Building an AI-Powered Quantitative Trading System with Hermes Agent and IBKR How I set up a multi-signal ETF trading bot that runs on autopilot — and the 7 things that broke along the way I wanted a system that watches the market 24/7, analyzes technical indicators across multiple ETFs, and executes trades automatically. No manual chart-checking. No emotional decisions. Just cold, calculated si
Kubernetes and AI have become unlikely bedfellows—and the numbers prove it. New data from CNCF and SlashData reveals that two-thirds of organizations running generative AI models have standardized on Kubernetes for orchestration. But here's the thing: it's not because Kubernetes magically solves AI problems. It's because the engineering fundamentals that make Kubernetes valuable—standardization, r
The most basic concept in test doubles is the dummy. When testing a function, there are usually two kinds of input: Meaningful input Data that affects the result of the function. Dummy input Data that is required by the function, but does not affect the behavior we are testing. Below is an example of meaningful data vs dummy data. This is a calculateShipping function: function calculateShip
I was skeptical at first. Not about AI in general — but about whether it would actually fit into my workflow. I work mostly with legacy PHP and jQuery. The kind of codebase that was written before half the frameworks people talk about today even existed. Some Vue.js here and there in newer parts, but a lot of the core is raw PHP, procedural logic, and jQuery doing things you probably don't want to
In March 2026, a rogue AI agent at Meta triggered a Sev 1 security incident. Sensitive company and user data was exposed to unauthorized employees for nearly two hours. The agent held valid credentials. It operated inside authorized boundaries. It passed every identity check. And yet. Identity and Access Management answers one question: Is this agent who it says it is? It doesn't answer: Was this
Applicant Tracking Systems used to be boring. For most of the 2010s, an ATS was essentially a database with a careers page bolted on top: a place to dump resumes, push them through a pipeline of stages, and email rejections in bulk. The interesting work happened around it, not inside it. That has shifted in the last two years, and the shift is deeper than the marketing pages suggest. I have spent
Three weeks ago I shipped IndieOps — a free invoicing and client management tool built specifically for freelancers. Here's the honest version of how it went. It handles the boring-but-critical stuff that eats freelancer time: creating professional invoices, collecting payments via Stripe, sending automatic payment reminders, and keeping a client directory. All free. No "upgrade to send more than
Why Do We Need Specialized Vector Databases? In the first five articles, we figured out how to chunk documents and generate embeddings. Now where do these vectors live, and how are they efficiently retrieved? You might wonder: "Can't I just store vectors in Redis or PostgreSQL?" No — traditional databases are designed for exact queries (e.g., WHERE id = 123), while vector retrieval is Approximat