A year ago, half the marketing world told us "AI search" was overhyped. The other half was shipping "ChatGPT SEO checklists" in a week.
We ignored both.
Instead we did one boring thing: we scraped LLM citations. Every day. Across ChatGPT, Perplexity, Gemini, Claude. For hundreds of brands. And we asked one question when AI recommends a product, where does that recommendation actually come from?
Here's what we found that nobody was talking about:
I formally studied marketing as a university program (5 years), and due to inspiration on social networks, it feels completely natural to do it, even easy to learn (because most of the time you just guess what might work for you).
We launched here back in 2024, which gave us a real platform to kickstart our journey, and the feedback from this community has genuinely shaped how we built Inr .
I've always been on the personal brand side. More and more founders are building it now (sometimes even before the product is ready while it's still in development, before seed fundraising). The CEO builds their position so the product sells more easily at the official launch.
But I have experience with people who built the product, scaled it, and only then did we discover who was behind it.
Honestly, with the first approach, I'd be concerned that people invest more in me as a person than in the product. People would idealise the founder and overlook the product's flaws (which could hurt development and constructive feedback).
+ I noticed the most common mistake that many people who started building a personal brand first, connected their product to their personal accounts (emails, social media, etc.) and started having a problem selling these things, because they cannot "give someone keys" to their personal profiles.
Hey everyone! With the landscape for building voice agents shifting lately, it feels like we re moving away from heavy, manual API orchestration toward something more streamlined.
How you re currently architecting voice agents. Specifically: Have you used the Model Context Protocol (MCP) to build or provide real-time data/context to your voice agents? Does it actually streamline your tool-calling, or is it more trouble than it's worth?
Would love to hear what's working (and what's breaking) in your current workflow. Drop your thoughts below!
In a discussion forum with @monatruong_murror , we talked about how AI can help us learn things that aren t naturally familiar to us, like programming.
The biggest challenge was/is: Getting AI to guide you toward a solution, instead of just giving you the answer.
Last week Garry Tan (CEO of Y Combinator) shared his entire Claude Code setup on GitHub and called it "god mode."
He's sleeping 4 hours a night. Running 10 AI workers across 3 projects simultaneously. And openly saying he rebuilt a startup that once took $10M and 10 people. Alone, with agents.
Let me start from the creator s perspective: I personally don t have a product (apart from hiring people for creative work or offering personal consultations).
But as a creator, I constantly share content, insights, and information, value that helps me build trust (for free). Based on that perceived expertise, people eventually decide to work with me (a paid service).
Today, I m doing a slightly more relaxed and bizarre corner.
The internet is full of things that are either amusing or scary, but mostly things that capture something outside the norm (and over time, even these weird things tend to become normalised).
Today, the productivity domain in tech is very well developed - there are tools for almost any need!
But at the same time, there s always a feeling that there might be something else, something better. All the time.
What I like about this space is that once people start using tools like Miro, Notion, Trello, ClickUp, etc., they tend to keep testing new things and experimenting with different tools.
AI is everywhere right now - from copilots and chat assistants to analytics, research, and planning tools. But beyond the hype, I m curious about what s truly useful in day-to-day product work.
From a PM or founder perspective:
Where has AI genuinely saved you time?
What tasks do you trust AI with - and what do you never delegate?
Has AI changed how you write specs, manage roadmaps, or talk to users?
What AI use cases sounded great in theory but failed in practice?
Personally, I see a lot of potential, but also a lot of noise. I believe that in the future, AI should help us much more. Create good roadmaps, convert product specs into concrete tasks, prioritise them, assign people, push for realisation, and much more.
