Small on-device vision models and what they let you build
Small vision models, fine-tuned for specific jobs, can now run on a phone with accuracy that used to need a server. Here is what that changes for image-driven apps.
engineering
How to choose between prompting, RAG, and fine-tuning
Three ways to make an AI feature work, three very different price tags. Here is the decision framework we use to pick the right one, written for the person signing off on the budget.
Scaling PostgreSQL for high-traffic web applications
PostgreSQL scales further than most teams realise. The trouble is rarely the database itself, it's the order in which teams reach for fixes. Here is the layered hierarchy we use with clients before reaching for distributed PostgreSQL.
How to Prepare Your Company's Data for AI Automation (Without Creating a Security Nightmare)
Before your company can use AI, your data has to be clean and secure. Here is how we help businesses organise their messy databases and API silos so they can safely deploy AI agents without accidentally exposing confidential data.
Google's Gemma 4 is Apache 2.0, and That Changes the Local Model Conversation
Gemma 4 ships under Apache 2.0 with native audio, vision, reasoning, and function calling. Here is what it means for teams running local models and building AI-powered products.
What happens when you let an AI agent loose on your production infrastructure
A developer lost 2.5 years of data in seconds after Claude Code ran a Terraform destroy on live infrastructure. Here is what actually happened, and why the same mistake is easier to make than you think.
