Hey PH!
I m Roy, one of the makers of Surf a research copilot for crypto folks. Super excited for our first launch.

I ve read a ton of guides but would love real advice from makers:

1. Expectations / forecasting

Do you usually have a sense of how many participants will show up on launch day or a rough range of upvotes/comments? Any rules of thumb you ve used (e.g., % of Teaser followers who visit, comment to upvote ratios, or targets you set for the first 4 hours)?

We started as a sustainable made-to-order clothing brand. Custom pieces, small batch, everything done internally sourcing, pattern making, grading, cutting, production. The unit economics actually worked because we controlled the whole chain. We even partnered with creators in the thrifting space to get the word out.

But we still couldn't reach enough people. Getting a small brand in front of a large audience without a huge marketing budget is a different kind of impossible. We were bootstrapping and every channel felt like shouting into the void. The product was good. The audience just couldn't find us.

That was the moment realizing we didn't have a product problem, we had a distribution problem. And no amount of improving the clothes was going to fix it.

On launch day, you only get one shot to capture attention. Your landing page is where curiosity turns into clicks and clicks into sign-ups. If it s unclear, slow, or uninspiring, even the best product will slip through the cracks.

A great landing page on Product Hunt should:

Grab attention with a crystal-clear value proposition in the first few seconds.

Show social proof or credibility to build trust fast.

Disney just announced a licensing deal with OpenAI that will allow users of OpenAI s Sora to generate short videos of its characters.

Just in case you ve forgotten what s in Disney s IP library, it s way more than Mickey and Goofy. It spans Marvel, Star Wars, Pixar, and a bunch of other properties. Not everything is included in the deal, though. For example, you ll get the illustrated Tony Stark, not the Robert Downey Jr. version.

I've been thinking a lot about audience monetization lately, especially for founders and indie makers.

Many of us slowly build small but valuable audiences over time:

• newsletters about startups or AI

• niche blogs or dev communities

• SaaS products with engaged users

• waitlists or early adopter communities

• founder-focused X / Twitter audiences

At some point sponsorships start to make sense.

Vibe coding seems to be a popular concept these days. Instead of writing all the codes by themselves, developers are turning to natural language prompts to simplify the programming process. It seems much more accessible, efficient, and beginner-friendly.

So what about data analysis? It still seems highly professional now, and the majority of people naturally think that they cannot do the data work but have to resort to analysts for help. But maybe with the advance of AI data analysts, everyone can get a customized tool for them to do 'Vibe Data Analysis'--have the data analyzed simply by asking questions to AI.

They just need to upload their dataset, however large it is, ask questions in plain language, and wait for the tool to process. The tool analyzes the data and responds with clear summaries, visualizations of all kinds of charts, and actionable insights, enabling users to make decisions based on solid evidence, without having to spend hours learning softwares, coding skills, or just waiting for an analyst to free up.

For data analysts, their work may become much more easier, as the tools can take over and automate much of the tedious work like data cleaning and calculatiion. They can focus on more creative and valuable aspects, like digging deeper into the data, interpreting the results, and delivering insights to their clients.

One piece of advice we hear all the time in startups is: launch fast. Ship early, test with real users, and improve along the way.

But lately it feels like the landscape is changing. With so many products launching every day, sometimes a half-baked launch disappears instantly, while more polished products seem to get better traction.

So I m curious about your experience:
Is launching fast still the best strategy?
Or is it better to wait a bit longer and launch something more refined?
Have you seen examples where speed helped a product win, or where quality at launch mattered more?

Would love to hear how other makers here approach this balance between speed and quality.

Hello PHs!!

With every new release, OpenAI is killing many startups and smaller companies. For example, the latest OpenAI Agents will surely make some existing AI agents obsolete...

How do you foresee the future of the industry? Do you think the future of AI is heading toward a concentration of power among big whales like OpenAI, Google, Meta, etc.?

Or do you think there will always be untouched categories where these big corporations won t venture, allowing smaller companies to emerge?