Architecture always leaks
Auditing data-heavy companies reveals the same pattern: asynchronous data processing crammed into the synchronous web stack. The contention shows in performance, delivery, and team dynamics. Isolation fixes all three.
Brenden Cambier
"Good news, I built it in Lovable.": an engineer's guide to surviving that sentence
Business users love Lovable. Engineers tend to panic. A real-world case study of how to wrap an AI builder in guardrails so non-technical teams can move fast without quietly rewriting the systems that give your product its edge.
Mental capacity is a bottleneck
AI removes bottlenecks until it reaches the one that doesn’t move: human cognition. The faster AI makes your system, the more your team’s mental capacity becomes the constraint. You can’t add more of it.
Three Claudes walk into a codebase
The machines aren't replacing developers, they're promoting them. You're no longer just writing code; you're managing agents, reviewing output, and setting standards. Three Claudes walk into a codebase, and suddenly you're a manager.
Bootstrapping a birding database using GenAI (Part 2)
Part 2 of the article about Mossie, when it was faced with scaling to include every bird in the world, complete with photos, sounds, and icons.
Bootstrapping a birding database using GenAI (Part 1)
A small birding app with 300 manually entered species faced an ambitious challenge: scale to include every bird in the world, complete with photos, sounds, and icons. This article explores how the team used GenAI to bootstrap a comprehensive birding database from scratch.
The full-stack enigma
Early-stage startups want full-stack unicorns who can do it all on a tight budget, but asking one dev to wear every hat is less strategic hiring and more duct-taping a rocket and hoping for the best.
Cloudy with a chance of function calls
In the first of a series exploring infrastructure fundamentals, Brenden addresses the most frequently asked questions about what's really happening under the hood with complex pipelines and AI/data systems, bringing the cloud to life.
Offboarding: The security hole nobody wants to talk about
Offboarding is a crucial part of security. Forgotten accounts and overlooked credentials can expose your systems to risk. This post offers real examples, a checklist approach, and clear steps to make offboarding more reliable across teams.
Due diligence for AI startups: what actually matters
Founders and investors due diligence: how to dig beneath the “AI-powered” facade and verify that their systems won’t break in secret.
Three cursors walk into a codebase
A cautionary tale about interns, AI tools, and outsourcing delivering 80% of a project—leaving internal teams with the clean-up. Learn why shortcuts often come with hidden costs.
AI overdose: When developers stop thinking and start prompting
AI tools are reshaping how junior engineers approach problems, often replacing simple solutions with overly complex ones. Here’s why foundational thinking still matters. A real-life case of AI over-engineering gone wrong highlights why understanding problem domains still beats prompting.
Tech stack decisions for AI startups: what you need to know
Selecting the right tech stack is critical for AI startups. Python is essential for data science, but the backend, frontend, and infrastructure choices determine scalability and efficiency. Explore the best tech stack combinations, hosting tools, and ETL solutions to future-proof your AI startup.
Building the dream team for an AI startup
Building an AI startup demands specialised roles like data scientists, engineers, and analysts to drive innovation. Discover what roles you need to hire first for a strong foundation to success.
Even ETL needs a roadmap
ETL (Extract, Transform, Load) is vital for data-heavy businesses but often begins with manual workflows. Companies should identify inefficiencies, prioritise automation, and design a scalable ETL roadmap that integrates human reviews and evolves with business growth.