The problem0%

Personal Subscription Dashboard

April 2026 (Work in Progress)

The problem

I knew my monthly total. What I didn't know was the real number. The cumulative spend since day one on each service, which tools were quietly duplicating each other, and which ones I hadn't actually touched in months. The information existed, scattered across bank statements and signup confirmations. There was no single place that made it legible.

What I want to build

A personal subscription dashboard that treats financial clarity as a design problem. Not a budgeting app — something narrower and more honest: a tool that shows you exactly what you're paying for, whether it's worth it, and when to stop.

Subscriptions · Dashboard Version I (Work in Progress)

15 active · 5 inactive

monthly

€205.93

yearly

€2.471

active

15

could save

€57.94

by category

AI & DevEntertainmentInfrastructureProductivityOtherFinance

breakdown

AI & Dev€86.40
Entertainment€63.92
Infrastructure€40.99
Productivity€6.63
Other€5.00
Finance€2.99
  • Apple OneEntertainment€34.95
  • NetflixEntertainment€13.99
  • IONOSInfrastructure€12.00
  • OpenAIAI & Dev€20.59
  • ScalableFinance€2.99
  • Claude ProAI & Dev€21.42
  • CursorAI & Dev€20.59
  • Amazon PrimeEntertainment€8.99
  • HBO MaxEntertainment€5.99
  • FigmaAI & Dev€23.80
  • RaindropProductivity€2.40
  • MediumProductivity€4.23
  • CongstarInfrastructure€27.00
  • Mein GrundeinkommenOther€5.00
  • Google DriveInfrastructure€1.99

The decisions I'm designing around

  • Cumulative spend over monthly cost. Seeing €21.42/month feels manageable. Seeing €514 since you first subscribed to something you barely use doesn't. The real number changes the conversation.
  • Duplicate detection by category. Not just a list — an overlap layer that flags when two services cover the same ground. Running Claude Pro, OpenAI, and Cursor simultaneously shows up as a pattern, not three separate line items.
  • AI-powered recommendations in context. The analysis layer shouldn't just see costs — it needs the full picture: usage, category, overlap, role. The output should be a direct call: keep, review, or cancel. Specific reasons, no hedging.
  • API-connected usage data where possible. OpenAI's usage API tells you whether you're actually using what you're paying for. Recommendations grounded in reality hit differently than ones based on assumptions.
  • Cancellation reminders. Passive renewal is the business model. A well-timed reminder breaks that default.

Why it's worth building

This is a small project, but the kind of problem I find interesting: a clear gap, a sharp point of view about what information actually changes behaviour, and a constraint — build something useful without overbuilding it. The "brutal honesty" framing isn't aesthetic. It's a product decision. If the tool softens the numbers, it doesn't work.