From our SaaS audits
What we find inside SaaS companies
Patterns from the technical due diligences we run for investors and boards.
AI
Vibe coding and the junior developer dilemma
AI tools are transforming how we code, but they're not replacing the experience needed to build real software. Let’s talk about what vibe coding gets right—and what it gets very wrong.
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.
Exploring LLM Options: Proprietary vs. Open Source
AI can be challenging. We break down three AI integration tiers—proprietary models, open-source solutions, and custom-built systems—to help you choose the right approach. From quick MVPs to scalable solutions, discover how to leverage AI effectively for your product.
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.
Revolutionising diagnosis: How large language models can drive a 10x change
Large language models (LLMs) transform problem-solving by enabling natural, iterative conversations, ideal for fields like healthcare and legal services. They scale expertise and accessibility but face challenges like reliability and cost.
A company's first steps in AI
Many product companies are eager to leverage tools like ChatGPT. But how do you go from experimenting to running in production? Let's explore choosing the right large language model (LLM) to understand hosting options, ensuring an efficient and sustainable AI implementation.
The fate of being too early
Strava's new AI-driven Athlete Intelligence brought me back to Addapp, the startup I co-founded nearly a decade ago. We used data from devices like Apple Watch to offer insights, like how your run affected your sleep, using traditional data science. Being ahead of time is not always good.
Integrating AI into your engineering team
How to integrate AI into your engineering team. This guide explains how AI can make your team more productive.
Why AI?
Will AI take over our jobs? Should we, as software developers, be using AI? How should I use AI in my job? How will this all evolve? Here's my personal findings on the topic.
ChatGPT and the future of software specs
In case you just woke up from a coma and missed the news that’s whipping Tech Twitter into a frenzy, OpenAi just released ChatGPT.
Learning Machine Learning: Neural Networks
This is a follow-up article on Machine Learning, Part 2. Have you read the introduction to Machine Learning (Part 1) already? The next thing
Learning Machine Learning
I’ve always been fascinated by science fiction and the advances of technology. And AI/ML has often represented a huge chunk of that because,