Recent content on Eoin Hurrell
Every time you choose to add complexity rather than measure and improve the system you already have, you gamble with your time and your focus. The new component arrives with a tax — latency, cost, more moving parts to operate — and it pays off only when the thing underneath it was already doing its job. Without a solid foundation and good measurement, the upgrade is motion, not progress. This is a…
I’m excited to announce that Inboxer , a small Obsidian plugin I created for my own workflow, is now available in Obsidian through Community Plugins. As an ML engineer, I spend a lot of time in Obsidian managing research notes, and I kept running into an annoying workflow issue. The Problem I’d found there was a lot of friction when trying to add details to a note, say a project or an…
I’ve been investigating multi-agent systems and their applications in development workflows. After reading Anthropic’s recent guide to agent coordination patterns , I wanted to experiment beyond established patterns like Evaluator-optimizer and Orchestrator-worker workflows. For this exploration, I’ve chosen automated code review as a sufficiently complex problem domain.…
TL;DR This is a quick write-up of RetentionCast, a system I built to predict customer churn in real-time using Change Data Capture. The system processes live transaction data through Kafka and Debezium to calculate churn probability and Customer Lifetime Value. Check out the GitHub repo The Churn Problem If you’ve worked in e-commerce or SaaS, you know customer churn is the silent killer of…
I’ve been kicking the tires on Typesense , an open-source search engine and alternative to Elasticsearch , for a personal project. So far I’m quite impressed with it. This is a small post recording how easy it was to set up vector embedding fields using a custom model. It is getting much easier to build semantic search applications. In previous work when I was setting up semantic…
One issue I have using Obsidian is that I feel I don’t use enough of its feature set. I have lots of notes, but few are tagged particularly well, so it makes tags less useful overall. I would like to be able to make surprising connections and surface related items, which means imprecise tags are preferable to no tags, so false positives are less of a concern to me, and very low risk.
I’ve been researching LLMs, trying to find practical ways I can use them for myself, in my work or at home. One area that interests me is as tool to better understand the notes I’ve taken, topics I frequently learn about and areas I might want to explore more. I’m not the first to explore this territory ( https://x.com/fortelabs/status/1749861644245848361) . I’ll be looking…
The below was originally written for and published on the Cohort company blog, Oct 6, 2017. Dr. Eoin Hurrell leads Data at Cohort. Here, he reflects on what’s involved in designing a system to ingest and analyze massive volumes of public data, to make predictions on relationships, and things people are knowledgable on. Cohort helps you find the people you need, through the people you already know…