Podcasts about Product Hunt

Daniel and Tamara have a channel that's underutilized… There are a TON of different distribution tools at our fingertips. Product Hunt is the product marketer's best friend. It's basically free distribution, you can get ahead, and it's not used as much as it should be. BUT, you need the right prep and strategy to maximize your time using it. How do you craft a winning strategy? Plus, what are the rules of the channel? Turns out, algorithms are catching onto upvotes from your friends. Daniel breaks down the rules and explains how to play the game.   If you're looking to focus on launching and distributing, this is the episode for YOU…and it's short and sweet. ⌛ Follow Tamara: LinkedIn: https://www.linkedin.com/in/tamaragrominsky/ Follow Daniel: YouTube: https://www.youtube.com/@themarketingmillennials/featured Twitter: https://www.twitter.com/Dmurr68 LinkedIn: https://www.linkedin.com/in/daniel-murray-marketing Sign up for The Marketing Millennials newsletter: www.workweek.com/brand/the-marketing-millennials Daniel is a Workweek friend, working to produce amazing podcasts. To find out more, visit: www.workweek.com

In today's episode of Technical Tips, we're joined by Semaphore engineer Lucas Pinheiro. He's here to share insights on self-hosting agents — including the challenges our engineering team has faced and the solutions we've implemented to manage agents reliably at scale. Whether you're working with self-hosted systems or navigating cloud-specific hurdles, this episode is packed with valuable takeaways. Like this episode? Be sure to leave a ⭐️⭐️⭐️⭐️⭐️ review on the podcast player of your choice and share it with your friends.

Infinite Loops: Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- Nathan Baschez is the cofounder and CEO of Lex, an AI word-processor. He also cofounded Every, was the first employee at Substack AND co-created Product Hunt. Suffice to say, Nathan knows a thing or two about building on the internet. He joins the show to discuss how AI is changing writing, why it's time to rethink the article, the rise of solo founders and MUCH more. I hope you enjoy this conversation as much as I did. For the full transcript, episode takeaways, and bucketloads of other goodies designed to make you go, “Hmm, that's interesting!”, check out our Substack. Important Links: Lex Twitter Substack LinkedIn Show Notes: Lex: Your Spotter In the Writing Gym Letting People Into Your Creative Process Collaboration-as-a-Service Creation Is Fundamentally About Choices What Will Become of the AI Holdouts? AI Is Like the Internet In 1995 Can AI Unfuck the Government? Blindspots While Working In Organizations Rethinking The ‘Article' As A Medium Memes Are Dense Information Packets It's Time for Solo Founders Why Learning About Cumulative Cultural Evolution Is Vital What's Next for Lex? Writing As A Way To Design Thoughts Nathan As World Emperor Books Mentioned: A Swim in a Pond in the Rain: In Which Four Russians Give a Master Class on Writing, Reading, and Life; by George Saunders The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous; by Joseph Henrich The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter; by Joseph Henrich

Infinite Loops Key Takeaways  AI is not thinking-as-a-service but collaboration-as-a-service: The wrong way to approach AI is to sit back and see what it comes up with; the right way is to tinker with it and poke it in different ways so that novel patterns emergeBanning AI in schools is like stopping humans from using fire because they might burn themselvesAfter discovering how to use fire, we created fire departments, firemen, and fire exits; the same thing will happen with AI“AI is a mirror, not a mold.” – Jim O'Shaughnessy Creation is fundamentally about choices: The choices reflect what the creator considers; the creation is the result of what the creator decidesThe best writers use the best prompts – the same skills that make them great writers help them get the best from AI“A lot of things that are revolutionary in the history of technology have taken something that was encoded into the substrate and then made it an abstraction.” – Nathan Baschez AI in 2025 is roughly where the internet was in 1995: So even if there is an AI ‘crash', value creation will take place post-crash just as it did with internet companies following the Dot Com BubbleUnderstanding cumulative cultural evolution: Recognizing cultural shifts before others do gives you a competitive edge in both life and business The thinking that you should let the world pull companies out of your creative projects may be wrong; the vast majorities of successful businesses were started by people who wanted to create a successful business  Do not fall victim to the “Disney Princess Co-Founder” Fallacy: Instead of waiting to start because you have not found the perfect co-founder, just start on your idea!A common misconception about writing is that it is not only a way to communicate our thoughts, but also a way to formulate our thoughts Learn more by doing: Set aside preconceived expectations and follow your curiosityRead the full notes @ podcastnotes.orgNathan Baschez is the cofounder and CEO of Lex, an AI word-processor. He also cofounded Every, was the first employee at Substack AND co-created Product Hunt. Suffice to say, Nathan knows a thing or two about building on the internet. He joins the show to discuss how AI is changing writing, why it's time to rethink the article, the rise of solo founders and MUCH more. I hope you enjoy this conversation as much as I did. For the full transcript, episode takeaways, and bucketloads of other goodies designed to make you go, “Hmm, that's interesting!”, check out our Substack. Important Links: Lex Twitter Substack LinkedIn Show Notes: Lex: Your Spotter In the Writing Gym Letting People Into Your Creative Process Collaboration-as-a-Service Creation Is Fundamentally About Choices What Will Become of the AI Holdouts? AI Is Like the Internet In 1995 Can AI Unfuck the Government? Blindspots While Working In Organizations Rethinking The ‘Article' As A Medium Memes Are Dense Information Packets It's Time for Solo Founders Why Learning About Cumulative Cultural Evolution Is Vital What's Next for Lex? Writing As A Way To Design Thoughts Nathan As World Emperor Books Mentioned: A Swim in a Pond in the Rain: In Which Four Russians Give a Master Class on Writing, Reading, and Life; by George Saunders The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous; by Joseph Henrich The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter; by Joseph Henrich

Infinite Loops Key Takeaways  AI is not thinking-as-a-service but collaboration-as-a-service: The wrong way to approach AI is to sit back and see what it comes up with; the right way is to tinker with it and poke it in different ways so that novel patterns emergeBanning AI in schools is like stopping humans from using fire because they might burn themselvesAfter discovering how to use fire, we created fire departments, firemen, and fire exits; the same thing will happen with AI“AI is a mirror, not a mold.” – Jim O'Shaughnessy Creation is fundamentally about choices: The choices reflect what the creator considers; the creation is the result of what the creator decidesThe best writers use the best prompts – the same skills that make them great writers help them get the best from AI“A lot of things that are revolutionary in the history of technology have taken something that was encoded into the substrate and then made it an abstraction.” – Nathan Baschez AI in 2025 is roughly where the internet was in 1995: So even if there is an AI ‘crash', value creation will take place post-crash just as it did with internet companies following the Dot Com BubbleUnderstanding cumulative cultural evolution: Recognizing cultural shifts before others do gives you a competitive edge in both life and business The thinking that you should let the world pull companies out of your creative projects may be wrong; the vast majorities of successful businesses were started by people who wanted to create a successful business  Do not fall victim to the “Disney Princess Co-Founder” Fallacy: Instead of waiting to start because you have not found the perfect co-founder, just start on your idea!A common misconception about writing is that it is not only a way to communicate our thoughts, but also a way to formulate our thoughts Learn more by doing: Set aside preconceived expectations and follow your curiosityRead the full notes @ podcastnotes.orgNathan Baschez is the cofounder and CEO of Lex, an AI word-processor. He also cofounded Every, was the first employee at Substack AND co-created Product Hunt. Suffice to say, Nathan knows a thing or two about building on the internet. He joins the show to discuss how AI is changing writing, why it's time to rethink the article, the rise of solo founders and MUCH more. I hope you enjoy this conversation as much as I did. For the full transcript, episode takeaways, and bucketloads of other goodies designed to make you go, “Hmm, that's interesting!”, check out our Substack. Important Links: Lex Twitter Substack LinkedIn Show Notes: Lex: Your Spotter In the Writing Gym Letting People Into Your Creative Process Collaboration-as-a-Service Creation Is Fundamentally About Choices What Will Become of the AI Holdouts? AI Is Like the Internet In 1995 Can AI Unfuck the Government? Blindspots While Working In Organizations Rethinking The ‘Article' As A Medium Memes Are Dense Information Packets It's Time for Solo Founders Why Learning About Cumulative Cultural Evolution Is Vital What's Next for Lex? Writing As A Way To Design Thoughts Nathan As World Emperor Books Mentioned: A Swim in a Pond in the Rain: In Which Four Russians Give a Master Class on Writing, Reading, and Life; by George Saunders The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous; by Joseph Henrich The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter; by Joseph Henrich

Nathan Baschez is the cofounder and CEO of Lex, an AI word-processor. He also cofounded Every, was the first employee at Substack AND co-created Product Hunt. Suffice to say, Nathan knows a thing or two about building on the internet. He joins the show to discuss how AI is changing writing, why it's time to rethink the article, the rise of solo founders and MUCH more. I hope you enjoy this conversation as much as I did. For the full transcript, episode takeaways, and bucketloads of other goodies designed to make you go, “Hmm, that's interesting!”, check out our Substack. Important Links: Lex Twitter Substack LinkedIn Show Notes: Lex: Your Spotter In the Writing Gym Letting People Into Your Creative Process Collaboration-as-a-Service Creation Is Fundamentally About Choices What Will Become of the AI Holdouts? AI Is Like the Internet In 1995 Can AI Unfuck the Government? Blindspots While Working In Organizations Rethinking The ‘Article' As A Medium Memes Are Dense Information Packets It's Time for Solo Founders Why Learning About Cumulative Cultural Evolution Is Vital What's Next for Lex? Writing As A Way To Design Thoughts Nathan As World Emperor Books Mentioned: A Swim in a Pond in the Rain: In Which Four Russians Give a Master Class on Writing, Reading, and Life; by George Saunders The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous; by Joseph Henrich The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter; by Joseph Henrich

In this episode of Semaphore Uncut, Patrick Debois—Generative AI and DevOps specialist —joins Darko Fabijan to share his perspective on how AI intersects with DevOps, DevSecOps, and infrastructure as code. Patrick discusses everything from generative tooling to failure handling, and what makes this era of automation both exciting and risky.Like this episode? Be sure to leave a ⭐️⭐️⭐️⭐️⭐️ review on the podcast player of your choice and share it with your friends.

When Abdulla Abdurazzoqov Reflects on Selling His SaaS Startup, He Offers These Takeaways:Build organic traffic early with free toolsChoose peace of mind over uncertaintyA strong Product Hunt launch can drive your first paying users fastAbdulla built AIHumanize.com as a solo founder and sold it within just nine months for a six-figure deal on Acquire.com. His platform helped users rewrite AI-generated content to sound more human and bypass AI detectors—attracting a surprising customer base of students and lawyers.The decision to sell came from a desire for financial stability and less stress, especially as he supported his family full-time. Listing on Acquire.com brought over 30 NDAs in the first few days, and after a smooth diligence process, he found the right buyer who understood both the niche and the tech stack.Today, Abdulla is working full-time on his next project: DetectingAI.com, which identifies whether content was generated by AI, already serving over 250,000 users.Tune into the Acquire podcast with Andrew Gazdecki and Abdulla as they discuss: ► How Abdulla built and sold his SaaS in under a year ► Launch strategies that convert free users into paying ones ► What to expect emotionally and financially after an exitFollow Abdulla's journey through the link below:LinkedInFacebook

It's not often we see a new social app launch. A drone accelerator program deadline approaches. Tons of events from AI gatherings to small business support to Portland Startup Week 2025. All that and more in Portland Oregon startup news this week. PORTLAND STARTUP LINKS- Save the date https://www.meetup.com/pie-portland-startup-community/events/307183761/- Dayo https://dayo.co - Launch Party https://www.eventbrite.com/e/dayo-launch-party-90s-dance-jam-tickets-1295003964719 - Dayo on Product Hunt https://www.producthunt.com/products/dayo-2 - GeekWire on Dayo https://www.geekwire.com/2025/get-paid-to-limit-social-media-time-portland-startups-app-monitors-usage-and-gives-rewards/- Oregon UAS Accelerator application https://www.einpresswire.com/article/801430762/oregon-uas-accelerator-opens-applications-for-summer-2025-cohort- Portland Office of Small Business "Show Up for Small Business" https://www.meetup.com/pie-portland-startup-community/events/307221148/- Portland AI Engineers https://www.meetup.com/portland-ai-engineers/- Portland Startup Week 2025 https://lu.ma/pdxstartupweekPORTLAND STARTUP NEWS00:00 Portland startup news intro00:14 Portland startup community gatherings new and old02:05 Dayo takes on social media05:09  @OregonUASAccelerator  applications are due06:42 Show up for small business09:56 New AI meetup for Portland AI Engineers12:12 Portland Startup Week 202514:13 Celebrating AAPI founders and community leadersFIND RICK TUROCZY ON THE INTERNET AT…- https://patreon.com/turoczy- https://linkedin.com/in/turoczy- https://bsky.app/profile/turoczy.bsky.social- https://siliconflorist.substack.com/- https://pdxslack.comABOUT SILICON FLORIST ----------For nearly two decades, Rick Turoczy has published Silicon Florist, a blog, newsletter, and podcast that covers entrepreneurs, founders, startups, entrepreneurship, tech, news, and events in the Portland, Oregon, startup community. Whether you're an aspiring entrepreneur, a startup or tech enthusiast, or simply intrigued by Portland's startup culture, Silicon Florist is your go-to source for the latest news, events, jobs, and opportunities in Portland Oregon's flourishing tech and startup scene. Join us in exploring the innovative world of startups in Portland, where creativity and collaboration meet.ABOUT RICK TUROCZY ----------Rick Turoczy has been working in, on, and around the Portland, Oregon, startup community for nearly 30 years. He has been recognized as one of the “OG”s of startup ecosystem building by the Kauffman Foundation. And he has been humbled by any number of opportunities to speak on stages from SXSW to INBOUND and from Kobe, Japan, to Muscat, Oman, including an opportunity to share his views on community building on the TEDxPortland stage (https://www.youtube.com/watch?v=Cj98mr_wUA0). All because of a blog. Weird.https://siliconflorist.com#pdx #portland #oregon #startup #entrepreneur

In this episode of Technical Tips, Tommy shares 10 expert tips to keep your CI pipeline fast and efficient. Learn how to improve performance, reduce errors, and ship quality software faster!Like this episode? Be sure to leave a ⭐️⭐️⭐️⭐️⭐️ review on the podcast player of your choice and share it with your friends.

In this episode of the SaaS Sessions podcast, Jacob Bank, founder of Relay.app, shares his journey from academia to startup founder, discussing the challenges of building a product in the AI space. He emphasizes the importance of validating ideas, finding early customers, and experimenting with various marketing channels. Jacob also highlights the significance of cohort retention as a measure of product-market fit and the need to balance innovation with competition in a rapidly evolving market.Key Takeaways –1. You're not failing - your distribution isBuilding is easy; getting customers is the battlefield.Early channels (Reddit, network, cold email) only take you so far.Most startup advice is outdated or irrelevant to your context - test everything yourself.2. Validate with precision, not egoThe Mom Test changed how Jacob gathered honest feedback.10 well-run interviews can kill or greenlight an idea.Getting “likes” is not validation - retention and willingness to pay are.3. Every growth stage needs a new motion0 → 10: Scrappy hustle (Reddit, LinkedIn DMs, direct outreach).10 → 100: Partner marketing, SEO, and high-intent blog content.100 → 1000: Viral LinkedIn content + YouTube for education + community-led growth.4. PMF isn't hype - it's cohort retentionRetention is the only true sign of product-market fit.Competitive pressure is a forcing function to build better products.Don't be afraid to pick a fight in a crowded space - just know your edge.Connect with Jacob Bank:

n 2007, the hashtag was a simple, yet revolutionary, idea that changed the way we organize and amplify content. Today, it is either endangered or more useful than ever, depending on whom you talk to. On the open social web, hashtags are an important unifying mechanism — not just for content but for people too. Why is that? How did we get here? What's next for this small but mighty feature and for the web at large? Here to tell us is Chris Messina, the inventor of the hashtag, the creator of the DiSo Project, and the No. 1 hunter on Product Hunt. In this episode, Messina goes wide to explain where this next 20-year cycle of the internet is taking us. From the community-pulling power of the hashtag to decentralization and the massive shifts ignited by AI, he threads the needle on it all.Addressing Elon Musk's disparaging comment about hashtagsThe history of the hashtagUnder-appreciated elements of the hashtagGrappling with identity and reputation in a decentralized worldAlignment between ActivityPub and LLMsMentioned in this episode and/or acronyms for clarity:bitly.com/tagchannels - original hashtag specDID stands for “decentralized identifier” and is a self-owned, verifiable digital identity that operates without a central authorityPGP is an encryption standard used for securing communication, data integrity, and authentication 

In this episode of the Zen Business Podcast, you'll hear what it takes to launch a successful Product Hunt campaign for your startup.John will delve into all the details on how to do this properly without breaking the bank!

In this episode, I discuss launching the Convertify apps on Product HuntShow Notes:Convertify AppsThis podcast is proudly hosted on Caproni.fm.

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 1621: Authenticity and critical thinking are essential in both writing and decision-making. Ryan Hoover explores the power of honest storytelling, showing how vulnerability fosters trust and emotional connection with readers. At the same time, he warns against blindly following popular advice, emphasizing that wisdom is contextual and must be questioned. "Good writing is honest. Great writing makes you feel something." Just as authentic writing requires self-reflection, so does evaluating guidance because "The best advice encourages you to think for yourself, not just follow blindly." Read along with the original article(s) here: https://medium.com/@rrhoover/honest-writing-a0eb6f39bf7f & https://medium.com/@rrhoover/dangerous-advice-5351109d9f6e Quotes to ponder: "Good writing is honest. Great writing makes you feel something." "Writing is a reflection of thought. If you're struggling to write, maybe you're still figuring out what you believe." "Bad advice is easy to follow. It's confident, simple, and dangerously persuasive." Learn more about your ad choices. Visit megaphone.fm/adchoices

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 1621: Authenticity and critical thinking are essential in both writing and decision-making. Ryan Hoover explores the power of honest storytelling, showing how vulnerability fosters trust and emotional connection with readers. At the same time, he warns against blindly following popular advice, emphasizing that wisdom is contextual and must be questioned. "Good writing is honest. Great writing makes you feel something." Just as authentic writing requires self-reflection, so does evaluating guidance because "The best advice encourages you to think for yourself, not just follow blindly." Read along with the original article(s) here: https://medium.com/@rrhoover/honest-writing-a0eb6f39bf7f & https://medium.com/@rrhoover/dangerous-advice-5351109d9f6e Quotes to ponder: "Good writing is honest. Great writing makes you feel something." "Writing is a reflection of thought. If you're struggling to write, maybe you're still figuring out what you believe." "Bad advice is easy to follow. It's confident, simple, and dangerously persuasive." Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Kate Syuma dives into a conversation with Marie Martens, co-founder of Tally, to explore the journey of building a profitable business around a product that is 99% free.Listen now on Apple, Spotify, and YouTube.—This episode is brought to you by Amplitude — сheck new Guides and Surveys to deliver helpful, well-timed messages: https://amplitude.com/guides-and-surveys—One more special update for you

In this episode, we sit down with the ever-innovative Chris Messina—creator of the hashtag, top product hunter on Product Hunt, and trusted advisor to startups navigating product development and market strategy.Recording from Ciel Media's new studio in Berkeley, we explore the evolving landscape of generative AI and the widening gap between its immense potential and real-world usability. Chris introduces a compelling framework, distinguishing AI as a *tool* versus a *medium*, which helps explain the stark divide in how different users engage with these technologies.Our conversation examines key challenges: How do we build trust in AI? Why is transparency in computational reasoning critical? And how might community collaboration shape the next generation of AI products? Drawing from his deep experience in social media and emerging tech, Chris offers striking parallels between early internet adoption and today's AI revolution, suggesting that meaningful integration will require both time and a generational shift in thinking.What makes this discussion particularly valuable is Chris's vision for the future of AI interaction—where technology moves beyond query-response models to become a truly collaborative medium, transforming how we create, problem-solve, and communicate.Links:Chris: https://chrismessina.meCiel Media: https://cielcreativespace.com

Have you ever fantasized about quitting your job and living off savings until you built a successful business or ran out of cash? SaaS builder Kamil Zowczac nearly accomplished both at the same time. While living in Bali off the money he'd earned as a project manager at a startup, Kamil taught himself to code and built a fast AI reel-video creation app called Copycopter. Marketing his product was slow at first. Kamil started offering a free subscription and then mysteriously went viral among Chinese Tiktok (Douyin) users after posting about it on Twitter. After installing a paywall and successfully launching on Product Hunt, he began to make a bit of money, but it was all too little too late. He'd spent too much time building his product and not enough time marketing it and was out of money. He needed an exit, fast. After posting about his dilemma on Twitter, Acquire.com's CEO, Andrew Gazdecki, helped Kamil list his business for free. After a few difficult negotiations with buyers and one deal that fell through, Kamil encountered his dream buyer. Within 24 hours, he'd finished all the deal paperwork and completed the sale of Copycopter. Now Kamil is back to building businesses in Bali living comfortably off his acquisition and resting easier knowing if he needs an exit, serious buyers are only a few clicks away. Listen to Kamil's interview with Andrew post-acquisition as they discuss: Kamil's tech recommendations for anyone wanting to get into AI video editing. The unfair marketing trick that applications like Copycopter can pull off better than any other SaaS. Andrew's top piece of advice for sellers who don't want to get locked into negotiations with an unserious buyer. Kamil is just getting started and you can follow his journey below: Twitter: @ky__zo

У новому випуску зустрілись з Володимиром Жуковим — серійним підприємцем, Board Advisor, та консультантом з діджитал-трансформації. Ви дізнаєтесь більше про те, як, коли і навіщо бізнесам впроваджувати штучний інтелект. Володимир поділиться власним досвідом співпраці зі Stanford та Bloomberg, розповість про отримання гранту від OpenAI та про те, як його проєкт став №1 на Product Hunt. Більше актуальних новин в Telegram: https://t.me/theceoneedsstrategy

In this episode of the podcast, host Dan Hafner interviews Grady Teske, a seasoned expert with over 250 B2B tech launches under his belt. Grady shares invaluable insights on effective product launches, marketing strategies, and building sustainable distribution channels. Key Points: Distribution is King: Grady emphasizes the importance of building and owning distribution channels. He argues that focusing on distribution often leads to better product outcomes than obsessing over product perfection. Consistent Launching: Rather than aiming for one perfect launch, Grady advocates for multiple, iterative launches. This approach allows for continuous learning and improvement. Data Capture and Engagement: A crucial aspect of successful launches is capturing user data and maintaining ongoing communication with your audience. Grady stresses the importance of owning your user database. Leveraging AI in Marketing: Grady discusses how AI can automate analytics, assist in content creation, and even manage aspects of marketing. He predicts the rise of client-specific AI agents in the near future. Content Strategy: Creating high-quality, intent-driven content is vital. Grady advises using platforms like Reddit, Quora, and YouTube for long-tail SEO benefits and building authority. Community Engagement: Utilizing community platforms like Product Hunt and partnering with influential community members can significantly amplify reach during launches. Podcast Marketing: Grady highlights how podcast appearances can be effective for building authority and should be integrated with other marketing efforts. If you're ready to take your product launches to the next level, start by evaluating your current distribution channels. Visit gradytesky.com to learn more about Grady's fractional CMO services and Rising Tides agency. Don't forget to subscribe to his newsletter for free insights on building systems that become businesses. Remember, keep building and launching – the simpler and faster you can create and launch products, the better off you'll be in this era of rapid innovation.

Welcome to another insightful episode of "Accelerate Your Business Growth" with your host, Diane Helbig. Today's episode is all about scalable strategies to win customers, featuring the acclaimed MarTech expert and CEO of McGaw.io and UTM.io, Dan McGaw. Dan shares invaluable advice for small business owners on carving out a niche in content marketing, navigating the competitive landscape with innovative approaches like direct mail, and the art of monetizing product features. We delve into effective marketing strategies for different product types, from door-to-door sales for local businesses to leveraging platforms like Product Hunt and AppSumo for SaaS products. Dan emphasizes the importance of understanding customer needs, consistent content creation, and the critical role of tailored marketing strategies based on unique business strengths. Get ready to uncover practical tips on choosing the right tools, developing successful referral programs, and the power of customer feedback in driving product development. Plus, Dan offers listeners a free copy of his book, "Build Cool Shit," to further enhance your marketing toolkit. Tune in as we explore these game-changing strategies with one of the industry's leading minds. Let's dive in! If you are a small business owner or salesperson who struggles with getting the sales results you are looking for, get your copy of Succeed Without Selling today. Learn the importance of Always Be Curious. Accelerate Your Business Growth is proud to be included on the list of the 45 Best Business Growth Podcasts. Each episode of this podcast provides insights and education around topics that are important to you as a business owner or leader. The content comes from people who are experts in their fields and who are interested in helping you be more successful. Whether it's sales challenges, leadership issues, hiring and talent struggles, marketing, seo, branding, time management, customer service, communication, podcasting, social media, cashflow, or publishing, the best and the brightest join the host, Diane Helbig, for a casual conversation. Discover programs, webinars, services, books, and other podcasts you can tap into for fresh ideas. Be sure to subscribe so you never miss an episode and visit Helbig Enterprises to explore the many ways Diane can help you improve your business outcomes and results. Learn more about your ad choices. Visit megaphone.fm/adchoices

Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM

Dans cet épisode de bannouze le podcast du marketing digital, Enfin une personne qui me parle d'IA sans buzzwords ni bullshit ! Un grand merci à Laurent Duverney-Guichard, CEO & co-fondateur de spyne.app. Avec lui, nous allons retracer la chronologie de l'avènement de l'IA dans notre secteur, explorer les outils disponibles, analyser les risques et mettre en lumière les bénéfices. Quelles sont les ressources pour mieux comprendre ces concepts. Bonne écoute ! Les liens cités dans l'épisode : ProductHunt.com ` https://news.ycombinator.com https://www.youtube.com/@3blue1brown La page Linkedin de Laurent Duverney-Guichard : https://fr.linkedin.com/in/duverney Le site de Laurent : https://www.spyne.app/

In this episode of the CRM Sushi Podcast, I interview Aurelien Vasinis, a tech entrepreneur with a unique background in political science and finance. https://www.youtube.com/@UCzGYzumNezGjNVcz2cjEk4A https://www.youtube.com/@UCy27aBEnFjXU8kSqGjuropQ Aurelien shares his journey from studying political science to becoming a hands-on product manager and developer in the tech industry. He discusses the importance of distributed teams, the evolution of his current business, Kuration AI, and how it leverages agent workflows to automate lead generation and market research. The conversation also touches on the differences between Kuration AI, Clay, and Zapier, and the significance of targeted lead generation strategies for small businesses. Aurelien concludes with a demonstration of Kuration AI's capabilities, showcasing its user-friendly interface and powerful features. We get into the challenges and solutions in data management, particularly for small businesses and solopreneurs. He emphasizes the importance of automation tools like Zapier, the complexities of data subscriptions, and the need for customizable data filters. Aurelien also shares insights on effective cold emailing strategies, the evolving landscape of platforms like Product Hunt, and the pricing structure of his service, KurationAI, which aims to simplify the research process for users. GUEST INFO: Guest Site: https://www.kurationai.com/ Guest LinkedIn: https://www.linkedin.com/company/kuration-ai/ Guest LinkedIn: https://www.linkedin.com/in/vasinis/ Chapters 00:00 Introduction and Background 03:01 Transitioning from Political Science to Tech 05:56 Building Distributed Teams 08:59 The Evolution of Curation AI 11:51 Understanding Agent Workflows 15:05 Curation AI vs. Clay and Zapier 17:58 Targeting and Lead Generation Strategies 21:01 Demonstration of Curation AI 28:57 Leveraging Automation Tools for Data Management 30:10 Navigating the Complex Landscape of Data Subscriptions 32:09 Customizing Data Filters for Targeted Outreach 34:49 Building a User-Friendly Research Tool 40:45 Creating a Sustainable Business Model 43:35 Effective Cold Email Strategies for Lead Generation 49:02 Evaluating the Impact of Product Hunt 50:24 Understanding Data Point Pricing and Usage Join The Inner Circle: https://info.wesschaeffer.com/inner-circle-silver Market like you mean it. Now go sell something. SUBSCRIBE to grow your sales. https://www.youtube.com/@TheSalesWhispererWes ----- Connect with me: Twitter -- https://twitter.com/saleswhisperer LinkedIn -- http://www.linkedin.com/in/thesaleswhisperer/ Podcast -- https://feeds.libsyn.com/44487/rss BUSINESS GROWTH TOOLS https://12WeeksToPeak.com https://CRMQuiz.com https://MakeEverySale.com

The Current State of Podcasting: A Comprehensive GuideIn the latest episode of our podcast, we had the pleasure of hosting Jeff Umbro, the CEO of Podglomerate. Jeff shared his extensive knowledge about the podcasting industry, offering valuable insights into its growth, best practices for independent podcasters, and effective monetization strategies. This blog post will break down the key points discussed in the episode, providing actionable advice and thorough explanations to help you navigate the podcasting landscape.Jeff begins by highlighting the impressive growth of podcasting over the past 16 years. According to the Edison Infinite Dial Report, approximately 132 million people in the U.S. listen to podcasts regularly, averaging about seven shows each month. This growth has been further accelerated by the pandemic, which increased streaming audio consumption. Podglomerate, founded in 2017, is a podcast services company that focuses on producing, marketing, and monetizing podcasts. They work with a diverse range of clients, from large corporations like Netflix and PBS to small businesses and individual creators. Jeff emphasizes that their goal is to help podcasters create high-quality content and effectively reach their target audience.Jeff also discusses the recent consolidation in the podcasting industry, with major players like Spotify, SiriusXM, and Apple acquiring smaller companies. This consolidation has led to a shift in the types of shows being produced, with a growing focus on ad sales and listener engagement. Despite these changes, advertising on podcasts remains highly effective. Jeff notes that podcast ads often outperform other digital mediums, attracting more brands to the space. He emphasizes the importance of creating quality content that resonates with listeners and advises podcasters to focus on engagement metrics such as social media mentions, listener feedback, and overall consumption patterns. For those with limited budgets, Jeff recommends leveraging owned properties like websites, newsletters, and social media to promote their shows and suggests cross-promotion with similar shows as a more effective strategy for audience growth.About Jeff Umbro:Jeff Umbro is the founder and CEO of The Podglomerate, the award-winning company which produces, distributes, and monetizes podcasts. The Podglomerate is a boot-strapped organization which now works with more than 70 podcasts and more than 30 million monthly podcast downloads. Jeff has written for and been quoted in Bloomberg, Morning Brew, Adweek, Quartz, Hot Pod, Paste, The Daily Dot, and more. Prior to launching the Podglomerate, Jeff had his hands in audience growth and business development for companies like Product Hunt, Serial Box, VotePlz, Talkshow, and Goldberg McDuffie Communications.About Podglomerate:The Podglomerate has been producing, distributing, and monetizing podcasts since 2016. Now representing more than 70 podcasts accounting for over 30 million monthly downloads, The Podglomerate's clients have topped the podcast charts and have received features on every major podcast distribution app and national coverage in print, digital, radio, and television. The Podglomerate has worked with Freakonomics Radio, PBS, NPR, A+E, Lifetime, History Channel, Harvard Business School, MIT, Stanford, Lit Hub Radio, NPR stations (including KPCC/LAist, NHPR, WHYY, WUNC, VPM, WPM, GBH), WNET, Substack, Magnificent Noise, Expedia, Optum, CVS Health, Hubspot, and Hoff Studios, among many others.Apply to be a Guest on The Thoughtful Entrepreneur: https://go.upmyinfluence.com/podcast-guestLinks Mentioned in this Episode:Want to learn more? Check out Podglomerate website athttps://podglomerate.com/Check out Podglomerate...

#198: Learn to find anything online quickly, effectively and securely with Chris and Steph Smith. We discuss essential tools and tips for navigating the web, from uncovering hidden information to leveraging AI and keyboard shortcuts to validating sources. Whether you're a seasoned researcher or a casual browser, you'll gain practical strategies to unlock the internet's full potential. Steph Smith is a writer and the host of the a16z and Sh*t You Don't Learn in School podcasts. She builds online projects like Internet Pipes and Trends, and has launched several websites that topped Product Hunt. Previously, she led HubSpot's Creator Program and worked at The Hustle. Link to Full Show Notes: https://chrishutchins.com/steph-smith-navigating-the-internet-pipes Partner Deals Copilot: Free 2 months access to my favorite personal finance app with code HACKS2 Bilt Rewards: Earn the most valuable points when you pay rent MasterClass: Learn from the world's best with 15% off Notion: Try Notion AI free to automate tedious tasks and streamline your work Fabric: Affordable term life insurance for you and your family DeleteMe: 20% off removing your personal info from the web For all the deals, discounts and promo codes from our partners, go to: chrishutchins.com/deals Resources Mentioned Steph Smith: Website | X | Podcast Internet Pipes (Get $200 off with code ALLTHEHACKS) Browser Extensions Keepa Honey Capital One Eno Points Path Keywords Everywhere Grammarly Library Extension Similarweb Ghostery Wappalyzer | changedetection.io | Distill Text Blaze OneTab Tools Syften Arc Rectangle Superhuman (Use code ALLTHEHACKS for a free month) Search Engines Reddit Search: GigaBrain | Redditle Academic Search: Google Dataset Search | Consensus AI Tools Perplexity Globe Explorer Note Taking: Obsidian | NotebookLM Cubby PodEngine Email Contact Searches: Lusha | Hunter | Muck Rack RadRunner Electric Utility Bikes Unclaimed Money Database Supplement Validators: Examine | Pillser Tools for Storing Info Evernote Notion (Try Notion AI free here) Apple Notes Save to Notion Arc Search Audio and Video Editing: Descript Business Ideas ImportYeti Harper's Index ResearchRabbit AppMagic Web Scraping: Web Scraper | Browse AI ATH Podcast Chris' Newsletter: Sign Up | Past Issues Ep #78: Action List: Everything You Can Do To Protect Your Identity, Accounts, Credit and Family Ep #85: Building a Second Brain to Organize Your Digital Life with Tiago Forte Full Show Notes (00:00) Introduction (02:16) Most Common Mistakes in Finding Info Online (04:45) Browser Extensions for Better Research (09:47) Online Research Tools (11:49) Keyboard Shortcuts (14:54) Different Types of Search Engines (19:45) Why Reddit Is the Most Underutilized Search Engine (23:51) How Information Available on Each Platform Differs (28:38) Ways to Leverage ChatGPT (36:26) Google's New Notebook LM (44:40) How to Effectively Search for Email Addresses (46:23) How to Delete Your Personal Information Online (50:00) The Importance of Digital Privacy (52:56) Ways to Validate Online Sources and Info (55:45) Validating Expertise (58:20) The Most Efficient Ways to Store Info (1:06:01) Internet Pipes: Researching Business Ideas and Trends (1:09:17) Web Scraping (1:11:14) Steph's Final Piece of Advice for Online Research (1:14:10) Where to Find Steph and Her Work Connect with Chris Newsletter | Membership | Twitter | Instagram | LinkedIn Editor's Note: The content on this page is accurate as of the posting date; however, some of our partner offers may have expired. Opinions expressed here are the author's alone, not those of any bank, credit card issuer, hotel, airline, or other entity. This content has not been reviewed, approved or otherwise endorsed by any of the entities included within the post.

I share the rollercoaster journey of my startup, Podsqueeze. I talk about our initial success, hitting a growth plateau, and the strategies we used to bounce back, like investing in SEO and launching new features. I dive into the emotional highs and lows of entrepreneurship, the importance of teamwork, and the challenges of maintaining motivation. I speak about the impact that ProductHunt and Backlinks had on our growth.Mentions Tools and Websites- "Podsqueeze" (00:24:13) - https://podsqueeze.com- "Product Hunt" (00:27:31) - https://www.producthunt.com- "Indie Maker Merch" (00:37:51) - https://www.indiemakermerch.com- "Tiago's Twitter" (00:36:38) - https://twitter.com/@wbetiagoTimestampsIntroduction to the Episode (00:00:01)  Tiago introduces the episode and its focus on overcoming challenges at Podsqueeze.Background of Podsqueeze (00:02:20)  Tiago shares the journey of Podsqueeze, detailing initial success and past entrepreneurial attempts.Initial Growth and Challenges (00:03:17)  The startup experiences rapid growth but soon faces a plateau in user engagement.Berlin Conference Experience (00:04:26)  Tiago describes attending a podcasting conference in Berlin, highlighting co-founder stress.SEO Strategy Implementation (00:06:40)  The team begins investing in SEO to improve website traffic and user engagement.Hiring External Help for SEO (00:07:35)  Tiago discusses the decision to hire an SEO auditor to enhance their strategy.Content Creation and Clusters (00:09:51)  The importance of keyword clusters and content creation for SEO is emphasized.Hiring a Content Writer (00:10:51)  Tiago reflects on the positive impact of hiring a content writer for their team.Launch of Video Clips Feature (00:11:51)  The team launches a new feature for creating video clips from podcasts.December Decline (00:13:00)  Tiago notes a significant drop in user engagement during December.New Product Launch (00:14:04)  A new product launch in January aims to revive interest and engagement.Mixed Results from Product Launch (00:15:23)  The latest product launch brings in traffic, but not as much as hoped.Continued Growth and Decline (00:16:25)  Despite initial recovery, the team notices another decline in user engagement.Discovery of Domain Authority (00:19:56)  The concept of domain authority is introduced as crucial for improving SEO.Building Backlinks for SEO (00:21:03)  The team focuses on acquiring backlinks to enhance their domain authority.Summer Struggles (00:23:13)  The summer months prove challenging, with a notable decline in user engagement and morale.The Initial Thoughts on Growth (00:24:13)  Tiago discusses his desire for more growth and the challenges of current earnings.New Product Strategies (00:25:13)  Plans to start a new product while maintaining focus on Podsqueeze's development.Traffic Insights from India (00:26:18)  Discovery of increased traffic from India but low conversion rates.Impact of Product Launch on Rankings (00:27:31)  Analysis of how the product launch affected Google rankings and traffic.Challenges with Indian Market Conversion (00:28:35)  Struggles to convert Indian users and advice received from industry friends.August Struggles (00:29:46)  A tough month for Podsqueeze with low engagement and user activity.September Growth (00:30:53)  A significant uptick in traffic and user engagement in September.Domain Authority Improvement (00:32:01)  Efforts to enhance domain authority through strategic backlinks and exchanges.Price Experiment Results (00:33:15)  Findings from A/B testing price changes and their impact on conversion rates.Reflections on Podsqueeze's Journey (00:34:24)  Tiago reflects on the evolution and resilience of Podsqueeze over the past year.Competitive Edge through Marketing (00:36:38)  Emphasis on the importance of marketing and SEO as a competitive advantage.Closing Thoughts and Future Outlook (00:37:51)  Tiago expresses excitement for the future and invites audience engagement.

“Distraction is not a technology problem or a moral failing. The vast majority of distraction is simply that you have not learned the skill of dealing with discomfort. Master internal triggers, or they will become your master.” Nir Eyal is a best-selling author and instructor working at the intersection of psychology, technology, and business. In a research-driven conversation about his latest book “INDISTRACTABLE - How to Control Your Attention and Choose Your Life,” we learn about what really drives distraction — which is less about flashy external stimuli, and more about our own internal discomfort. The good news is, it's not out of reach, by better understanding our internal triggers - it just might be possible to get back to a focus that feels so far gone. The good news is, It's not out of reach, and Nir shares some great tips to get started.  Indistractable received critical acclaim, winning the Outstanding Works of Literature Award as well as being named one of the Best Business and Leadership Books by The Globe and Mail and Amazon, and among the Best Personal Development Books of the Year by Audible. Nir is also the author of “Hooked: How to Build Habit-Forming Products” — his books have resonated with readers worldwide, selling over 1 million copies in over 30 languages.  Nir's writing and work has been featured in The New York Times, Bloomberg Businessweek, The Harvard Business Review, The M.I.T. Technology Review, Time Magazine, Psychology Today. Nir co-founded and sold two tech companies since 2003, and invests in habit-forming products that improve users' lives including Eventbrite (NYSE:EB), Anchor.fm (acquired by Spotify), Kahoot!, Canva, Homelight, Product Hunt, Marco Polo, Byte Foods, FocusMate, Dynamicare, Wise App, and Sunnyside. Nir previously taught in the Business and Design schools at Stanford University, and Nir attended The Stanford Graduate School of Business and Emory University. This is an episode from friend-of-the-pod Sharad Lal's podcast “HOW TO LIVE” - for lots more great conversations with Sharad and thought leaders, subscribe to How To Live wherever you get your favorite podcasts, or visit howtolive.life  Learn more about your ad choices. Visit megaphone.fm/adchoices

Nir Eyal is the author of Hooked: How to Build Habit-Forming Products and Indistractable: How to Control Your Attention and Choose Your Life. Previously, he taught as a Lecturer in Marketing at the Stanford Graduate School of Business and Design School, and he sold two technology companies since 2003.For most of his career, Nir worked in the video gaming and advertising industries, where he learned and applied (and sometimes rejected) the techniques used to motivate and manipulate users.Nir writes to help companies create behaviors that benefit their users while educating people on how to build healthful habits in their own lives.Nir is an active investor; he puts his money where his mouth is by backing habit-forming products that improve lives. Some of his past investments include Eventbrite (NYSE:EB) and Kahoot! (KAHOOT-ME.OL), Anchor.fm (acquired by Spotify), Canva, Refresh.io (acquired by LinkedIn), Product Hunt (acquired by Angelist), Homelight, Marco Polo, Byte Foods, FocusMate, Dynamicare, Wise App, and Sunnyside.Although Nir received most of his education by earning an advanced degree from The School of Hard Knocks, He also received an MBA from the Stanford Graduate School of Business.Please enjoy my conversation with Nir Eyal.Become a supporter of this podcast: https://www.spreaker.com/podcast/next-level-soul-podcast-with-alex-ferrari--4858435/support.

“Distraction is not a technology problem or a moral failing. The vast majority of distraction is simply that you have not learned the skill of dealing with discomfort. Master internal triggers, or they will become your master.” Nir Eyal is a best-selling author and instructor working at the intersection of psychology, technology, and business. In a research-driven conversation about his latest book “INDISTRACTABLE - How to Control Your Attention and Choose Your Life,” we learn about what really drives distraction — which is less about flashy external stimuli, and more about our own internal discomfort. The good news is, it's not out of reach, by better understanding our internal triggers - it just might be possible to get back to a focus that feels so far gone. The good news is, It's not out of reach, and Nir shares some great tips to get started. Indistractable received critical acclaim, winning the Outstanding Works of Literature Award as well as being named one of the Best Business and Leadership Books by The Globe and Mail and Amazon, and among the Best Personal Development Books of the Year by Audible. Nir is also the author of “Hooked: How to Build Habit-Forming Products” — his books have resonated with readers worldwide, selling over 1 million copies in over 30 languages. Nir's writing and work has been featured in The New York Times, Bloomberg Businessweek, The Harvard Business Review, The M.I.T. Technology Review, Time Magazine, Psychology Today. Nir co-founded and sold two tech companies since 2003, and invests in habit-forming products that improve users' lives including Eventbrite (NYSE:EB), Anchor.fm (acquired by Spotify), Kahoot!, Canva, Homelight, Product Hunt, Marco Polo, Byte Foods, FocusMate, Dynamicare, Wise App, and Sunnyside. Nir previously taught in the Business and Design schools at Stanford University, and Nir attended The Stanford Graduate School of Business and Emory University. This is a re-airing of a podcast conversation from P&G Alumni Podcast co-host Sharad Lal's other top podcast “HOW TO LIVE” - for lots more great conversations with Sharad and thought leaders, subscribe to How To Live wherever you get your favorite podcasts, or visit howtolive.life Got an idea for a future “Learnings from Leaders” episode - reach out at pgalumpod@gmail.com

Guiding you through the world of growth, performance marketing, and partner marketing.We sit down with growth and marketing leaders to share tests and lessons learned in business and life.Host: Tye DeGrangeGuest: Greg RollettHype man & Announcer: John Potito

Welcome to another exciting episode of The SaaS CFO Podcast! In this episode, we are thrilled to have Roee Barak, founder, and CEO of Upward AI, as our guest. Roee, based in Tel Aviv, shares his fascinating career journey that took him from law school to the tech industry. With nearly a decade of experience in various business roles, including business development, sales, and marketing, Roee decided to launch his dream venture, Upward, to revolutionize AI-powered research and productivity tools. Join us as we dive deep into Roee's story, exploring the genesis of Upward and the innovative solutions it offers for high-stakes research in various industries. Roee provides a detailed walkthrough of how Upward helps users perform significant research, from defining objectives to gathering and synthesizing data, all the way to creating comprehensive reports and presentations. We also discuss Upward's go-to-market strategy, initial success on Product Hunt, and its impressive growth to over 1,000 paying subscribers in its first year. Lastly, Roee shares valuable insights on fundraising, team building, and the challenges of staying ahead in the fast-evolving AI landscape. Hear firsthand how Upward plans to transition from a single-user focus to a collaborative platform, aiming to bring substantial productivity gains to teams and enterprises. This episode is packed with lessons and inspiration for aspiring founders and SaaS enthusiasts. Don't miss it! Show Notes: 00:00 Need for a knowledge tool and productivity. 05:24 Content sourcing, research hub, notepad for outcomes. 06:56 Focused on end-user research tool before organizations. 11:33 Co-founders' AI journey: from skepticism to mainstream. 15:10 Key metric: New research projects from customers' usage. 16:46 Upward transitioning to collaborative research platform development. Links: SaaS Fundraising Stories: https://www.thesaasnews.com/news/upword-raises-3-million-in-pre-seed-round Roee Barak's LinkedIn: https://www.linkedin.com/in/roee-barak-20882887/ Upword AI's LinkedIn: https://www.linkedin.com/company/upwordai/ Upword AI's Website: https://www.upword.ai/ SaaS Metrics Course here: https://www.thesaasacademy.com/the-saas-metrics-foundation-live-cohort-new?mc_cid=b89d27187d&mc_eid=20fbb3e1b5 To learn more about Ben check out the links below: Subscribe to Ben's daily metrics newsletter: https://saasmetricsschool.beehiiv.com/subscribe Subscribe to Ben's SaaS newsletter: https://mailchi.mp/df1db6bf8bca/the-saas-cfo-sign-up-landing-page SaaS Metrics courses here: https://www.thesaasacademy.com/ Join Ben's SaaS community here: https://www.thesaasacademy.com/offers/ivNjwYDx/checkout Follow Ben on LinkedIn: https://www.linkedin.com/in/benrmurray

In this week's “Failure Friday” segment, we hear from a hacker who develops an app to solve a common coding problem, but launches before conducting proper market research. Oops! Back to the drawing board.  Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week. Show notes: SideHustleSchool.com Email: team@sidehustleschool.com Be on the show: SideHustleSchool.com/questions Connect on Instagram: @193countries Visit Chris's main site: ChrisGuillebeau.com Read A Year of Mental Health: yearofmentalhealth.substack.com If you're enjoying the show, please pass it along! It's free and has been published every single day since January 1, 2017. We're also very grateful for your five-star ratings—it shows that people are listening and looking forward to new episodes.

Sign up for our free 7 day "Creator Blueprint" email course! In today's episode of The HeyCreator Show, Matt Ragland (⁠@mattragland⁠) and Tim Forkin (⁠@timforkindotcom⁠) interview Jason Levin (@iamjasonlevin), a creator, entrepreneur, author of Memes Make Millions, and self-proclaimed memelord who currently serves as the Head of Growth at Product Hunt. Jason walks us through how to start thinking in memes, the reality of how memes can make millions for your company, and why being funny on the internet is the best long-term play for any brand. (0:00) — Intro (1:01) — How do I start thinking in memes? (7:07) — How do memes make millions? (14:56) — Someone sold their business for $100M by using memes (19:04) — “Dumb memes, smart threads” (25:15) — The secrets to growing company social Connect with us: Join the HeyCreator Community Submit a question to Creator Advice Use Automatic Evergreen to send profitable newsletters on autopilot

The joy of building a startup is that you get to learn from a lot of different people, but usually these meetings are not recorded. In this season of Building Jam we're sharing these raw conversations where we ask the experts about our startup. This week we're learning from Product Hunt's Head of Growth, Jason Levin.(00:49) Jason's top lesson for community growth: doing things that don't scale to connect with people.(03:07) Our top community challenge is trying to scale as our userbase grows - so what do we do?(07:10) The reason Jam doesn't have a community Slack & Jason's experience on why maybe we should(11:33) Jason wrote a book about memes, so we asked him for advice on how to make Jam a meme (philosophically)(16:23) Brand marketing vs. performance marketing w/ a Nike cameo(18:49) Jason, the Head of Growth at Product Hunt, says we should embrace the cringe(22:41) Our 3rd Product Hunt launch: Jam Genies(29:46) Jason's feedback on Jam's approach to community-led growthSubscribe to Building Jam on YouTube, Spotify, and Apple Podcasts. New episodes drop every Friday at 10AM ET. See you there!

Running a startup is a lot of throwing things against the wall to see what sticks (and what doesn't). The hard truth is, majority of things you do won't work...but you need to do them anyway to find the few things that do.On this episode, Melissa and Todd dives into the 10 go-to-marketing strategies that didn't work for them, why, and what they learned from each one.Takeaways: • Think twice before putting effort into Product Hunt.• Things they wasted too much money on too early.• What they thought would have a huge impact, but didn't.• Should you pay to be in a newsletter with massive reach?• There are a lot of things you think will move the needle, but do they?Contact ProfitLed Tweet us at @profitledfm. Find show notes of each episode on ProfitLed.fm. Connect with our host Follow Melissa Kwan on LinkedIn where she share stories & lessons from her founder journey weekly. Follow @themelissakwan on Instagram, TikTok, Twitter and YouTube where she shares short videos of business advice and other truth-bomb sound bites. This podcast was brought to you by eWebinar.Learn more at ewebinar.com and find out how you can turn pre-recorded videos into automated webinars that perform better than a live webinar.Thanks for listening!

Daryna Kulya, Co-founder of OpenPhone, is with us today. She enjoys serving consumers with the best customer experience by creating products that suit their needs. OpenPhone is an app built for teams and individuals so they can level up and use their phones for business anywhere. It's everything that you and your team need in a phone system! Daryna gives us an overview of how they were able to come up with this unique vision and how they skyrocketed from 0 to 1000 customers. Are you on the lookout for a business phone? If so, then catch her on the show and stay tuned for more. Show Notes [4:47] They want to be a part of an environment that is a lot more inspiring and that allows them to make progress. [5:50] The reason why they joined Velocity [6:47] Why did they initially give their product for free? [9:42] People should be getting value out of the product, so see if that's true [12:54] They always knew that OpenPhone would ultimately end up being a product that starts with one person in the company and then scales to the whole team [19:26] One of the most fundamental lessons learned was that a lot of times you overcomplicate things unnecessarily. [23:17] Daryna shares some of the biggest milestones that they have achieved throughout the years [27:08] How were they able to build a team and what did that journey look like? [34:27] Biggest leadership mistakes and lessons learned from the presence of scale [35:57 Daryna's advice on delegation, building a business, and scaling it up About Daryna Kulya Daryna Kulya is the COO and co-founder of OpenPhone. She was previously a product manager at Vidyard, where she helped to establish and grow Vidyard GoVideo (ViewedIt). She also worked at Deloitte's Digital Innovation Lab, and helped them with their prototypes and innovations. Back in 2014, she established Product Hunt Toronto, one of the city's largest product events and the world's first Product Hunt community-run meeting. But what's more interesting is Daryna is adventurous. She loves hiking trails in her free time.   Link OpenPhone Profile Daryna's LinkedIn

Episode 16: How is AI transforming the future of SEO and job markets? Matt Wolfe (https://x.com/mreflow) and Nathan Lands (https://x.com/NathanLands) are joined by innovator Greg Isenberg (https://x.com/gregisenberg), founder of Late Checkout and Boring Marketer. Greg hosts “The Startup Ideas Podcast”. In this episode, the trio delves into the shift from traditional SEO practices to AI-powered SEO 2.0. Greg explains the role of AI in creating personalized content, the pros and cons of AI note-taking tools, and the potential impacts of AI and automation on job markets. They also touch on the environmental and political implications of AI advancements and explore strategies for generating high-quality, interactive content that ranks well on search engines. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) AI to revolutionize content creation and ranking. (06:30) Maximizing SEO through embedding tools and calculators. (08:13) AI for SEO needs differentiation and interactivity. (11:21) Create high-quality content for organic website traffic. (14:56) Turning a stranger into a fan – the business game. (20:18) AI will scrape internet data for convenience. (21:30) Explore SEO 2.0, create AI content. (25:32) Using Otter app for easy conference note-taking. (28:28) There will be less certain types of jobs. (30:54) Share evolving opinions openly and be open-minded. (34:05) Access to global knowledge and market simplified. (40:08) Climate, AI power usage, tech solutions narrative. (43:13) AI conversation should unite, not divide. (44:12) Humans polarized, but AI offers positivity. — Mentions: Greg Isenberg: https://www.gregisenberg.com/ Late Checkout: https://www.latecheckout.studio/ Boring Marketer: https://www.boringmarketing.com/ The Startup Ideas Podcast: https://podcasts.apple.com/us/podcast/the-startup-ideas-podcast/id1593424985 Otter.ai: https://otter.ai/ Product Hunt: https://www.producthunt.com/ Perplexity: https://www.perplexity.ai/ — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

Dive into the latest episode of the B2B Go to Market Leaders podcast, where Leo Bosuener founder of a product launch agency specializing in Product Hunt shares his career journey from freelancing as a B2B consultant to focusing on product launches. He emphasizes the importance of aligning product, sales, marketing, and customer success teams for a successful go-to-market strategy. Leo discusses the iterative nature of go-to-market efforts, the role of clear messaging and case studies, and the value of patience and continuous learning. 

Anuj Abrol, Creator, former Chief of Staff for Justin Kan and ex-McKinsey, and Sarah Wright, head of content at Product Hunt, join Erik Torenberg of Turpentine to discuss building successful newsletters. In this conversation, they trade innovative strategies for creating high-quality content that attracts readers derived from the Turpentine podcast network, the importance of understanding your audience and social network engagement, and whether vertical newsletters can succeed. Grow and monetize your newsletter with Beehiiv: head to https://Beehiiv.com and use code “EMPIRES” for 20% off your first three months.

【友站訊息推廣】對於CASETiFY「強悍防摔手機殼」跟「多功能手機背帶」有興趣可點擊以下連結，前往挑選心儀款式！

資深研究員 Jason 出任務啦！受邀到 ICRT BreakDown 節目與主持人 Tim 和 Paz 暢聊總經與投資。 此次特輯也同步上架，讓大家學習英文之餘也更了解M平方與研究員！

No, we're not talking about Product Hunt (the website) today.  This one's about:Hunting for new product ideas.  Categories in AI.  Finding a partner.  Divesting in audience.  Hobbies. Attention diets.  Mothers day. Connect with Brian: Brian's Product Consultancy:  Instrumental Products Brian's SaaS, Clarityflow  Brian on Twitter: @casjam Brian on Threads: @brian.casel Connect with Jordan: Jordan's company, Rally Jordan on Twitter: @jordangal Jordan on Threads: @jordangal

Episode 565: Shaan Puri (https://twitter.com/ShaanVP) and Sam Parr (https://twitter.com/theSamParr) are dropping an emergency pod to break down Paul Graham's essay about Reddit's IPO yesterday.  Want to see Sam and Shaan's smiling faces? Head to the MFM YouTube Channel and subscribe - http://tinyurl.com/5n7ftsy5 — Show Notes: (0:00) Intro (1:59) If you want to learn, teach.  (3:45) Show up – the ultimate high agency move (5:54) Trust your gut (7:47) How to get the best ideas (12:30) Don't be precious about the name (13:33) How Reddit faked early traction (16:45) Talent filters (19:37) “The best products are you pushed out” (23:42) We read Chris Saccsa's early emails (26:32) Reddit's exits to Conde Nast, then buys it back (29:20) 19 years later and still not profitable  — Links: • Y Combinator - https://www.ycombinator.com/ • Reddit - https://www.reddit.com/ • Paul Graham Essays - https://paulgraham.com/articles.html • Product Hunt - https://www.producthunt.com/ — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano Past guests on My First Million include Rob Dyrdek, Hasan Minhaj, Balaji Srinivasan, Jake Paul, Dr. Andrew Huberman, Gary Vee, Lance Armstrong, Sophia Amoruso, Ariel Helwani, Ramit Sethi, Stanley Druckenmiller, Peter Diamandis, Dharmesh Shah, Brian Halligan, Marc Lore, Jason Calacanis, Andrew Wilkinson, Julian Shapiro, Kat Cole, Codie Sanchez, Nader Al-Naji, Steph Smith, Trung Phan, Nick Huber, Anthony Pompliano, Ben Askren, Ramon Van Meer, Brianne Kimmel, Andrew Gazdecki, Scott Belsky, Moiz Ali, Dan Held, Elaine Zelby, Michael Saylor, Ryan Begelman, Jack Butcher, Reed Duchscher, Tai Lopez, Harley Finkelstein, Alexa von Tobel, Noah Kagan, Nick Bare, Greg Isenberg, James Altucher, Randy Hetrick and more. — Other episodes you might enjoy: • #224 Rob Dyrdek - How Tracking Every Second of His Life Took Rob Drydek from 0 to $405M in Exits • #209 Gary Vaynerchuk - Why NFTS Are the Future • #178 Balaji Srinivasan - Balaji on How to Fix the Media, Cloud Cities & Crypto • #169 - How One Man Started 5, Billion Dollar Companies, Dan Gilbert's Empire, & Talking With Warren Buffett • ​​​​#218 - Why You Should Take a Think Week Like Bill Gates • Dave Portnoy vs The World, Extreme Body Monitoring, The Future of Apparel Retail, "How Much is Anthony Pompliano Worth?", and More • How Mr Beast Got 100M Views in Less Than 4 Days, The $25M Chrome Extension, and More