Skip to content
View LeonelSalvo's full-sized avatar
🎯
Focusing
🎯
Focusing

Block or report LeonelSalvo

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
LeonelSalvo/README.md
Leonel Salvo — Lead AI Developer · neural networks from first principles

pinguinista.com LinkedIn Email profile views


🧠 About

Lead AI Developer — production AI infrastructure by day, neural networks rebuilt from scratch by night, so I actually understand what I ship.

Full-Stack Engineer  ➜  Product Discovery Lead  ➜  Lead AI Developer


Four directions, one method: build it myself first, then trust the tool.

  • 🔬  Research — I rebuild modern architectures from first principles: no nn.Transformer, no wrapper I can't explain.
  • 🤖  Work — I lead a private, multi-tenant AI platform: composable agents + RAG over a customer's own documents, in production.
  • 🎮  Games — a one-person Godot studio, Pinguinista: two games of my own in production, nothing released yet.
  • 🧰  Tools — for working with coding agents without losing seniority: Seniority Rounds, opening soon.
  • 📚  Permanent student: a degree in hardware / electronics, and pushing from LLMs toward CUDA and quantum.

🔬 Research — LLMs from scratch

The modern stack rebuilt one self-checking component at a time, in PyTorch, on a single RTX 3090.


modern-nanoGPT
dense transformer · RMSNorm · RoPE · SwiGLU · GQA

stars

The modern dense transformer. The GPT-2 skeleton upgraded to the 2026 open-weight stack — RMSNorm · RoPE · SwiGLU · GQA · tied weights — built one self-checking component at a time. Trained on a single RTX 3090 (val ≈ 1.48).

nano-moe-mla
sparse MoE + MLA · routing probe · ablation

stars

The sparse template: MoE + MLA in one model. Built from scratch with the instruments to study it — a labeled multi-domain corpus, a router-specialization probe (mutual information), and a seed-averaged stack ablation that confirms the balancing ↔ specialization tradeoff.

frontier-llm-techniques-2026-Q1
Muon · Multi-Token Prediction · BPE

stars

Frontier techniques, from scratch. The Muon optimizer (used to train Kimi K2) and Multi-Token Prediction (DeepSeek-V3, Gemma 4, GLM, Qwen), plus a base BPE tokenizer — each self-contained and self-checking, with an AdamW-vs-Muon benchmark.

🤖 Work

I lead a private, multi-tenant enterprise AI platform — composable agents and retrieval over a customer's own documents, shipped to production. (Architecture kept private.)


🎮 Games — Pinguinista


🐧 Pinguinista
my one-person game studio

Two games of my own, both in production, neither released. Hand-drawn pixel art, Godot, one person. The dock, the field and how they run today are on pinguinista.com.

Records of the Truth Bureau
noir detective · writing to think

In development. The evidence goes on a corkboard, and the Bureau only accepts method.

Next game
TBD

TBD. One field, from dawn to midnight. In silent production — more when there is something to show.

🧰 Tools — for working with coding agents


🃏 Seniority Rounds
TypeScript · React · three.js · a JSON contract any agent can write

Avoid atrophy. Understand what your agents are producing, in a didactic, learning-in-the-loop way — cards, not diffs. The agent says what it wasn't sure about and what you didn't ask for; you predict before you look. Stay senior on what your agents are producing.

🔒 Private while I polish it — opening soon. The stance behind it: the Sovereign Seniority Manifesto.


🧬 Reproduced from scratch

I didn't invent these — I learned them by rebuilding and verifying each one in code, following Karpathy's Zero to Hero and the open-model papers.

Built What it is Learned from
micrograd a tiny autograd engine — backprop by hand Karpathy · Zero to Hero
makemore MLP, BatchNorm, manual backprop, WaveNet Karpathy · Zero to Hero
self-attention the attention mechanism, from the math up Attention Is All You Need
modern-nanoGPT the modern dense transformer (RMSNorm · RoPE · GQA · SwiGLU) nanoGPT + Llama / Mistral / Qwen
nano-moe-mla sparse MoE + MLA in one model, with a routing probe + ablation DeepSeek-V2/V3 papers
Muon optimizer orthogonalized-momentum (Newton-Schulz) + a benchmark Keller Jordan · Moonshot (Kimi K2)
Multi-Token Prediction a 2nd head predicting t+2 — a denser training signal DeepSeek-V3
BPE tokenizer byte-pair encoding, exact round-trip Karpathy · minBPE

Method: from-scratch first; a wrapper is only allowed once I can name what it wraps and the trade-off.


📖 Learning path

✅ Done

  • Karpathy — Neural Networks: Zero to Hero (10/10)
  • Stanford CS229 — Machine Learning (Andrew Ng)
  • fast.ai — Practical Deep Learning, Part 1

🔄 In progress

  • Hugging Face — LLM Course (transformers, fine-tuning, reasoning)
  • Reinforcing attention internals (RoPE · KV-cache · RMSNorm)

⏭️ Next

  • nanochat (Karpathy) — the full ChatGPT pipeline
  • Raschka — Build a Reasoning Model (From Scratch)

🧪 Later

  • Triton → GPU-MODE + PMPP (CUDA kernels)
  • Mamba / SSM hybrids · Diffusion LLMs · Quantum

🛠️ Stack

AI / ML  

Backend  

Frontend  

Infra & also  


📊 Stats

followers stars profile views



pinguinista.com

Pinned Loading

  1. modern-nanoGPT modern-nanoGPT Public

    From-scratch PyTorch GPT: the GPT-2 skeleton plus the components of current open LLMs — RMSNorm, RoPE, SwiGLU, GQA, no-bias + tied embeddings. Each isolated and self-checking.

    Python

  2. frontier-llm-techniques-2026-Q1 frontier-llm-techniques-2026-Q1 Public

    From-scratch PyTorch: frontier LLM techniques as of 2026-Q1 — the Muon optimizer and Multi-Token Prediction, plus a base BPE tokenizer. Self-contained, self-checking modules.

    Python 2 1

  3. nano-moe-mla nano-moe-mla Public

    DeepSeek-style MoE + MLA from scratch in PyTorch, with a router-specialization probe (mutual information) and a dense/+MoE/+MLA ablation. Educational, nano-scale, measured.

    Python 2 1