
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.
Founders and investors due diligence: how to dig beneath the “AI-powered” facade and verify that their systems won’t break in secret.
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 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.
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 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.
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.
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.
Learn how to explain technical debt to investors effectively. This guide simplifies its impact on growth and strategies to manage it, helping you enhance company value and align business goals.