Mozilla recently announced that they're shutting down Pocket. I used to use Pocket a lot back in the day, but I don't find myself regularly saving articles that much now.

For those that are still using Pocket, what are you planning to switch over to?

YC published a list of themes they want to invest in:

1. Full-stack AI Companies

2. More Design Founders

3. Voice AI

4. AI for Scientific Advancement

5. AI Personal Assistant

6. Healthcare AI

7. AI Personal Tutor for Everyone

8. Software Tools To Make Robots

9. The Future of Education

10. AI Residential Security

11. Internal Agent Builder

12. AI Research Labs

13. AI Voice Assistants for Email

14. AI for Personal Finance

Of course there are many projects and startups that launched on Product Hunt in each of these categories.

From TechCrunch:

"Windsurf, the maker of a popular AI coding assistant, is in talks to be acquired by OpenAI for about $3 billion, Bloomberg reported."

This is pretty crazy especially since the OpenAI Startup Fund is one of @Cursor 's biggest investors (source).

Hey everyone

I ve been building products for a long time (15+ years), and I recently tried using v0.dev for the first time. Honestly didn t expect much, but I was surprised how quickly I got something real off the ground - not just a playground UI, but a fully working fitness app with protected routes, dashboards, flow logic, the works.

It s called The HIIT PIT and it s live, but that s not why I m posting.

I m more curious to hear from other devs and indie makers:

Let s be honest: AI is getting eerily good at sounding human. It can craft hyper-personalized content and emails, and handle entire conversations. It s also made sales outreach noisier and eroded trust in once-reliable channels like email, chat, and even voice and crazy enough, video. But it s so seductive and fast. It s learning and adapting faster than ever. And it s not going to stop: in Q1, it already represented the majority share of global venture capital funding.

But one thing AI still can t fabricate is a real, trusted human relationship. These can t be made up or scaled with prompts. They re built through shared experiences, mutual trust, and real-world context.

And yet, in today s business world, precious human connections are slipping through the cracks. They re buried in inboxes, lost in forgotten LinkedIn threads, siloed in the minds of your team and stakeholders. The right lead, investor, or candidate is often just one intro away, hidden in the network you already have. LinkedIn is great if you re an individual building an audience but it fails to unlock the collective relationship capital of an entire company.

Let s be honest: AI is getting eerily good at sounding human. It can craft hyper-personalized content and emails, and handle entire conversations. It s also made sales outreach noisier and eroded trust in once-reliable channels like email, chat, and even voice and crazy enough, video. But it s so seductive and fast. It s learning and adapting faster than ever. And it s not going to stop: in Q1, it already represented the majority share of global venture capital funding.

But one thing AI still can t fabricate is a real, trusted human relationship. These can t be made up or scaled with prompts. They re built through shared experiences, mutual trust, and real-world context.

And yet, in today s business world, precious human connections are slipping through the cracks. They re buried in inboxes, lost in forgotten LinkedIn threads, siloed in the minds of your team and stakeholders. The right lead, investor, or candidate is often just one intro away, hidden in the network you already have. LinkedIn is great if you re an individual building an audience but it fails to unlock the collective relationship capital of an entire company.

When integrating internal knowledge into AI applications, three main approaches stand out:

1. Prompt Context  Load all relevant information into the context window and leverage prompt caching.
2. Retrieval-Augmented Generation (RAG)  Use text embeddings to fetch only the most relevant information for each query.
3. Fine-Tuning  Train a foundation model to better align with specific needs.

Each approach has its own strengths and trade-offs:

I used to be a startup scout, and one of my sources of deal flow was actually the Product Hunt leaderboard. For fun, I m going to pick the top five products I find most interesting that launched in the last week from a scout POV. I ll try to veer away from the top products each day to keep things interesting.

@Check Supply (Send checks in the mail) --> Takes something that s been around for decades and feels like it s stuck in the Stone Age, and lowers the friction to accomplish the task at hand. Do young Gen Z ers even know how to write a check? I don't know, but I know many systems still rely on these. This makes sense.

@PickR (free e-sports prediction game) - Betting is in. I can bet on political events via @Polymarket. I can bet on sports. It s natural to assume betting on e-sports is going to be just as big (if not bigger). I see PickR building the stepping stones to unlock the next wave of betting, for better or for worse. (I don t even know if this is their intent, but this is where my brain went.)