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Hey everyone!

Got an email from a user wanting to switch from Plausible and asking about data migration. Instead of just answering, we shipped it.

This meme made me laugh because it is painfully real.

We built a model to generate 1,000 questions that people actually ask.
Not random prompts.
We scraped 50,000 real user queries from search logs, forum threads, and support tickets across 12 industries.
We clustered them by intent and generated 1,000 representative questions.

We asked those same 1,000 questions to 5 AI models: ChatGPT (GPT-4), Gemini (Ultra), Perplexity (Pro), Claude (4.5 Sonnet), and Llama (3).
We ran the experiment daily for 30 days. We tracked every citation at the source level.

The goal: measure citation overlap.
How often do these models cite the same source for the same question?

The dataset:

Hi everybody, have a great Friday

Just wanted to thank you all for the support over the launch of Toone this week couple days ago!

Hey, I was wondering how many of you are coding from the phone - it's easier to do it with AI coding agents now, so I'm wondering how often developers are doing it.

Personally, I do some coding tasks daily from my phone and I'm curious how others are doing it?

If you have 20 minutes to spare today, I highly recommend you watch this talk by Rory Sutherland.

I was completely engrossed for the full 20 minutes because he delivers so many interesting insights.

The builder internet has one dominant religion: ship fast, learn fast, iterate. And honestly? It's mostly right. I'm not here to argue against iteration.

But I've been noticing a pattern in products that actually lasted and it's uncomfortable: A lot of them were embarrassingly slow at the start. Not because the founders were lazy but because they were obsessive about the wrong thing to ship first.

Figma spent years just making the multiplayer cursor work flawlessly before talking about anything else. Notion had a tiny, nearly unusable v1 that they kept showing the same 500 people. Linear said no to mobile for two years while everyone said they were crazy.

You built something remarkable.

The code works. The design sings. Customers who find you, love you.

But here's the problem AI will never just know.

Unlike Google, which crawls everything and figures it out eventually, AI learns from patterns. And if your product doesn't fit those patterns, you simply don't exist.

Road to 1,000,000 Votap users Day 63 | Current: 1346
Soon you ll be able to tell your fellow citizen to go to hell
or, ideally, have a proper debate. I m about 90% done building a dedicated thread system for Votap.
You comment on a politician or an article.
Someone replies you get notified (push + in-app).
You tap it and land straight in a thread.
You ll see:
the original comment that started it
your conversation
and the full back-and-forth
No more lost replies. No more chaos. Just actual discussions.
(And yes sometimes that might include telling someone to go to hell )
Update coming soon.
Download Votap from the App Store!
More tomorrow.

Yesterday I showed you how to audit your AI visibility. Today I'm going to show you exactly what to do with those findings.

After analyzing 50,000+ AI answers at Rankfender, we've identified clear patterns. Certain content types get cited 3x more often than others.

Here are the 7 content types that win AI citations with real examples you can steal.

First, the data:

What are the alternatives to Stripe?

Paddle

got rejected

Apple is reportedly pushing back on AI vibe coding apps like @Replit and @Vibecode App, tools that let users create apps just by typing prompts.

The issue isn t a new rule. Apple is enforcing an old one:

• Apps must be self-contained

• They can t download or run new code that changes functionality after review

Launched last week, open-source frontier model @MiniMax M2.7 scores 56.2% on SWE Bench Pro, converging towards the best proprietary models like @Claude by Anthropic Opus 4.6.

I've been thinking a lot about what separates AI products that people actually stick with from those they try once and forget. The pattern I keep noticing is that the ones that win aren't necessarily the most powerful they're the ones that feel like they understand your context.

Think about it: most AI tools today are essentially fancy command lines. You give them an instruction, they spit out a result. But the products gaining real traction are the ones that remember what you care about, adapt to how you work, and meet you where you are emotionally not just functionally.

We just shipped Krisp MCP as a connector in Claude, and it's changing how we think about this. Instead of treating meetings as isolated events, your AI assistant can now search transcripts, pull action items, and reference meeting context on the fly.

The bigger question I keep coming back to: what's your system for turning meeting context into actual work? Would love to hear what's working (or not) for others here.

New update brings a few minor fixes. Mostly documentation and under-the-hood stuff.

Changed

• Added books section to REFERENCE and README documenting directory structure, chapters, manuscript, and workflow.

• Added Ctrl+M (compile manuscript) to the README keybindings table.

After launch day, something interesting happens. You start seeing clearly all the things you should have done before it.

The most common regrets I hear:

Hey Product Hunt!

We ve all been there. You design a beautiful, complex layout in Figma, only to have it die a slow death during the "handover." Either the developer says it s impossible, or you spend three days fighting with nested tables and inline CSS to get it to look half as good in an inbox.

Some of the most inspiring startup journeys of the last few years happened in plain sight.

@levelsio built Nomad List and Remote OK live on Twitter sharing revenue numbers, failures, and pivots in real time. @marclou does the same, shipping products publicly and turning his audience into his distribution. Both have built massive followings and real businesses partly because of how openly they build.

Tomorrow we're back on Product Hunt with something big.

Last time we launched, you gave us #7 Product of the Day and 488 upvotes. That meant everything to us.

Questions:
-> Ubuntu + x86_64 only. No ARM, no Mac (unless you run a VM) that cuts out a big chunk of hobbyists and researchers right away
-> Prompt sensitivity is a real concern if vague prompts give generic results, the "just describe it in plain English" pitch needs some asterisks
-> Multi-robot setups are experimental, which is a big gap for anything beyond single-robot prototyping
-> Requires internet to process every prompt so your AI copilot goes dark without a connection, even for local sims
-> Still early (v1.0.15 beta) rough edges are expected, but production teams should be cautious
-> The 60% stat frames simulation as a problem to skip, not a skill to build engineers who skip that struggle badly at sim-to-real transfer
-> Terminal-only, Gazebo-specific workflow is a hard limit if your stack uses Isaac Sim or Webots
-> And any proprietary AI agent in your core workflow = vendor lock-in risk
Curious how it handles URDF validation and collision mesh accuracy?
None of these are dealbreakers for students or solo researchers. But for engineering teams evaluating it seriously, these constraints matter.
Answers:
1. Our initial reach is for robotics developers who already use ROS and simulators, so they are already on linux. Most of the robotics frameworks are optimised and built for linux. You will have a hard time figuring things out on Mac with the all the open source tools and libraries.
2. Definitely. The more articulate you are with explaining what you want, better drift will run. "describe in plain english" is to communicate that this is better than before where devs have to hustle on their own.
3. Yes, and that is why we are solving it, and making this better.
4. We are working to bring integration for local deployments.
5. We are monitoring all bugs and crashes in production.
6. The skill to master simulations is not to be skipped at all, we want to accelerate the skill up instead with Drift, so that engineers actually focus on the physics and behaviour of robots in the sim.
7. That's why we are bringing MuJoCo, Isaac Sim and other simulators and plugin support really soon.
8. If the vendors are getting your work done from months to days, that is not a worry. And we will always have competitors so you have plenty of options! :)

a lot of AI coding discussions focus on before or after

March was a big one for @Pretty Prompt.

Did you know we don t follow a strict roadmap?

We take a more "nimble" approach:

~2x faster conversion while the others designs all floundered. That was the result of simulations doing the talking while building password only access for Vibe Check instead of arguing in circles.

"Failing" designs cost the collective time of every user and then again by a Product team working on needless iterations. We ve baked this simulation first mindset into our new tool, Vibe Check. We launch on Product Hunt next week! Support us and ensure actual conversion on your next product designs.