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Observatory · Barcelona

Javier Hengda

AI Software Engineer

Currently at SPRK

Plate II · Selected work

Five constellations

Each project is charted as a figure in the sky. The case studies carry the evidence: problem, approach, result, with real numbers wherever there are any.

  1. sprk-ai autocategorization

    Libra

    Production transaction categorization at SPRK — a rule-first cascade with one grounded, abstaining LLM call.

  2. tun-vpn

    Draco

    A VPN from first principles: Linux TUN devices and CA-pinned TLS, in 560 lines of Go.

  3. SignalML

    Lyra

    An arXiv ingestion + LLM-classification pipeline on Supabase — the data layer of a future reader.

  4. metrics-server-dashboard

    Horologium

    A self-hosted Mission Control — Go WebSocket fan-out at 1 Hz, memory bounded at every layer.

  5. Globe visualization

    Armillary Sphere

    A config-driven 3D globe instrument — seven render strategies, runtime GLSL, live re-mapping.

Plate III · The observer

About

I'm an AI software engineer in Barcelona, building production ML systems by day and charting personal projects — from a hand-rolled VPN to real-time observability — by night.

This site maps that work as a sky: each project a constellation, drawn as the case studies are written.

Read the CV Write to me →

Observer's log

Currently
ML Engineer at SPRK
Software & Tech · since Mar 2026
Station
Barcelona, Spain
41.4° N · 2.2° E
Focus
LLM systems in production, MLOps, and the measurement discipline that keeps them honest
Degree
B.Sc. Computer Engineering
Expected Dec 2026
Instruments
  • Python
  • Go
  • TypeScript
  • AWS
  • Postgres

Plate IV · Field notes

Writing

  • Personal ·

    Something That Matters to Me

    If no one needed anything from me—and no one was there at all—would I still have something I wanted to do?

  • Technical ·

    Frozen Models Can Drift from Their Dataset Hashes

    What a stored content hash protects in Pydantic v2, where shallow freezing falls short, and the measured cost of hashing a dataset twice.

  • Technical ·

    Building an Event-Driven AI Monorepo

    Why treating AI microservices as async data pipelines — not REST call chains — solves the scaling problems monoliths can't

All writing →

Plate V · Correspondence

The observatory is open

For roles, collaborations, or a good question — write.

hello@javierhengda.dev

GitLab LinkedIn CV