Hello, Guang here.

I work around BI, reporting, dashboards, and the messy middle between raw data and business decisions. A lot of my work is helping teams clean up spreadsheet-heavy processes, automate recurring reports, define KPIs properly, and build dashboards that people can rely on in meetings.

I'm especially interested in tools that make analytics work more practical:

• cleaner data imports,

• better refresh schedules,

• easier reporting automation,

• stronger QA checks, and

Around October last year, this was still just an idea in my head.
I m not an engineer.
I don t come from sales.
I don t come from marketing.
My background is FP&A finance.
So I understood P&L, margins, COGS, unit economics, and the feeling of looking at numbers that seem fine on the surface but don t fully explain what is really happening underneath.
That was the starting point for Okiela.
A simple belief:
Shopify founders should not need 30 hours of spreadsheets just to understand what is actually left after all the costs.
But building even a simple version of that has been anything but simple.
Behind Okiela, there have been months of working through things I had never touched before:
Shopify data.
CSV uploads.
Refund logic.
Discount logic.
COGS assumptions.
Shipping cost gaps.
Payment fee estimates.
Profit waterfalls.
Dashboard bugs.
Billing flows.
Security checks.
Real-world test cases.
Some days I felt proud.
Some days I honestly felt completely out of my depth.
There were moments where one small issue could block the whole product for hours. Sometimes days.
But I kept going because the problem still feels real.
A lot of ecommerce founders can see revenue.
They can see orders.
They can see ROAS.
But they still struggle to answer the question that matters most:
after everything, what is actually left?
That s what Okiela is trying to make clearer.
It is still early.
Still imperfect.
Still being improved every day.
But it is real.
Built by one solo founder from Vietnam, with a finance background, learning in public and trying to make something useful for sellers who deserve better profit clarity.
If you know a Shopify founder or ecommerce seller who is tired of guessing their real margin, I d really appreciate you sharing Okiela with them <3
And if you try it, honest feedback means more than you probably realize.
Still learning.
Still building.
Still here.

#buildinpublic #shopify #ecommerce #saas #founderjourney #profitability #Okiela

Hey Product Hunt

I m a full-stack developer and tech lead building products around discovery and distribution.

I'm Erin Espinoza, a professional photographer based in Yucaipa, California.

After years of shooting newborn, child, maternity, and family sessions, I finally built a proper home for my work. Today I'm launching Captured By Erin a clean, easy-to-use website where families can browse my style, see real galleries, understand the packages, and book sessions directly.

My goal has always been simple: create relaxed sessions and timeless images that families will actually want to hang on their walls for decades, not just scroll past.

Would love your honest feedback!

I ve been building SeatForAll after realizing something kind of absurd:

We ve spent years making spaces compliant while somehow forgetting to make them feel understandable.

A venue can have ADA seating, elevators, accessible entrances, sensory rooms, shuttle services and people will STILL arrive stressed because finding and navigating everything feels like solving a side quest.

What started as helping people navigate only stadiums has slowly turned into a much bigger realization:

Vedic astrology isn't just zodiac signs. There are documented

systems Samudrika Shastra for palm reading, Mukhanga Shastra

for face reading, full Jyotish for birth charts each with

thousands of specific rules that have been studied for centuries.

Hey everyone,

I'm Henrietta! I'm a developer and researcher focused on digital archives and data pipelines.

Hi, I'm a research data engineer working with academic teams on data collection, cleaning, versioning, and reproducible analysis.

A lot of research data work breaks down before the model, chart, or paper ever happens. Sources change, scripts are undocumented, licenses are unclear, and six months later nobody can explain exactly how the dataset was created.

I'm especially interested in open science, dataset provenance, archival workflows, and practical ways to make research easier to rerun without slowing everyone down too much.

Curious how others here handle this: when you publish or share a data project, what do you document first... raw data, collection timestamps, cleaning scripts, licenses, environment files, or something else?

Hi, I'm Ueder Cardoso, and I'm from a small city in S o Paulo (a brazilian state).

I come from a poor family. My parents only finished elementary school, but didn't go to middle school because they had to start working early. And my father was a gas deliveryman for the rest of his life, and my mother has been a homemaker and helped in my father's business. Currently, my father is retired on a minimum wage, and my mother works as an elderly caregiver, but earns the minimum wage.

Hey everyone

I m Nick, founder of Desiree.

We built Desiree around a simple idea:
What if an AI conversation actually felt real?

Not robotic. Not scripted.
Something you can open instantly and just start talking.