I posted a random thread on X about the cost of living in the Netherlands. Nothing about what we're building. Just genuine thoughts about life in the Netherlands.
It hit 1M+ impressions. And here's the weird part we got a ton of signups and paid users for Starnus from it. Without ever mentioning the product.
Meanwhile, my "here's what Starnus does" posts? Way less engagement.
This genuinely messed with my head. I'm sharing the actual X post below
I have been thinking about situations where clients specifically ask for AI agents to simplify a process. On the surface, it sounds reasonable. They want something intelligent to classify, route, or decide. But when we go deeper into the actual workflow, we often find that the logic is completely structured. It might just be routing leads based on budget, geography, or service type. In those cases, a simple if-else condition or a fetch record from a table would solve the problem cleanly.
Another common case is using AI to analyze structured form submissions. If the inputs are predefined dropdowns and checkboxes, there is nothing to interpret. A fetch record or rule-based filter is cleaner, cheaper, and easier to maintain.
So the real question is this: are we adding AI agents because they actually do the job better, faster, or more efficiently? Or are we just throwing AI into the mix because it sounds cool and everyone else is doing it?
We're launching Rover on Feb 25th, but the preview is live right now and we want your honest takes before we go big.
What it is: One script tag on your website your users get an AI agent that takes real actions inside your UI. Clicks buttons, fills forms, runs checkout, guides onboarding. Through conversation.
Hi Product Hunt! I m Lili, one of the Engineers at Warp who built Oz an orchestration platform for cloud agents. Oz helps devs run coding agents at scale safely with orchestration, observability, a unified local <> cloud experience. I worked on setting up our cloud environments with all the tools agents need to run code (there is a lot to unpack here, getting cloud agents to work on arbitrary base docker images was more challenging than you might think!), surfacing agent runs and artifacts like PRs and plans in our desktop and web apps, and integrating the Oz agent directly into GitHub actions. Building with and on top of Oz has been so much fun. The platform is incredibly flexible, and the primitives we ve built unlock a whole new level of experimentation and automation. AMA about how we built Oz, our favorite use cases, and where we see the platform going!
I d like to share iPhotro v4.0.0, a free and open-source, local-first photo manager that recently gained a set of advanced color grading tools.
This release focuses on giving photographers precise control over color and tone, while keeping a clean, non-destructive workflow and a familiar, macOS-like interface.
We launched dark mode for Tonkotsu earlier this week. It was written entirely by Tonkotsu with 63 completed tasks. My involvement was exclusively during planning and verification (the classic barbell shape described here).
We're looking for early users willing to test Polyvia and
give feedback. Ideal if you're:
- Building AI agents or multimodal workflows
- Working with documents heavy on charts, diagrams,
infographics
- Frustrated with RAG missing visual data What you get: Early access to Polyvia API + MCP Server Direct line to the founders for support Input on what we build next Interested? Sign up here: https://polyvia.ai/#access Or drop a comment happy to answer any questions first!
A 3-year search for a simple tool to track both personal and business finances in one place. Nothing fits.
Website owners constantly need minor edits in the admin panel. They are forced to pay specialists for 5-minute tasks. We need an AI agent that does this on command in the browser.
An indie hacker spends 20-30 hours manually cold launching each new product in directories, Reddit, and blogs. There is no tool that fully automates this and proves its effectiveness.
A freelancer often loses in proposal competitions due to the inability to quickly create personalized and visual website concepts for each job order.
A Telegram channel owner is losing their audience without understanding the reasons for unsubscriptions. There is no simple tool for automatically collecting feedback from departed subscribers.
We shipped it. Halo is your AI assistant, embedded directly on your website. Customers ask questions. Halo answers. They book appointments. Halo handles it. One script tag, 5 minutes, done.
New AI models pop up every week. Some developer tools like @Cursor, @Zed, and @Kilo Code let you choose between different models, while more opinionated products like @Amp and @Tonkotsu default to 1 model.
Curious what the community recommends for coding tasks? Any preferences?
This community helped turn a scrappy weekend project into something used by 25,000+ people from all around the world. So it felt right to share this here first:
On Jan 31, we are launching Pretty Prompt 1.0 right here on Product Hunt.
Mastra (YC W25) is launching on Product Hunt today, releasing stable 1.0 for building AI apps and agents, and announcing 300,000+ weekly npm downloads, 19,400+ GitHub stars, and production use at companies like @Replit and @WorkOS.
I had a blast working on this launch with @calcsam @smthomas3 @bookercodes and team, and wanted to share with you some thoughts we put into the ops.
For teams using Taskade, it often becomes the place where planning turns into action. Tasks, automations, and AI features all living in the same loop instead of scattered across tools.
If Taskade is part of how you run work, tell us how. What workflow did you set up that stuck? What changed once it was in place?
If you re reading this and you have a pull request open somewhere right now, you re already doing something most people never do.
We spend a lot of time talking about scale, growth and velocity. We talk less about the quiet grind of keeping quality high while shipping continuously.
Most ideas never leave a Notion doc. Most repos never see consistent PRs. Most teams never build the habit of review, they rely on heroics instead.
At the beginning, my reason was very simple: I needed a job and I genuinely liked the product.
I graduated with a Marketing degree, but I never felt like I belonged in agencies or similar environments. It just wasn t for me. At the same time, I didn t have much experience in tech either. So I took a leap of faith and applied for a Customer Support role, almost blindly.
The early days were tough. I had no technical background, no real understanding of how apps were built, and everything felt overwhelming. But the product itself became my motivation. I started from the most basic things: learning simple technical terms, understanding how an app is structured, and slowly exploring how everything works behind the scenes.
