Product Hunt Launch Secrets with Rajiv Ayyangar: From Tagline to PMF

If you’re building something in the AI wave right now, Product Hunt is still one of the best places on the internet to get in front of early adopters.

In this episode of AI Insights San Francisco, I sat down with Rajiv Ayyangar, CEO of Product Hunt, to unpack real, practical Product Hunt launch secrets, not hacks, not gimmicks, just what actually works for founders trying to launch, find PMF, and grow.

Why Product Hunt still matters for builders

Product Hunt started as a daily leaderboard of new products. Today, it’s:

  • A launchpad for new tools

  • A discovery engine for what the best builders are using

  • A community with Shout Outs and Product Forums where makers trade tips and feedback

Rajiv describes it as “a place where you realize you’re not the only weirdo in your friend group building things.”

“A Product Hunt launch is an open game anyone can play, no matter where you live.”

If you’re working on an AI product, dev tool, SaaS app, or anything in that space, your launch there is basically your public dress rehearsal for the rest of the internet.

No virality without clarity

Rajiv is blunt about the first rule:

“There is no virality without clarity.”

On Product Hunt, your 5–7 word tagline is doing almost all the heavy lifting. People scroll fast. If they don’t understand your promise in one glance, they move on.

“Those five to seven words in your tagline might be the most important copy you ever write.”

Founders love to say they’re building “an AI platform for everything.” That’s not a tagline; that’s a fog machine. Clear and specific always beats grand and vague.

Ask yourself:

  • Can a stranger repeat your one-liner to a friend without messing it up?

  • Would someone in your target audience instantly know whether it’s for them?

If the answer is no, you’ve found your first bottleneck.

Promise-market fit vs product-market fit

One of my favorite ideas from the episode is how Rajiv splits PMF into two stages:

  1. Promise-market fit – Do people get excited by the promise you’re making?

  2. Product-market fit – Does the actual product deliver on that promise?

“Promise-market fit is your ability to make a clear, compelling promise; product-market fit is your ability to keep it.”

A successful Product Hunt launch hits both at once:

  • The tagline + description make a sharp, specific promise

  • The product, once people try it, feels like it actually does that thing

If your launch gets attention but nobody sticks, you have promise-market fit but no product-market fit. If people love the product but nobody tries it, you might have the reverse.

How to find your one-liner (especially for AI products)

Complex AI tools are notoriously hard to explain. Founders want to say:

“We’re a flexible AI platform for end-to-end orchestration across agents, workflows and knowledge graphs.”

Cool, but nobody knows what that means.

Rajiv’s take:

“Great one-liners are earned in the wild, not invented in a vacuum.”

Some tactics that came up in the conversation:

  • Launch small and ask users:

    • “How would you describe this to a friend?”

    • “What’s the main benefit you get from it?”

  • Listen for what sticks. The words your users use are more valuable than your internal jargon.

  • Be concrete. “Virtual office for remote teams” or “apps from Google Sheets” are simple but powerful.

“If I can’t explain your product to a friend in ten seconds, it won’t spread.”

If you’re working on an AI tool, your first win is not “AGI-level intelligence.” It’s “I never have to do X by hand again.”

AI as a platform shift (and what that means for your launch)

Rajiv compares this moment to the early mobile era:

“AI is a platform shift, but the fundamentals of listening to users and shipping don’t change.”

Every product category now has an “AI-native” version emerging. But that also means noise. Launching just another “AI thing” won’t cut it.

Your edge:

  • A sharp problem statement

  • A focused use case

  • A community that actually cares about that problem

Product Hunt is less about “AI magic” and more about “Does this solve something real for me, today?”

Using Product Hunt like a laboratory

One thing I love from Rajiv’s mindset: treat your Product Hunt launch as an experiment, not a verdict.

“Founders overestimate how polished they need to be and underestimate how fast they need to launch.”

Instead of waiting until everything is perfect:

  1. Get a working version.

  2. Shoot a simple demo video: screen share + voiceover showing how it solves the problem.

  3. Launch, listen, and iterate.

On the video specifically:

“Your launch video doesn’t need Hollywood polish; it needs to show the product solving a real problem.”

Builders on Product Hunt want to see the tool, not a brand film.

Real Product Hunt launch secrets (the simple version)

Here’s the distilled playbook from the episode:

  1. Start with the tagline.

    • 5–7 words

    • Clear, specific, tangible outcome

  2. Write a promise users actually want.

    • “AI note taker for meetings” beats “Intelligent productivity platform.”

  3. Record a straightforward demo.

    • Loom-style is fine. Show the UI. Talk through the use case.

  4. Activate your community.

    • Tell your email list, Discord, Slack, WhatsApp groups you’re launching.

    • Ask people to comment, ask questions, and share their honest feedback.

  5. Be present on launch day.

    • Answer comments

    • Clarify use cases

    • Treat it like a live user interview marathon

“Talk to people who tried a similar idea and failed; you either find a blind spot or deepen your conviction.”

And after launch, don’t vanish. Update your product, respond in the Product Forums, and keep refining your promise based on what people actually cared about.

Scaling what you built (when AI wrote half your code)

We also got into the reality of building with AI coding tools:

  • It’s easier than ever to ship an MVP

  • It’s still hard to maintain a large codebase

Rajiv mentions a wave of tools emerging for navigating and refactoring big codebases. The point for founders:

  • Don’t assume “AI built it” means you’ll never touch it again

  • Do think about how you’ll maintain, debug, and scale if your Product Hunt launch takes off

Your Product Hunt launch secrets don’t stop at launch; they extend into how you keep the thing alive once people actually care.

The deeper mindset: launch sooner, iterate in public

At the end of the episode, Rajiv brings it back to something simple:

“Great products are made through iteration, and you can’t improve if people never see it.”

This is the real meta-lesson:

  • Ship earlier than feels comfortable

  • Use Product Hunt as a feedback engine, not just a trophy shelf

  • Stay close to users in the forums and DMs

  • Let your tagline, product and roadmap evolve as reality pushes back

“Your first launch is not your final judgment; it’s your first real data point.”

“A quiet launch that teaches you something is better than a perfect idea that never ships.”

If you remember nothing else from this post, remember this:

“Launch sooner than you think – the internet can’t help you until your product is out in the open.”

How to use this episode

If you’re prepping a launch right now, here’s a simple way to use this content:

  1. Watch the full AI Insights San Francisco episode with Rajiv.

  2. Write three different taglines for your product.

  3. Show them to 5–10 potential users and ask which one they’d repeat to a friend.

  4. Ship a small version of your product.

  5. Launch on Product Hunt, then hang out in the comments all day.

Then come back and improve. That’s where the real Product Hunt launch secrets pay off.

If you want more notes like this from AI founders, builders and platform leaders, you can:

AI for Agricultural Lending, From 60 Days to 5 Minutes with Agxes

What this conversation is about

If you work anywhere near agriculture, finance or climate, you have probably heard a lot of buzzwords already. So let’s keep it simple.

This conversation is about one core idea

Right now, many banks take around sixty days to decide on a single farm loan

With the right mix of data and smart workflows, that decision can drop to under five minutes

That is what AI for agricultural lending looks like in real life. Not a robot in a field, but better, faster and more transparent credit decisions.

In this episode of AI Insights San Francisco, I sat down with Victoria Tostado Bringas, CEO and Co Founder of Agxes, to talk about how her team is rebuilding the way lenders look at farmers. We dig into:

  • Why agricultural loans are so painful to underwrite

  • How Agxes turns messy farm data into clear explanations for lenders

  • How this helps small and medium farmers finally show up on the radar

  • What new regulation means for lenders and why technology is no longer optional

It is a very grounded view of AI for agricultural lending, told by someone who has been both a grower and a founder.

What is AI for agricultural lending, in plain language

When I say AI for agricultural lending, I am talking about a system that:

  • Collects structured and unstructured data about a farm

  • Understands what that data means for risk and cash flow

  • Translates it into something a loan officer can read and trust

  • Documents every step so the decision is transparent and repeatable

Victoria describes it like this

“We are combining the raw data. We are making sense of unstructured data to analyze the farm, then we explain that to a person at a bank. We are using language models to explain in simple words what you are seeing in a satellite image, or what you are seeing in a market report.”

So the goal is not to replace people

It is to give lenders a much better view of what is actually happening on the land, without needing a room full of agronomists and data scientists.

Why farm loans are so slow and frustrating

Agriculture is one of the hardest sectors to underwrite. There is a lot going on:

  • Different crops, cycles and regions

  • Seasonality and weather

  • Water access and soil conditions

  • Market prices and logistics

Victoria explains the problem from her own experience as a producer

“Agriculture is one of the hardest loans to process because of the amount of information you need to analyze. You do not only analyze the farmer. You need to analyze the business, the operations and the cash flows. If you produce bananas and I produce strawberries, our cycles are different, our businesses are different, even if both are in the same region.”

Now imagine a lender with limited staff and time. They have a choice

They can spend sixty days analyzing a giant corporate client

Or sixty days analyzing a small or medium farmer

“They say it is going to take me sixty days to analyze Nestlé and sixty days to analyze Victoria. I will put my effort into the huge producer and disregard the small farmer because I do not have more time.”

This is where a lot of farmers become almost invisible to the system.

The communication gap, lenders vs growers

There is also a basic language problem between both sides.

Lenders speak in:

  • Ratios

  • Credit history

  • Compliance rules

Growers speak in:

  • Seeds

  • Fertilizers

  • Water, soil, equipment, harvest

Victoria captures this perfectly

“The lenders speak finance and the growers speak operations. They speak tons, fertilizers, trucks. It is hard for finance people to understand how this information translates into cash flows.”

So farmers end up doing a mountain of paperwork and explaining their operation from scratch. Even then, the lender might still not really understand how that translates into predictable cash flow and risk.

This is exactly the kind of gap AI for agricultural lending can help close, by turning operational signals into financial language.

How Agxes works behind the scenes

Agxes sits at the heart of this problem with an AI engine and a set of workflows that mirror the jobs humans do today in a lending team.

Some key ideas from Victoria

  1. Making sense of messy data

    Agxes pulls in raw data, including satellite imagery and operational information, and turns it into something structured and readable.

    “We are combining the raw data. We are making sense of unstructured data to analyze the farm. We explain to the lender what they are seeing in a satellite image or a market report.”

  2. Using deterministic models, not guesses

    In lending, hallucinations are not an option.

    “We are doing a deterministic model. We are using real information and real cases, and that is how we are avoiding these potential scenarios with hallucinations.”

  3. Standardizing decisions and documenting every step

    Today, a lot of decisions depend on the individual loan officer. That creates inconsistency and room for bias.

    “With a system like Agxes, you follow a process that is standardized. You can demonstrate at every single stage how the decision was taken and what the thinking process was behind it.”

This is where AI for agricultural lending really shows its value. It is not just faster, it is also more transparent and more consistent.

From KYC to “know your farmer”

Most financial people know the term KYC, know your customer.

Victoria reframes it for agriculture

“We call it the cliff instead of KYC. We call it know your farmer.”

Instead of only looking at bank statements, legal documents and credit history, Agxes looks at:

  • Operations on the farm

  • Seasonality and cycles

  • Water access

  • Crop type and production reality

That means the lender is not only judging a farmer by their past financial record, but by the real potential of their operation.

This is a big shift in how risk is viewed and how opportunity is measured.

Regulation is pushing lenders in this direction

Even if a lender is not actively looking for innovation, regulation is moving in.

Victoria mentions a new policy that will require lenders in the United States to collect more than twenty data points for every small and medium business loan, including agriculture.

Done manually, that would mean:

  • More forms

  • More people

  • More time and cost

Agxes starts at the very beginning of the process and tracks everything through servicing.

“We step in since the know your farmer process. We collect all the data from the beginning to the servicing part. Everything is documented and standardized.”

This is another strong argument for AI for agricultural lending. It is not only about speed and accuracy, it is also about surviving the next wave of compliance requirements.

Fairness, bias and who gets funded

One part of the conversation that really stood out was around fairness and bias.

Victoria points out that women often get worse conditions when it comes to credit

“It is very well studied that women have less attractive conditions for loans. Women pay higher interests than men.”

When decisions are documented and the same rules are applied to everyone, it becomes much harder for hidden bias to drive outcomes.

“If now you can apply the same rules to every farmer and prove that through the system, you do not have that problem anymore and everything is transparent.”

So AI for agricultural lending is not just a technical upgrade. It is also a chance to rebuild trust and fairness into the system.

What changes for small and medium farmers

If platforms like Agxes scale, here is what it can mean in practice:

Faster answers

You do not wait months to know if you can plant, expand or invest.

More visibility

You are not automatically pushed aside just because you are not a giant corporate client.

Better storytelling

Your real operation, from soil to harvest, shows up inside the decision, not only your bank statements.

More confidence for lenders

They see a clear, documented path from data to decision, which makes it easier to back farmers they might have avoided before.

Victoria sums up the goal beautifully

“Farmers and regular people do not have to be technical. They just have to be good at their work. They can be seen and valued for their potential, not just as a sign of risk.”

That is the promise of AI for agricultural lending when it is done right.

Final thoughts

Agriculture has been around as long as humans. Finance has been around for centuries. Artificial intelligence is the new kid in the room.

The real magic happens not when we chase another buzzword, but when we combine all three in a way that actually works for people on the ground.

This conversation with Victoria is a clear example of that. AI for agricultural lending can:

  • Cut underwriting time from sixty days to minutes

  • Help lenders handle new regulation without burning out their teams

  • Bring millions of small and medium farmers into view

  • Build a more transparent and fair credit system

If you want to go deeper, check out:

  • The full episode of AI Insights San Francisco with Victoria Tostado Bringas

  • Books by Vaclav Smil on how we feed the world and how food systems evolve

  • Agtech and fintech resources that look at both tech and finance, not only machines in the field

  • https://www.agxes.com/

Because sometimes the most important innovation is not a robot, it is simply giving one lender a clear, honest view of one farmer and letting a good decision follow from that.

AI Filmmaking, for Real — Inside My Conversation with Matty Shimura from ElevenLabs

In this episode of AI Insights San Francisco, I had the chance to sit down with Matty Shimura, one of the leading voices connecting creativity, storytelling, and artificial intelligence. Matty currently leads Creator Competitions at ElevenLabs, where he’s helping run the Chroma Awards — an ambitious global challenge bringing together filmmakers, musicians, and game creators under one roof.

But our conversation wasn’t just about technology. It was about what remains human in this new era of AI-powered filmmaking.

From Filmmaker to AI Trailblazer

Matty’s story starts in traditional cinema — over 17 years of filmmaking experience, from short films to VR storytelling. When he discovered the new generation of AI tools like Stable Diffusion, it unlocked a new creative lane.

That spark led to Project Odyssey, which quickly became the world’s largest AI film competition by number of submissions. The success of that initiative evolved into the Chroma Awards, now backed by ElevenLabs and partners like ByteDance, Gemini, and Andreessen Horowitz.

As Matty put it, “It’s like running the Olympics for creators.”

Chroma brings together film, music, and game creators worldwide — and the best part? Anyone can enter, regardless of budget, background, or geography.

The Craft Still Wins

What stood out to me is how Matty repeatedly returned to craft. AI tools evolve daily, but the fundamentals of storytelling haven’t changed: framing a shot, pacing a scene, designing sound, and understanding emotion.

“The ones who really excel,” he said, “come from traditional creative backgrounds. They know how to tell stories — and they use AI to reach the precision they used to spend hundreds of hours achieving in VFX.”

AI filmmaking, in his words, isn’t about skipping the process — it’s about accelerating creativity while keeping the human touch in the loop.

Building Competitions Like Startups

Running these global competitions isn’t easy. Behind every open call are thousands of moving pieces — marketing, partnerships, community management, customer support, and education.

“It’s like running a startup within a startup,” Matty laughed. And it really is. The Chroma Awards are not just contests; they’re ecosystems where companies and creators learn from each other.

I found this part especially relevant for anyone building AI products today: community and competition are fast feedback loops for innovation. They push tools and creators to evolve side by side.

Judging, IP, and Ethics in AI Film

Matty also shared how Chroma tackles one of the most difficult questions in AI art — what’s fair, original, and ethical?

Each submission is human-reviewed, not ranked by views or upvotes. Every piece is scored across clear criteria: production value, sound design, creativity, and adherence to category guidelines. The top 25 per category move to a final round judged by domain experts — from filmmakers to composers to game designers.

On the topic of intellectual property and likeness, ElevenLabs enforces a strict standard. No brand logos. No unlicensed likeness. No shortcuts.

“We want people to create something they couldn’t have made before — not just a rehash of Star Wars or Wes Anderson,” he said. It’s about originality over imitation, a principle I deeply agree with.

Voice, Sound, and the Future of Storytelling

As someone who also uses ElevenLabs tools in production, it was exciting to hear what’s next:

  • Voice cloning for multilingual production and quick fixes

  • Text-to-sound effects and music APIs for faster workflows

  • New emotion and intonation controls that finally make AI voices sound truly human

For me, this is where things get powerful — AI doesn’t replace the creative process; it multiplies it. When used well, it helps creators scale their storytelling, not just their output.

The Human Element Never Leaves

Toward the end, Matty reminded me that filmmaking, even when powered by AI, remains a collective act of creativity. It’s not about pushing buttons alone at home — it’s about community, collaboration, and shared imagination.

“Creativity is part of human nature,” he said. “It doesn’t matter if you’ve never made a film before. Just start.”

That line hit me. Because beyond all the hype cycles and tech trends, that’s what this entire movement is about: empowering people to tell stories again — faster, cheaper, and with fewer barriers.

🎥 Watch the full episode

“AI Filmmaking for Real — Matty Shimura, ElevenLabs” on YouTube

🔗 Links

 

AGI vs Human Intelligence – Which Will Drive the Next Wave of Innovation? | Chappy Asel, AI Collective

Couple of weeks ago on the AI Insights San Francisco podcast, I sat down with Chappy Asel—Co-Founder and Executive Director of The AI Collective—to dissect one of the biggest questions in tech: Can artificial general intelligence (AGI) out-innovate the human mind, or will our future be built on collaboration between the two?

Below is a written deep-dive for readers who prefer text over video, complete with timestamp references, standout quotes, and practical resources.

1. Why Chappy Calls AGI “Mankind’s Final Challenge” (0:00 – 1:45)

“Once we create an intelligence greater than ourselves, it can solve everything else for us.”

Chappy frames AGI as the ultimate engineering milestone—one that could automate scientific discovery, fix supply-chain bottlenecks, and accelerate cures for disease. But he also cautions that humanity must tackle trust, governance, and safety before the technology reaches runaway velocity.

2. The AI Collective’s Grassroots Origin (1:46 – 3:14)

What began as three friends debating AI in a San Francisco living room has exploded into 40 000+ members, 25 chapters, and 200+ events worldwide. Their secret? Micro-communities:

  • Demo Nights – 2-minute no-slide product demos with live feedback

  • Discussion Pods – themed breakout groups on policy, research, or applied ML

  • Singularity Fest – a city-wide festival slated for November, blending research, art, and hackathons

3. Trust Is the New Operating System (8:00 – 11:55)

Chappy argues that modern institutions—government, media, even healthcare—are experiencing an unprecedented trust deficit.

His remedy:

  1. In-person dialogue – nuanced conversation beats flame-wars.

  2. Open-source insights – translate event takeaways into public white-papers.

  3. Local ownership – every chapter elects volunteer organizers to keep incentives aligned with their city’s unique AI ecosystem.

4. Vibe-Coding & the Democratization of Creativity (11:55 – 17:39)

“Vibe-coding” is Chappy’s shorthand for LLM-powered app generators that translate plain-language prompts into runnable code. The gap between amateur and expert output is shrinking fast:

2015

2020

2024

Static templates

No-code editors

Prompt-to-production apps in minutes

For entrepreneurs, this means lowered barriers—but also a ticking clock. Waiting six months could mean your idea is already commoditized.

5. A Three-Part Playbook for the Next 12 Months (17:40 – 20:52)

  1. Events & Community – double the number of city chapters, triple the cadence.

  2. Institute – publish “frontier insight briefs” for policymakers and industry.

  3. Collective Investments – match founders with angels and VCs aligned on ethical AI.

6. How to Stay Ahead: Explore > Exploit (20:53 – 24:11)

Chappy’s rule of thumb:

Spend at least 50 % of your week in exploration mode—testing new APIs, reading cutting-edge papers, or brainstorming with builders. The rest can go to shipping product.

Two books he recommends for a macro lens:

  • Life 3.0 by Max Tegmark

  • The Singularity Is Near by Ray Kurzweil

7. My Takeaways

  • Relational advantage – Human-to-human trust will likely be the last moat machines can’t replicate.

  • Community flywheel – Feedback loops between local chapters and global leadership unlock compounding insight.

  • Execution window – In AI, a six-month roadmap is a long-term plan; iterate weekly.

Get Involved

Are We Trusting AI Too Much? | AI Insights San Francisco w/ Tiffany Saadé

I had the pleasure of recording an AI Insights San Francisco episode with Tiffany Saadé, a researcher at Stanford who lives at the crossroads of cybersecurity, public policy, and artificial‑intelligence ethics. Tiffany isn’t the kind of guest who stays in the safety of theory—she has helped draft her home country’s first national AI strategy and routinely red‑teams models for real‑world vulnerabilities. From the moment we hit “record,” I knew this would be a conversation that forces all of us—builders, policy folks, and everyday users—to slow down and ask: Have we started trusting AI a little too much?

Why “human‑AI teaming” can slip into dependence

We opened with a blunt observation: the efficiency of large models is intoxicating. Automating meeting notes? Great. Offloading scheduling? Even better. But Tiffany warns that, somewhere between “helpful” and “hands‑off,” we cross a line. If we defer our cognitive autonomy—our own judgment and skepticism—then we risk letting hallucinations, malicious prompts, or poisoned datasets steer the ship while we’re asleep at the wheel.

I could feel the room get quieter when she put it this way:

“Descartes said ‘I think, therefore I am.’ What happens when we stop thinking?”

Security by design… or by regret

Tiffany’s security background showed up in every example. She compared today’s AI rush to building a skyscraper without fire codes—impressive until the first spark. Organizations love the productivity boost, but ransomware, jailbreak prompts, and membership‑inference attacks are growing just as fast. Her advice? Bake in red‑team drills, data minimization, and explainability from day one—before a breach drags you into million‑dollar compliance failures.

Agents, feedback loops, and the vanishing human

When we drifted into agentic AI, Tiffany’s tone shifted from caution to outright concern. Multi‑agent systems swap data and refine one another’s behavior so quickly that a single glitch propagates before any human can hit pause. She’s researching ways to use agents for diplomacy and conflict‑resolution, but the same architecture can turbo‑charge misinformation or automate cyberattacks. The takeaway: “autonomy at scale” cuts both ways, and policy needs to keep pace.

A personal journey from Beirut to Silicon Valley

One of my favorite moments was hearing Tiffany describe leaving Beirut after the 2020 explosion and promising herself she’d return, armed with technology, security, and leadership. That promise now guides her Stanford work and her advisory role to Lebanon’s Ministry of IT & AI. It’s easy to talk abstractly about “global impact”; it’s rarer to meet someone turning that phrase into policy drafts and capacity‑building at home.

What startups (and the rest of us) should do next

Tiffany’s call to action is simple but demanding:

  1. Get an AI‑policy voice on your team early. Waiting until regulators knock is a losing strategy.
  2. Prioritize AI literacy—company‑wide. Engineers, marketers, and executives all need a shared language for risk.
  3. Think of security as a feature, not overhead. Users want speed, but they also want safety; build both.

Why this episode matters

Recording this conversation left me energized and a little uneasy—in the best possible way. I’m bullish on AI, but Tiffany reminded me that progress isn’t just bigger models and smarter apps. It’s also the messy, essential work of aligning incentives, guarding data, and making sure humans stay in the loop.

If you’re building with AI, regulating it, or simply curious about where the tech is headed, give the full episode a watch. I think you’ll walk away re‑examining your own “automation comfort zone” and, hopefully, adding a few guardrails before the next big leap.

Thanks for reading—and big thanks to Tiffany for bringing both expertise and heart to the table. See you in the next AI Insights SF episode.

Get Paid for Your Data: How MindMe Uses AI to 10× Personalization

“There’s a gap in how consumer data is collected, managed, used today.” — Archana

Traditional ad giants harvest data without clear consent, leaving users uncompensated and brands dependent on opaque algorithms. Apple’s ATT and Google’s cookie phase-out prove the model is crumbling. MindMe’s answer: build an AI-driven data marketplace where transparency, rewards and first-party insights win.

How the Marketplace Works

Opt-In Rewards for Consumers

  • Browser extension captures purchase & browsing signals (opt-in only).

  • Users earn cash, discounts & loyalty perks.

  • Full dashboard shows what’s shared—no hidden trackers.

Real-Time AI for Enterprises

  • Large-language-model layer segments audiences automatically.

  • Creative agents produce images, copy & product recommendations.

  • “Dynamic frames” (low-code widgets) serve the right content on site, email or SMS—boosting ROAS even as ad costs climb.

From Harvard to Shopify: The Founder’s Journey

Archana’s path: Harvard economics & neuroscience → McKinsey (blue-sky consumer tech) → Deliver (Shopify-acquired logistics SaaS). Across roles she saw one constant: customer data is the #1 value driver. MindMe emerged to give companies cleaner data and consumers a fair share.

Why Now? The Post-Cookie Privacy Wave

  • Apple ATT blocks automatic mobile tracking.

  • Chrome will drop third-party cookies.

  • Regulators push GDPR-style fines worldwide.

Brands need deterministic, permissioned data. MindMe supplies it—at scale—while keeping users happy.

Go-to-Market: Students First, Retailers Fast

  • Consumer side: 18-35 y/o students = privacy-aware + eager for passive income. Wait-list already “hundreds strong.”

  • Enterprise side: design partnerships with P&G, Athletic Brewing, D2C leaders—ready to convert once beta ends.

Funding & What’s Next

Fresh pre-seed round closed; priority is scaling the consumer dataset. Archana is open to investors who bring operational expertise in AI, martech or data privacy. Contact: archan@mindmedata.com.

The 5-Year AI Outlook

Archana predicts specialised agents will “10× every role”—but only if models train on ethically sourced data. She calls for bias checks, consent rails and transparent value-sharing.

Links

  • Join the MindMe wait-list → mindmedata.com

  • Apple App-Tracking Transparency overview

  • Google Privacy Sandbox timeline

Enjoyed the episode? Subscribe to the AI Insights YouTube channel or follow us on Spotify for weekly founder deep-dives.

🎨 When AI Meets Ink: How Matija Pavicevic Is Transforming Tattoo Art with Technology

How can we embed intuition into AI?

That’s the big question that kicked off my conversation with Matija Pavicevic, the founder of Art Nova Technologies—and honestly, it stuck with me. We’re living in a world where AI is reshaping industries left and right, but the idea of blending AI with something as personal as tattoo art? That’s next-level.

In this episode of AI Insights, I sat down with Matija to dive deep into his journey, his company, and how AI is making tattoos more creative, customizable, and, well… a little less permanent.

Meet Matija Pavicevic

Before diving into AI and tattoos, Matija took a pretty wild journey. He’s originally from Montenegro—a small, beautiful country in Southeast Europe—and started out studying economics and business. But life had bigger plans.

At 21, he packed his bags and headed to China to study business Chinese, with dreams of bridging business ties between China and Montenegro. He spent six years working in the renewable energy sector, but like many of us, the pandemic threw him a curveball.

That’s when Matija moved back to Montenegro before making another big leap—this time to San Francisco to pursue an MBA. And guess what? That’s where he caught the AI bug. 🤖💡

Art, AI & Tattoos: The Birth of Art Nova

So how does someone go from renewable energy to AI-powered tattoos?

During his time in San Francisco, Matija became fascinated with how generative AI could be used in creative spaces. He noticed a gap in the tattoo industry—people often get permanent tattoos without fully thinking them through (and end up regretting them). Plus, there wasn’t an easy way to test out designs before committing.

Enter Art Nova Technologies.

Matija and his team built a platform that lets users create personalized temporary tattoos using AI. Whether you want to test out a design before going permanent or just want something cool for a festival or party, Art Nova gives people the chance to experiment—risk-free.

Why Temporary Tattoos?

Let’s be real—tattoo regret is a thing. People get impulsive (especially in their early 20s), pick a design on a whim, and a few years later, they’re Googling tattoo removal services.

Matija’s solution? Let people test-drive their tattoos.

The AI lets users either design something from scratch or upload a personal image, then transforms it into a unique tattoo. You can try it out, see how it feels, and if you love it—maybe make it permanent. If not? No big deal. It washes off.

Plus, it’s not just about testing tattoos. People use it for:

  • Festivals & events
  • Parties (hello, bachelor/bachelorette vibes)
  • Fashion statements
  • Sporting events (yes, custom team tattoos!)

 The Tech Side (But Not Too Techy)

We kept the conversation easygoing, but Matija did share some behind-the-scenes stuff about how the AI works.

The biggest challenge? Embedding intuition into AI.

It’s easy for an AI to generate an image, but how do you make it feel personal? How do you make it capture someone’s vibe, their story, their emotions? That’s the mountain Matija’s team is climbing.

They’re constantly tweaking the system to make the designs more user-centric—and it’s working. The platform picks up on user preferences, helping create tattoos that actually mean something to the person.

 AI Won’t Replace Artists—It’ll Empower Them

One thing Matija made clear: AI isn’t here to take over art.

Instead, it’s here to help artists (and regular folks like us) get creative faster. The AI serves as a tool—a way to bring ideas to life quickly and experiment without limits.

And honestly? It’s kind of cool to think that AI can help someone bring a deeply personal design to life without the fear of “What if I hate this in five years?”

 What’s Next for Art Nova?

The company is pushing hard into e-commerce, making it easy for people to design, order, and rock their tattoos wherever they are. Plus, they’re adding features like:

  • Image Uploading – Got a design in mind? Upload it and turn it into a tattoo.
  • AI-Powered Customization – Blend real photos with AI-generated art for totally unique designs.
  • Data-Driven Styles – The AI tracks user preferences, highlighting trending styles like minimalism and realism.

Matija and his team also landed a spot in Microsoft’s startup program, giving them access to powerful tools to keep refining their product.

 Final Thoughts

Talking with Matija felt like chatting with an old friend who’s super passionate about what he does. His vision isn’t just about tattoos—it’s about self-expression, creativity, and making art more accessible.

Whether you’re someone who’s been too scared to commit to a tattoo or you just want something fun for your next event, Art Nova Technologies is making it easier (and safer) to experiment.

“AI won’t replace artists. It’ll help people express themselves in new, exciting ways.” — Matija Pavicevic

Curious to try it out? Check out Art Nova Technologies and create your own AI-powered tattoo! And if you enjoyed this convo, don’t forget to like, share, and subscribe for more deep dives into AI, tech, and the people pushing the boundaries. 🚀

 

Fireflies AI CEO Krish Ramineni on Disruptive Tech | AI Insights SF Ep. 9

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The Journey of Fireflies AI: A Conversation with Krish Ramineni

In this episode of AI Insights San Francisco, I had the privilege of sitting down with Krish Ramineni, the CEO and co-founder of Fireflies AI. Krish is not only a visionary in the AI space but also someone who truly understands the value of solving real-world problems through technology.

Our conversation was fascinating, diving deep into how Fireflies AI—a groundbreaking AI-powered note-taking platform—came to life, the challenges of scaling disruptive technology, and its ambitious vision for the future of work.

The Birth of Fireflies AI: From University to Entrepreneurship

Krish shared how his passion for note-taking started during his academic years, where he was always jotting things down to stay organized. This passion stayed with him during his time at Microsoft, where he saw firsthand how much time product managers spent on tedious administrative tasks.

He realized there had to be a better way. “I was spending so much time in meetings, and I wanted a solution that would save me time,” Krish explained. He became his own first user, building a tool that could capture and organize meeting notes seamlessly. What started as an experiment soon became the foundation for Fireflies AI.

Overcoming Challenges: Disruption and Trust

Krish didn’t shy away from discussing the challenges of introducing disruptive technology. Breaking into industries resistant to change or dominated by established players is no small feat.

“When we started, we didn’t have marketing in place or even know much about enterprises. We just cared about solving a problem for people like us”

Krish shared. Slowly but surely, Fireflies gained traction by focusing on automation and serving small businesses. The turning point? Leveraging OpenAI’s advancements and price cuts to commercialize their product effectively.

Scaling and Data Security: Lessons Learned

As the platform grew, particularly during the pandemic, Fireflies faced major scaling challenges.

“From 2020 to 2021, we were dealing with an explosion in usage”

Krish noted.

Data security became a core focus. Fireflies ensures that customer data isn’t used for training its models and offers private storage options to maintain user trust. “At the end of the day, the data is yours,” Krish emphasized, highlighting their commitment to transparency and privacy.

The Fireflies Business Model: Simple, Affordable, Effective

Krish explained how Fireflies’ pricing model—seat-based licenses and utility pricing—keeps the platform accessible to businesses of all sizes. “We try to be the most affordable platform possible,” he said. By relying on word-of-mouth marketing and a self-service approach, Fireflies has grown rapidly while maintaining its affordability.

The Future of Fireflies: From Note-Taker to AI Agent

Looking ahead, Krish envisions Fireflies evolving into much more than a note-taking tool. The goal? To become a full-fledged AI agent that collaborates with users, automates tasks, and even takes meetings on their behalf.

“Imagine asking Fireflies to perform specific tasks during a meeting or even attending a meeting for you”

Krish said. He believes this shift could redefine how we work, allowing people to focus on creativity and strategy while AI handles the busywork.

The Broader Impact of AI on Work

We also talked about the broader implications of AI. Krish sees AI driving efficiency and reducing costs but stresses the importance of upskilling. “AI won’t replace jobs entirely, but it will transform them. The best way to adapt is to learn how to use AI effectively,” he concluded.

This conversation with Krish left me inspired about the endless possibilities of AI, not just in improving workflows but also in reshaping how we think about work itself.

What do you think the future of AI in the workplace looks like?

Lets connect!

https://www.linkedin.com/in/-roan/ 

Why AI Is Taking Over: Insights from a Tech Leader & Professor | AI Insights San Francisco Episode 7

In this episode of AI Insights San Francisco, I sit down with Thomas Kurnicki, a professor at HULT and Vice President of Quantitative Analytics Specialist at Wells Fargo, to explore the fascinating world of AI and its growing impact across industries.

We dive into key topics, including:
– What exactly is AI, and why has it grown so quickly in recent years?
– The key differences between a Data Scientist and a Data Analyst.
– How AI is transforming the financial sector, particularly within banks like Wells Fargo.
– Thomas’s unique perspective as both a professor and an executive in finance.
– What the future holds for AI in various sectors and how businesses should prepare.

If you’re interested in the intersection of AI, finance, the differences between data roles, or the future of artificial intelligence, this conversation is packed with insights you won’t want to miss!

Let’s connect: https://www.linkedin.com/in/-roan/

 

Boosting Buyer Engagement with AI | AI Insights San Francisco – Criya (Episode 5)

In this episode of AI Insights San Francisco, I sit down with Deboshree Dutta, the founder and CEO of Criya AI, to explore how AI is transforming the sales landscape. We dive into the power of effective follow-ups, the role of technology in boosting sales, and how AI can create stunning presentations in just seconds.

Deb shares her journey with Criya AI, a Y Combinator and venture-backed startup that enables sales teams to generate perfectly designed collateral in just 20 seconds. With over 40,000 users in 2023, Criya AI is making waves in the tech world by helping sales professionals communicate more effectively and close more deals.

We also discuss the future of AI beyond generative content, focusing on how AI can drive deeper insights and enhance our search capabilities. Don’t miss this conversation packed with valuable insights on the future of sales and AI.

Criya website: https://www.criya.co/

Let’s connect: https://www.linkedin.com/in/-roan/

Watch the full video: