cryptography

Anyone cracked the mystery behind Stripe making paid trials (not free ones!) so painfully hard—or outright impossible—to set up? Their checkout session page keeps defaulting to 'free days' no matter what I try. Is this a bug, docs issue, or intentional? Devs who've fought this beast, what's the workaround?

I am wondering if any hacker here built the stack fully AWS-free

Hey everyone!

I'm doing a small research project for my studies to better understand what makes certain accounts go viral. What actually makes them stand out? Is it mostly about the content itself, or does the creator’s background/personality play a big role?

I know this is a broad question, but I’d really appreciate it if you could share a few things that make you enjoy someone’s content — and maybe even follow them. What grabs your attention and keeps you coming back?

Thanks in advance!

Imo, Twitter's 280-character constraint made ideas sharper and forced people to share high-density knowledge. Not sure it was a good idea removing it as a pro feature

I've been using Cursor and I'm genuinely curious about something.

When you paste a screenshot of a broken UI and it immediately spots the misaligned div or padding issue—is it actually doing visual analysis, or just pattern-matching against common UI bugs from training data?

The speed feels almost too fast for real vision processing. And it seems to understand spatial relationships and layout in a way that feels different from just describing an image.

Are these tools using standard vision models or is there preprocessing? How much comes from the image vs. surrounding code context?

Anyone know the technical details of what's actually happening under the hood?

I asked Claude about a library released in March 2025 (after its January cutoff). Instead of saying smth like "I don't know, that's after my cutoff," it fabricated a detailed technical explanation - architecture, API design, use cases. Completely made up, but internally consistent and plausible.

What's confusing: the model clearly "knows" its cutoff date when asked directly, and can express uncertainty in other contexts. Yet it chooses to hallucinate instead of admitting ignorance.

Is this a fundamental architecture limitation, or just a training objective problem? Generating a coherent fake explanation seems more expensive than "I don't have that information."

Why haven't labs prioritized fixing this? Adding web search mostly solves it, which suggests it's not architecturally impossible to know when to defer.

Has anyone seen research or experiments that improve this behavior? Curious if this is a known hard problem or more about deployment priorities.

I have primarily worked with web applications, and tools like JustBeepIt or Superglow have been invaluable for creating tasks and gathering feedback on live websites. Recently, however, I’ve been involved in mobile application development and am finding it challenging to find similar tools or alternatives.

Since my workflow was entirely web-based until now, JustBeepIt (https://justbeepit.com) was a perfect task management platform for me and my clients to create and manage tasks. Recently, I've started working on native mobile apps as well, but unfortunately, JustBeepIt isn’t supported on phones yet (I reached out to their team, and they mentioned future support). If you know of any alternatives, please share them here. Thanks!