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Self-initiated demonstration, not client work. Three Python pipelines pull auth-free public data over REST APIs and emit deduped CSVs in which every row carries the public source URL it came from, so any single row can be re-verified independently.
Snapshot counts the scripts actually returned: 1,056 pest-related citations across 829 NYC restaurants (NYC Open Data 43nn-pn8j, window 2026-08-07 to 2026-09-02); 5,041 debris-generating DOB build permits issued since 2026-08-07 (rbx6-tga4); 1,952 open postings across 28 live public Greenhouse/Lever boards, scanned 2026-09-07. These are point-in-time snapshots, not a live index, and no claim is made that any record converted into business for anyone.
Full case study, sanitized sample rows and honest limits: https://contra.com/p/rkorFQlE-nyc-public-data-lead-engine
Same pipeline as a scoped deliverable, Data pull in a day, $150: https://contra.com/products/1u2QatvL-data-pull-in-a-day
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Wow! It's amazing
Brand Identity for Wujhat, a tourist resort owning and managing a number of tourist rest stops located on one of the farms in the fertile Wilayat of Barka in Oman.
Brief context, the logo features a refined, custom-made wordmark subtly inspired by the elegance of Arabic letterforms, simulating branches of trees wrapping around their flowers & fuits, evoking a sense of regional heritage and royal sophistication.
Would you take a rest here?
🧠 GROWTH TIP 02/30: OPTIMIZE FOR STATE CHANGE, NOT SESSION COMPLETION
A meditation session can be completed and still create zero product value. For this wellness product, we changed the growth logic from content consumption to state change.
The framework:
Signal → Selection → Shift → Proof → Adaptation
SIGNAL
We identified what the user actually needed in the moment.Calm. Focus. Reset. Energy. Sleep.
SELECTION
Instead of pushing users into a library of sessions, we reduced choice friction and surfaced one relevant next action.
SHIFT
We measured whether the recommended practice actually moved the user toward the desired state.
The key metric became: State Shift Rate Successful desired state changes divided by completed recommended sessions.
PROOF
I showed users what was actually working for them.
Stress ↓18% after Evening Unwind
Best time: 8:30 PM
Insights stopped being passive analytics and became part of the retention loop.
ADAPTATION
Every completed outcome improved the next recommendation.
The loop became:
Intent → Recommendation → State Change → Proof → Better Recommendation → Repeat Use
📈 RESULTS
35% faster Time to Confident Choice
25% higher recommendation acceptance
20% higher repeat practice rate
15% lift in 30 day retention
25%+ improvement in successful state shifts as personalization learned
💡 GROWTH INSIGHT 02
The biggest shift was not getting users to complete more sessions It was increasing the percentage of sessions that actually delivered the outcome users came for.
More sessions did not create the growth. Better outcomes did.
That is when a wellness app stops behaving like a content library and starts becoming a product that learns what actually works for each user.
#MobileAppGrowth #ProductGrowth #WellnessApp #MeditationApp #Retention #Personalization #BehavioralDesign #MobileUX #ProductStrategy #AsolDesign
Nice App
