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Building AiSDR has made me rethink how much of sales can be solved by automating the tasks.

We started with prospect research, writing outreach, and handling replies. But I kept joining customer calls to help answer more basic questions: Who should we target? What should we offer? Why would someone care right now? What should we change when nobody responds?

One interesting finding from our database is that the number of competitors appearing in AI answers does not determine a brand's share of voice.

In one category with 1,868 named brands, a single brand held 21% share of voice. Meanwhile, two brands competing against fewer than 400 held under 8%.

Three weeks ago I posted a list of things I learned on launch day. I left one off, because I only noticed it after the dust settled.

Almost every good thing that happened to Construct came through someone we already knew.

Not cold DMs. Those mostly went nowhere. It was a friend of a friend of a friend, and the ask worked because the person in the middle could say "yeah, I know them, they're legit."

Building projects requires collaboration.

Share your models directly from Autonomyware.
Turbofan Engine

Confession time. MCP was one of the last things we shipped before this launch, almost a last minute decision. And honestly, I now think it s the most valuable part of k dlo.

Here s why. Ever since we started showing k dlo to people, we kept getting the same question. Why would I use this if I already have Claude Cowork / ChatGPT / my own agent? Fair question. We tried to answer it by explaining the architecture, the shared memory, how all the agents work from the same context. People nodded, but it didn t really land. Nobody gets excited about architecture diagrams

With MCP, they don t have to. You just plug k dlo into the tool you already use and feel the difference right away.

People already sell with their own AI every day. What they re missing is the context. k dlo keeps everything about your leads in one place, so it doesn t matter if you work in Claude, ChatGPT or a coding agent. That context goes with you, and the selling you already do just gets bigger. On top of that, k dlo gives you what a chat window can t, a real place to list your leads, sort them into campaigns and work on docs and everything else a deal needs.

Every time I hit a small problem, I face the same choice: find a tool that already does it, or build my own quick fix. Building feels exciting, but I m not sure it s the smart choice.

Last month I spent a whole weekend setting up my own system to track my reading list. It worked, but a free app I d skipped would have done 90% of it in ten minutes. I still can t tell if the weekend was wasted or if I learned enough to make it worth it.

It made me wonder if I ll just build it myself is often a way of avoiding the boring work of searching for what already exists.

How do you decide between building and using something that already exists? Has building your own ever paid off, or did you end up wishing you d just used the existing tool?

Hey everyone,

Andriana here, co-founder of k dlo. Today is our launch day and I just wanted to stop and say thank you.

Every upvote, comment and question has meant so much to our small team. We read all of it, and your feedback is already shaping what we build next. Honestly, this is the most valuable part of launching for us!

The day isn t over yet, though, and we re still pushing. If you like what we re building, an upvote and a share with a friend or your team would help us a lot in these hours

Hey makers,

Ever had your fans kick in and no idea why? Today's update answers that from the notch.

In the first half of 2026, 3,869 products shipped on Product Hunt. We ran the whole stream through LinkedIn and Crunchbase to see what people launch, who the founders are, where they came from and what actually improves your odds.

Part 1. The landscape: the AI wave and the dilution of attention

Hi everyone,

UX HeatGrid 2.0 launches on Product Hunt today. It is a Chrome extension that opens in the side panel and shows you any page from two angles: what its structure makes stand out, and how it is actually used.

Predict

ReSO AI is at #1 right now and we re barely a few hours away from Product Of The Day!

ReSO is a generative engine optimization platform that helps brands show up in AI search results. It s built for founders, CMOs, and growth teams who want to turn buyer intent in ChatGPT, Perplexity, and Google AIO into visibility, citations, and traffic.

If you believe LLMs are reshaping search behaviour in real-time, please support us on Product Hunt. We absolutely value your honest feedback on what works, what feels unclear, and you d like to see next.

biggest upgrade since launch. until now your AI could only read about the videos on refs, now it sees them

new

frame sheets: a real frame for every shot in all 3,138 videos, plus hook sheets of the first 3 seconds

Something pretty important changed in AIRUNCODE.

The model is becoming interchangeable.

Claude, GPT, other providers, an API endpoint, Chinese models, or a model running locally on your own machine can now operate inside the same environment and get access to the same AIRUNCODE tools.

That means you don't need a frontier model burning expensive tokens for every stupid little step of a workflow.

A couple of my recent posts led to good conversations. Now I m trying to figure out what gets someone from reading about a product to actually trying it.

For those who ve launched something: where did your first users come from? Were they following what you were building, or did you find them somewhere completely different?

I m getting Inquir Compute ready to launch. It s a platform for running apps, databases and background jobs. I ve been sharing what I m working on here, but I m still figuring out how to reach the people who might use it.

Share your product and one specific thing you did that brought in users.

@Mac Duo 1.1 is out, with a few changes focused on making the fold feel more natural while keeping Mac Duo lightweight when you're just using your Mac normally.

One brand went from zero AI visibility to 22% share of voice across 180 prompts in 90 days, with structured data, AI-readable product pages, and a few technical fixes.

I've been watching how brands measure SEO success, and the gap between what gets tracked and what actually drives decisions is widening. Traffic and rankings still dominate most dashboards. But in a world where over 60% of searches end without a click, those numbers tell an increasingly partial story.

Hi everyone,

Thank you again for the support on launch day. Ghostifier finished #9 with 111 upvotes, and I m still reading every comment.

I just set up an Instagram account, @ghostifier_app, to share what we re learning as we build:

The ranking is definitely exciting, but the BTS, the months of build-up, the entire village that took off to create this one product in one cramped room, and now it is available to such a HUGE audience is absolutely amazing. 

What really stood out to me is how people are actually concerned about the actual problem where LLMs are actually taking charge of search.

The best feedback has come from people who d never heard of us before.

Maker update: five recent Termexo releases focused on a practical question for people running several coding agents: which terminal needs attention, and what can you trust when you come back to it?

Agent status now follows the actual lifecycle. In v0.10.8 I checked Claude Code, Codex, OpenCode, Grok Build, and Antigravity against their installed CLI behavior. A late startup or idle event no longer overwrites a newer running state. OpenCode 2 and Grok Build also received updated integration paths for execution, input requests, failures, and completion.

v0.10.9 puts compact status icons in workspace order: gray for idle, animated green for running or thinking, red for errors or approval requests, and a green check for completion. Hovering shows the terminal's current name, including renames. Completion is a reminder to review the result: open the terminal, check output and code changes, then use Confirm completion to clear it.

Founders launch on @producthunt with one goal: Rank as high as possible, but the first question should be: what is your launch objective?

Maybe your goal is to:
Test an idea and see if there s real interest

• Get more traffic

• Get new users

• Increase conversions

• Get feedback

• Make people try your free plan

• Get your first paying customers

• Build awareness around your product

• Get backlinks ...

Depending on your goal, the way you prepare your launch and how you measure if it was successful can be completely different.
For those who launched on Product Hunt: what was your main goal, and did you achieve it?

DungeonQ is designed to give security teams time and space to investigate and respond while a designated suspicious session works inside a persistent synthetic world.

The current public source now makes both sides easier to operate:

1. Participant workspace: read an order, save a multiline review note, read the saved value back from the server, and use a world-only ticket for a bounded read.

2. Control room: with separate operator authority, inspect the same world records, successful observations and a request timeline correlated with route evidence. A participant's account of what happened is not the operator's only source.

We launched CoWork, a mobile app testing solution, on PH a while back and got a lot of useful feedback. Some of the most interesting comments came from developers who wanted to test their mobile apps before the merge, not after.

So we built QApilot MCP, that lets Claude, Cursor or Codex drive a real Android device. It launches Saturday.

HtmlDrag is back on Product Hunt today.

Once you have an existing or AI-generated HTML page, there are often a few things left to fix: an image, some text, or part of the layout.

I built HtmlDrag to make that editing pass more direct. Upload or paste your HTML and make visual adjustments. When you want AI s help, select the elements you want to work on, describe the change, and review the result.

I think finding good design references is the easy part. Figuring out what to take from them is much harder.

For example, you find a dashboard with a really clean layout. But is it good because of the spacing, the information hierarchy, or simply because it has less data than your product?

I've been thinking about a few things worth checking before borrowing a design pattern:

• Context: What problem is this design solving, and is it similar to ours?

• Structure: Does the layout still work when the content gets longer or more complex?

• Interaction: What happens in empty states, errors, or when users need to go back?

• Tradeoffs: What did this product simplify or leave out to make the experience feel clean?

I ve worked in investing and trading since 2008, and one problem has followed me throughout that time: investors have access to more data than ever, but turning that data into a clear decision remains difficult.

That s why I built Stocks 2 Buy, a platform designed to help investors: