From our SaaS audits
What we find inside SaaS companies
Patterns from the technical due diligences we run for investors and boards.
AI
How we replaced Fyxer overnight
Fyxer's per-seat price more than doubled, so we built our own meeting notetaker in one night with Claude Code. A blind benchmark on our own bilingual meetings, EU-only storage, and a per-hour cost that puts the whole company below what nine Fyxer seats now cost.
How AI made me twenty times faster at making mistakes
When implementation becomes cheap, what's left? I stopped writing code, ventured into machine learning and 3D game development, and discovered that AI made me remarkably efficient at making mistakes. Fortunately, it also made me much faster at learning from them.
Owning the software factory
My first software factory flooded the team with pull requests nobody asked for. The second one had the same problem, just with better visibility. It only started working when the team took ownership and the factory began to encode what we believe good engineering looks like.
Relive your childhood memories, now with no bugs
As a kid, your skills could only get you so far in a game. Sometimes the game itself was the problem. AI fixed a decades-old goalkeeper bug from a short video, and the real shift is what that means for anyone with an idea.
Token spend is the new cloud bill
The token bill for an AI agent is the visible 10 to 30% of what it costs. Review, supervision, rework and ownership make up the rest, and unlike a salary, almost nobody forecasts it.
Worktrunk takes the friction out of Git worktrees
Git worktrees let you run agents in parallel, but every new worktree starts cold, fights over ports and leaves junk behind. Worktrunk wraps worktrees in a few simple commands and a hook system, so one config file gives the whole team warm, isolated environments that clean up after themselves.
AI needs more uncertainty
AI has a confidence problem. Human intelligence was shaped by an uncertain world, and uncertainty is exactly the ingredient AI is missing.
Your codebase is part of the prompt
Agents follow the patterns in the surrounding code more reliably than they follow your rules, so every inconsistent naming scheme and half-migrated architecture is a lesson you're teaching them. The fix isn't another rule file. It's deleting the bad example.
Three Codexes walk into a codebase: The return of the king
Two agents implemented and reviewed a full architecture refactor across five iterations, with zero lines of production code written by hand. What neither agent caught was four innocent lines in a coordinator that had quietly started making policy decisions.
Three Codexes walk into a codebase: The two agents
Codex got the same god class Claude had failed to remove, plus one thing Claude never had: a target architecture. An implementing agent and an independent reviewer then took three rounds to converge, because agents renovate around bad architecture rather than delete it.
Three Codexes walk into a codebase: The fellowship of the god class
Handing an AI your engineering conventions cleans up the code without fixing the architecture. A 5,000-line god class shrank to 500 lines across tidy domain files, and an independent reviewing agent still returned FAIL: authority never left the central object.
How to become an AI-native SaaS company in the next 3 years
Becoming AI-native in three years does not need a three-year technology roadmap. It needs a usage policy, a map of your data, one bounded experiment, and both the enthusiasts and the sceptics in the room. Build the ability to change, not the overhaul.
AI chat is a brain in a jar
Hitting a wall with AI usually means hitting the wall of the chat box, not the wall of AI itself. Give a model memory, tool access and a browser, and the interfaces we built for humans become the integration. The bottleneck stops being the model and starts being your imagination.
Your second brain knows more than you do
An AI-assisted note system captures faster than you can understand, and the gap compounds into internalisation debt: a dense graph of links attached to a thin mental model. The test of a knowledge base is not how much it holds, but how much you could still explain with the file closed.
Curl says it's fast. Your users disagree.
Synthetic checks like curl and Lighthouse measure a request your users never make. A HAR file captures the real logged-in flow, and handed to Claude alongside the codebase it turns a wall of JSON into a ranked list of fixes that get implemented in the same conversation.