting, preprocessing, and storing TB of data into their platform.

➞ 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 to the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of a 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐧𝐠 >𝟓 𝐓𝐁 of data in 𝐨𝐧𝐞 𝐠𝐨 in a reliable, fast, and secure way.
➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a scalable 𝐝𝐚𝐭𝐚 𝐩𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports multiple media types (images, videos, audio, 3D objects) and creates small preview files that are cheap to transfer, store and visualize. The 𝐨𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐟𝐢𝐥𝐞𝐬 are 𝐚𝐫𝐜𝐡𝐢𝐯𝐞𝐝 on an affordable storage solution 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐜𝐨𝐬𝐭𝐬 𝐛𝐲 𝐮𝐩 𝐭𝐨 𝟖𝟏%.

Everybody talks about shipping AI products, but nobody shows you how.

These 5 books are all you need (and yes... I've read them all):

𝟭/ 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗯𝘆 Chip Huyen 
🔗: https://lnkd.in/dwmcghyA

One of the best books I've read on designing ML systems.

It's beginner-friendly.

And it provides a strong intuition into what an end-to-end ML system looks like.

𝟮/ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝗯𝘆 John Berryman 𝗮𝗻𝗱 Albert Ziegler 
🔗: https://lnkd.in/dGXBYAFU

If you're obsessed with seeing the big picture and thinking in systems (like me), you'll love this book.

It will show you how to engineer your prompts effectively.

You will also learn how to manipulate your prompt, make it flexible, scalable, and squeeze the most out of your models.

P.S. As John said, this book should be called 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀.

𝟯/ 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗯𝘆 Chip Huyen 
🔗: https://lnkd.in/dPKStJW3

This book has one of the best (and clearest) explanations of:

• AI vs. ML Engineering
• RAG (from classic text search to more complex, semantic-based systems)
• Building agentic systems
• LLMOps (Especially observability and user feedback)
  
𝟰/ 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗟𝗟𝗠𝘀 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗯𝘆 Louis-François Bouchard 𝗮𝗻𝗱 Louie Peters 
🔗: https://lnkd.in/d9v6HyZr (𝟭𝟱% 𝗼𝗳𝗳 𝘂𝘀𝗶𝗻𝗴 𝗮𝗳𝗳𝗶𝗹𝗶𝗮𝘁𝗲 𝗰𝗼𝗱𝗲 𝗣𝗮𝘂𝗹_𝟭𝟱)

This book will walk you through how to implement all the core algorithms required to build LLM applications.

You'll use LangChain and LlamaIndex to implement all kinds of RAG techniques (starting from the vanilla architecture).

It's great for people who are mid-to-entry-level engineers.

𝟱/ 𝗟𝗟𝗠𝘀 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗯𝘆 Chris Brousseau 𝗮𝗻𝗱 Matthew Sharp 
🔗: https://lnkd.in/d_xqpfUz

This book focuses entirely on shipping AI.

Specifically, it does a great job of explaining:

• How to optimize LLMs using different tools
• The data engineering involved in building an LLM app
• Training and serving LLMs at scale
• How to think about infrastructure when serving LLMs
• etc.
  
𝗕𝗢𝗡𝗨𝗦: 𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿'𝘀 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 𝗯𝘆 𝗺𝗲 𝗮𝗻𝗱 Maxime Labonne 
Link: https://lnkd.in/dRmBZXdR

I couldn't list a bunch of books without mentioning my baby.

This book merges my obsession with thinking about end-to-end systems with Maxime Labonne 's love for fine-tuning.

We walk you through all the steps to build an LLM & RAG application.

Oh… and we’re working on a 2nd edition due to popular demand.

P.S. How many of these books have you read?

Senior AI Engineer • Founder @ Decoding AI ~ Building an army of AI agents and teaching you about the process.

90% of AI engineers build LLM apps the same way.

They spin up a RAG system, tweak a few prompts, and then ask:

"Does it work?"

Or worse... "Should I switch to the new model that just dropped?"

But these are the wrong questions to be asking.

Why? Because you have no way to know.

It's impossible to answer these questions without an evaluation framework.

This is why Hugo Bowne-Anderson suggests we use Evaluation-Driven Development (EDD).

The concept isn't new (especially for those with a background in ML).

The idea is to shift the development mindset from launch first, evaluate later to evaluate first, launch better.

Here's how it works:

1. 𝗨𝘀𝗲 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮 to uncover failure modes before launch.
2. 𝗟𝗮𝗯𝗲𝗹 𝗯𝘆 𝗵𝗮𝗻𝗱 to build intuition about what “good” looks like.
3. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗵𝗮𝗿𝗻𝗲𝘀𝘀 - even a spreadsheet is enough.
4. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 it to track changes over time.
   
This loop will help you compare models, debug failures, and build confidence that your system works before it hits production.

You wouldn’t build a search engine or recommender system without robust evaluation.... 
So why build an LLM app without one?

Hugo Bowne-Anderson explains EDD beautifully in a guest post he wrote for Decoding AI Magazine.

It’s one of the most practical, no-fluff guides I’ve read on building reliable LLM systems.

Read it here → https://lnkd.in/dWpSyfcq

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