gradiente.ai
Omar U. Espejel

I’m Omar U. Espejel. I work on AI and crypto at Starknet Foundation. Before that I worked on crypto at StarkWare and machine learning engineering at Hugging Face.

Here I write notes and essays about AI engineering, crypto, proof systems, and product work. A lot of the pieces start as research in progress. I try to make the idea clear enough that someone else can inspect it, reproduce it, or disagree with it.

Open-source AI

40,509

downloads of models and datasets I publish

18 models, 8 datasets and 85 followers on Hugging Face, publishing there since 2022

All-time figures, read when this page is built. Downloads are the sum of downloadsAllTime over every public repository, so check the numbers yourself: models, datasets, profile.

Teaching

I teach 4 courses on Platzi, in Spanish: 94 classes and about 14 hours of recorded material, rated 4.8 out of 5 across 472 student reviews.

Course Classes Rating Reviews
Curso de LangChain 37 4.8 192
Curso de Transfer Learning con Hugging Face 17 4.8 114
Curso de Redes Neuronales con PyTorch 24 4.7 93
Curso de Experimentación en Machine Learning con Hugging Face 16 4.8 73

Course figures read from the public Platzi course pages on 2026-08-17.

Podcast

I host Hacia Afuera, a Spanish-language deep tech podcast: 85 episodes and 76 hours of conversation since 2021, with researchers and builders in AI, quantum computing, cryptography and neurotechnology. It is on Spotify, Apple Podcasts and Deezer.

Recent essays

  • Omar U. Espejel 13 min

    ERC-8004 listed 418,665 agents. Where were the customers?

    • agents
    • ethereum
    • onchain-data

    I checked 457,778 Ethereum and Base reviews. Two files point to settled transactions; only one, worth one cent, names both payment parties.

  • Omar U. Espejel 17 min

    Does Proof Cost Follow What the AI Actually Runs?

    • zkml
    • benchmarking
    • mixture-of-experts
    • proof systems
    • verifiable ai

    An illustrated guide to testing proof-carrying sparsity: installed experts, activated work, batching, timing boundaries, and a qualified DeepProve comparison.

  • Omar U. Espejel 16 min

    Prove Only What Runs: Inside a SparseProve Receipt

    • proof systems
    • mixture-of-experts
    • starks
    • verifiable ai

    A beginner's illustrated walkthrough of how one MoE claim becomes commitments, transcript challenges, authenticated openings, algebraic checks, and a verifier decision.

  • Omar U. Espejel 17 min

    An AI Chooses Which Experts to Run. How Do You Prove It Chose Correctly?

    • mixture-of-experts
    • sparse computation
    • zkml
    • starks
    • verifiable ai

    A beginner's illustrated guide to Mixture-of-Experts, sparse computation, and a proof receipt that binds router scores to selected expert work.

All essays