Paolo Perrone

OpenAI rejects 99% of AI engineers.
The 1% who get in? They all watched the same YouTube playlist.

It's Karpathy's Neural Networks: Zero to Hero.

Most courses teach you to call APIs. Karpathy teaches you to be a builder:
→ Implements backprop from scratch (finally understood it)
→ Builds GPT from bare NumPy (no magic imports)
→ Shows the actual math without the fluff
→ Live codes everything (mistakes included)

The difference is brutal:

Course graduates: "How do I import torch.nn?"
Karpathy students: "Here's how attention actually works."

Guess who gets hired?

Start with micrograd. Thank me at your OpenAI interview.
Full playlist in the comments 👇

MIT Press will charge $80 for this textbook in 2026.

You're reading it for free today.

"Machine Learning Systems" — The missing education between algorithms and production.

What's inside (save this):

Part 1: Foundations (4 chapters)
→ Data pipelines that don't break at scale
→ Training systems beyond Jupyter notebooks
→ Benchmarking that predicts real performance
Skip this → Your models die in production

Part 2: Deployment (4 chapters)
→ Edge AI on $10 hardware (TinyML)
→ Cloud patterns that actually scale
→ MLOps beyond "it works locally"
Skip this → Inference costs eat your runway

Part 3: Advanced Systems (4 chapters)
→ Distributed training that works
→ Hardware acceleration (GPUs → TPUs → custom)
→ Model compression without accuracy loss
Skip this → You're stuck at prototype scale

Part 4: Responsible AI (4 chapters)
→ Privacy systems, not just policies
→ Carbon footprint measurement
→ Security against adversarial attacks
Skip this → Your AI becomes a liability

Bonus: TinyTorch framework included.
Build neural networks from scratch. No black boxes.

10,300 engineers starred it.
Prof. Vijay Janapa Reddi updates it weekly.
MIT Press publishes it 2026.

You're reading it today.

📖 Read: mlsysbook.ai
📄 PDF: mlsysbook.ai/pdf
🔬 GitHub: https://lnkd.in/ef_SKs6G

Time to complete: 8 weeks, 2 hours/day.

💾 Save this before it costs $80
♻️ Repost before someone pays MIT Press for what's free today.

David Kimai just open-sourced the Context Engineering handbook.

7.1K stars in 2 weeks.

Here's what's actually inside:

The Core Idea:
Prompt engineering = what you type
Context engineering = everything else the model sees

Most people optimize the prompt.
Smart people optimize the context.

What You Get:

📁 00_foundations/
Basic theory in plain English.
Why context beats prompts every time.
Token budgets that don't blow up.

📁 10_guides_zero_to_hero/
Start here. Literally.
Minimal examples that actually run.
No 500-line boilerplate.

📁 20_templates/
Copy-paste context patterns.
Memory systems. Tool integration. Control flow.
YAML configs you can steal.

📁 30_examples/
Full implementations:
- Chatbots with real memory
- Agents that don't hallucinate
- RAG that actually retrieves

📁 40_reference/
Deep dives on:
- Why attention windows matter
- Context pruning strategies
- Evaluation metrics that work

The Practical Stuff:

Every concept has:
✓ Runnable Python code
✓ ASCII diagrams
✓ Before/after metrics
✓ "Why this matters" sections

No slides. No theory dumps.
Just: problem → solution → code.

My Favorite Parts:

The biological metaphor:
atoms → molecules → cells → organs
(single prompts → few-shot → memory → multi-agent)

The token calculator:
Shows exactly why your context explodes.
And how to fix it.

The "cognitive tools" templates:
Prompt programs that make models think step-by-step.
No training required.

Who This Is For:

- Engineers tired of prompt tweaking
- Anyone hitting token limits
- Teams building production agents
- People who want stuff that works

The repo assumes you know Python.
Everything else is explained.

Start with 10_guides/01_min_prompt.py
Run it. Break it. Understand it.

Then steal whatever you need.

https://lnkd.in/gpWCq58e

What part of context engineering confuses you most?

♻️ Repost to help someone graduate from prompt engineering

The only Agentic AI roadmap you need for 2026.

No fluff. Just what works. With actual links.

Phase 1️⃣: Foundations (2 weeks)
→ Math: 3Blue1Brown Linear Algebra: https://lnkd.in/ewiPRVuG
→ Python basics: https://lnkd.in/eDSYRAkg
→ ML fundamentals: https://lnkd.in/eYZfefYP
Skip this → You'll be learning theory forever

Phase 2️⃣: Build Your First Agent (2 weeks)
→ ReAct pattern tutorial: react-lm.github.io
→ LangChain quickstart: https://lnkd.in/eZCZHnv7
→ Build memory + tools: https://lnkd.in/e53rpuev
Project: Agent that searches web + executes code
Skip this → You'll never understand why agents fail

Phase 3️⃣: Advanced Architectures (2 weeks)
→ Multi-agent systems: https://lnkd.in/ganTtyg7
→ AutoGPT architecture: https://lnkd.in/gQBfXtnf
→ RLHF fundamentals: huggingface.co/blog/rlhf
Project: Agent that improves its own prompts
Skip this → Your agents stay shallow forever

Phase 4️⃣: Production Systems (2 weeks)
→ FastAPI deployment: fastapi.tiangolo.com
→ Docker + agents: https://lnkd.in/eb4tmubv
→ LangSmith monitoring: smith.langchain.com
Reality check: 90% of "AI agents" die here
Skip this → Your demo stays a demo

Phase 5️⃣: Pick ONE Specialization
🤖 Robotics: https://lnkd.in/e5sA7XnS
💼 Business: docs.crewai.com
🔬 Research: paperswithcode.com

Best resources that actually deliver:
📚 Theory: https://lnkd.in/envUC_aC
🛠 Practice: python.langchain.com/docs
🔥 Deep understanding: mlsysbook.ai/tinytorch
🚀 Deploy: railway.app or vercel.com

Time: 8 weeks, 3 hours/day.
Cost: $0 (all resources free).

The difference:
Others: 50 tutorials → Maybe build something
You: 5 working agents → Understand everything

💾 Save this before 2026 hits and you're still "planning to learn agents"
♻️ Repost if someone in your network has been "about to start" for 6 months

Elon Musk tweeted about Cartesia voice AI, so I tested it against 11Labs and OpenAI.

Sent it to 50 people asking which sounded most human.
The results shocked me.

Sonic-3 just changed the game.

3-5x faster than OpenAI.
More accurate than ElevenLabs.
$100M in funding because it actually works.

But here's the insane part:

IT LAUGHS.

Like, actually laughs. Not robot "ha ha ha."
Real, contextual, human laughter.

And it speaks 42 languages. Including 9 Indian languages.
Hindi, Tamil, Telugu - all native fluency.
One voice. Every language. Zero accent bleeding.

The features that blew my mind:

🎯 SPEED CONTROL THAT WORKS
"Say that slower" → It actually slows down
"Say that faster" → Speeds up mid-sentence
No other TTS can do this. I tried them all.

🔥 VOICE CLONING IN 3 SECONDS
Not 30 minutes of recording. Not 3 minutes.
3. Fucking. Seconds.

🌍 HANDLES THE IMPOSSIBLE
Email addresses: john.smith_92@outlook.com ✓
Heteronyms: "Present the present" ✓
Indian names: "Venkatasubramanian" ✓
Your current TTS would stroke out.

This isn't incremental improvement. This is voice AI that ships and converts.
♻️ Repost if you're done with voice AI that sounds dead inside

A Fortune 500 CEO typed one sentence.

"Migrate all cloud environments from AWS to Azure.
End to end. Secure Compliant. Human review on critical steps."

That's the input.

Here's what happened:

→ Full project scoped in minutes
→ Agents assigned by role (infrastructure, security, compliance)
→ Tasks executed in parallel, maintaining state
→ Human checkpoints for critical decisions
→ Migration completed overnight

No sprint planning.
No hiring.
No "we'll get to it in Q3."

This is engineering velocity.

Same team. 10x output.
Scale capacity without scaling headcount.

The platform that enables it?

Kubiya

Not an AI that autocompletes your code.
An AI org that ships your roadmap.

The question isn't whether agentic engineering teams are coming.

It's whether you'll deploy and manage them — or they'll replace you.

👾 https://www.kubiya.ai/
📄 https://lnkd.in/e58M_PPw

💾 Save this for the next "we need to prioritize" meeting that changes nothing
♻️ Repost if your backlog has a backlog

30B parameters running on 24GB. Not a typo.

NVIDIA AI dropped a banger MoE model.

Nemotron 3 Nano.

Runs on 24GB. Only 3.6B active during inference. 1M context window.

I ran it on my DGX Spark. Here's the verdict:

𝗦𝗲𝘁𝘂𝗽: → Clone llama.cpp → Build with CUDA → Pull the GGUF from Hugging Face → Running in 20 minutes

𝗪𝗵𝗮𝘁 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲𝗱 𝗺𝗲:
→ 3.6B active params competing with models 3x the size
→ Built-in reasoning with tokens – no prompt hacks
→ Native tool calling. No gymnastics.

𝘃𝘀. 𝗼𝘁𝗵𝗲𝗿 𝗹𝗼𝗰𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀:
→ Llama 3: More VRAM, no native reasoning
→ Qwen: Close, but loses on coding benchmarks
→ Mistral Large 3: Similar speed, but 1/4 the context

𝗧𝗵𝗲 𝗴𝗼𝘁𝗰𝗵𝗮:
Watch your context size. I started at 1M and hit OOM. Dial it back to 32K-64K unless you've got headroom.

𝗤𝘂𝗶𝗰𝗸 𝘀𝘁𝗮𝗿𝘁:
./llama.cpp/llama-cli \\
-hf unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL \\
--jinja --ctx-size 32768 \\
--temp 0.6 --top-p 0.95

This is the best "run it on your own hardware" model I've used.

💾 Save for your next "which local model?" decision
♻️ Repost if your AI stack is going fully open source in 2026

AI agents won’t fix your data science workflow. Unless you give them the right environment.

This is how we deliver Data Science projects in 2025 in seven steps:

Start with a prompt, not a blank notebook.
Let the Zerve agent generate the initial workflow and code.
Review the plan and data previews in the full IDE.
Iterate with natural language; the agent stays context-aware.
Scale experiments from one run to thousands instantly.
Trust that every result is tracked and reproducible.
Deploy your work as a workflow, API, or app.

Get Zerve here: https://bit.ly/41i1A61

AI agents won't replace data scientists.
But a data scientist using an agentic environment like Zerve will.