u/Large-Army-3441

Dunno why, but I feel like I've already pushed myself to the limit with all the usual advice😅😅😅

My 401(k) is maxed out, and my HSA is also covered. Grateful for that, but my tax bill still feels like a burden to me (still pretty heavy 😮‍💨). I've heard my colleagues talk about tax strategies beyond 401k for high income earners. But the more I try to understand it, the more vague it becomes.

If you've done anything that actually made a noticeable difference, what was it?

I've been experimenting with a few AI wireframe tools recently, and the speed is genuinely impressive.

Generating an initial wireframe from a prompt is one thing, but what I'm really curious about is how these tools perform once stakeholder feedback starts coming in.

Most projects go through multiple rounds of revisions, changing requirements, and new feature requests. That's usually where the real work begins.

Ideally, I'd like a tool where I can describe changes in plain language and have the wireframes evolve without rebuilding large sections from scratch.

For those using AI wireframe tools regularly, have they become a meaningful part of your workflow, or are they still best suited for quick concepts and early ideation?

How well do they handle iteration compared to traditional design tools like Figma?

Lately I've been seeing more discussions and job postings focused on AI Product Management, and I'm trying to figure out how different the role really is from traditional PM work.

At a high level, a lot of the responsibilities seem familiar: customer research, prioritization, roadmap planning, stakeholder management, experimentation, and delivering value to users.

The obvious difference is working with AI capabilities and constraints, but I'm curious how much that changes the day-to-day reality of the job.

For those working in AI Product Management, do you need a significantly deeper technical understanding than a traditional PM? Are there new frameworks, metrics, or decision-making processes that become important when building AI-powered products?

What skills have turned out to matter most, and what does the role actually look like in practice?