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Practitioner-level writing on building with AI.
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98 posts across 13 categories
The "missing traffic" trap: why most conversion fixes fail when the real issue is distribution
Most teams optimise the funnel they can see. But if nobody’s reaching your signup page, conversion rate improvements are solving the wrong problem entirely.
Metric overload as a product bug: dashboards that look "rigorous" but reduce decision accuracy
When your dashboard has 47 metrics, nobody makes faster decisions — they make fewer. Rigour without focus is just noise with better formatting.
Hypothesis theatre: when teams "test" things without a falsifiable claim
Running A/B tests without a falsifiable hypothesis isn’t experimentation — it’s a performance of rigour that produces no learning.
Instrumentation before iteration: why shipping faster can make you slower if you can’t observe outcomes
You can’t ship faster if you can’t observe what happened. Velocity without observability is just moving faster in the dark.
Leading vs lagging indicators in SaaS: what to track when revenue is too slow to teach you anything
Revenue tells you what happened months ago. If that’s your primary signal, you’re steering by the wake, not the road ahead.
User feedback as a noisy sensor: how to treat anecdotes like data, without ignoring them
Anecdotes aren't useless, but they're not data either. The skill is treating them as signals without over-weighting any single one.
The hidden cost of "one more metric": cognitive load and analysis paralysis as measurable waste
Every metric you add competes for attention. At some point, the dashboard itself becomes the bottleneck to good decisions.
Designing reports that force decisions: how to structure outputs so "next action" is unavoidable
A report that doesn’t make “what do we do next?” obvious has failed at its only job — regardless of how well it visualises the data.
When customers ask for features they already have: UX discoverability as the real problem
Before you build it, check whether the real problem is that nobody can find the feature you already shipped.
The trust arc: why AI tools need to show their work before showing a diagram
The core UX of AI design tools isn’t generate-then-view. Users move through a trust arc — from scepticism to confidence — built by visible reasoning, trade-off explanations, and intelligent pushback handling. The diagram is proof the thinking happened, not the product. Voya Genie’s itinerary builder is a good example of what this looks like done well.
The "Illuminate → Solve → Refine" pattern: a minimalist loop that beats most fancy prompting
Forget elaborate prompting frameworks. This three-step loop handles 90% of real tasks better than any chain-of-thought template.
Prompt chains as product UX: the difference between "chatting" and "operational workflows"
The gap between chatting with an AI and building an operational workflow is prompt chaining — and most products haven't made that leap yet.