What if students could build real products with strangers and prove their skills along the way?
What should an AI receptionist do when it doesn’t know the answer?
I made a book buddy app that matches book lovers (basically Tinder for readers)
Avid book readers, I would love your feedback. I created Litpeer, an app that matches readers one-on-one with other readers who have the same book tastes.
We're still super early and basically have no users yet. But I genuinely believe this could be something people want. I just need to figure out if it actually is and how we can improve it.
Banc3 Technology — Company & Technology Discussion
A discussion space for sharing and discussing information about Banc3 Technology, including its technology, products, innovation, business developments, emerging technologies, and industry contributions. The thread can also cover relevant developments in defense technology, augmented and mixed reality, software, engineering, startups, and other areas connected to Banc3's work.
How to secure AI agents without killing latency?
Hey Product Hunt community!
As we see more companies moving from simple LLM wrappers to fully autonomous AI agents, a massive security gap is opening up: How do you safely give an AI agent access to your internal VPC, APIs, and databases?
I built an all-in-one AI short drama creation platform — would love some feedback
I ve been experimenting with AI video and short drama creation, and I kept running into the same problem: making a short drama usually means jumping between a bunch of different AI tools for scripting, characters, images, video generation, and editing.
So I started building DramaPilot an all-in-one AI platform for creating short dramas.
It s designed to help with the whole creative process, including story/script generation, characters, scenes, storyboards, and AI video generation, all in one workflow.
I m still actively developing and improving it, so I d really love to hear feedback from people who are interested in AI filmmaking, short-form video, or storytelling.
Lessons learned from building a hyper-local demand forecasting engine
Hey PH community!
While working on local inventory optimization, we hit an interesting technical bottleneck: standard time-series models struggle with neighborhood-level demand spikes caused by micro-weather shifts, local events, and regional supply chain delays.
To tackle this, we shifted our architecture to blend real-time sales signals with regional contextual data.
A few key takeaways from our build process:
The first bad review you got — how did you actually handle it?
Getting your first negative review hits differently than you think it will.
I was sitting at my desk reading it three times, trying to figure out if the person was wrong or if they had a point.
The work you forgot to do is still costing you time
Most productivity systems track what you planned to do. But the real problem is often what never made it onto the list. A reply you forgot. A follow-up that never happened. A promise buried in an old conversation. A task that became irrelevant without anyone noticing. This is the work that creates the most mental overhead because you are constantly trying to remember what might be slipping.
Luna is built around this problem. Instead of only helping you manage your tasks, Luna looks at the context around your work and helps surface what needs your attention.
Sena Slowblog is live
I quietly launched Sena Slowblog on Product Hunt recently.
Sena is a writing platform built around a simple idea: what if a publishing platform encouraged you to slow down instead of asking you to publish more?
There are intentional limits on how often you can publish. Free members can publish once every six days, while Sena Circle members can publish once every three days.