Hi everyone!

I built ReelGenius AI, a Windows desktop app that creates complete viral quiz videos in minutes.

Just enter a topic, and the app generates the questions, voiceover, captions, visuals, and a fully rendered vertical video ready for YouTube Shorts, TikTok, Instagram Reels, and Facebook Reels.

The goal was simple: eliminate the repetitive editing work so creators can focus on publishing more content instead of spending hours producing each video.

Integrating proxies into your stack sounds simple until you're debugging why your requests are randomly failing at 2 AM. After building and scaling multiple data pipelines, here are four lessons that would have saved us weeks of headache.

1. Test authentication before you write a single line of scraping logic

Proxy auth failures are the #1 reason integrations stall. Test your credentials with a simple curl command first before you embed them in your application code. Confirm whether your provider uses username/password or IP whitelisting, and make sure both sides are configured correctly.

2. Build a proxy manager, don't hardcode proxies

I never planned to build a PDF toolkit.

It started because I needed to merge a few PDFs and every website wanted something in return:

• Upload your files

• Sign up

• Wait in a queue

• Upgrade to Pro

It felt wrong especially for documents containing personal or business information.

Some of you might remember my post about shipping a whole product without a UI designer. This is that product.

BettaScore pulls product reviews from Reddit, YouTube, Hacker News, Wired, RTINGS and Google onto one page, scores the tradeoffs, and links every point back to its source. The idea came from spending three evenings and fourteen tabs researching one action camera, then realizing the real problem wasn't too little information. It was that I couldn't trust any single source alone, so I was cross checking everything by hand.

Most people want to journal.

They just don t stick with it.

Hi everyone!

I'm an indie maker passionate about building AI-powered tools that solve everyday problems. My latest project is an AI face shape analyzer that helps people find hairstyles, glasses, and grooming styles that suit them best.

Building this product has taught me a lot about computer vision, user experience, and the importance of validating ideas with real users instead of making assumptions. Every piece of feedback has led to improvements I wouldn't have thought of on my own.

Every file-upload bug seems to start with the same request:

Can you find me a 10 KB PNG?

Do we have a video with a predictable size?

What happens if the extension and file signature disagree?

Over the past few weeks, I've been building ReadRiff, an independent publication focused on helping people discover thoughtful, high-quality content instead of endless information overload.

When I first shared the project, the feedback was incredibly valuable.

Several people pointed out that while the idea was clear to me, it wasn't obvious enough for a first-time visitor. Others suggested explaining the mission better, making the waitlist more compelling, and being more transparent about what readers could expect after signing up.

So I went back to work.

I'm building an open source experiment called DevTokn and would love honest feedback before investing more time into it.

The idea is simple: an AI coding terminal where users can optionally view unobtrusive sponsor placements to earn credits that offset AI costs. Sponsors never see your repository, prompts, or code.

I'm mainly trying to answer three questions:

1. Would this save enough money to interest you?

2. What would make you trust a tool like this?

3. What's the biggest flaw in the idea?