We've added OpenClaw to our template library. Now launching your AI agent in the cloud is even faster.

We've automated deployment for AI projects:

1. Connect a repository

TwelveLabs just introduced Pegasus 1.5, their most significant leap in generative video AI, transforming video into a queryable, structured data asset.

They're launching today on Product Hunt.

We ve all been there: You check your storage, and there s a massive yellow bar labeled 'Other' or 'System Data' taking up 50GB+.

In OptiClear, I built the Large & Aging Files analyzer specifically to hunt these down. It's often forgotten .dmg installers from 2 years ago or massive log files that serve no purpose.

Yesterday, a user told me they found 12GB of old screen recordings they forgot they ever made!

Question for the community: What was the weirdest or largest 'forgotten' file you ever found while cleaning your drive?

Built for the folks who work with photos and videos on a daily basis, Studio is the workflow-agnostic media workspace; It doesn t just analyze the media you upload to it, it builds an agentic visual memory and runs workflows to understand and act on your team s photos and videos.

Support our launch https://www.producthunt.com/prod...

In November 25, AI Context Flow was #1 Product of the Day and #1 Productivity Tool of the Week. It was surreal.

Since then, we have been building in public, together with this amazing community here.

You believed in this before it was polished. You gave us feedback when it was rough. You kept asking for more and that pushed us to build more, and we delivered more.

@charlie_hb started quite an interesting thread where he outlined on which days people lose their streaks the most often.

He showed some stats regarding days in a week, but I am still lacking the reasons.

The most common ones I have heard were:

The first week felt like validation. 340 customers, $50K in the bank, near the top of the charts. I thought I'd solved the cold start problem.

What I hadn't worked through: I'd acquired 340 customers who paid once and had no incentive to churn. Which meant I had no recurring signal on what actually needed fixing. The feedback was noisy because everyone bought at different price points with different expectations. Support was immediate and permanent. When I raised prices six months later to attract monthly subscribers, existing LTD holders treated it as a personal betrayal.

We just shipped Projects in Room Service.
A new way to inspect your local development folders and understand what s actually taking up space.
Projects analyzes each repo and groups its contents into things like assets, generated data, git storage, and logs. So instead of just seeing folders, you can understand what each part represents and why the project is large.
It also surfaces things you don t normally see while working, like build outputs, caches, and repository internals.

Hey everyone! With the landscape for building voice agents shifting lately, it feels like we re moving away from heavy, manual API orchestration toward something more streamlined.

How you re currently architecting voice agents. Specifically: Have you used the Model Context Protocol (MCP) to build or provide real-time data/context to your voice agents? Does it actually streamline your tool-calling, or is it more trouble than it's worth?

Would love to hear what's working (and what's breaking) in your current workflow. Drop your thoughts below!