Journal entries
Journal 043
A growing category is trying to preserve AI conversations, memory and context across models. That may let Wayli focus on a narrower problem: preserving the state a person has actually accepted.
Journal 042
Portable AI memory may solve the problem of repeating ourselves. Threads is our experiment in solving the problem that comes next: preserving what the user actually decided.
Journal 041
AIR produced deterministic, repeatable results, but checking six assessments against the websites exposed something more important: a reproducible interpretation can still be wrong.
Journal 040
Making AIR's scoring more transparent exposed an Audience score that could never be reached. Testing the problem led us to simplify the question rather than weaken the evidence standard.
Journal 039
Testing AIR on Wayli exposed a weakness in where the assessment looked, leading to a more representative bounded acquisition method.
Journal 038
Wayli began with calculators, questions and a growing realisation that the real product was understanding.
Journal 037
A technically healthy website can still produce a poor AI answer when the retrieval system selects the wrong evidence.
Journal 029
As AI becomes a new audience for the web, websites are beginning another period of adaptation. This is why we're choosing to build AI Readiness in public.
Journal 030
Six independent AI reviews reached different scores but repeatedly identified the same questions about clarity, audience, trust and evidence.
Journal 027
While testing Wayli with browser AI, we discovered something unexpected: sometimes AI isn't looking at the same information as you.
Journal 028
We discovered our own assessment could never award a perfect score. The problem wasn't the methodology. It was our implementation.
Journal 026
Building an AI Readiness product taught us something unexpected: the most trustworthy AI report might be the one that AI never writes.
Journal 025
The biggest improvement to AI Readiness wasn't a better engine. It was realising what people should not have to pay for.
Journal 024
The biggest improvement to our latest AI Readiness report wasn't caused by changing the engine. It happened because we acted on what the previous report told us.
Journal 023
We thought we were improving an AI Readiness score. What we were really improving was how clearly we explained ourselves.
Journal undefined
We don't publish because we have all the answers. We publish because we're still looking for them.
Journal undefined
A plain English explanation of how AI forms an understanding of a business and how Wayli measures it.
Journal 018
Testing Wayli didn't just improve our website. It changed what AI Readiness was becoming.
Journal 019
Testing one website taught us about Wayli. Testing one hundred began to teach us about AI.
Journal 020
Our first benchmark suggests many businesses may be harder for AI to understand than their owners realise. It's an early observation—not yet a conclusion.
Journal 021
We thought we'd find one big problem. Instead, we found lots of small ones. That changed how we think about AI Readiness.
Journal 022
One hundred businesses gave us our first evidence. It also reminded us how much we still have to learn.
Journal 016
The first question is rarely the real problem. Wayli exists to understand what people are actually trying to decide.
Journal 017
Before asking anyone else to trust our Understanding Engine, we pointed it at ourselves.
Journal 015
Looking back, Wayli didn't change direction. Each project simply revealed a deeper question than the one before it.
Journal 014
We set out to improve a mortgage page and ended up questioning whether Wayli should generate reports at all.
Journal 013
The report looked incomplete, but the missing understanding was already there.
Journal 012
The biggest breakthrough wasn't teaching AI to make better decisions. It was deciding that it shouldn't make them at all.
Journal 011
AI Readiness wasn't built by looking for customers. It started by trying to understand whether AI understood Wayli.
Journal 010
One of the hardest lessons wasn't deciding what to build. It was deciding what not to build yet.
Journal 009
We weren't trying to build another product. We were trying to understand how AI would understand Wayli.
Journal 008
The biggest breakthrough wasn't building another calculator. It was realising they all followed the same pattern.
Journal 007
Building the Retirement Planner changed the question from 'When can I retire?' to 'When does work become optional?'
Journal 006
While comparing mortgage overpayments with investing, we discovered that people weren't always trying to maximise wealth. Sometimes they were trying to buy time.
Journal 005
Three simple scenarios revealed that the most important number wasn't the repayment. It was the point where the decision changed.
Journal 004
I expected to spend most of my time writing code. Instead, I spent it learning how to ask better questions.
Journal 003
The biggest improvement wasn't better code. It was asking better questions.
Journal 002
AI helped me learn faster, but it never replaced the thinking behind the project.
Journal 001
I thought I was building better calculators. I slowly realised I was trying to solve a different problem.