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Before adding another feature to a half-built AI product, run one core journey through four checks:1. Flow: can a new user finish without explanation?2. Evidence: can you prove the path works, not just demo it once?3. Resilience: do loading, empty, error, and retry states keep trust?4. Release: are the acceptance criteria and go/no-go decision explicit?If one fails, “more AI” usually isn’t the next move—the bottleneck is release readiness. I built this scorecard to scope one fixable journey into a seven-day diagnostic.
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WIP! Designing a hero section for an AI automation brand.
Nothing final yet — just testing background visuals.
Which one do you like more — A or B?
9 voted
60%
6 voted
40%
15 votes
Closed
Personal finance dashboard — from an hour of admin to two minutes
A weekly household finance workflow (manually pulling bank CSVs, splitting and categorising spends across two people, cross-checking shared expenses, reviewing totals against savings goals) was taking about an hour every week and still made it hard to tell at a glance whether spending was on track. Designed and built a dashboard that automates the pull, categorisation, and monthly view.
My role:
Research (mapped the existing manual workflow and pain points), UX design, and AI-assisted build of the dashboard.
Outcome:
Cut the weekly admin task from roughly an hour to about two minutes.
Note on scope:
Personal side project, not a client engagement. Included here as a live example of the kind of workflow-to-dashboard problem I take on for clients (repeat-purchase and admin-heavy flows, see Pet Circle and SafetyCulture project cards).
Project details up on my profile.
This sounds like a game-changer for weekly finances! How did you automate the CSV pulls?
Currently building autonomous AI systems that actually survive production — not just the demo.
Most AI projects work beautifully in a controlled test and then quietly fall apart on real data, edge cases, and concurrency. I focus on the unglamorous engineering that closes that gap: data validation pipelines, boundary checks, error handling, and knowing exactly where deterministic logic should own the decision instead of the model.
Recent work includes agentic workflows built with Python and FastAPI, an LLM reasoning layer for the genuinely ambiguous decisions, and automation orchestrated through Make with MCP. My background is technical support and operations, which means I design for the failure case first — I've spent years watching systems break in ways their builders never anticipated.
If you have an AI pilot that impressed everyone in the demo and then stalled on the way to something dependable, that gap is exactly what I build for.
Open to Applied AI / AI automation projects.
Keep it up!