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 👇

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

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

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.