I run a few businesses at once, and the hard part was never information, it's attention. Everything has a dashboard, but nothing tells you which fire is actually worth putting out today versus which one will burn itself out on its own. Curious how other multi-business folks decide what gets their focus on a given day, gut feel, a spreadsheet, something else?

Event technology is a loose label, some firms just do AV, others build full hybrid platforms, and a few own everything from planning to post-event analytics. Most organizers don't think about it until something breaks on show day: the mic cuts out, the stream freezes, remote attendees can't find the Q&A.

For anyone here who's run hybrid or virtual events, I'd love to hear what you've seen:

• What actually broke on the day?

• What separated a partner who handled it well from one who didn't?

• How much do you weigh accessibility (captions, sign language, screen-reader support) when choosing?

Mostly want to hear your experiences.

AI support tools increasingly promise to resolve most customer questions without human involvement.

That works well for simple questions, but it becomes more complicated when a conversation involves billing, account access, an upset customer, or something the knowledge base does not cover clearly.

After years working in software support, I have started to think the best approach may be a combination:
AI handles repetitive questions automatically, drafts responses for uncertain cases, and sends sensitive or complex issues to a person.

I am currently building a support product around this model, but I am still trying to understand where founders draw the line.

I build a fragrance app, so I spend a lot of time in how people describe what they own. One pattern keeps surprising me. The bottle someone bought and never reaches for predicts their taste better than the ones they love. A favorite tells you what you aspire to. A regret tells you what you actually are, and that turns out to be the more useful signal.

So a question for the room, in two parts. If you have built recommendation systems in other categories, do you find negative signal beats positive signal for figuring out a person? And a question for the fragrance people here. What bottle do you regret buying the most, and what did it teach you about your taste?

I got recently an request to build an API from a user and I wanted to ask if people here have experience in designing one. Earlier we've only considered building a pull API, but this user requested us to build specifically a push API.
This got me to thinking that I don't know nearly enough about how to design a well rounded API. What I'm wondering about is:

• While I want to give our customers maximum privacy while using our API, I also want to have the ability to detect issues and potential attacks in real time. Are there best practices regarding analytics for the API usage?

• Usage based pricing is all the rage now which makes sense with AI agents and all. But I'm thinking about a flat monthly fee with some (upgradeable) limits, particularly considering the push model. Would really appreciate if anyone could share their experiences on how to discover the right pricing model for an API?

• I've also seen a few API providers making a Model Context Protocols to deliver their data once the user has subscribed. Any experiences with making them (and providing data through them as well)?

Really appreciate any help and insights!

Most performance advice I read assumes a recent phone on good wifi. Our app runs in Cameroon, where plenty of users are on older Android devices and connections that stall for tens of seconds. Almost nothing I believed about making an app feel fast survived contact with that.

Three things moved the needle, and none of them were the things I expected.