Zero Grappler is a small no_std crate that applies a data-pipeline mindset to embedded ML: three traits, two async tasks, compile-time buffer sizing, zero allocations. This post is about the design choices — not yet a hardware report. The Pico 2 W smoke test on real silicon is still ahead of me.
Lance is a columnar storage format built for machine learning workloads — fast random access, native vector indexing, and zero-copy Arrow integration. This article walks through the format itself, how LanceDB builds on top of it, and how I wired it into a live NATS stream to build a simple semantic search layer over real-time events.
Most ML pipeline failures are not exotic model bugs — they are data issues that nobody encoded as checks. This article walks through building guardrails using pandas, Apache DataFusion, data contracts, and the Arrow C Data Interface.
Claude interviews Andrea Bozzo about a full year of using Claude Code in the terminal — the workflow, the custom skills, the rough edges, and the nuked database.
Two Rust projects, one conceptual divide. Ares fetches arbitrary web pages and uses LLMs to extract structured data; Ceres harvests metadata from CKAN portals and indexes it semantically. Together they show what it looks like to move from scraping scripts to production data pipelines.
A design story of dataprof: why I built a profiler around Apache Arrow, how it changed the architecture, and how this journey led to contributions to arrow-rs’ Parquet reader.
A technical exploration of async/await in Python and Rust: how the same syntax hides completely different execution models, with practical examples from contributions to Tokio and Python projects.
A deep dive into Mosaico, the robotics data platform written in Rust: client-server architecture, semantic ontologies, data-oriented debugging, my journey within it, and the integration with Data Contract Engine.
Ceres is a semantic search engine for CKAN portals. Built in Rust with Tokio and PostgreSQL+pgvector, it bridges the gap between how people search and how public administrations name their datasets.
Polars completes the Rust data engineering ecosystem: lazy evaluation, Apache Arrow, and native Iceberg V3 integration for performant analytics that compete with distributed clusters. The third pillar of the RisingWave + Lakekeeper + Polars stack.