DEV Community: cherry yan

New Choicer Voicer packs usually fill their Top 100 leaderboard within an hour 😅 Think you can grab a spot? 🎙️ Play free: https://choicervoicer.org/ #gamedev #indiedev

cherry yan — Wed, 26 Aug 2026 06:32:12 +0000

The Choicer Voicer | Voice Impression Party Game

Play The Choicer Voicer in your browser. Load voice packs, perform impressions into a microphone, and let judges, local players, or Twitch chat score your delivery.

choicervoicer.org

I Got Tired of Hunting for Choicer Voice Packs, So I vibe Coded My Own Game — 200K People Played It

cherry yan — Wed, 26 Aug 2026 06:24:34 +0000

It started with a $5 game on itch.io.

The concept was simple and ridiculously fun: listen to a character, imitate the voice, and see how well you can match it.

I loved it.

The $5 wasn’t really the problem.

The problem was that I quickly ran out of things I wanted to play.

I wanted more packs～

For this kind of game, the mechanics are only half of the experience.

The packs are the game.

Once you’ve played the scenes you’re interested in, you immediately start wanting more:

More characters.

More movies.

More weird voices.

More scenes that make you think:

“Wait, I can totally do that voice.”

So I started looking for more material myself.

And that’s where things got ridiculous.

I was spending more time searching for scenes and preparing things I wanted to imitate than actually playing the game.

At some point I had a very developer-shaped thought:

Why am I spending all this time looking for packs when I could just build my own version?

So that’s what I did.

I AI-coded my own take on it

I started building Choicer Voicer, my own browser-based voice imitation game.

👉 https://choicervoicer.org/

I used AI heavily while coding it.

Instead of spending weeks getting every small piece working from scratch, I could describe what I wanted, prototype it, test it, break it, fix it, and iterate extremely quickly.

The goal was simple:

Open a link → choose a pack → listen → record → get scored.

No installation.

No account.

No $5 purchase just to see whether you’ll enjoy it.

Just open the browser and start making questionable voice impressions.

But coding wasn’t actually the hard part

This was the unexpected part.

AI made building the software dramatically easier.

But it didn’t solve the reason I started the project in the first place:

I wanted good packs. Lots of them.

So after building the game, I ended up spending a huge amount of time creating, finding, preparing, testing, and reviewing pack content.

And I became surprisingly picky.

A clip being recognizable doesn’t automatically make it fun to imitate.

A good pack needs something more.

The voice needs character.

The delivery needs to be interesting.

The clip needs to be the right length.

The difficulty needs to feel achievable.

And most importantly, after getting your first score, you should immediately think:

“No. I can do better than that.”

I’ve now created and reviewed a large number of packs for the game.

Ironically, AI made the coding fast enough that content curation became one of the most time-consuming parts of building the product.

Then 200,000 people showed up

I originally built this because I wanted something I could play myself.

Then other people started finding it.

A few users became thousands.

Thousands became tens of thousands.

And now more than 200,000 people have played Choicer Voicer.

That was when I realized the problem wasn’t unique to me.

People didn’t just want the mechanic.

They wanted more things to try.

They wanted new packs.

They wanted to retry the same voice.

They wanted to compare scores.

They wanted to climb the leaderboard.

And, occasionally, they wanted proof that their terrible impression was at least slightly less terrible than their friend’s.

A score shouldn’t just be a number

This led to another problem.

Originally, finishing a recording could simply give you something like:

62%

Technically, that works.

But it’s boring.

If someone just performed an absurd movie impression into their microphone, the game responding with a sterile percentage feels like a missed opportunity.

So I started turning the result itself into entertainment.

Depending on your performance, the game can react like a tiny virtual movie set:

Director says:

“One more take!”

Crowd reaction:

“Wait… that was kinda good.”

And I’m experimenting with performance titles that change with your score.

The idea is that getting 95% should feel worth bragging about.

But getting 32% should potentially be even more worth sharing because the result is hilarious.

That’s something I didn’t understand when I started building the game:

Failure can be content too.

AI changed where I spend my time

Before this project, I would have expected most of my effort to go into engineering.

Instead, AI coding shifted the bottleneck.

I spend much more of my time thinking about:

What makes a good pack?

Which clips should I reject?

Where do players get bored?

What score makes someone retry?

What makes a bad result funny instead of frustrating?

What makes someone send their result to a friend?

Those aren’t primarily coding problems.

They’re product and taste problems.

And I suspect this is going to become increasingly common for independent developers using AI.

When implementation becomes dramatically cheaper, the question changes from:

“Can I build this?”

to:

“Can I make something people actually want to keep using?”

AI can generate a lot of code.

It can help me iterate incredibly quickly.

But it still can’t sit in my chair, play twenty packs, reject seventeen of them, and decide that one ridiculous three-second delivery is somehow the funniest thing in the entire game.

At least, that’s still my job.

You can try it

Choicer Voicer is free and runs directly in the browser:

👉 https://choicervoicer.org/

Pick a pack, turn on your microphone, and see what score you get.

And if you run out of packs you like…

Well, that’s literally why I built this thing.

I’m still making and reviewing more of them.

If you try it, I’d love to know two things:

What score did you get?

And more importantly:

What pack should I make next?

⸻

How I Built an AI Avatar Generator That Outputs Perfect Square Images

cherry yan — Wed, 28 Jan 2026 04:52:36 +0000

Why I Built a Square Avatar Generator (and What I Learned)

When building side projects or small tools, you often don’t realize how many tiny problems add friction—until they stack up.

One issue I kept running into was avatars.

Most platforms (GitHub, social profiles, dashboards, comment systems) expect square images.

But real user photos? Rarely square.

Cropping manually works… until you have to do it over and over again.

So I decided to solve it properly.

The Real Problem with “Simple Cropping”

At first glance, this feels like a solved problem:

Upload an image
Crop to 1:1
Done

In practice, it breaks down fast:

Faces get cut off
Important details fall outside the crop
Automatic center-cropping fails on non-standard poses

What I actually needed was:

Face-aware positioning
Consistent square output
No manual tweaking for every image

That’s when I decided to experiment with an AI-based approach instead of traditional cropping.

From Manual Tools to AI-Based Generation

Rather than just resizing or cropping, I explored using AI to:

Detect the subject properly
Reframe the image into a square
Preserve facial proportions and composition

The result is a workflow where you upload a photo and get a clean, square avatar without thinking about margins, alignment, or cut-off edges.

While working on this, I ended up turning it into a small standalone tool:

SquareFace Generator

It’s focused on one thing only—turning photos into square avatars that actually look right.

What I Learned Building This

A few takeaways from this mini project:

“Small” UX problems are often under-engineered

Cropping sounds trivial until quality actually matters.
AI shines when rules break down

Once images stop being predictable, heuristics fall apart fast.
Constraint-first tools are underrated

Doing one thing well is often better than shipping a toolbox.

Final Thoughts

This wasn’t meant to be a startup idea—just a way to remove friction from my own workflow.

But it reminded me how many developer tools start exactly this way: scratching a very specific itch.

If you’re building side projects, I’d highly recommend paying attention to the small annoyances you repeatedly ignore.

They’re often the best ideas hiding in plain sight.

Happy building 👋

I’m building a small tool to help people understand relationship misunderstandings

cherry yan — Wed, 28 Jan 2026 04:49:30 +0000

Hi DEV 👋

I’m currently building a small side project called DecodeYou.

The idea came from personal experience.
I’ve been in a long-distance relationship, and even when both people care a lot,
misunderstandings still happen — not because of bad intentions,
but because we interpret situations differently.

For example:
the same action can feel “reasonable” to one person,
and deeply hurtful to the other.

Instead of giving advice or labeling people,
DecodeYou focuses on helping users:

reflect before reacting
notice emotional and communication patterns
separate what actually happened from what we assumed it meant

Right now the project is very early.
I’ve open-sourced the repo to document the ideas and structure as I build.

👉 GitHub repo: https://github.com/cherryyan1987/cherryyan1987

I’d really appreciate any feedback, thoughts, or even skepticism.
Happy to learn from this community.

I built a global GLP-1 telehealth directory (and why it’s harder than it looks)

cherry yan — Wed, 28 Jan 2026 04:45:09 +0000

GLP-1 medications (like semaglutide and tirzepatide) have exploded in demand, but finding legitimate, accessible telehealth providers is still surprisingly hard — especially outside the US.

So I built GLP-1 Patches, a global directory of GLP-1 telehealth providers.

What problem I was trying to solve

Most existing resources are:

US-only
SEO farms or affiliate-heavy
Opaque about pricing, prescriptions, or regions served

If you’re not in the US (or you’re comparing options), the information gap is huge.

What the directory includes

Telehealth providers offering GLP-1 medications
Regions served (US / international)
Prescription model (subscription vs one-time)
Transparency around eligibility and process

No prescriptions. No medical advice. Just structured, comparable information.

The hard parts (that I underestimated)

Regulatory differences across countries
Providers changing terms quietly
Avoiding becoming an affiliate spam site
Balancing “useful” vs “compliance-safe” content

Health + software is a very different beast.

Current state

The project is still early, but already useful for:

Users comparing GLP-1 access options
Builders researching the space
Anyone trying to understand how fragmented this market is

👉 Website: https://glp-1patches.org

Feedback welcome

If you’re building in healthtech, telemedicine, or regulated markets, I’d love feedback:

What would make this genuinely useful?
What would you absolutely avoid adding?
Any red flags you see immediately?