Your next hire will use AI in their technical test. Obviously.
As AI becomes a standard part of every developer’s toolkit, tech hiring needs to evolve from policing its use to understanding how candidates apply, evaluate, and collaborate with it.
Ideas and insights
95 posts
Claude as tester using Playwright and GitHub MCP
Claude can now test your frontend. With a bit of config, the Playwright MCP server lets Claude run browser tests, find bugs, and even generate reusable test code. This could be a game-changer for startups without QA.
Fitness for purpose: taking risks with quality
When product teams obsess over perfect quality, they risk standing still, but by embracing a 'fit for purpose' mindset and planning for instability, they can move faster and smarter.
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.
These questions will be answered after technical due diligence
Investing without technical due diligence is like buying a used car without opening the bonnet. This article demystifies the audit process, shares what red flags we look for, and explains why investors should care deeply about code, processes and product.
Technical due diligence is broken: going beyond the checklist
Most technical due diligence audits focus on code quality and tooling, but the real risk lies in invisible processes, fragile teams, and product chaos, which is why madewithlove audits the full stack of your organisation, from code to culture.
What would it cost to rebuild this software from scratch?
Evaluating the cost of rebuilding software from scratch involves more than counting development hours; it requires recognising the invisible value of user feedback, lessons learned, and embedded experience.
Making progress without a technical leader
Startups without a technical co-founder can still build great products, but only if they avoid the usual traps of overengineering, needless infrastructure, and late developer involvement.
AI writes bad code, but what if that’s the good news?
Should engineers fear messy AI code or embrace it as a tool for fast product validation? This story reveals why your cleanest code might not be your most valuable contribution. Why founders will use AI generated code more and more for MVPs and prototyping and why engineers should embrace this.