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Practical writing on software architecture, SaaS products, AI automation, legacy modernisation, and the business of building reliable systems.
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Version 9.3.7 of the Elastic Stack was released today. We recommend you upgrade to this latest version. We recommend 9.3.7 over the previous versions 9.3.6 For details of the issues that have been fixed and a full list of changes for each product in this version, please refer to the release notes.
Hi, I'm Ryan, CTO at airCloset. In Part 1, I wrote about unifying 46 repositories of production code into a single knowledge graph via static analysis. The graph itself got built, but I closed the post with four open issues: no semantic search, node explosion, having to open the file to actually know what a function does, and the cost of writing a new parser every time a new boundary pattern showed up. This Part 2 is about how I solved the first one — the entry-point problem (no semantic search)
Online clothing returns cost the industry over $816 billion a year. The root cause is simple: shoppers can't see how clothes look on their body before buying. We built StyleSense to fix that — an AI-powered virtual wardrobe where you upload a selfie, add clothes from any product URL, and instantly see yourself wearing them. Here's how we built it, and why Amazon Aurora PostgreSQL and Vercel were the right choices for a 72-hour hackathon build. Frontend: Next.js 14 App Router + TypeScript + Tailw
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Beyond the Timer: How I Built BrewCore for the H0 Hackathon Home espresso is a paradox. Baristas obsess over the micro-details the gram weight, the seconds on the clock, the pressure profile-but we usually track it all on sticky notes or fragmented mental maps. When that morning shot turns sour, we don’t have a database to query; we have a bad cup of coffee and a Reddit thread from 2019. For the H0 Hackathon, I wanted to bridge that gap. I've built BrewCore: a full-stack coffee companion that
TL;DR Build AI-agents in 5 lines of code. Skip the set up & infrastructure. Live and running. from custodian_labs import Custodian app = Custodian( model="gpt-4o", system_prompt="You are a concise, helpful assistant.", ).deploy() print(app.chat("Hello!").response) print(app.chat_url) # ← live, shareable URL Runnable Colab notebook here. DIYing this: FastAPI behind a reverse proxy + Redis for sessions + a small frontend + an auth layer + a vector DB and ingestion pipeline. All of it
I built Kajota Pulse and wrote this article as my entry for the **AWS × Vercel "H0: Hack the Zero Stack"* hackathon (#H0Hackathon). Live app: kajota-pulse.vercel.app · Code: github.com/KaJota-inc/kajota-pulse* Across African micro-commerce, "co-sellers" buy stock from wholesalers and resell to their network for a markup. There's a whole industry of tools for writing the listing. There's almost nothing for the question that actually decides whether a co-seller makes money: what should I stock thi
Location sharing has become normal. Most people use it for simple reasons: checking if family arrived safely coordinating with friends helping elderly relatives finding children handling emergencies But most existing apps solve this with the same model: continuous passive tracking. That model always bothered me. Not because it doesn’t work. But because it changes the relationship between trust and location. Most location-sharing apps work by keeping your position visible all the time. 24/7. Even
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