u/Dull_Gift_8242

Hey everyone, how you’re approaching SEO/GEO these days, especially for AI search.

We’ve been working on SEO for a new product and tried to make the site much clearer for both Google and AI. We focused on a few main search intents, rewrote the homepage around a clearer H1/value proposition, added pages like How It Works, About, For Users, etc., expanded the FAQ, improved internal linking and structured data, and made the main answers more explicit in the actual HTML.

We’ve also started building the blog and have 7-8 articles up so far, plus some external mentions and content outside the main site. The idea is basically to make the product/entity easy to understand and build up mentions across different sources.

I'm looking more for an experts advice. What you guys are doing for GEO specifically. What has actually worked for getting a product picked up or mentioned by ChatGPT, Perplexity, Google AI, etc.? Thanks a lot.

Hey everyone, how you’re approaching SEO/GEO these days, especially for AI search.

We’ve been working on SEO for a new product and tried to make the site much clearer for both Google and AI. We focused on a few main search intents, rewrote the homepage around a clearer H1/value proposition, added pages like How It Works, About, For Users, etc., expanded the FAQ, improved internal linking and structured data, and made the main answers more explicit in the actual HTML.

We’ve also started building the blog and have 7-8 articles up so far, plus some external mentions and content outside the main site. The idea is basically to make the product/entity easy to understand and build up mentions across different sources.

I'm looking more for an experts advice. What you guys are doing for GEO specifically. What has actually worked for getting a product picked up or mentioned by ChatGPT, Perplexity, Google AI, etc.? Thanks a lot.

It’s been around 50 days since our start. A lot changed.

Over this time, we’ve been building the platform and we’re finally getting close to the first beta test.

We migrated to a new infrastructure AWS, spent time testing different analytics tools, and figured out which solutions actually fit our needs. Now we’re moving into stress testing.

Along the way, we also explored a lot of interesting opportunities and met some great people. From making first tries joining accelerator programs and taking part in their courses, to getting access to startup credits for different tools and connecting with founders and people who already have experience building and investing in startups. These conversations and learnings helped us look at us from different perspectives.

Working with video formats became a bit easier, but it's still feels impossible to bring it for more people. So I have a question. HOW?

On the content side, text-based updates and not only on social platforms have been performing well, so we’re continuing to use them.

Btw, I finally found and managed a cheap paid ads channel, with an average CPA of $0.013 per user.

Right now, we’re waiting for the final legal approvals and, if everything goes as planned, we’re aiming to launch in August.

For those who don't know (you're all) - Tributly is a platform that shares advertising value with users. Instead of your attention creating value only for platforms, users get a share back.

Your attention has value. We’re building a way for users to benefit from it.

Share ur experience. Still have a lot of questions.

Tributly is a platform that shares advertising value with users. Instead of your attention creating value only for platforms, users get a share back.

Your attention has value. We’re building a way for users to benefit from it.

Hey guys, now we’re migrating a project to AWS, and our builds are taking around 20–25 minutes.

On top of that, things keep randomly breaking/failing, so we often have to stop, fix something, and run the build again. This makes the whole process even more painful.

From what we’ve seen, we could probably get the build time down to around 10–15 minutes by using a different approach, but that would also increase our AWS infrastructure costs by roughly 10–15%.

Is there any way to optimize this further without increasing infrastructure costs? Maybe there are some AWS/build optimizations we’re missing that could both speed things up and make the builds more reliable?

Would appreciate any advice.

Hey guys, now we’re migrating a project to AWS, and our builds are taking around 20–25 minutes.

On top of that, things keep randomly breaking/failing, so we often have to stop, fix something, and run the build again. This makes the whole process even more painful.

From what we’ve seen, we could probably get the build time down to around 10–15 minutes by using a different approach, but that would also increase our AWS infrastructure costs by roughly 10–15%.

Is there any way to optimize this further without increasing infrastructure costs? Maybe there are some AWS/build optimizations we’re missing that could both speed things up and make the builds more reliable?

Would appreciate any advice.

What stage are you at ur project? Any difficulties right now?

Or maybe you have any good stuff you’ve found recently - resources, funding, growth tips, tools, whatever? What stage are you on? Did you finally got your first users / funding / launch? Share something here

Hey everyone.

If you were migrating your infrastructure today, would you choose AWS or Azure?

On paper the credits and pricing look pretty similar, but from what I’ve seen Azure can become more expensive and a bit harder to manage at scale.

Which one would you pick and why?

Hey everyone.

If you were migrating your infrastructure today, would you choose AWS or Azure?

On paper the credits and pricing look pretty similar, but from what I’ve seen Azure can become more expensive and a bit harder to manage at scale.

Which one would you pick and why?

Hey everyone.

If you were migrating your infrastructure today, would you choose AWS or Azure?

On paper the credits and pricing look pretty similar, but from what I’ve seen Azure can become more expensive and a bit harder to manage at scale.

Which one would you pick and why?

I can't decide which project is the most promising.
I’m asking for your opinion and would appreciate real feedback.

My current ideas:

• Cloud file storage
• Life OS / personal tracker

Which one would you focus on and why?

I can't decide which project is the most promising.
I’m asking for your opinion and would appreciate real feedback.

My current ideas:

• Cloud file storage
• Life OS / personal tracker

Which one would you focus on and why?

For those of you building side projects while having a full-time job, how do you handle everything besides coding?

Like marketing, content, SEO, getting feedback from users, analytics, support, all that stuff that comes after you actually build something.

Do you do all of it yourself or do you have a co-founder / someone helping you out?

So how everyone else is managing this in real life?

For those of you building side projects while having a full-time job, how do you handle everything besides coding?

Like marketing, content, SEO, getting feedback from users, analytics, support, all that stuff that comes after you actually build something.

Do you do all of it yourself or do you have a co-founder / someone helping you out?

So how everyone else is managing this in real life?

Is there actually a best day or time to launch on Product Hunt, or does it mostly depend on how much traffic you can bring yourself?

Are there hours when activity is naturally higher, and times when launching is basically pointless?

Is there actually a best day or time to launch on Product Hunt, or does it mostly depend on how much traffic you can bring yourself?

Are there hours when activity is naturally higher, and times when launching is basically pointless?

What if instead of just removing ads, an ad blocker could create a cleaner experience and give users some value back from the attention they already spend online?

Still just an idea we’re testing. Curious what people think.

What was your startup when you applied, and what did YC help you with besides the investment?

What was the biggest value?

I’m interested in feedback from people working in fraud prevention, trust & safety, or risk engineering.

Imagine a digital platform where users can receive benefits based on verified activity. The main challenge is preventing organized abuse while keeping the experience simple for legitimate users.

A possible approach is a multi-layer anti-fraud system using:

• device reputation and fingerprinting;
• IP/network analysis;
• VPN/proxy/datacenter detection;
• behavioral signals;
• account trust scoring;
• graph analysis to identify connected accounts;
• risk-based limits and delayed payouts;
• manual review and feedback loops.

The goal is not to create a system that fraudsters can never bypass - that is probably unrealistic. The goal is to increase the cost and complexity of abuse until large-scale attacks are no longer profitable.

The question:

From a fraud prevention perspective, can this approach realistically change the economics of organized abuse?

For example, even if attackers can technically scale using automation, virtual environments, proxies, and other tools, they still have operational costs and need to maintain account reputation over time.

At what point does the cost of running a fraud operation exceed the expected return?

I would appreciate feedback from people who have dealt with real fraud networks:

• What attack vectors would you expect against a system like this?
• What signals are usually overestimated or underestimated?
• Where do these systems typically fail?

Looking for criticism rather than validation.

Hey everyone.

I’m interested in opinions from people working in ad-tech, fraud prevention, or traffic quality.

Hypothetical scenario:

A platform where advertisers pay for verified user interactions rather than raw traffic volume. The biggest challenge is maintaining traffic quality and preventing large-scale automated abuse.

Instead of relying on a single protection layer, the platform uses multiple signals:

• device reputation and fingerprinting;
• IP/network quality analysis;
• VPN/proxy/datacenter detection;
• behavioral analysis;
• account trust scoring;
• connections between accounts/devices;
• delayed or risk-based payouts;
• manual review and feedback loops.

The goal is not to create a system that bots can never bypass (probably impossible), but to make large-scale abuse financially unattractive.

The question:

From an ad-tech perspective, does this kind of approach actually change the economics of fraud?

For example, even if a sophisticated operation can scale using automation and changing infrastructure, they still have costs: infrastructure, traffic sources, maintenance, adaptation, and lower-quality accounts that may not pass verification.

If the potential revenue per user is limited (more like side income rather than a high-value payout), does fraud still make sense at scale?

Would love feedback from people who have dealt with invalid traffic, bot networks, or advertiser fraud. What weak points would you expect in a system like this?