Most of my day is spent in Codex, Claude Code & Cursor building DunSocial.

Opening another dashboard just to save memories, write product updates, draft posts & schedule them always broke my flow.

Now I just connect the DunSocial MCP.

When I finish a feature, I ask my AI to create a PR. It saves the latest updates to memory, drafts a post, finds the next free publishing slot, schedules it & creates the PR. I never leave my editor.

I want to talk about something that doesn t get discussed enough in product development: what happens when you actually listen to your community instead of your roadmap.

When we launched v1 of Second Brain here on Product Hunt, we had a plan. Then the comments came in. People told us newer isn t always more correct. That conflicting memories needed an authority model. That compression should preserve the through-line.

Netfox 0.15.0 is out https://netfox.app

This one answers two questions a network monitor should be able to answer and Netfox couldn't.

The big one first: octoscope has a real guide now. Ten pages at its own address Getting started, Authentication, The tabs, Drill-ins & scan, Live & reliable, Themes, Configuration, Scripting, plus a reference pair for every CLI flag and every keybind. https://gfazioli.github.io/octos...

The octoscope guide

Nowadays the AI is dramatically improved product visual creation, but every workflow still seems to have one step that takes more time than expected. For me, maintaining consistency across a growing catalog and creating multiple variations still requires careful review.

I'm curious how others approach it:

• What's your current workflow that gives a better result?

• Do you still rely on manual refinements?

Hey everyone

A while back, I shared NoteUX here on Product Hunt, and the support from this community meant a lot.

Since then, I've been quietly working on something much bigger.

Today, I'm excited to share Konxios - an AI Operating System designed to bring models, agents, workflows, files, and tools together in one place.

We got 272 founders to sign up for Causo after launch.

Nice number. Felt good for about 12 seconds.

Then we looked at the funnel properly and got humbled pretty fast.

253 created a company profile.
92 completed onboarding.
90 got matched with investors.
38 actually launched outreach.
18 became paying customers.

It's been 4 hours since launch, and Adomate is currently holding the #1 spot on Product Hunt!

If you haven't supported us yet, we'd greatly appreciate your help with an upvote and comment. Every bit of support helps us stay at #1 and reach more people.

https://www.producthunt.com/prod...

Thank you for being part of the journey!

When we first built Murror, our AI would automatically surface emotional patterns after every journal entry. "You've mentioned feeling anxious about work 4 times this week." "Your mood tends to dip on Sunday evenings."

Users loved it in demos. They called it "impressive" and "insightful." Our internal metrics looked great -- pattern detection accuracy was 87%.

I keep digging into what actually gets a product mentioned in ChatGPT or Perplexity answers, and the data keeps pointing somewhere that makes me question the whole "GEO/AEO" hype.

Ahrefs looked at 75k brands and found branded mentions correlated with AI visibility way more than backlinks 0.664 vs 0.218. It's correlation, not causation, so take it with salt. But the gap is big enough that I can't ignore it.

Which makes me wonder if we're overcomplicating this. Maybe AI visibility isn't a new discipline at all. Maybe it's just the old thing: be genuinely talked about in the right specific places.

Three weeks post-launch, real product, real infrastructure, a handful of free users, but the jump from "people trying it" to "people paying for it" feels like a genuinely different problem than everything before it.

Curious how others here actually closed that gap. Was it a specific conversation, a pricing change, a feature that tipped the decision, or just volume and patience? Not looking for generic "provide value" answers, more the specific thing that actually worked for you.

Paid someone to do a code review last week, first actual engineer to ever see what I'd built. Watched her scroll for a solid two minutes without saying anything. Then: "okay, so, this works, but why does this file talk to three different places to do one thing?"

I didn't have an answer. I'd asked the AI for a feature, it worked, I moved on, asked for the next feature, it worked, moved on again. Never once looked at whether feature three quietly duplicated logic from feature one. The app functioned perfectly the whole time. It just also had, apparently, three slightly different versions of the same idea living in different corners of the codebase.

What got me wasn't that it was bad, she was actually kind about it, said this is completely normal for anything built fast. What got me was realizing I'd been measuring progress purely by "does it work when I click it," and that measurement had been quietly lying to me the entire time.

I don't think the answer is doing it slower from scratch. I think it's that at some point, someone who actually reads code needs to look at the whole thing, not feature by feature.

Hey hunters!

I built @Tamadoggo so pet parents can track their dog or cat's whole life - health, walks, meals, milestones. But a few PH community folks told me: more than one person cares for the pet, so why does only one phone hold the journal? They were right!

Today we're fixing that with Family Sharing:

Free on every plan. Sharing never costs extra. Invite with a private, single-use link you share however you chat.

Everyone logs to one shared timeline, each entry tagged with who added it

If the pet's owner is Pro, everyone sharing that pet gets its AI features too

Also new in 1.2.2: a friendly Family Welcome Hub for new members, a smoother in-form date picker, full-screen event photos you can swipe through, and snapping photos straight from the camera.

This one's personal. I'd love to hear how your family uses it, especially anyone co-raising a pet across two homes.

What would make it feel even more like home?

A timestamp can tell you when something happened. It doesn't always tell you why.

That's not usually a problem with traditional software. If a user updates a record or changes a setting, the event itself is often enough. With AI agents, the situation feels a little different because the outcome is often the result of multiple decisions happening along the way.

Imagine an agent researching information, selecting a tool, retrieving data, generating a recommendation, and then triggering an action. An audit log might show each step occurred. What it may not capture is the reasoning behind those steps. What was the agent trying to accomplish? What information influenced the decision? Was there a review before the action moved forward? If something was modified, what concern or feedback led to the change?

The gap becomes more noticeable when someone tries to reconstruct an AI workflow weeks or months later. A sequence of events is useful, but context is often what turns a log into something explainable. Understanding intent, decision paths, review history, and the factors behind an outcome can be just as valuable as knowing the exact time it occurred.

An phone app with sync'ed notes could be convenient, but we then lose the co-edit feature on the phone. It's more a quick notes. Eventually we can setup co-edit with the agent on the cloud. So phone can also enjoy the co-edit. Currently leave a todo for the agent on phone is definitely do-able.

Any thoughts?

Your Mac was full of Xcode caches while Cursor was still running and holding half of them hostage. DevCleaner 1.10.1 doesn t fight the open app. It cleans around it.

We ve been living in the menu bar for months. This release makes that feel like the product - not a shortcut to a bigger window.

What s new in 1.10.1

I ve been using Codex, Claude Code, and OpenClaw for different kinds of work:

• Codex (now @ChatGPT by OpenAI )is strong at long-context planning and execution.

• @Claude Code acts like a supervisor and a second pair of eyes.

• @OpenClaw is like my daily operations intern.

I know there are many other agent harnesses. Many are open-source, including OpenHands, @Pi Coding Agent , and Hermes.

Hi,

To summarise

Sometimes I look at Product Hunt and think

Do people really need this many products?

AI has made it so much easier to turn an idea into something real.

An idea that maybe would have stayed in someone s head for years can now become a product in a few days.

One thing I ve always wanted as a cloud engineer is the ability to experience major cloud incidents instead of just reading the postmortem.

We ve started adding real-world cloud incidents as scenarios in Cloud World Model so engineers and AI agents can safely explore how systems behave during major outages.

What a week!

CodexBar Lite closed at #13 Product of the Day with 97 points.
For a first Product Hunt launch, I honestly couldn't have asked for more.

I want to thank everyone who supported the launch, whether you upvoted, commented, tried the app, shared feedback, or simply stopped by to take a look. Every interaction meant a lot.

The launch may be over, but the project certainly isn't.

A few months ago, I started building StealthHound with one goal: make invisible web tracking visible.

Most people know about cookies, but far fewer know about browser fingerprinting a technique that can identify your device without relying on cookies. I wanted to build a tool that could expose and block these techniques in real time while keeping everything local and privacy-first.

Hi everyone!

We've just released Motionly v1.2.3, and the biggest change is a major upgrade to our AI.

Semantic search

You can now search your meeting history by meaning instead of exact words. It all runs on-device, and it works across languages, so a Japanese query can find an English meeting.

Some notes on how this got built, because it didn't go the way I planned. My first version used Apple's built-in embedding API (NLContextualEmbedding), mainly because it needs no model download. I really wanted that to work. But it's a general-purpose model, not tuned for retrieval: short filler lines like "yeah, okay" matched almost any query, and the similarity scores moved around so much that I couldn't find a threshold separating real hits from noise. I tried the usual tricks (centering the vector space, weighting by length) and they helped, but on my real transcripts it was still a coin flip. So I gave up on it and switched to Qwen3-Embedding-0.6B, a model actually trained for retrieval, running locally through llama.cpp. That costs users a one-time 640 MB download, which I was reluctant about, but the results on my own messy meeting data were good enough that I stopped second-guessing it.

Dotient v0.2.4 is a total UI upgrade.

UI

• New search bar design

• lower contrast sidebar to put less stress on your eyes

• feels very cozy!