Connect with the Product Hunt community

During today s standup meeting, an idea came up about improving our presence on Reddit (for LLM search visibility and similar reasons).

One of the suggestions was to look for high-karma accounts and possibly buy them to appear more credible when posting and mentioning the product within the posts/comments. It s a tactic, sure, but to me it already feels like it crosses an ethical line. I sometimes worry they can seriously damage a company s reputation.

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.

Been thinking about this after something that came up recently. Imagine an AI agent makes a recommendation that ends up influencing a customer workflow. The recommendation gets reviewed, approved, and eventually becomes part of how the team operates. Fast forward a few months and someone wants to understand why that decision was made.
The interesting part is that the technical history is usually still available. You can find the output. You can find the prompt. You can usually figure out which model generated it. What can be surprisingly difficult to find is the human context around the decision. Who reviewed the recommendation? Who approved it? What information did they have that made the recommendation seem reasonable at the time?
The more AI becomes part of everyday workflows, the more I find myself paying attention to that layer. Understanding the output matters, but understanding why someone trusted that output often matters just as much. A lot of conversations around AI accountability focus on the model. I suspect a lot of the missing context lives around the people making decisions with it.
Curious how your team is keeping track of that today, lets discuss it below...

The funds will boost open-source / Postgres tools, grow the platform, and liquidity for the employees.

Two months ago, @Supabase announced 8,000,000 developers building with the platform. [1] They're now nearly 10 million, with databases launches growing by 600% over the past year.

There s a point where products stop being fully understandable.

Too many features
Too many dependencies
Too much history

And decisions start getting made with partial context.

Hey PH! I'm a bit obsessed with Pricing, and just started testing a new experiment for my startup, @Pretty Prompt:

Remove the monthly tier; offer only Free or Yearly (basically no monthly plan available).

Road to 1,000,000 Votap users Day 75 | Current: 1358

Hey everyone!
Three major features just landed in OpenCut AI and they all run locally on your machine.
1. AI Co-Pilot Agent

This is the big one. Tell the editor what you want in plain English and it creates a step-by-step plan, then executes it.

Examples:

- "Make this a 60-second vertical reel with captions and trending music"

I've been working with LineageLens and just added a custom agent adapter so our internal CLI tool is attributed with prompts, model metadata, and confidence evidence. The registry design makes this surprisingly low-friction: implement a detect(input) that returns a NormalizedAgentContext (tool name, model, session ids, confidence, and evidence), register the adapter, then run the quickstart proxy to validate captures.

Why this matters: your team can capture private or bespoke tools without sending data to a vendor, and you get prompt code linkage in PR reviews and dashboards. I followed the recent repo changes (custom agents landed in late May) and found the adapter API predictable: detection should be conservative, emit evidence items, and choose appropriate ordering so your specialist adapter wins over the fallback.

If you ve extended LineageLens for an internal tool, what heuristics did you use to build confidence and avoid false positives?

My instinct is always to polish. I want the first thing someone sees to be good, because I figure you only get one first impression and an embarrassing v1 sticks to you.
But almost every product I actually respect started out rough. The early versions were janky and half-broken, and the founders shipped anyway because real users teach you things a year of polishing never will.
So I go back and forth on this constantly.
The case for shipping ugly is that you stop guessing. You find out what people actually care about instead of perfecting features nobody asked for. The case for waiting is that a bad first experience can kill momentum before it ever gets a chance to stick, and you don't always get told why people left.
I think the answer depends on how reversible the launch is. A small audience that forgives you is very different from a big public moment you can't take back.
But I'm probably biased toward polishing longer than I should, and I've definitely buried good ideas thinking they were not ready yet.
Where do you land: ship the ugly version and learn, or hold it until you're proud of it?

last week, I shared an update on everything Inr has shipped over the last 20 months in automation, CRM, and integrations.

today I am doing a final update on the bigger shift coming this Saturday 25th: Inr is now an AI-first platform, and here's what that actually means.

Over the last decade, countless people have launched products here every single day, so there's certainly no shortage of inspiration.

And I have to say, the bar keeps getting higher (helped in part by the fact that some launches are filtered through the Featured section).

Excited that we made the #3 spot via just word of mouth, no paid marketing. If you have any questions after trying https://zero.xyz (it's free! tell your agent to install the CLI!) with the $5 welcome gift, ask 'em here!

The Apple Worldwide Developers Conference (WWDC26) kicks off June 8.

It's Tim Cook's last big event as CEO, and the company is expected to use the occasion to present a complete overhaul of Siri, the voice assistant that's been built into every iPhone since October 2011. Starting with iOS 27, Siri is expected to look a lot more like OpenAI's ChatGPT or Anthropic's Claude.

Has it ever occurred for you, that X(Twitter), brought in a huge traffic to your product?
btw we are also live on x as https://x.com/trybitgrain : )

When I notice a project dragging, my reflex is always to cut scope. Trim the feature down, ship the smaller version, get something out the door instead of polishing the grand plan forever.

And usually that's the right call. Most of the time I was building more than the problem actually needed, and cutting it loose feels great.

Thanks for checking out Audex Trace.

Trace is a small macOS app for Apple Music that shows what s actually playing not just the badge.

It can show:

• The actual playback format: AAC / ALAC / Lossless / Hi-Res / Dolby

• The Mac output sample rate

• Whether playback and output are matched

• Playing Next quality estimates

• Skip warnings before a queued track may fail or skip

OliverZhaohaibin/iPhotron-LocalPhotoAlbumManager: A macOS Photos style photo manager for Windows folder-native, non-destructive, with HEIC/MOV Live Photo, map view, and GPU-accelerated browsing.
We re excited to share the latest release of iPhotron, a desktop photo library app focused on local-first browsing, organization, People discovery, albums, maps, and metadata workflows.

This release brings together several major improvements across recent versions: automatic People face clustering, multi-person groups, richer location workflows, improved map runtime behavior, large-library performance work, and a new internationalized UI with English, German, and Simplified Chinese support.

What s new

People, Face Clusters & Groups

Hey PH community

We're in the middle of our AppSumo launch for AI Context Flow, and I wanted to start a real conversation here rather than just drop a link.

The problem we're solving: most people using AI tools daily are paying what I call an "AI context tax", the time spent re-explaining your project, background, and goals every single session, to every single tool.

ChatGPT doesn't know who you are. Claude doesn't know who you are. Gemini doesn't know who you are. And if you're jumping between them (like most power users are), you're paying that tax multiple times a day.

Someone on the team finally looked at our production client bundle, which we hadn't checked in a while, and it was 25.67 MB. We got it down to 18.34 MB without removing a single feature. Most of the wins were stuff we were shipping to customers that had no business being there.

Bundle size is the metric that drifts up one dependency at a time until somebody notices. We noticed, turned on rollup-plugin-visualizer, and read the treemap. That treemap is the most useful 30 seconds of the whole project. You stare at the rectangles and immediately have opinions about every one of them.

Two honesty fixes this round: octoscope now tells you when a newer version is out, and it counts your whole account instead of just the first 100 repositories.

The update notice

On launch and hourly after that, on its own timer separate from the dashboard refresh octoscope checks the public Releases API and, if there's something newer, drops a quiet one-line notice under the banner. The upgrade command is context-aware: it detects how you installed octoscope and suggests the right one brew upgrade, go install, gh extension upgrade, or a download link.

It's nice to see that App Store reviews tend to move much faster after the initial approval.
48 hours for the initial review and just 8 hours for the latest update.
Anyway, Tamadoggo just got a new release, and starting today, Crib is now open to all cats!
If you're looking for a pet journal that happens to be pretty smart for your feline companion, I'd love for you to give it a try and share your feedback.

Hey Product Hunt,

I just shipped Privacy Watch in Room Service 1.4.0.

Vercel Day is live on Product Hunt today.

We teamed up with @Vercel for a special launch day, which means there s a dedicated leaderboard full of teams shipping on Vercel, all in one place. More launches, more competition, more reasons to spend too long refreshing the page.