Podcasts about Saas

Have you ever had someone believe in you before you fully believed in yourself?For me, that person was my mentor, Susan.I first discovered Susan on Clubhouse while I was building Social Curator and trying to figure out the world of SaaS. After months of listening to her advice, I finally worked up the courage to ask her a question—and what she shared helped me raise $555,000 in less than an hour.But the money isn't what changed my life. Her belief in me did.In this episode, I'm sharing the business lessons Susan taught me about focusing on the plan instead of constantly reacting to problems, deciding what to keep, delegate, or let go, and why diluted focus creates diluted results.And then? EXECUTE.Because ideas and strategies are great, but they don't change a business until we act on them.This episode is also much more personal. I share what happened when Susan unexpectedly entered palliative care and the incredible gift of getting to tell her—in person—how profoundly her mentorship changed my life.Friend, this episode is about business, but it's also about something bigger: Tell people what they mean to you. Use what they taught you. And pass it forward.Click play to hear the lessons from one of the greatest mentors I've ever had—and why I believe her impact doesn't stop with me.Click play to hear all of this and:[00:00] Why having someone believe in you can completely change your trajectory[00:56] How I accidentally found the mentor who would transform my business[01:48] How one piece of Susan's advice helped me raise $555,000 in under an hour[03:22] Why founders need to give themselves credit for what's already working[05:20] How to stop reacting to problems and build around your 12-month plan[06:29] How to decide what to keep, delegate, or completely let go[07:14] Why execution matters more than another great idea or strategy[32:05] Why I finally told my mentor exactly what her belief in me meantListen to Related Episodes:Strategies that Have Transformed My Business with (My Coach) Susan SierotaEavesdrop on a Private Call with Me and My Business MentorBusiness Questions Answered: Roundtable Discussion with Brilliant and Successful Women

Bob Serling is the Founder of Licensing Lab, where he helps businesses improve their products, positioning, offers, and marketing through deep customer intelligence. He created the Customer Created Framework, which uses insights from actual customers to uncover why they buy and what experience they want. His work includes developing a skateboard toy featuring Tony Hawk's branding and co-creating licensed assessment software used by Fortune 500 companies. Bob also helps businesses identify valuable intellectual property and turn it into new revenue streams through licensing. Johann Nogueira is the Director at Ai Tech Advisory, a consulting company that helps businesses implement AI solutions and navigate digital transformation. He is a serial tech entrepreneur, SaaS founder, and AI strategist with more than two decades of experience building and growing companies. Johann has founded and scaled multiple technology ventures, including White Label Suite, and has completed several business exits. He is also the Founder of Business Authorities, an entrepreneur education platform focused on helping business owners scale through strategic insights and resources. In this episode… Licensing can turn existing ideas, processes, and expertise into scalable revenue without requiring an entirely new business model. How can entrepreneurs turn what they know into scalable revenue streams without adding more complexity or overhead? Veteran licensing strategist Bob Serling believes entrepreneurs often overlook the value of their own intellectual property, from specialized knowledge and business processes to marketing systems and unique solutions. He explains that licensing is less about creating something entirely new and more about finding ways to package existing assets so others can use, sell, or benefit from them. Johann Nogueira, a serial entrepreneur and business builder, shares how he applied these concepts across his companies through referral models, software licensing, and strategic partnerships. Their experiences reveal how licensing can create leverage, unlock new income opportunities, and help businesses grow beyond traditional models. In this episode of the Inspired Insider Podcast, Dr. Jeremy Weisz sits down with Bob Serling and Johann Nogueira to discuss how entrepreneurs can license intellectual property to create new revenue streams. They explore the toll booth method, strategic partnerships, and turning internal systems into valuable assets. Bob and Johann also share lessons from the Tony Hawk licensing deal, White Label Suite's growth, and using AI to uncover business opportunities.

All links and images can be found on CISO Series LinkedIn used to have a decent reputation among cybersecurity professionals. But lately, it feels like our feeds are relitigating the same debates every day, just with different faces. Has the situation changed? Check out this post by Allan Alford of NTT Global Data Centers for the discussion that is the basis of our conversation on this week's episode co-hosted by me, David Spark, the producer of CISO Series, and Geoff Belknap. Joining us is Howard Holton, founder, Phronia Counsel. In this episode: The clichés that say nothing A discipline without shared answers Old topics, new lenses A people problem, not a security problem A huge thanks to our sponsor, Teleskope Most DSPMs stop at finding the risk. Teleskope fixes this: it automatically finds sensitive data, including IP documents or board decks, and remediates exposure across cloud, SaaS, and AI environments natively, with human-in-the-loop controls, improving your team's efficiency tenfold. Trusted by Ramp, Polymarket, and Chevron Phillips, and more. teleskope.ai

Patrick McKenzie (patio11) reads his Bits about Money essay on how credit cards make money. The prompt was a listener who wondered how a card can include free travel insurance for someone who never carries a balance. He covers the four ways a card earns revenue (net interest, interchange, fees and marketing contributions), and explains why rewards competition makes some customers in the middle of the credit score ladder persistently unprofitable. He also explains why Europe's interchange cap left cards at about half of electronic payments, while Japan's uncapped interchange quietly pays for the rest of its consumer banking. In a new postscript, he walks through what a proposed 10% APR cap would mean for cardholders at the low end of the market, and how First Republic made sub-10% unsecured loans work by treating them as a way to win deposits.–Full transcript available here: https://www.complexsystemspodcast.com/why-banks-pay-you-to-use-their-credit-cards/  –Presenting Sponsors: Mercury & GranolaComplex Systems is presented by Mercury—radically better banking for founders. Mercury Spend hands your team and agents their own cards with limits you set once, so nobody waits on you to approve a SaaS invoice and nobody chases a receipt. Apply online in minutes at https://mercury.com/. If meetings consistently leave you with hazy action items and lost context, Granola handles the transcription so you can actually participate and gives you searchable notes afterward. Try it free at granola.ai/complexsystems with code COMPLEXSYSTEMS–Links:How credit cards make money: https://www.bitsaboutmoney.com/archive/how-credit-cards-make-money/ –Timestamps:(00:00) Intro(01:11) How credit cards make money(01:55) Bundling and unbundling(03:12) Revenue levers for credit cards(03:23) Net interest(06:19) Interchange(07:34) Interchange makes cards so valuable you're paid to use them(10:02) Fees(10:50) Marketing contributions(13:05) Debit cards: a horse of a different color(13:32) Sponsors: Mercury | Granola(16:54) Postscript(25:27) Wrap

 EPISODE DESCRIPTION In this episode, I sit down with Denis from Humea , a product and research company that has catalyzed over $1 billion in ARR for their clients. We go deep on what it actually takes to achieve product market fit, and why most founders are looking in completely the wrong direction. Denis brings a rare dual background in physics and psychology, and he uses it to reveal how human behavior , not technology , is the real driver behind whether a product succeeds or dies. We talk about why Web3 is uniquely challenging because money arrives before product market fit, the hidden psychology of why founders resist pivoting, how Doordash generated over $300 million in additional revenue through better user understanding, what blockchain banks must do to win trust, and why simplifying your message for non-technical decision makers might be the most valuable thing you do this year. Whether you are building in DeFi, fintech, AI, or SaaS, the principles Denis shares here are universal and immediately actionable. DISCLAIMERNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/ CONNECT Humea Website: https://humea.com/Humea LinkedIn: https://www.linkedin.com/company/humeaWeb3 with Sam Kamani: https://www.web3pod.xyz/ KEY POINTS WITH TIMESTAMPS • [00:00] Humea has catalyzed over $1 billion ARR for clients across fintech, banking, and infrastructure• [02:49] Denis explains his dual background in physics and psychology and how it shapes his approach to product research• [05:57] The difference between output and outcome , and why measuring the wrong thing gives you false positive data• [08:54] Why working with an agency should always start with a friendly conversation to find genuine alignment before any contract• [14:13] In Web3, money comes before product market fit , the complete opposite of the Web2 world• [17:42] The IKEA effect and sunk cost fallacy explain why founders get emotionally attached and resist pivoting• [23:08] Why mid-stage startups throw money at ads instead of fixing the underlying product and customer understanding• [28:24] Doordash case study , course correcting the user journey generated over $300 million in additional annual revenue• [31:45] Blockchain banking case study , turning a deeply negative NPS into a positive one through empathy-driven research• [33:20] The four things users want from banking: returns, versatility, problem solving for their segment, and trust• [34:04] Trust in crypto products is not about gaining confidence , it is about removing doubt• [38:19] Why human-crafted content creates energy and connection that AI-generated content currently cannot replicate• [43:58] Even in cutting-edge Web3, all major deals are still done human to human the old-fashioned way• [45:24] Most B2B decision makers are not technical , your communication strategy needs to reach non-technical buyers• [47:30] Polygon CDK dashboard case study , a brilliant engineer could not explain the interface to a colleague in another department

In this episode of FP&A Unlocked, host Paul Barnhurst and co-host Glenn Snyder sit down with Valerie Martin to discuss how AI is changing FP&A, leadership, and business partnering. They explore why greater efficiency does not automatically create greater value and how finance teams can use AI without losing judgment, context, or trust with the business.Valerie Martin is a Finance Executive specializing in FP&A and strategic finance, with experience across GTM, SaaS, AI, and value creation. A former Autodesk finance leader and San Francisco FP&A Board Ambassador, Valerie brings extensive experience in business partnering, finance transformation, and helping organizations make better strategic decisions.Expect to Learn:Why AI efficiency does not always create more value.How AI is changing the skills FP&A teams need.Why judgment and business context still matter.How leaders should rethink training and development.Why communication and trust remain critical in FP&A.Here are a few relevant quotes from the episode:“AI could help us get the answer faster, but you can't outsource accountability.” - Valerie Martin“The measurement is about the value, not the production.” - Glenn SnyderValerie explains that AI can automate variance analysis, reporting, data cleanup, and other repetitive work, but faster production is only useful if teams turn that saved time into greater business value. As AI takes on more technical work, FP&A professionals need to strengthen their judgment, curiosity, business acumen, and communication skills.Follow Valerie:LinkedIn: https://www.linkedin.com/in/valerie-martin-342878/Email: valsoniamartin@gmail.comFollow Glenn:LinkedIn: linkedin.com/in/glenntsnyderDisclosure: Portions of this episode (such as the introduction or promotional segments) use AI-generated voice narration produced under human editorial review.Explore Lineos Reporting Intelligence:Ready to transform financial reporting? See how Lineos Reporting Intelligence helps finance teams connect ERP data directly to Excel, access current data, and spend less time assembling reports.Watch the Reporting Intelligence demo:https://www.insightsoftware.com/lineos/resources/Reporting-intelligence-demo/?utm_source=thefpaguy&utm_medium=newsletter&utm_campaign=lineos-influencer-oct&utm_content=newsletter1Earn Your CPE CreditFor CPE credit, please go to earmarkcpe.com, listen to the episode, download the app, answer a few questions, and earn your CPE certification. To earn education credits for the FPAC Certificate, take the quiz on earmark and contact Paul Barnhurst for further details.In Today's Episode:[00:00] -Trailer[01:17] - Hard-Coded Opinion[04:54] - How AI Is Changing FP&A Skills[06:16] - Where AI Adds the Most Value[13:51] - When AI Efficiency Doesn't Create Value[15:03] - Judgment, Context & Accountability[19:46] - Redesigning FP&A Work for AI[20:51] - Why Communication Still Matters[27:35] - Turning Saved Time Into Greater Value[34:20] - What Differentiates Finance in the AI Era[41:22] - How FP&A Professionals Stay Valuable[46:27] - Closing & CPE Information

Chris Wysopal was one of the first hackers to go public. As "Weld Pond" at the L0pht hacker collective in Boston, he testified before the US Senate in 1998, where the group delivered the soundbite that they could take down the internet in 30 minutes. He also wrote the Windows version of Netcat. In 2006 he co-founded Veracode, which by his account has now analysed trillions of lines of code. Today he's the company's Chief Security Evangelist. Tobi and Chris talk about what AI changes for attackers and defenders. Attacks are getting cheaper and faster, and a custom exploit no longer tells you a nation-state is behind it. Chris argues this follows a familiar cycle: attackers adopt a new class of tool first, defenders catch up, and parity returns. That only holds if defenders actually adopt the tools, especially underfunded organisations like hospitals, schools and utilities. They also cover the new attack surface created by agents, plugins and MCPs, why prompt injection may never be fully solvable, and how Chris would handle security debt when acquiring a small SaaS company. CTOs will leave with a concrete shortlist: avoid memory-unsafe languages, put a package firewall in front of open source, run AI-assisted static analysis with real architectural context, and give coding agents explicit security intent rather than hoping "make it secure" does the job. - From the L0pht and BBS culture to the professional security industry - The BGP flaw behind "30 minutes to take down the internet" - How AI changes the cost and speed of attacks - Prompt injection, agents, MCPs and least privilege - Security debt in B2B SaaS acquisitions - Secure on first write: security intent, context and pre-merge testing - How engineering and security roles change over the next two years

Jeff Mains sits down with James Gourley, a patent attorney and IP litigator who has spent 20 years on both sides of intellectual property disputes — from filing patents and trademarks to defending companies against infringement claims. This episode is a practical, plain-English guide to the IP fundamentals every SaaS and tech founder needs to get right before the scary letter arrives. They cover trademark due diligence, the "work made for hire" trap with contractors, the unsettled legal frontier of AI-generated code, how trade secrets actually work, why software patents are getting slightly easier to obtain, and what a well-structured IP portfolio does for your valuation at exit. James's core message: IP protection is a rounding error in cost compared to what it saves you on the back end — and the regret is always about what you didn't do early enough.Key Takeaways0:00 — A trade secret is anything that gives your company a competitive advantage that you've taken reasonable measures to keep secret.1:46 — Most founders don't find out whether they actually own their company name until the worst possible moment — right before a raise or mid-acquisition.4:22 — The first thing founders should do when naming a company is check whether anyone else is already using a similar name for something similar.6:36 — Before you build and ship, run a freedom-to-operate search to see if someone already holds a patent you might be infringing.9:29 — To get a patent through the USPTO, your invention must be both novel and non-obvious over everything that came before.11:19 — Patent examiners are time-constrained and often miss prior art, so invalid patents do get issued — but they can be invalidated in court.16:18 — If you hire a contractor and the contract has no IP assignment clause, the contractor owns the work by default — not you.18:46 — As the law stands today, AI-generated code and content cannot be copyrighted — nobody owns it.21:12 — Trade secrets require you to take reasonable measures like restricting access on a need-to-know basis, not just calling something "secret."25:14 — Filing a trademark gives you presumptive nationwide rights as of the filing date, and it's incredibly cheap relative to other business costs.27:14 — Software patents may be getting slightly easier to obtain after a decade of difficulty, thanks to new USPTO examiner guidance.29:51 — When filing a software patent, think through future roadmap variations and alternative implementations — not just what you're shipping today.32:35 — Don't take matters into your own hands when someone copies you — get legal counsel to calibrate how aggressive you should be.34:15 — Never throw an infringement letter in the trash — an attorney can quickly tell you whether the plaintiff is serious or a paper tiger.36:02 — Fake, AI-generated law firms are sending cease-and-desist letters as shakedown scams — always verify the sender is real.38:21 — In the next 30 days, start with trademark registration — it's the cheapest, highest-leverage IP move you can make early.Tweetable Quotes"If you have AI create what would otherwise be a copyrightable work, nobody owns the copyright." — James Gourley, 18:46"You gotta have a good contract if you want to own the IP." — James Gourley, 18:22"It's never too early to start looking at protecting the trademark." — James Gourley, 39:04"The regret is usually not that I spent money on lawyers to protect the IP. It's that we tried to save a little bit of money and it's costing us a lot on the back end." — James Gourley, 39:14"IP isn't paperwork you get around to later. It's the ground your company is actually standing on." — Jeff Mains, 41:51"If you can be aggressive in response, sometimes it makes them go away." — James Gourley, 13:48SaaS Leadership Lessons1. Trademark diligence is free — skipping it is not. Before you name your company or product, run a basic search. The standard isn't exact match — it's "likelihood of confusion." A name that's spelled slightly differently or uses a shared key term can still trigger a dispute years later when both companies grow into overlapping markets. The cost of a trademark search is zero; the cost of rebranding mid-acquisition is enormous.2. No assignment clause, no ownership. The "work made for hire" doctrine covers employees by default — but contractors, agencies, and freelance developers own what they create unless your contract explicitly says otherwise. The wedding photographer example says it all: you hired them, you paid them, but without a transfer clause, they own the photos. Every contractor agreement your company signs should include an IP assignment provision. One clause changes everything.3. AI-generated IP is legally unownable — plan accordingly. If AI writes your code or generates your logo, you likely can't copyright it. The current legal landscape says AI-created works have no copyright owner — period. For hybrid human/AI codebases, you'd need to disclaim the AI-generated portions in a copyright registration. This is a bleeding-edge issue that will be litigated for years. Until then, founders building on AI-assisted code should understand they may have no legal recourse if that code is copied.4. Trade secrets demand least-privilege access, not just labels. Calling something a "trade secret" doesn't make it one. You have to show a judge you took reasonable measures — and that means limiting access to people who genuinely need it. A CRM that every employee can open weakens your claim. Restrict permissions, use document management tools, and document your access controls. If someone walks out with the data, you need to prove you tried to protect it.5. Patent for the roadmap, not just the release. Software evolves faster than the patent process. If you file on what you're shipping today but your product migrates six months later, your patent may no longer cover what you actually built. When filing a software patent, describe not just your current implementation but alternative approaches and future variations you can foresee. The patent is only as valuable as its ability to still cover what you're doing two years from now.6. Never ignore legal letters — and never respond emotionally. The two worst responses to an infringement letter are throwing it in the trash or firing back in anger. A 20-minute call with an experienced IP attorney can tell you whether the plaintiff has a history of filing suit or is just a copyright troll sending shakedown letters. The same attorney can tell you whether your own case is strong enough to be aggressive or whether you should approach gently. Calibrate before you act.Guest Resourcesgourley@caglaw.comwww.caglaw.comhttps://www.linkedin.com/in/jamesgourley/Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/https://jeffmains.com/books/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

On this episode, Jesse Bendit, Principal at Apax Digital, joins the show to unpack how a private equity firm builds and funds a 30-person internal operating team without letting it become bloated. Learn why a pull model beats a push model for utilization, how staying concentrated in 15 to 20 investments per fund lets a team build real sector depth, and how that depth shows up during diligence, not just after close.Hear why trust—rather than traditional SEO signals—is the new currency of visibility as search moves into LLMs. Plus, learn how to separate genuine AI risk from AI opportunity in a portfolio company, including why seat-based SaaS pricing is losing its power as a value proxy and how deep data and integration advantages can make AI a moat rather than a threat.The information contained in this podcast is not intended to constitute, and should not be construed as, investment advice.

a16z's David George, Sarah Wang, Alex Immerman, and Santiago Rodriguez unpack 25 key charts from the latest State of Markets presentation, from the scale of the AI infrastructure buildout to what adoption looks like inside companies today.They examine why rising markets have so far been supported by earnings rather than multiple expansion, why hyperscaler CapEx is approaching $1 trillion annually, and why demand for compute continues to outrun supply. They also look at the downstream effects of that spending across chips, power, construction, and physical infrastructure. State of MarketsThen they move up the stack: OpenAI and Anthropic's revenue growth, the gap between AI deployment and measurable enterprise impact, the rise of agents, falling inference costs, and what all of this means for SaaS. They close with where the team is spending time next, including consumer agents, robotics, autonomy, AI and biology, personal health, defense, and the continued diffusion of AI across the enterprise. State of MarketsResources:Follow David George on X: https://x.com/DavidGeorge83Follow Sarah Wang on X: https://x.com/sarahdingwangFollow Alex Immerman on X: https://x.com/aleximm Follow Santiago Rodriguez on X: https://x.com/santiago__rdz Read David's piece ‘There are only two paths left for software': https://a16z.com/there-are-only-two-paths-left-for-software/ Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Jason Zigelbaum has grown Zigpoll into a one-person SaaS generating about $142,000 in MRR, or roughly $1.7 million in annualized revenue. In this episode, he shares how 90% of the business comes through the Shopify App Store and why reviews, churn reduction, onboarding, and customer feedback have driven much of its growth.   Jason also explains how he uses AI across product development, marketing, design, and data analysis while still handling customer support himself. We also discuss pricing, annual plans, moving upmarket, and his plans to take Zigpoll from $2 million toward $4 million in annual revenue.     Sponsor: Quiet Light Get a free, confidential valuation at https://quietlight.com/!     Be sure to get more content like this in the Niche Pursuits Newsletter Right Here: https://www.nichepursuits.com/newsletter ----------------------------------------------------------------------------------------------------------------------- Want a Faster and Easier Way to Build Internal Links? Get $15 off Link Whisper with Discount Code "Podcast" on the Checkout Screen: https://www.nichepursuits.com/linkwhisper ----------------------------------------------------------------------------------------------------------------------- Get SEO Consulting from the Niche Pursuits Podcast Host, Jared Bauman: https://www.nichepursuits.com/201creative -----------------------------------------------------------------------------------------------------------------------     Links & Resources Learn more about Zigpoll: https://www.zigpoll.com/ Install Zigpoll: https://apps.shopify.com/zigpoll Connect With Jason: https://www.linkedin.com/in/jason-zigelbaum      - You can learn more about me and get additional website tips at: https://www.nichepursuits.com - You can learn about optimizing your site with internal links using a WordPress Plugin I created right here: https://www.linkwhisper.com   Thanks for watching and please consider subscribing to the main Niche Pursuits YouTube channel by clicking here: https://www.nichepursuits.com/youtube 

Un agent IA fouille vos comptes bancaires, retrouve vos abonnements oubliés et récupère des centaines de dollars en moins de deux heures. Muse, l'agent de Meta, ne répond pas : il agit. Derrière son adoption fulgurante se cache une question que personne ne pose encore — les boîtes construites sur votre inattention sont-elles déjà condamnées ?Zuckerberg joue un contre-pied radical. Pendant que les grands labos agitent la peur pour vendre, il mise sur l'utilité immédiate. Ce choix n'est pas anodin : c'est une bataille narrative qui va redessiner toute l'industrie.En Europe, on hésite encore. Et cette hésitation a peut-être déjà un coût.===================⏱️ DANS CET ÉPISODE :===================00:00 — Sommaire02:27 — Muse : l'agent IA qui agit vraiment à votre place05:40 — Muse Charm : Meta glisse son agent dans votre poche06:20 — Zuckerberg contre le récit apocalyptique de l'IA07:36 — La peur, arme commerciale des grands labos IA10:14 — L'IA peut-elle vraiment libérer du poids de l'administratif ?11:52 — [Sponsor] : Qonto, gérez votre compte pro avec l'IA !13:12 — Ces agents pourraient-ils provoquer un bank run ?15:10 — Les SaaS bâtis sur votre inattention sont en danger17:05 — Seules les boîtes à valeur réelle survivront19:03 — L'IA compresse le temps, pas les emplois21:14 — Utiliser l'IA : liberté créative ou macartisme ?23:23 — Pourquoi les meilleurs ne craignent pas l'IA25:53 — L'agent vous éclaire, mais vous décidez toujours26:47 — Réguler ou adopter : l'Europe fait-elle fausse route ?==================

Et si le principal frein de votre entreprise, c'était vous ?Pendant des siècles, on a construit les entreprises autour des humains.Des process pensés pour des humains, des outils pensés pour des humains, un savoir qui vit dans la tête des humains.Pierre-Louis Biojout pense que ce modèle est en train de s'effondrer.Sa thèse est simple : une entreprise ne fera plus travailler des salariés, mais des agents IA.Des systèmes qui lisent l'information, prennent des décisions, agissent seuls, et tournent pendant que vous dormez.Donc si une boîte fonctionne pour faire tourner des agents IA, alors ils peuvent opérer toute la structure.C'est là que le rapport de force s'inverse.Une multinationale doit réorganiser des milliers de personnes, des outils fermés et des années de contexte jamais écrit nulle part.Idem pour un solopreneur qui démarre aujourd'hui, il peut tout construire avec et pour les agents, et aller infiniment plus vite.Pour le prouver, Pierre-Louis a fait une expérience.Il a donné à un agent ce dont un fondateur a besoin pour faire ses premiers euros : une carte bancaire, un nom de domaine, un email, un serveur et des objectifs.Le lendemain matin, il y avait deux ventes et une semaine plus tard, sept.On parle dans cet épisode :Des métiers d'exécution qui vont disparaître, et de pourquoi il y voit une libération plutôt qu'une menaceDes agents trop motivés, capables de tenter de pirater un fournisseur pour atteindre leur objectifDu coût réel de l'intelligence, qui baisse à tâche égale mais dont la facture exploseDe la course entre modèles américains / chinois, et de la place de l'EuropeUn épisode pour définitivement comprendre que l'IA fera votre travail. À vous de choisir si c'est à votre place ou avec vous.Vous pouvez contacter Pierre-Louis sur Linkedin et X.Je vous ai négocié un code promo : GDIY2026 pour -50% sur les deux premiers mois de Nanocorp.TIMELINE:00:00:00 - Qu'est-ce qu'un agent IA et comment ça marche concrètement00:16:59 - Polytechnique et Y Combinator : un parcours atypique00:30:20 - Se confronter à l'échec rapidement00:40:29 - Allons-nous devenir des managers d'IA ?00:49:49 - Comment rendre nos IA autonomes ?01:02:40 - Le commencement de Nanocorp01:11:06 - Peut-on vraiment créer des entreprises avec Nanocorp ?01:21:54 - Concurrencer les gros SaaS en quelques sessions de code01:28:33 - Les projets les plus fous créés sur Nanocorp01:40:38 - Comment réduire le coût de ses tokens01:47:58 - Quels sont les prochains gros changements des modèles ?01:55:07 - Des entreprises sans aucun humain ?Les anciens épisodes de GDIY mentionnés : #564 - VO - Mati Staniszewski - ElevenLabs - "Humans were never meant to write, but to speak."#564 - VF - Mati Staniszewski - ElevenLabs - « L'humain a été fait pour parler, pas pour écrire »#560 - Rudy Gobert - 4x meilleur défenseur NBA - Le druide de la longévité#531 - Mathias Frachon - The Product Crew - IA et agents, tout part en vrille, il est temps de vous y mettre#429 - Nicolas Dessaigne - Y Combinator - Le berceau des futurs géants de la techNous avons parlé de :Y CombinatorÉcole PolytechniqueSan Francisco / Silicon ValleyNanocorpAnthropic (Claude)Claude CodeOpenAI (ChatGPT)StripeNvidiaIntelligence artificielle générale (AGI)AirbnbDeepSeekMistral AIHugging FaceAlain Aspect (Prix Nobel de Physique)Les recommandations de lecture :Ultra-intelligence : Jusqu'où iront les IA ? de Emery CrouchetUn grand MERCI à nos sponsors : Squarespace : https://squarespace.com/doitQonto: https://qonto.com/r/2i7tk9 Brevo: brevo.com/doit eToro: https://bit.ly/3GTSh0k Payfit: payfit.com Club Med : clubmed.frCuure : https://cuure.com/product-onely (code DOIT)Rejoignez-nous sur la communauté GDIY : https://communaute.gdiy.fr/c/bienvenueNos formations avec l'EDHEC : https://www.letincelle.edhec.edu/Vous pouvez retrouver la liste de tout le matériel utilisé pour enregistrer nos épisodes sur cette page.Vous souhaitez sponsoriser Génération Do It Yourself ou nous proposer un partenariat ?Contactez mon label Orso Media via ce formulaire.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

On the podcast: growing for four years on press and organic alone, affiliate deals as an underexplored revenue stream, and why deciding to pivot is the hardest part.Top Takeaways:

Alex Bovee, CEO and co-founder of C1.ai (formerly ConductorOne), returns for his third appearance on Identity at the Center. Alex walks Jim and Jeff through the rebrand to C1.ai and why the .ai matters: identity now covers humans, workloads, and agents. The conversation opens on launch week and the new App Hub, built around a question many CIOs and CISOs are facing. What happens when everyone in the company becomes a builder and vibe-coded apps start running critical workflows?From there, Alex frames the difference between humans, software, and agents using two dimensions, trustworthiness and determinism, and explains why agents that "goal max" call for runtime enforcement instead of relying only on after-the-fact reviews. He breaks down the Hugging Face breach, where an OpenAI agent under evaluation escaped its sandbox in pursuit of better eval results, and lays out six control points for agent security: identity, the harness, network egress, data and tools, the LLM gateway, and credentials.The group also covers why IGA fundamentals speed up AI adoption, three buckets of agents (SaaS, enterprise, and personal productivity), agents evaluating other agents, governed swim lanes over shutting things down, and the slept-on problem of credentials sprawling across endpoints. Plus girl dads, boy families, and the return of the Royal Octopus.Made possible with support from C1. Learn more at c1.ai/idacConnect with Alex: https://www.linkedin.com/in/alexbovee/Learn more about C1: https://www.c1.ai/idacConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at idacpodcast.com00:00 Intro00:33 Welcome back, Alex Bovee01:28 From ConductorOne to C1 and the .ai rebrand04:14 Launch week and the App Hub07:07 Farm-to-table software and the limits of off-the-shelf SaaS09:56 The real risks of vibe-coded apps12:52 Humans, software, and agents: trust and determinism17:10 UARs matter more for agents17:59 What happened in the Hugging Face breach21:23 Six control points for securing agents25:38 Where should teams focus first?31:33 Meeting customers where they are33:02 Why IGA basics come before AI adoption34:27 AI accelerates bad IAM35:45 Governance versus business speed37:51 SaaS, enterprise, and personal productivity agents41:19 Runtime enforcement and agents evaluating agents44:22 Can you train an agent not to goal max?47:33 Governed swim lanes, not shutdowns50:33 Publishing a vibe-coded app through App Hub53:03 The slept-on credential problem56:04 Girl dads, boy families, and the Royal Octopus1:00:54 Wrap-upIDAC, Identity at the Center, Jeff Steadman, Jim McDonald, Alex Bovee, C1, C1.ai, ConductorOne, Sponsor Spotlight, identity and access management, IAM, identity governance, IGA, AI agents, agentic AI, agentic enterprise, non-human identity, NHI, workload identity, machine identity, vibe coding, App Hub, Hugging Face breach, OpenAI, agent evals, goal maxing, runtime enforcement, runtime security, MCP, MCP gateway, LLM gateway, network egress, agent harness, just-in-time credentials, secrets management, shadow AI, user access reviews, UAR, joiner mover leaver, delegated authorization

Three months ago Dwarkesh, who has been posting incredible blogs and episodes about RL, posted a framing question for his video essay on RLVR which upset a lot of Computer Use folks:We are no strangers to learning in public and are no strangers to the stress of getting things wrong when you have a big platform. However, we were at Anthropic for the Computer Use launch, there for Claude Cowork with the first big podcast on it, organized the first Computer Use track at AIE presenting the state of the art, and were close to the OpenAI-Sky Software acquisition that now powers the complete domination of computer use that Codex enjoys today. This is why we're excited to bring you today's first guest, Ari Weinstein, cofounder of Sky and now leading all the amazing CUA progress that casuals might miss:Ari explains why Computer Use is now “180 degrees different” from where it was months ago, how agents are learning to debug and recover from failures, why combining screenshots with accessibility data, the DOM, Playwright, and generated code changes the speed equation, and why the next frontier is making agents literally superhuman at using software.OpenAI clones JevIn the second half, Nikunj Handa from OpenAI's API team breaks down the new developer stack: async tool calling, mid-turn steering, WebSockets, UltraFast inference, the Decisions API, prompt caching, pre-warming, compaction, and the Agents API. Given that we were the first Jev podcast, we particularly focus on the unusually fast sprint on the Decisions API:And why it is just a Luna wrapper for now but the team is motivated and egoless enough to clone what they consider to be good patterns.We discuss:* Why OpenAI thinks Computer Use has changed dramatically in just the last few months* Dots and what changes when every agent gets its own Linux computer* Why Computer Use can now complete some tasks faster than the average human* The path from human-level to “literally superhuman” computer use* Why modern agents are much better at debugging and recovering from failure* How screenshots, accessibility trees, the DOM, Playwright, and generated JavaScript work together* App Shots and why they give models much richer context than ordinary screenshots* Why Computer Use can close the loop between writing software and testing it* Trust, permissions, and safety when agents can make payments and operate websites* Async function calling and why models no longer need to stop reasoning while tools run* Mid-turn steering, WebSockets, and the architecture behind more responsive agents* UltraFast inference and how OpenAI is pushing frontier models toward much lower latency* The rapid internal story behind the Decisions API* Why Decisions API is more than structured outputs at low latency* GPT Live, fast tool calling, and real-time computer control* How OpenAI is already using Decisions API for support classification and internal workflows* Longer prompt caching, cache pre-warming, and cache-aware applications* Server-side compaction vs manual compaction for long-running agent threads* What should live inside an Agents API versus a developer's own harness* OpenAI as an “AI cloud” and the search for higher-level primitives beyond raw model APIsAri Weinstein* Product & Engineering, Computer Use at OpenAI* X: https://x.com/AriX* LinkedIn: https://www.linkedin.com/in/weinsteinari/Nikunj Handa* Product, API at OpenAI* X: https://x.com/nikunjhanda* LinkedIn: https://www.linkedin.com/in/nikunjhanda/Timestamps00:00:00 OpenAI DevDay: Dots, GPT-6.1, Agents API, and Decisions API00:02:52 Dots and Personal Cloud Computers00:04:59 Why Computer Use Is “180 Degrees Different”00:06:04 From Sky to Self-Debugging Computer Use Agents00:09:24 How Computer Use Sees and Operates Software00:12:09 From Faster Than Humans to Superhuman Computer Use00:16:03 Agents API: Trust, Permissions, and Safety00:17:31 Computer Use for Coding, Testing, and QA00:19:14 GPT-6 APIs, Async Tool Calling, and UltraFast Inference00:23:21 The Rapid Story Behind Decisions API00:25:32 What Decisions API Is and How It Works00:30:24 What OpenAI Is Building With the New APIs00:32:23 Prompt Caching, Pre-Warming, and API Performance00:35:20 Context Compaction for Long-Running Agents00:37:13 Memory, Higher-Level APIs, and the AI CloudTranscriptIntroduction: OpenAI DevDay and the New Agent StackVibhu [00:00:00]: Okay. We're very excited to be here. Today is OpenAI DevDay. Special podcastSwyx [00:00:08]: We're the first podcast after your livestream.Vibhu [00:00:10]: First podcast. We have Ari here, who leads the product and engineering team for Computer Use agents. Before we kick in and dive deep on Computer Use, you wanna give a quick recap? What was announced? What's the quick slew of announcements you guys had today?Ari Weinstein [00:00:24]: Yeah. yeah, it was a super exciting day. we just got out of the keynote. It was really sick. there were a bunch of Computer Use announcements that I think are worth thinking about. We have, Dots, which is the new, sort of personal assistant product, and, that has some really exciting Computer Use features. There's GPT-6.1 Sol, which is this amazing new model, that I think is particularly great for Computer Use ‘cause of, sort of the cost and speed, advantages. I think, I think we shared that it's, a fifth of the cost of Astra and a seventh of the cost if you're looking at Computer Use specifically, which is really amazing. sorry, there were so many things. I'm trying to sort through it.Swyx [00:01:02]: And the API.Ari Weinstein [00:01:03]: Agents API, which now has Computer Use in it, which is really cool, ‘cause now developers can build on the same Computer Use, that is part of Codex, and ChatGPT. and then there were some demos of our existing Computer Use features, like app shots, where you can take the context of something you're doing on your computer and bring it into Codex and ChatGPT really fast. And then, like, native Computer Use on your Mac, where Roman had it taking screenshots of his app, automatically, and he could do other things on his computer while Computer Use was using his applications. so yeah, really exciting keynote.Swyx [00:01:35]: And not to mention the Decisions API.Ari Weinstein [00:01:37]: Decisions API.Swyx [00:01:38]: Off the bat, are they all the same model? Like, this is. Or the same dataset distilled to different models?Swyx [00:01:44]: Like, basically, like, is Computer Use using Decisions API, or are they, like, kinda separate?Ari Weinstein [00:01:49]: So what's really cool about the Decisions API is it, you know, it has all these new capabilities. It does inference in parallel. it doesn't have reasoning. It's a smaller model, than the ones we use for Computer Use. and so those capabilities make it really fast.Dots and Delegating Work to a Cloud ComputerSwyx [00:02:07]: Yeah.Ari Weinstein [00:02:07]: They also make it a little bit less good at doing, like, long horizon, sort of sophisticated tasks. And so I think I would say it's still an open area of research for how we, like, bring those approaches together. But, yeah, I'm really excited to see what people build with the Decisions API.Vibhu [00:02:24]: One of the interesting things is Dots now have attached personal computers.Ari Weinstein [00:02:28]: Yeah.Vibhu [00:02:28]: So it seems like they're very much more persistent. You've been using them for a while. How should people push the bounds? Like, what should people aim for? What should they try? Personally, right now I use it for a lot of customer service. LikeAri Weinstein [00:02:41]: CoolVibhu [00:02:41]: “Oh, this was wrong. I don't wanna sign in. I don't wanna authenticate.” Find whatever and just get it fixed.Ari Weinstein [00:02:45]: Yeah.Vibhu [00:02:46]: How should we push further? What should people try?Ari Weinstein [00:02:50]: Dots Are a really cool product because each Dot has access to its own Linux virtual computer in the cloud, which is different from our other products. you know, traditionally, we've have access to a browser in the cloud, or it has access to your own computer, but now you get your own entire Linux computer in the cloud. And so it can run full desktop applications, and it can also use a web browser. And so, yeah, you know, I think the powerful thing about Computer Use and the reason why I think it's so, exciting is because it makes it so that the agent can do anything you as a, as a person can do, because all the software in the world was designed for humans, and now agents can use that same software, and you can delegate to the agent. So, yeah, like, anything that you would do on a computer, you can ask a Dot to do. Yeah, I think what particularly is useful is gonna really depend on who the end user is and what- what's valuable in their life. but yeah, I would just start by thinking about, like, one of the things that you spend time on and how could you delegate those to an agent.Swyx [00:03:47]: Yeah, a lot of flight booking and shopping and honestly even, like, playing a game or whatever, right?Ari Weinstein [00:03:52]: Totally.Swyx [00:03:52]: Yeah.Ari Weinstein [00:03:53]: Yeah, I don't know. For me, something I did recently, I've been working on. I've, subscribed to a meal prep service ‘cause I was trying to, like, eat healthy, you know? And I really like this meal prep service I found because it lets me customize the meals I order to, like, a high degree of granularity. So I can say, like, “I want this many grams of chicken and this many grams of rice.” but it was so complicated. It took me two hours to do an order, and I found that I could ask Computer Use to do it for me, and it did it in 15 minutes. so I actually saved two hours. it both did it eight times faster than I could, and it saved me two hours on GPT-6.1 Sol.Swyx [00:04:32]: Yeah.Ari Weinstein [00:04:32]: So those are the kinds of tasks that I feel like, are really powerful.Swyx [00:04:36]: As a creator, I can tell you automatically, immediately, my number one use case is automating YouTube.Ari Weinstein [00:04:40]: Nice.Swyx [00:04:40]: Because, YouTube doesn't expose a lot of things via API.Ari Weinstein [00:04:43]: Yeah.Swyx [00:04:43]: And you have to just put it in a VM and just, like, run it, for, like, let's say, let's say their AB testing feature or making community posts. None of this is available by API ‘cause they hate developers.Swyx [00:04:53]: Anyway, so,Ari Weinstein [00:04:55]: I've heard that from our developer experience team too. They use it with YouTube a lot. Yeah. It's really awesome.Swyx [00:04:59]: So I wanna draw for, you know. let's say, I wanna get a little bit spicy. One of our, the leading AI podcasts, our friends, is famous for saying that Computer Use hasn't advanced in the last two years.How Computer Use Has Changed in the Last YearAri Weinstein [00:05:12]: Yeah.Swyx [00:05:13]: Which is a very interesting statement, and I think you're one of the best people in the world to talk about this, like, how have things have progressed, right?Ari Weinstein [00:05:20]: Yeah. You know, they said that a few months ago, I think, and I hope they have a different perspective now because Computer Use is, like, 180 degrees different than it was.Swyx [00:05:26]: He's a, he's a tough guy to impress.Ari Weinstein [00:05:27]: Yeah, okay. well, we're working on it.Swyx [00:05:30]: But, you know, you worked on. You've, like, basically spent your whole career working on, like, some kind of computer automation, right?Ari Weinstein [00:05:34]: Yeah.Swyx [00:05:34]: Like shortcutsAri Weinstein [00:05:35]: YeahSwyx [00:05:35]: At Apple, and then Sky, and then, and then joining OpenAI. Can you draw, like, what your through line is for, like, what is driving you and what- you, what wasn't possible back then maybeAri Weinstein [00:05:47]: Yeah.Swyx [00:05:48]: And, like, what your sort of milestones were.Vibhu [00:05:49]: I guess to add on to that as a follow-up question, what's the major change from using Codex Computer Use from, like, last weekAri Weinstein [00:05:57]: YeahVibhu [00:05:57]: Through to today? Is it model? Is it dots? Is it harness? So all the history plus what really just changed in today's announcements?Ari Weinstein [00:06:04]: Yeah. On the through line, I guess I've always been excited about automation and helping people automate tasks because then you can, like, save time in your life and focus on things that are more important to you than, like, operating a computer very intricately. And so, yeah, that was why we worked on some of those products. I was at Apple before. we made a company called Sky. we ended up joining OpenAI, which is really exciting. and I think something that wasSwyx [00:06:27]: And almost like you have to hack around Apple until Apple was like, “Fine, like, we'll just hire you and you can just work on the inside,” right? Like.Ari Weinstein [00:06:35]: It was, it was a cool place to get to work. what was really interesting looking back at Sky is we were, we were working on Computer Use there as well, and the models were so much less capable. And now the models, just in the last one year, have become extraordinarily capable at Computer Use. I think the biggest delta that I see is before they could, like, reliably start tasks, but then they would run into problems, and now they're really good at debugging. They're really good at trying again, introspecting what is and isn't working. and I think we've also brought the Computer Use the Computer Use field itself has moved forward. I think we're using more techniques. now Computer Use, often writes code. So if you actually look at it in Codex and you expand the tool calls manually, you can see that it's not just doing one action at a time. It's actually writing JavaScript code that it executes, that the computer executes to perform sometimes many actions at once, which is a great, you know, speed up and great capability. We use more accessibility, sort of multimodal interfaces. So, the model may use screenshots, it may use accessibility, it may use Playwright. it can use a lot of different mechanisms, based on the task at hand. and then, yeah, the model acceleration has been, has been just amazing. So, yeah, what's different today? I think we're making computers better all the time, so I think just, like, one day's difference, is probably a little bit less consequential than, like, even the past month or the past two months. but, yeah, I think the Computer Use in Dot is really exciting as well as, the new model that we came out with.Measuring Computer Use and Improving the HarnessVibhu [00:08:03]: On the keynote, Tejal was mentioning 7x improvements in Computer Use speed, a lot better on a few benchmarks. How do you guys think about measuring it? Computer Use is one of those things where, as you say, you know, it's improvements over time.Ari Weinstein [00:08:20]: Yeah.Vibhu [00:08:20]: Is it harness? Is it model? Is it post-training?Ari Weinstein [00:08:22]: Right.Vibhu [00:08:22]: How do you guys look at it internally about measuring how good it is, and what were the changes with the new model?Ari Weinstein [00:08:29]: We actually have a bunch of different ways of measuring it, some of which are on different permutations and configurations of the harness. It's a bit of a complicated story because, you know, our production products have, you know, some more safety checks, and, you know, those are configured differently based on the needs of the, of the task at hand. So there's a lot of ways to measure it, but I think regardless of how we measure it, we find pretty consistent gains. and those gains are, sometimes in the harness and sometimes in the model. and yeah, I was really excited by this result that GPT-6.1 is even more cost-effective for Computer Use than its baseline cost improvement as compared to Astra. It's, like, really cool to see.Swyx [00:09:10]: Yeah. I mean, one of the visuals I really liked from the livestream was that, you're sort of improving the Pareto frontier of, your, curve, and there was a lot of talking about how you're improving it together with the harness.Ari Weinstein [00:09:24]: Yeah.Swyx [00:09:24]: Can you give some examples of aha moments that you had, whether it's on, like, model driving the harness driving the model, whatever?Ari Weinstein [00:09:32]: I don't mean to repeat myself, but I think, like, introducing more modalities has been really powerful.Swyx [00:09:36]: Okay.Ari Weinstein [00:09:36]: One more specific example of that is, in the past, I think we saw a lot of Computer Use, products had to spend a lot of time, like, scrolling, you know? So it would, like, take a screenshot. It would try to do something. It would be like, “Oh, I gotta, like, scroll down to the next page of results,” and then it would take a screenshot, and then it would try to do something. It would scroll down again. And so I think, with accessibility and other. and, direct access to the DOM and other things like that, now the language model can actually see, like, an entire page or an entire application. It can write code that can do multiple steps at once. And so I think those have been probably the biggest single aha moments. There's, like, a lot of tiny ones that are less exciting in comparison, but actually we do find also that a lot of speed improvements are driven by, like, a lot of little paper cuts that we gotta go in and introspect.App Shots, Accessibility, and Better Computer ContextSwyx [00:10:21]: Yeah. A lot of really hard engineering.Ari Weinstein [00:10:23]: Yeah.Swyx [00:10:23]: I mean, app shots in general, right? Like, I think people don't quite get the difference if. because there's, like, a nice visual in Codex when itAri Weinstein [00:10:30]: YeahSwyx [00:10:30]: When you take an app shot, but they don't maybe they get the difference that, you are able to actually drive each button and you have the, you have each text, in a very optimal representation.Ari Weinstein [00:10:40]: Yeah. Exactly. Yeah. It's kind of fun actually. If you wanna be, like, really nerdy about it, you can go into Codex, take an app shot by hitting the two command keys. So you grab the content from whatever app you're working with, bring it into the, Codex or ChatGPT chat. And then the. if you click on the attachment and you click on this, like, little tiny button in the top right, you can see the raw text and you see the raw accessibility representation. And yeah, we've put a lot of work into, puttingSwyx [00:11:04]: Just dumping everything out. Yeah.Ari Weinstein [00:11:05]: Dumping it out, but also making it token-efficient, doing it efficiently. There's, like, a bit of an art to it. And, you know, it turns out that the same technology that was invented for humans, you know, who maybe have accessibility needs, who wanna use a screen reader technology, that technology is really helpful for them to be able to use computers. It's also really helpful for LLMs to be able to use computers. So that's been, like, really fun to get to work on.Vibhu [00:11:27]: For context, I feel like a lot of people don't understand app shots. They don't even know it's a feature.Ari Weinstein [00:11:30]: Yeah.Vibhu [00:11:31]: It's when you double hit command, it pulls in what looks like a screenshotAri Weinstein [00:11:34]: RightVibhu [00:11:34]: And you're like, “Oh, why have I opened up just a screenshot and thrown it in?” No, it's actually pulling all the metadata, all the code, everything.Ari Weinstein [00:11:40]: Yeah, exactly. Yeah. So it's like, you know, if you take a screenshot of a webpage that has a link- The screenshot doesn't include where the link goes. It doesn't include, you know, maybe you take a screenshot of your calendar, the ca- event ti- titles are truncated, you know? But when you take an app shot, it gives, like, the language model, like, full context about everything and, that lets it, just sort of, like, do much more.Swyx [00:12:02]: Yeah. For those who wanna see more, Jason Liu, I invited him to do a full workshop on this, at AI Engineer.Ari Weinstein [00:12:07]: Amazing.Swyx [00:12:08]: Did a great job.Vibhu [00:12:09]: I have a broader vision questionToward Superhuman Computer UseAri Weinstein [00:12:11]: YeahVibhu [00:12:11]: On Computer Use agents. So your example of take a screenshot, scroll page, take a screenshot is where we were.Ari Weinstein [00:12:17]: Right.Vibhu [00:12:17]: Today, they can automate a lot. what are the bottlenecks? Is it models? Is it harnesses? What. Where do you see it going in, like, two years? Do you see it just running for hours? How do we get there? Any predictions on where Computer Use goes?Ari Weinstein [00:12:32]: Yeah. I mean, I think what's really crazy that I think, You know, the team's accomplished over the past couple of months is that now Computer Use is, like, faster at accomplishing tasks than, like, the average human probably in most cases. and I think that the next frontier is to have Computer Use be, like, literally superhuman in its performance where it actually is as fast or faster at using software than, like, expert Computer Users like us. and I think that'll be really consequential and exciting when that happens because I think we'll be able to all of a sudden build products, that, provide just much more real-time experiences. And I think it'll also. lowering the barrier to entry of, or the activation energy, I suppose, of using Computer Use I think will make us start to default to doing certain things in agents that we've become accustomed to doing manually. And I think that's exciting also ‘cause it'll save us a ton of time. and I think there's a, you know, there are a lot of different little paper cuts and bottlenecks that are sort of standing in the way of that. I think that there's, yeah, there's things on the model side, there's things on the inference side, there's things on the harness side, there's things in the, in the representation. You know, we find that as Computer Use gets faster, we're increasingly bottlenecked by just, like, the speed of doing an operation. Like, for example, you know, a non-trivial amount of time in our benchmarks of Computer Use tasks is actually, like, let's say you're automating a task on doordash.com. Like, a lot of the time is actually waiting for doordash.com itself to load, you know?Swyx [00:14:04]: Yeah, then you just write a wait and then you execute the wait.Ari Weinstein [00:14:07]: Yeah, totally. And you wanna get. Yeah, actually, it's actually really important that you get that de- like, you want as little delay as possible between when it finally finishes loading and when you go andSwyx [00:14:16]: YeahAri Weinstein [00:14:16]: Trigger the LLM to do the next action, which is actually- itself a statistical science.Swyx [00:14:20]: Like an event-driven way maybe to do that.Ari Weinstein [00:14:22]: When possible, you want it to be event-driven.Swyx [00:14:24]: JavaScript has some load events.Ari Weinstein [00:14:25]: And JavaScript has load events for. or the web browser has load events for web navigation, but there's other types of events that actually really can't be event-driven. So there's a lot of complexityVibhu [00:14:34]: The one that comes to mind is, like, chatting with customer service.Ari Weinstein [00:14:37]: Yeah.Vibhu [00:14:37]: Replies could take 30 seconds, could take three minutes.Ari Weinstein [00:14:39]: Oh, right.Swyx [00:14:41]: I have dealt with so many bots with Codex. it's great, but I also wonder if the other side knows that they're talking to a bot ‘cause I'm, like, answering in complete sentences. Like, I'm capitalized correctly.Ari Weinstein [00:14:50]: That's hilarious.Swyx [00:14:51]: Like, I'm giving full num- full reference numbers and everything. Like, it's too. it's clearly too good. I don't care. Like Like, I'm just, like, trying to get my support case.Vibhu [00:14:58]: I've prompted it to, like, you know, “Don't pretend you're a bot. Be very annoyed human.”Vibhu [00:15:02]: Short one-liners, likeSwyx [00:15:04]: YeahVibhu [00:15:04]: Push it, do all this. I also tell it, “While you're waiting for responses, like, use subagents to research better ways to figure out what we need.”Ari Weinstein [00:15:12]: Nice.Vibhu [00:15:12]: It's just, like, human little intervention.Ari Weinstein [00:15:14]: That's awesome. I also feel like half the time it's a bot on the other end, so now youVibhu [00:15:17]: YeahAri Weinstein [00:15:17]: Got the bots talking to each other.Swyx [00:15:18]: Yeah. I will also say, you know, like, you know, one milestone of Computer Use that we are, we're at now is, you know, three, four years ago, we were scared of hooking up LLMs to the, to the web and toAri Weinstein [00:15:31]: YeahSwyx [00:15:31]: To our, to our devices. And now I'm having it configure DNS for me.Ari Weinstein [00:15:35]: Wow.Swyx [00:15:36]: I'm having it pay my bills, and, like, really, like, tens of thousands of dollars of, like, stuff I'm just sending it over and yoloing with Computer Use and, like, you know, what's the, what's the worst thing that can happen?Swyx [00:15:48]: So that- that's all, that's all really good.Building Safely With Computer Use in the Agents APIAri Weinstein [00:15:50]: Yeah.Swyx [00:15:50]: I think now that you've. you know, obviously, you also have to dogfood your own products and all these things. Now that you've sort of released this in API, what are some pitfalls or tips that you wanna tell developers, because they're about to, I guess, encounter all this, firsthand?Ari Weinstein [00:16:03]: First of all, I'm just really excited that we brought Computer Use into the Agents API. I think this is, really great because obviously a lot of developers are building applications that wanna be able to work with third-party websites and services. And so Computer Use has this universality to it. It can work with anything. So now all of a sudden, developers can build using the same Computer Use implementation that we're building on. I think there's great work to be done if you wanna build your own Computer Use harness, but it's hard. And also, we train our models on our Computer Use harness, so there is, an advantage to using the one that's in distribution for the model. There actually might be a speed and cost and accuracy advantage. So I think it's really great for people to get to build on top of that. And, yeah, you know, I think kind of to the point that you were making, like, I think we're all sort of still in the process and maybe, like, some of us are ahead of many people in the world of, like, getting comfortable with this technology and trusting it. And so I think it's incumbent on us to, sort of build that trust over time by making sure we're building things that are reliable, by building, the right kinds of safety checks, by asking for the user's consent before doing something consequential like making a payment, by, asking, you know, maybe depending on the application, making sure you're only letting it access the websites or applications that it actually needs for the task. So that's, I think, something important to think about. but yeah, I'd really encourage people to try the new Agents API, build all kinds of cool stuff on it. We'd love to hear your feed- feedback if, you know, depending on how it goes.Vibhu [00:17:31]: Have you seen any changes in the way it affects dev workflows? So one of the things with dots is, you know, you're seeing it in Slack.Ari Weinstein [00:17:38]: Yeah.Vibhu [00:17:38]: You're seeing people use voice and build. the example Roman showed of change this app and send me screenshots along the way and all this.Computer Use for Testing and Closing the Software LoopAri Weinstein [00:17:46]: Yeah.Vibhu [00:17:46]: Is anything that you're seeing there in adoption about how people are using Computer Use for coding workflows? Any tips people should take from that?Ari Weinstein [00:17:55]: One of my favorite use cases for Computer Use actually, and one that we see a lot in the wild, is Computer Use letting the agent- actually test the software that the agent has built, which is far more consequential than it sounds. Because traditionally, you know, you'd build something in Codex and then the-- and the Codex builds it for you, and then you have to test it, and you are now like QA for the agent, right? So with Computer Use, you can complete the develop-- the software development life cycle, where, the agent can build software, it can test it. So I have a lot of fun, you know, building stuff, having the agent test it. By the time it comes to me, it's already working. I have, extra fun because sometimes I'm, like, developing Computer Use itself, and so now I have a Computer Use agent that's using my Computer Use agent that's using something else. so yeah, I really, I really think this is a super powerful class of use case.Swyx [00:18:44]: I have a visual play test skill that I've developed that, really catches a lot of design issues,Ari Weinstein [00:18:49]: NiceSwyx [00:18:50]: That, you know, normally when you just look at code, you wouldn't really pick it up. it's also really good for cloning apps, though. If you're using a shitty SaaS and you wanna kill the SaaS You just clone it screen by screen by screen. and Obviously, Computer Use can completely drive everything, take screenshots, note it down, and then clone everything with Codex.Ari Weinstein [00:19:06]: That's really cool.Swyx [00:19:06]: But yeah, thanks for all your progress. I think, that isAri Weinstein [00:19:08]: AbsolutelySwyx [00:19:09]: Our time.Nikunj Handa: What's New in the OpenAI APIAri Weinstein [00:19:10]: Yeah.Swyx [00:19:10]: This is not the last that we're gonna talk.Ari Weinstein [00:19:12]: Yeah, cool. This has been really fun. Thank you guys for having me.Swyx [00:19:14]: All right.Vibhu [00:19:14]: All right. Okay, we're a strict cutoff. We're just gonna dive right in.Nikunj Handa [00:19:17]: Let's do it, yeah.Vibhu [00:19:19]: Okay, so, Nikunj, we're very excited to have you. You shipped a lot on the API side, like we justNikunj Handa [00:19:25]: YeahVibhu [00:19:25]: Talked about with Ari. You can now build with Computer Use agents. Anything you wanna highlight, the API side of changes, and introduce yourself a little and what you do?Nikunj Handa [00:19:34]: Yeah, for sure. My name is Nikunj. I lead product for the API team. Been here for roughly three years. been working on launching models. I feel like that's just been, like, a thing, constant thing throughout my time, here at OpenAI. And, with every new model, we try to, like, basically work super closely with the post-training team, the research team, to figure out what's new in it. and then we, like, expose those capabilities in the API. so that's, like, the basic way of putting it. and if you just look at, everything that's new with GPT-6, the cool new capabilities that we launched were, firstly, async function calling. so what you see with, like a lot of the things that you're seeing in, like, Codex and Dots and everything is that tool calls take so long that you don't have to, like, pause the model's execution while, the tool is running. So you could just, like, kick off a tool call, keep running, keep reasoning, and then check back in. so we launched async tool calling. We launched, like, mid-turn steering, so now you can, like, inject messages while the model is reasoning, in the middle. so as your tool call finishes, you can put in that instructions.Async Tool Calls, Mid-Turn Steering, and WebSocketsSwyx [00:20:43]: And that's also partially a model alignment capability, right?Nikunj Handa [00:20:46]: Yeah.Swyx [00:20:46]: Like, they have to train in the ability to train.Nikunj Handa [00:20:48]: Exactly, yeah. AndVibhu [00:20:49]: I feel like we've had it in the app. You could always, as it's reasoning, you could steer.Nikunj Handa [00:20:54]: Yes.Vibhu [00:20:54]: It wasn't the best. It's gotten much better.Nikunj Handa [00:20:57]: Yeah.Vibhu [00:20:57]: Excited to see how it does this in versionNikunj Handa [00:20:58]: Yeah, and I like our mainVibhu [00:20:59]: And nowNikunj Handa [00:21:00]: Goal in, our main goal in the API is to, like, put things in the API once it's trained into the harness. And so we kinda wait for that moment until it's good enough. And a lot of that is, like, actually being powered by WebSockets, which we launched, a few, I wanna say months ago. And so WebSockets just opens this, like, whole bidirectional, like, communication thing with the model. This is not, the GPT Life thing. I'm just talking about GPT-6. and you can do all these, like, async tool calling, async reasoning, injecting messages. It's a really fun API to work on. I think, like, really enjoying.Swyx [00:21:33]: Yeah. This is why we are the engineering podcast, because we get to talk about WebSockets.UltraFast and the Inference StackNikunj Handa [00:21:36]: Yeah.Swyx [00:21:37]: This also pairs very well with UltraFast, right?Nikunj Handa [00:21:39]: Oh, yeah.Swyx [00:21:39]: Like, that is now, like, I think for the first time ever available in the API.Nikunj Handa [00:21:43]: Yes.Swyx [00:21:43]: Which is, which is basically the theoretical fastest speed you can ever get, Frontier of Intelligence.Nikunj Handa [00:21:49]: Yeah. It's been so exciting to work on that project. I think, before I go into the API, the most fun part of, UltraFast has been just watching the inference team cook with Astra. Like, they're just, like, constantly having these, like, Codex agents running, trying to, like, squeeze out more performance. And, I would say, like, at least for a couple of months, a lot of it was focused on efficiency and driving the cost down, which is how we, like, were able to cut the Luna price by, like, 80%. It was, like, a lot of that was driven by, like, all the inference improvements they landed. And then now they've, like, shifted gears towards, like, how can we make this run as fast as possible? And so UltraFast has just been, like, amazing to see on a mo- on a model like Astra. Like, to go that fast has been really cool. And yeah, WebSockets is like. actually it was like the first time we launched WebSockets, it was for GPT, 5.3 Codex Spark, which was. Can't believe we named a model that, but, you know, that's what we launched it for. And obviously, it helps so much because, like, you gotta have the tool calls. you had, like, really reduced the overhead, of going back and forth with tools. And so, WebSockets is awesome for that.Swyx [00:22:57]: Yeah. it's always cute to see, like, I have my reset usage limit, and then I have my Spark usage limit that I never use.Nikunj Handa [00:23:03]: Yeah.Swyx [00:23:04]: Like, it's there if I want it.Nikunj Handa [00:23:05]: I think it's gone finally.Swyx [00:23:06]: It's gone. It's gone, yeah.Nikunj Handa [00:23:07]: I know it's gone, so.Swyx [00:23:08]: Yeah. you're slowly killing off all the, you know, theNikunj Handa [00:23:11]: The old ones, yeah.Swyx [00:23:11]: Oldies.Vibhu [00:23:11]: This is a great week. I mean, it was the first time we had Frontier Intelligence at extreme speeds.Nikunj Handa [00:23:17]: Yeah.Vibhu [00:23:18]: People really liked it.Nikunj Handa [00:23:19]: Yeah.Vibhu [00:23:19]: SoSwyx [00:23:20]: YeahVibhu [00:23:20]: First time it comes back.Swyx [00:23:21]: Yeah. for, 5.3 Spark is explicitly attributed to Cerebras. You guys are not confirming or denying that, UltraFast is related to Ce- Cerebras, but people are. I'll just say that people do care and, are wondering about it. And you have your own silicon as well. elephant in the room, decision models.Decisions API: OpenAI's Fast Decision ModelNikunj Handa [00:23:38]: Oh, yeah.Swyx [00:23:38]: Decisions API. We were the first podcast to do a big Jev, deep dive with, Diogo, and I also, you know, featured him at AI Engineer. How quickly did you see Jev and go likeNikunj Handa [00:23:49]: Oh my gosh. Yeah.Nikunj Handa [00:23:50]: Yeah. Firstly, like, huge props to Diogo and, like, the Jev team for, like, really inspiring theSwyx [00:23:55]: YesNikunj Handa [00:23:55]: Like, whole segment in the market. Like, obviously Jev comes out, everyone's, like, losing their minds over it. Our users are, like, hitting us up. But also, like, our internal teams are like, “We need, like, a much faster classification system.” We can. I don't wanna, like, get ahead of some of the dots features that are gonna comeSwyx [00:24:16]: WhooNikunj Handa [00:24:16]: But you're gonna see, like, some cool, like, really snappy, fast things built on top of the decisions API. but, you know, like, yeah. Props to Jev for, like, inspiring this whole thing. obviously a bunch of people at OpenAI get nerd sniped by that, and they're like, “How can we, like, make this work? We're not gonna, like-”Swyx [00:24:33]: Okay.Nikunj Handa [00:24:33]: “. train a new model.” ButSwyx [00:24:34]: Like, four weeks ago, this was not on the dev radar, right?Nikunj Handa [00:24:37]: No, not at all. No.Swyx [00:24:37]: Okay.Nikunj Handa [00:24:37]: This is likeSwyx [00:24:38]: WowNikunj Handa [00:24:38]: Jev-inspired and, likeSwyx [00:24:40]: I think you are officially the first one to your lab to, like, clone and, adopt this.Nikunj Handa [00:24:44]: Yeah. Yeah. I feel like, OpenAI has such a strong, like, hacker culture and, like, people are just, like, they get excited about things. And so, guy from inference, this one awesome guy from, the infra team are like, “ this is amazing. We're gonna, like, hack on it.” They build a prototype, it, like, works, and now we- we are just, like, hill climbing on latency and trying to make this as fast as possible, and we wanna, like, launch it in the coming days. so as soon as we hit our, like, latency target, we'll try to get this out.Vibhu [00:25:13]: It's interesting. At the same time of hacker culture, you also, as Sam said, like 99%, one of the most reliable APIs withNikunj Handa [00:25:20]: Mm-hmmVibhu [00:25:20]: I think probably the most usage, which is your team directly. how should people see decisions API? I feel like a lot of people saw Jev, heard the buzz, haven't built with it. You're making it very mainstream.What Decision Models Are Good ForNikunj Handa [00:25:32]: Mm-hmm.Vibhu [00:25:33]: What should people see it as? How should they use it?Nikunj Handa [00:25:36]: Yeah. I think the main use cases we've seen is, like, really fast classification. all the Computer Use demos have been amazing and really cool. I think there will be limitations, of course, in terms of, you know, having Astra, like, write, like, a JavaScript-like script to control your computer, versus having Luna pick, like, one action at a time. I think, it's not gonna be at the same intelligence level, but, like, maybe there's some Computer Use tasks that this is good enough for. So excited to see that come through. the other cool prototype I've seen internally is people hooking it up with GPT Live. So GPT Live is like, you know, our bidirectional, like, real-time,Swyx [00:26:14]: VoicingNikunj Handa [00:26:14]: A- API. And, it's built on this, like, model of front-end models and back-end models. So GPT Live is this, likeSwyx [00:26:20]: Think or talkerNikunj Handa [00:26:21]: Super fast. Yeah, think or, talker thing. So GPT Live is the talker, super fast, really good at delegation, and you have something like Astra sitting at the ba- at the back. But tool calling has always felt, like, really slow in GPT Live. and so people have been, like, putting together these, like, tool calling demos of GPT Live controlling a computer, and it just feels like so much more snappy and natural. So I'm, like, kinda excited to see, like, what people do with Live and with Luna on decisions API. so that'll be pretty exciting. Yeah.Swyx [00:26:55]: So I wanna iron this out for people, especially from the product side, because a lot of people have been putting out Jev clones. There's been about 100 in the last two weeks.What Makes a Decision Model DifferentNikunj Handa [00:27:01]: Oh, really? That's amazing.Vibhu [00:27:03]: The first couple days.Swyx [00:27:04]: But like, it. Like, they can clone a Jev API, which is honestly structured outputsNikunj Handa [00:27:09]: YeahSwyx [00:27:09]: Which OpenAI was first to.Nikunj Handa [00:27:10]: Yeah.Swyx [00:27:11]: Right? So, like, I think let's iron out for people what is a decision model, as far asNikunj Handa [00:27:16]: YeahSwyx [00:27:17]: As far as, like, what is important? It is not just latency. It's not just structured output, right? Because I could just have Luna as it'- The decision model is priced the same as Luna, right?Nikunj Handa [00:27:26]: Mm-hmm.Swyx [00:27:27]: Have turned off reasoning and then have structured output. Do I have a Jev? you know, no, right? And that's theNikunj Handa [00:27:33]: YeahSwyx [00:27:33]: That's the realVibhu [00:27:34]: There's a confidence there.Swyx [00:27:35]: Yeah.Nikunj Handa [00:27:36]: Yeah, totally. I think, the way that. So we haven't trained, like, a new model for this.Swyx [00:27:40]: Yeah.Nikunj Handa [00:27:40]: We're, like, building this purely on top of the same Luna weights that we have.Swyx [00:27:44]: Oh.Nikunj Handa [00:27:44]: So yeah. This is, like, really just Luna. And, on top of that, what you're doing is you're constraining. So, like, structured output's a big part of it. you're really optimizing the inference stack to, like, get very fast on TTFD. And because you can have multiple questions, what you do is, like, you basically run those in parallel,Swyx [00:28:05]: As a batch.Nikunj Handa [00:28:06]: Yeah. You run those in the-- as a batch. you-- All sorts of, like, inference techniques people are working on to try to make it as fast as possible. But I'd say, like, at least our implementation of it at the start and this first version is, like, zero-shotting this on top of Luna, to see how it goes. And obviously, you wanna, like, put it out there. Like, this is OpenAI's, like, classic iterative deployment thing. Put it out there, see what people think, and then, like, we'll make more model improvements, as needed. so yeah. That's, the decisions API.Swyx [00:28:38]: Yeah. And, obviously as a benefit, you have vision. They don't have vision, right?Nikunj Handa [00:28:42]: That's true.Swyx [00:28:42]: Obviously, Jev's comes withNikunj Handa [00:28:43]: Yeah. Like, we get it for free with Luna. Yeah.Swyx [00:28:45]: Yeah. I do think that, like, you know, some of the innovations, it sounds like, it's still to come if it's still the same Luna weights, which is, like, the confidence stuff, like, the in calibration is something that we've talked about on the podcast with, benchmarking calibration. ‘Cause basically, the whole point is that RLHF kind of collapses you towards what you want to hear.Calibration, Architecture, and the Open Research QuestionsNikunj Handa [00:29:03]: Yeah.Swyx [00:29:03]: But, like, not actually, like, what the amount of confidence is.Nikunj Handa [00:29:06]: Yeah. Yeah, totally. I'm eager to see how it pans out. Maybe there's, like, gonna be. These are gonna be, like, the key areas where we may have to, like, hill climbSwyx [00:29:15]: YeahNikunj Handa [00:29:15]: With the, with the future model release. But, yeah.Swyx [00:29:18]: And then architecture-wise, the other thing that's in the debate, obviously, you-- Nobody knows because Jev doesn't talk about it, but the two speculations are, one, maybe diffusion model instead of autoregressive.Nikunj Handa [00:29:28]: Mm-hmm.Swyx [00:29:29]: But you are able to achieve the parallel, generation in your way. And then the other one is some mech interp type thingNikunj Handa [00:29:37]: Mm-hmmSwyx [00:29:37]: That you're, like, analyzing the activations and then just outputtingNikunj Handa [00:29:40]: That would be coolSwyx [00:29:41]: The weights.Nikunj Handa [00:29:42]: Yeah.Swyx [00:29:42]: Which, like, you guys have all done the research on this. People have speculated.Vibhu [00:29:45]: There have been demos onSwyx [00:29:46]: YeahVibhu [00:29:46]: Both of these as well. I think Gemini shared a Gemini diffusion, Gemma diffusion on a Jev-style output.Nikunj Handa [00:29:53]: Oh, sick.Vibhu [00:29:53]: And, interp people have also, you know, pulled out interp from a middle layer, but this is all speculation.Swyx [00:29:59]: It's just like, what are you trying to aim for, right? Because you can achieve the API. Everyone can achieve the API. It's actually pretty trivial. But, like, then there's the speed, then there's the accuracy, then there's the other calibration features.Nikunj Handa [00:30:11]: Mm-hmm.Swyx [00:30:11]: I don't know what else.Nikunj Handa [00:30:13]: Yeah. Yeah. No, totally. It's so cool that this, like, whole space has been kicked off now and people are gonna do so much cool stuff and everyone's gonna learn from each other. And, yeah, I'm excited about it.What Developers Should Build NextVibhu [00:30:24]: I feel like being on the platform team, a lot of your job is to empower builders.Nikunj Handa [00:30:27]: Mm-hmm.Vibhu [00:30:28]: What do you think people should build with decisions API and also Computer Use agents? Any stuff that you've- been building with internally that you think really opens up after the new change?Nikunj Handa [00:30:39]: Yeah. okay, let's think. decisions API, use cases internally have been pretty obvious. Like, the user ops team was, like, jumping on it. We were like, “We gotta classify all of our support tickets.” what else came up? obviously, there were, like, the really cool GPT Live demos. I'm sure, like, the Codex app team might, like, pick this up and try to do something cool with it. So, you know, like, this whole thing started, like, a week ago, so it's, like, very early andSwyx [00:31:06]: Oh, one week.Nikunj Handa [00:31:07]: We're excited. Yeah. Yeah, pretty much.Vibhu [00:31:08]: There was a big push in, evals, LLM as a judge having really low latency there.Nikunj Handa [00:31:13]: Right. Yeah. That'll be interesting to see. and then, with the Agents API, we have-- we're basically, like, having a bunch of first-party products, like, at OpenAI built fully on top of it. we've had the Codex security stuff that just went out that's fully built on top of, the Agents API. We have, sort of the-- we- we are having, like, a meetings type of thing launching today.Agents API and OpenAI's First-Party ProductsSwyx [00:31:40]: Mm-hmm.Nikunj Handa [00:31:40]: I think there was, like, a demo. do you remember, like, the plugin extensions when Sam was showing it? There was, like, a demo for, like, you're in a calendar, you can sort of, like, have your meeting notesSwyx [00:31:51]: Like, drop into a singleNikunj Handa [00:31:52]: Flow into like your spaceSwyx [00:31:52]: Like, Google Docs type thing.Nikunj Handa [00:31:53]: Yeah.Swyx [00:31:54]: Right?Nikunj Handa [00:31:54]: And so the-- all of that stuff is, like, fully built on top of, the Agents API. and yeah, I'm, like, just excited to see. Like, we're just getting this out, and let's see what people build on top of it.Vibhu [00:32:04]: I think you showed it off very well. The whole edit spaces, pages, collaborate, add in your dot. Like, that's a lot, soNikunj Handa [00:32:12]: YeahVibhu [00:32:12]: There's a lot of inspiration people can go to.Nikunj Handa [00:32:14]: Yeah. All possible with Astra, you know. Like, thing- things just move so fast now. LikeSwyx [00:32:19]: YeahNikunj Handa [00:32:19]: People go from idea to execution so quickly, it's amazing.Swyx [00:32:23]: Is there something that you want, people to focus on to give you feedback? Like, what-- like, you know, maybe you're just putting this out there and you want-- and there's, like, a fork in the road and you want developers to help you decide.Responses API Performance and Long-Lived CachingNikunj Handa [00:32:35]: So I think Agents API and decisions API, they are like, these are our newest products. Would love, like, any and all feedback on that to figure out where to take them. I think, over here, we're, like, very open on Responses API, which is sort of like our workhorse over here. like, really focused on performance right now, and the performance comes in, like, two main ways. first is just, like, latency. We've been, like, rewriting the whole Responses API stack to, like, make it as fast as possible from a TTFT perspective, DVD perspective. So there's like-- that, like, continues to be, like, a main area of focus for us. The second thing we've been trying to do is, like, really go deep on caching, particularly with these, like, personal agents that are, you know, like, basically, like, a single thread that just goes on and on forever. We've been, trying to, like, really up our game on caching. We provide now guarantees of, like, cache hits within, like, 30 minutes. We're actually, like, we-- for one of our users, we just launched, like, a much longer cache window. So we have, like, a 12-hour caching guarantee, that we offer so that you have, like, guaranteed cache hits forSwyx [00:33:40]: Is that a public API?Nikunj Handa [00:33:42]: Not yet. That's in preview.Nikunj Handa [00:33:43]: We're gonna, like, try to get that out to everyone as soon as possible. But, like, just pay a little bit more for the cache write, and we, like, guarantee, like, cache reads for, like, a much longer period. So even if, like, your instinct thread, for example, like, you just, like, do something on it and then come back to it, like, three to four hours later, you- you're still getting the caching performance out of it. And launchedVibhu [00:34:04]: And you cut the cost there quite a bit too, right, with the new model?Nikunj Handa [00:34:07]: Oh, yeah. That's right.Vibhu [00:34:08]: Like, 25% cheaper, soNikunj Handa [00:34:08]: Yeah, with, like, driving down cache reads, yeah.Cache Pre-Warming and Cost-Efficient Agent ThreadsVibhu [00:34:10]: For builders, they should implementNikunj Handa [00:34:13]: YeahVibhu [00:34:13]: Because it's significantly cheaper.Nikunj Handa [00:34:14]: Yeah. Yeah. Just, like, building your apps with, like, to be very cache aware and sort of, like, use our prompt diagnostics or cache diagnostics tool to figure out, like, where things are dropping off. And, so the caching part is, like, really important. yeah, I also wanted to talk about pre-warming. We have that in the API now. So, like, if you know that, “Hey, I'm gonna get a cache,” like-- sorry, “I'm gonna get this prompt. I just wanna, like, pre-warm the cache, pay, like, the cache write fee right now, and then, like, have it sort of ready to go for the next 30 minutes for whenever.”Swyx [00:34:49]: And it can spawn many instances of that thread.Nikunj Handa [00:34:51]: Exactly, yeah.Swyx [00:34:52]: Yeah.Nikunj Handa [00:34:52]: You can just keep going and haveSwyx [00:34:54]: Yeah, just keep messing with the prompt thereNikunj Handa [00:34:55]: Tons and tons of that. and so, yeah, like, I'm very excited about getting feedback on, like, the low-level performance things that we can keep making Responses API the most performant and reliable way to, like, build on top of an LLM. And then you basically have our, like, new products where I'm just looking for, like, any and all feedback.Swyx [00:35:15]: Yeah, just use it, right?Nikunj Handa [00:35:16]: So yeah, just useSwyx [00:35:16]: Tell us what toNikunj Handa [00:35:17]: Yeah. Define our roadmap for us, please. So yeah.Swyx [00:35:20]: I think for me, the caching thing, great, right? Like, obviously very needed. But at the end of the day, you're still bumping up against a million-token contextCompaction and Managing Million-Token ContextsNikunj Handa [00:35:28]: Mm-hmmSwyx [00:35:28]: And that's probably not gonna change for the foreseeable future.Nikunj Handa [00:35:31]: Mm-hmm.Swyx [00:35:31]: Like, you still need good compression.Nikunj Handa [00:35:33]: Yeah.Swyx [00:35:33]: What is the best practice there?Nikunj Handa [00:35:34]: Yeah. Yeah, totally. so firstly, OpenAI has its own, like, proprietary compression, compSwyx [00:35:40]: Which is inNikunj Handa [00:35:41]: Compaction.Vibhu [00:35:42]: Compaction.Swyx [00:35:42]: It's in the agents.Vibhu [00:35:43]: It's in the API.Nikunj Handa [00:35:43]: Yes.Vibhu [00:35:43]: Agents API.Nikunj Handa [00:35:44]: Yeah.Swyx [00:35:44]: You decide for us, right?Nikunj Handa [00:35:45]: Yeah, exactly. So in the Agents API, it comes built into the harness. and if you're in Responses API, there's, like, two ways of doing it. One is what we call server-side compaction, which is you basically tell Responses API that if you ever hit this threshold of tokens, just auto-compact it and, like, go back, or sorry, like, reduce the context, being used. And the second way is, like, /compact, which is, like, if you want full control. So you can, like, /compact at any timeSwyx [00:36:15]: I hear youNikunj Handa [00:36:15]: Have your own logic on when to, likeSwyx [00:36:17]: It's not AGI.Nikunj Handa [00:36:18]: It.Swyx [00:36:18]: It's not AGI.Nikunj Handa [00:36:19]: Yeah. Yeah.Swyx [00:36:20]: Yeah. But it, I meanNikunj Handa [00:36:20]: YeahSwyx [00:36:20]: It is the manual override.Nikunj Handa [00:36:21]: Yeah, it is the manual way. And like, I don't know, but a lot of the big coding agents like to do it manually. I mean, like, if you look at the Codex implementation of it in the Code- open source Codex harness, you can see that they use /compact and do it. and, there's also, like, new, by the way, new compaction techniques that we are working on. Some of them you will be able to see in the Codex harness. Like, it's already implemented in the Codex harness. And so, they're like some file-based, systems that we are, like, experimenting with. So yeah, lots of cool stuff going on around in compaction as well.Swyx [00:36:57]: Cool. we are running out of time.Nikunj Handa [00:36:59]: Okay.Swyx [00:36:59]: I think you've talked about, a lot about performance and talked a lot about, the new APIs that you're launching. Can you give us any other hints as to things that you're interested in as far as the future of the platform is concerned?Higher-Level Platform Primitives and the AI CloudNikunj Handa [00:37:13]: We're obviously like very low level. Like, I used to work at Stripe before this, and, at Stripe a lot of the game was like building these higher level primitives and products on top of like the core payments primitives. and, I'm always like curious about what the best way of doing that is in AI. And I think we've had a couple of attempts at that. We like had launched assistance API like way back in the day, and like wasn't really the right fit. We were sort of like going off with this like Agents API, and, it gives you the codex harness, but like where's like the, what's the right amount of flexibility to give in that? That's like an open question. Like how should we like have memory walls and like all of these like higher level like API objects to take away, also like to abstract away more, like storage concepts. Like this is like a whole, like, there's a whole space that I'm like very curious about figuring out how we design. I think a lot of things in AI are just have a low-level API primitive and see an example harness and go and have your coding agent implement that. But how much of that do we build into the API is like a constant question that I'm thinking about.Swyx [00:38:24]: Yeah.Nikunj Handa [00:38:24]: So I don't know if folks have thoughts on that. If anyone has ideas, it would be super interesting to hear.Swyx [00:38:30]: Yeah. The analogy I always bring back to, and we'll end there, is, you're building an AI cloud, right?Nikunj Handa [00:38:35]: Mm-hmm.Swyx [00:38:35]: Like, which is, something that, Sam said a year agoNikunj Handa [00:38:38]: Mm-hmmSwyx [00:38:38]: Where, and you're, it's almost like you're kind of doing the AWS invention and you have to do, okay, this is EC2Nikunj Handa [00:38:45]: YeahSwyx [00:38:45]: And this is S3, and this is like. But you're doing the AI-native versions of each of these.Vibhu [00:38:48]: There are a lot of analogies, so you're pre-warming caches for stuff that you know will beNikunj Handa [00:38:53]: Yeah.Vibhu [00:38:53]: And it's nice that it's all exposed to buildersClosingNikunj Handa [00:38:56]: Mm-hmmVibhu [00:38:56]: ‘cause it just opens up ways that you can build new things.Nikunj Handa [00:38:59]: Yeah, absolutely.Swyx [00:39:00]: Okay.Vibhu [00:39:00]: Awesome. WellSwyx [00:39:01]: That's everything.Nikunj Handa [00:39:01]: Thank you, guys.Vibhu [00:39:02]: Thank you.Nikunj Handa [00:39:02]: Yeah. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

AI has spent the last few years helping people do their jobs faster. The next phase is different. What happens when AI doesn't just recommend what to do, but actually does the work? David Pourquery joins Galen Hair to talk about the shift from AI assistant to autonomous AI operator. David built GROAS after teaching himself Google Ads while running e-commerce businesses, eventually turning a basic Google Ads tool into an autonomous platform designed to manage campaigns without constant human intervention. In this episode: How David went from private equity to building an AI company Why he started with e-commerce and Google Ads How GROAS evolved from a simple SaaS tool into autonomous AI The difference between AI recommendations and AI actually doing the work Why David believes businesses are moving toward buying outcomes, not software Where the human element still matters Why customer success managers remain important even in an autonomous AI business What happens when AI starts replacing traditional agency work Why software engineering may be one of the first major areas disrupted Why anything done on a computer may be increasingly exposed to automation What business owners can do today to prepare for AI agents How AI could change CRM and sales management Why David chose to bootstrap instead of raise venture capital The lessons he brought from venture capital and private equity What it means to keep leveling up in an AI-driven world Reach out to us! Connect with David Pourquery https://groas.com/ Connect with Galen M. Hair https://insuranceclaimhq.com hair@hairshunnarah.com https://levelupclaim.com/

This week we are taking you back to Black Hat USA 2026 and exploring two sides of the security landscape.  First, Microsoft incident response experts Adrian Hill and Terry Mee break down identity-based attacks, from compromised credentials and MFA bypasses to containment, logging, access controls, and the growing risks surrounding AI agents.  Then, members of Microsoft's Defender Purple Team discuss how they recreate full attack chains, including emerging AI-driven techniques, to identify detection gaps, strengthen defenses, and use AI to accelerate security research while keeping human expertise in the loop.  In this episode you'll learn:  Why identity is at the center of modern security incidents  How attackers can bypass MFA and maintain access through sessions, tokens, and other forms of persistence  Why logging and long-term data retention are critical during incident response  How purple teams recreate full attack chains to uncover gaps in detection and response  Where AI can accelerate security research, and where deterministic systems and human oversight still matter  Why AI agents introduce new risks around access, permissions, and trust  Some questions we ask:  What should organizations be logging and retaining for incident response?  Where do organizations underestimate SaaS, OAuth, and identity persistence risks?  When should defenders contain an attacker versus continue gathering intelligence?  How does attacking an AI-enabled system begin to look like traditional offensive security?  Where should security researchers trust AI, and where should they rely on deterministic systems?  What should security teams understand about the exposure and permissions of their AI agents?     Resources:   View Elliot Volkman on LinkedIn     Related Microsoft Podcasts:                    Afternoon Cyber Tea with Ann Johnson  The BlueHat Podcast  Uncovering Hidden Risks        Discover and follow other Microsoft podcasts at microsoft.com/podcasts     Get the latest threat intelligence insights and guidance at Microsoft Security Insider    The Microsoft Threat Intelligence Podcast is produced by Microsoft, Hangar Studios and distributed as part of N2K media network. 

When you use an AI tool, how much time do you spend correcting it? And would your customers be willing to do the same?Kendra Vant, Chief Product Officer at Tapi, joins Randy Silver to explore the “reliability layer”: the people, checks and engineering that make AI products dependable. She explains why enthusiastic AI users often provide that layer themselves, and why product teams can't assume their customers will.Kendra shares four questions to ask before building AI into your product, from who catches mistakes to what reliability costs at scale. They also discuss designing for failure, the technical literacy she expects from product managers, and why faster prototyping makes experienced judgement so valuable.Chapters00:00 Introducing Kendra Vant02:51 The gap between an AI demo and a reliable product06:12 Accuracy, consistency and the user as the reliability layer13:31 Four questions to ask before building AI into your product18:27 Sponsor: Jira Product Discovery19:01 Planning for failure and protecting customer trust24:06 Customisation, pricing and the cost of scaling26:37 Do product managers need to learn to code?31:40 How Tapi uses AI to experiment and build33:25 Why small teams have an advantage35:05 Product judgement and the experience gap38:08 Using AI to maintain legacy code39:41 Ask better questions, write down answers and test your thinking43:11 Where to follow KendraKey takeawaysFind the work your users are doing for the AI. Correcting answers, refining prompts and spotting mistakes can make a tool feel more reliable than it is. Consider whether your customers have the time, motivation and expertise to do that work.Make someone responsible for reliability. Establish who specifies, builds and operates the checks around your AI. An impressive demo can conceal the fact that nobody owns this work yet.Price the whole product. Human review, additional models, guardrails and ongoing maintenance all affect the cost of delivery. Check whether your reliability layer remains commercially viable as usage grows.Design for the moments when it fails. Saying a model is wrong 15% of the time prompts a different conversation from saying it is 85% accurate. Identify the consequences for customers and plan how the product will recover.Build your technical literacy. Kendra expects product managers to be comfortable interacting with Git and learning from the codebase. Understanding how software works helps you ask better questions and collaborate with engineers.Recognise the value of experience. Faster tools increase what teams can build, but recognising flawed suggestions still requires judgement. Kendra raises an open question: how will newer practitioners develop that judgement as the work changes?Use writing to sharpen your thinking. Break difficult problems into smaller questions, write down your answers and explain them to a colleague. The gaps often become clearer when you have to articulate your reasoning.We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

Christian Ghigliotty is the Head of Cyber Defense Engineering at a well-known tech company. In this episode, he joins host Brian Contos to discuss the AI attack surface in SaaS. This series is brought to you by Mitiga, the leader in Agentic Runtime Security for cloud, SaaS, and AI, delivering Zero-Impact Breach Prevention. To learn more about our sponsor, visit https://www.mitiga.ai.

By Doug Green “Sales comes down to two things: be first to the customer and follow up.” In this Technology Reseller News podcast recorded at MSP Summit, Avinash Gujje, CEO and founder of Zuuz, discusses how the company is using AI to uncover sales opportunities that may be sitting unnoticed in email, calls and other customer communications. Gujje came to the idea from his own experience running a value-added reseller, distribution and MSP business. He says sales teams routinely lose opportunities not because customers say no, but because follow-up gets delayed, renewal conversations are missed or leads never make it into the CRM. Zuuz is designed to address that problem. The platform connects with a salesperson's mailbox, identifies potential sales opportunities and compares them with what is already recorded in the CRM. It can also work with call recordings, LinkedIn, WhatsApp and other customer communication channels. The goal is to find conversations that should have turned into pipeline but did not. Gujje says Zuuz is launching what it calls the Zuuz Challenge, looking back across roughly 90 days of communications and comparing identified opportunities against the customer's existing CRM. “If Zuuz finds 60 opportunities and only 30 are in your CRM, those other 30 are what we've recovered,” Gujje says. The company is pre-launch and is using MSP Summit to introduce the platform to the channel. Gujje says early testing with approximately 30 customers has identified a significant amount of potential pipeline, particularly among IT services companies, VARs and distributors where individual opportunities can be substantial. For MSPs, Gujje sees two opportunities. They can use Zuuz internally to improve their own sales follow-up, while also offering the platform to customers as they evolve toward what he describes as managed intelligence providers. Deployment is designed to be straightforward. Zuuz is offered as SaaS, with an on-premises option for customers with stricter security or data governance requirements. Gujje's larger message is that AI should help salespeople perform better rather than simply replace them. “Use AI to improve your sales,” he says. “Use AI to become a better salesperson.” Visit Zuuz.ai to learn more.  

In this episode of Future Finance, hosts Glenn Hopper and Paul Barnhurst sit down with Matija Nakic, CEO and Co-founder of Farseer, to discuss how AI is changing financial planning, enterprise software, and decision-making. Matija shares why asking the right questions matters, how AI can accelerate planning workflows, and why strong data foundations remain essential.Matija Nakic is the CEO and Co-founder of Farseer, an AI-native SaaS platform for business modeling, planning, and analysis. With a background in computer engineering, an MBA, and experience across B2B enterprise software, she progressed from developer to product director before co-founding Farseer.In this episode, you will discover:Why asking the right questions matters with AI.How finance can move toward a decision culture.Why clean data and context still matter.How AI can speed up modeling and planning.When Excel still makes sense for finance teams.Matija also pushes back on the idea that Excel is obsolete. She argues that spreadsheets remain excellent for rapid modeling and individual analysis, but become harder to manage when collaboration, large datasets, version control, and auditability enter the pictureFollow Matija:LinkedIn: https://hr.linkedin.com/in/matija-nakicCompany: https://www.farseer.com/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyDisclosure: Portions of this episode (such as the introduction or promotional segments) use AI-generated voice narration produced under human editorial review.Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[02:18] - Why Farseer Was Built[04:12] - Solving Complex Enterprise Planning[05:11] - AI, Vibe Coding & Enterprise Software[06:57] - Building Farseer's AI Modeler[10:05] - How AI Accelerates Development[11:33] - Building for Technical Finance Users[12:41] - Why Asking the Right Questions Matters[15:37] - AI Context, Guardrails & Governance[17:30] - Why Strong Data Foundations Still Matter[18:23] - Closing Thoughts

"You get launched over a barrier and now you're running. Everybody's yelling and you just have to get there." What happens when a psychology grad who grew up in tank tops and sundresses - and didn't own a single collared shirt - walks into a golf pro shop for the first time? She sees a disconnected, convoluted ecosystem and spends the next 19 years building the commerce layer that connects it all. In this episode of The ModGolf Podcast, host Colin Weston sits down with Meghann Butcher, Founder & CEO of RepSpark, the B2B wholesale commerce platform powering golf and outdoor lifestyle brands. From ordering 50 custom-logoed polos across 10 colorways to predicting what's trending in the market before orders are even placed, Meghann shares how she turned a sales rep's pad-and-paper problem into the platform the PGA of America calls "the commerce layer of golf." Whether you're a brand founder, a green grass buyer, or just golf-curious, this conversation is a masterclass in grit, empathy, and building infrastructure that makes everyone's life easier. 3 Key Takeaways you'll discover: 1. Psychology is the ultimate entrepreneur's superpower Meghann's BA in Psychology and Communications from Vanderbilt didn't lead her away from tech - it led her through it. From listening to customer pain points to building the right team, she credits her liberal arts background as the differentiator that helps her understand what people need, how they say it, and how to translate that into solutions. "I've actually used my psychology degree more than anything else." 2. Mass customization is the hardest problem in golf apparel - and RepSpark solved it It's not an e-commerce site where you put your name on a water bottle. It's 50 polos, 10 colorways, club logos on the correct side, correct colors, correct sizes - or the whole order comes back unsellable. Meghann explains how RepSpark spent years mastering this nuanced complexity, making it easy for sales reps and buyers alike to get it right the first time. 3. The future of golf commerce is predictive, intelligent, and inclusive With 9 million new golfers and brands like Lululemon and Fabletics entering the space, the game is expanding beyond traditional 18-hole courses. Meghann shares how AI is transforming RepSpark - from predictive ordering and order organization to auto-generated templates - and why the platform is leveling the playing field for up-and-coming brands to present a modern front as big as the enterprise players.

No episódio de hoje do Kiwicast, recebemos Balian e Lukas Lobo, sócios na operação de produtos digitais construída em cima de um dos maiores canais de YouTube do Brasil, com mais de R$ 1,6 milhão faturados e 10 mil alunos, sustentados por tráfego orgânico.Balian é criador de conteúdo desde 2016 e ficou conhecido pelos desafios extremos: tomou banho no rio mais poluído do Brasil, foi de Uber de São Paulo até a Bahia, passou 72 horas naAmazônia. Em 2019 ele colocou R$ 6 mil, quase toda a economia que tinha, em um único vídeo, e foi esse vídeo que virou o jogo do canal.Lukas Lobo é a metade que a audiência nunca viu. Ele tinha uma empresa de engenharia e construção em Lauro de Freitas, na Bahia, largou o trabalho, a namorada e a família e foi morar de favor na casa do melhor amigo em São Paulo. Hoje é ele quem administra a empresa por trás da marca, com dez anos de mercado digital e passagem por lançamento, perpétuo com tráfego pago, perpétuo com tráfego orgânico e SaaS.Os dois já testaram praticamente todo modelo de venda que existe no digital, e a conclusão a que chegaram é contra a corrente: confiar cem por cento no tráfego pago foi o que quase destruiu o negócio, e o que está mais subestimado hoje é justamente o tráfego orgânico. A tese central deles é que a maior parte do resultado vem de uma coisa só, linkar a ideia de conteúdo com o produto e com o lead, e que dá parafaturar sem sequer aparecer na câmera.No Kiwicast, eles falaram sobre:Como fazer a primeira venda no orgânico sem gastar com anúncioComo ganhar dinheiro no YouTube e no TikTok sem aparecerPor que confiar só no tráfego pago quase destruiu o negócio delesComo um produto e uma estratégia venderam R$ 300 mil em um único vídeoPor que eles limpam a operação a cada três mesesComo copiar uma referência de fora do Brasil sem copiar erradoAprenda com quem vive o mercado digital na prática.Dá o play e deixe nos comentários qual foi o melhor insight que você tirou do episódio.Nosso Instagram é @Kiwify

Senthu Velnayagam (@senthuvelnayagam) is a Canadian entrepreneur, investor and growth strategist who has built his career around scaling companies through execution, operational excellence and sustainable growth.Senthu is the co-founder and CEO of Kimp, one of the pioneers in the subscription-based creative services space. He is also the Managing Partner of Scale Shift Ventures, a venture studio focused on acquiring, building and scaling companies across proptech, AI, SaaS and automation.He joins Ara on this episode of #TheTamilCreator to discuss leaving York University to pursue entrepreneurship, why he favours bootstrapping over outside funding, building Kimp's subscription-based business model, investing in founders through Scale Shift Ventures, deciding when to hold or exit a company, evolving from an operator to an architect and why he believes formal education may become less valuable in the future.If you have ever wondered what it takes to build profitable, durable businesses without following the traditional startup playbook, this episode is for you.Follow Senthu:- Instagram (https://www.instagram.com/senthuvelnayagam/)    Timestamps00:19 — Ara introduces this week's guest, Senthu Velnayagam01:40 — Senthu speaks on his early years; born in Jaffna and moved to Canada at 1603:15 — His first few years of entrepreneurship; dropping out of York and building at 1807:20 — Why does Senthu favour bootstrapping?10:44 — From graphic designing and marketing (2003-2017/2018) to co-founding Kimp12:00 — Starting Buy.ca in 2024 with a cashback model13:35 — How does the model work?16:10 — Kimp; the pros and cons of the subscription-based creative service19:10 — Navigating resistance with customers20:50 — Scale Shift Ventures; why he started it, how it operates, and what's worked best25:20 — Writing cheques for founders despite not knowing what they were building25:54 — How Senthu determines when to hold companies versus exit27:36 — From operator to architect; how he stays in touch while thinking high-level31:26 — How he sources business deals31:58 — Going against the grain of the traditional Tamil mindset33:29 — What Senthu would do in the first 90 days if he had to start over from scratch34:39 — Building, investing in, and acquiring; what creates the most wealth?35:29 — Advice he would give his 16-year-old self35:56 — How Senthu wants to be remembered by friends and family36:27 — Thoughts around AI; how he uses it and how it jobs/industries40:34 — Creator Confessions44:19 — The Wrap UpIntro MusicProduced And Mixed By:- The Tamil Creator- YanchanWritten By:- Aravinthan Ehamparam- Yanchan Rajmohan    Support the show

EP 563 7 AI Side Hustles Ranked | Make Money Online With Zero HypeA 5 factor scorecard to help you choose a starting point before your evenings disappear. Make money online doesn't have to mean waiting months for your first dollar. Tracy ranks 7 AI side hustles by the real metric busy parents care about: how fast can you earn your first credible sale? Using a five-factor rubric (startup cost, skill barrier, weekly hours, speed to first dollar, risk), this episode breaks down which opportunities actually deliver velocity—and exposes the three telltale signs most "make money" content is selling a dream, not a path. https://DarkHorseSchooling.comSponsor - https://YourSuccessDNA.comTracy ranks seven popular AI side hustles by how quickly a busy nine-to-five worker can earn a first paid dollar, using a five-factor rubric: startup cost, skill barrier, weekly hours needed, time to first credible sale, and risk. It argues most videos hype income ceilings instead of the real metric—how long until a stranger pays you—and shares three signs that “make money online” content is selling a dream. The rankings place AI micro-SaaS last due to maximum skill barrier and longest time to first sale, with YouTube automation also slow because monetization is gated by subscriber and watch-hour thresholds. Mid-pack are AI consulting and AI art/print-on-demand (distribution bottleneck). Faster options are done-for-you local social media services and AI-assisted freelance gigs, while AI content clipping (Whoop/Viro) is #1 because it leverages existing audiences and payout systems. The host advises choosing one hustle, setting a 30–60 day proof-of-concept deadline, and not switching in week two. 00:00 Hype Versus First Dollar01:42 Ranking Rubric Explained02:32 Number 7 Micro SaaS04:35 Number 6 YouTube Automation06:04 Number 5 AI Consulting07:14 Number 4 AI Art POD08:32 Number 3 Local AI Services09:57 Number 2 Freelance Gigs11:39 Number 1 Content Clipping13:33 Whiskered Wisdom15:03 Mindset And Recap AI side hustles, Make money with AI, ChatGPT side hustles, Fastest side hustle to make money, Best side hustles, ai content creation, ai side gig, make money, online business, online entrepreneurship, online opportunities, side hustles, make money online, ai side hustle, fastest path to income, ai entrepreneur proof, side hustles ranked, ai side hustle, micro business, ai automation business, online side hustles, business motivation, work from home, business mindset, entrepreneur motivation, entrepreneurship, ai content creation, side hustle ideas, entrepreneur mindset, business tips, how to make money online, make money from home https://DarkHorseEntrepreneur.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Laila Marshall is the Founder & CEO of Beebizy, a B2B event management SaaS built for the lean teams running events that drive real business revenue. A previously exited founder in on-demand healthcare technology, Laila brings a rare combination of practitioner depth and product vision — having personally produced 500+ events before building the software she always wished existed. Based in Los Angeles, Laila is currently in active enterprise conversations with brands like Marriott, Hilton, and Eleventy Milano, while raising a seed round to fuel the next phase of growth. She also serves on the General Leadership Council of the Ayana Foundation, which focuses on capital access and development programming for female founders.

Ben Pines has spent the last 20 years helping SaaS companies turn strong ideas into marketing that people actually trust. As the first marketer at Elementor, he helped grow the company from launch to millions of users and nearly $100 million in annual recurring revenue. Today, he works directly with founders to uncover their unique point of view and turn one weekly conversation into consistent, high-quality LinkedIn content.

בעולם ה-SaaS בעבר, לקוחות ידעו בדיוק כמה הם משלמים: תקציב שנתי מסודר, בלי הפתעות. עם התבססות מוצרי ה-AI, הכל השתנה. עלויות הטוקנים אינן קבועות, הצריכה משתנה מיוזר ליוזר, ואף אחד לא יכול לומר ללקוח אנטרפרייז בדיוק כמה יעלה לו האייג'נט בסוף החודש, בדיוק כמו שאי אפשר להבטיח לו מראש כמה דלק תצרוך המכונית שהוא קונה. בפרק השבוע, רוני הרניב מדברת עם רועי מן, Co-Founder ו-Co-CEO של מאנדיי, על האתגרים בעולם התמחור כיום, ועל שאלות שעוד אין עליהן תשובות סגורות במאנדיי או למעשה בתעשייה כולה. רועי משתף במודלי התמחור השונים שנבחנים בחברה: מ-Pool ארגוני, דרך באקט לכל יוזר ועד שילוב של מאגר אישי וארגוני יחד. בנוסף, הוא מדבר על אתגרים והזדמנויות במודלים השונים, ואיך יוצרים Alignment בין הלקוחות שיקבלו הנחות משמעותיות ויפיקו את הערך המקסימלי מהמוצר, לבין החברה שתשמור על מרג'ין גבוה על קרדיטים שנרכשו ולא נוצלו. פרק מומלץ לכל מי שבונה מוצר מעל AI ומנסה להבין לא רק מה לתמחר, אלא איך לחשוב על זה. הפרק המלא בוידאו גם ביוטיוב: https://youtu.be/Ul0zk43QtRw האזינו גם לפרק על השינוי במודל העסקי בעידן ה-AISee omnystudio.com/listener for privacy information.

We are excited to have Anthropic share their latest AI x Finance work at AI Engineer New York, coming up in 2 weeks!In case you've been under a rock, here's a non-exhaustive list of what Anthropic has been shipping since closing the largest fundraise of all time in May at $47B ARR:* June: Launched Claude Tag and Sonnet 5 and Fable 5* July: Opus 5, /checkup. crossed $65B ARR* Last month: Fable/Mythos 5.1, and EFS (upcoming pod)* IPO target $2T, end 2026 ARR estimated $100B* Cowork/chat merged before did* Claude Mods* Dario endorses the same Pacing the Frontier message cosigned by all labs* Last week: Opus 5.5, Plugins portal, Cloud Sessions/Claude Projects* Today: Sonnet 5.5!Today's episode should catch you up, with Thariq Shihipar, the explainer-king of Anthropic, who we last caught up on Fable launch day with The Field Guide to Fable:The Future of Mutable SoftwarePay special attention to Claude Mods (especially the cheatsheet):In general this is also the inverse of the other viral tweet from Thariq:Cloud Brain, Local HandsAnd give a try to Claude Projects:The “hands” terminology is not just an analogy for the local/cloud paradigm that is being built up at frontier coding agent companies like Cognition, but is ALSO particularly relevant to the safety systems discussions that we'll be discussing with Anthropic in an upcoming episode as they prepare to pace to frontier with responsible AI deployment.For those who want Thariq's writing tips we teased at the start of the pod, watch the full video here:From the rapid rise of Claude Code to a future where agents can rewrite their own harnesses, collaborate across teams, and operate across cloud and local environments, the way we build software is changing extraordinarily fast. In this episode, Anthropic's Thariq Shihipar joins swyx and Vibhu to unpack how power users are actually working with Claude Code today, why prompting remains a high-skill discipline, and where Anthropic thinks the agent harness is headed next.We go deep on Claude Code's evolving interface: Ask User Question and elicitation, artifacts as persistent generative interfaces, Claude Tag for multiplayer agent workflows, Projects, model effort, implementation notes, and the new Claude Mods system for customizing the harness itself. Thariq explains why Claude.md may eventually disappear, why the smartest model could also become the cheapest model for many tasks, and why mutable software could become a new paradigm for how applications are built and customized.The conversation then turns to agent security and Anthropic's “Pacing the Frontier” argument. Thariq walks through recent incidents where agents discovered unexpected ways to communicate, exploit infrastructure, reverse-engineer benchmark scorers, and chain vulnerabilities together. We discuss sandboxing, prompt injection, autonomous agents, interpretability, constitutional classifiers, probes, fallbacks, Auto Mode, and why securing increasingly capable agents may become one of the defining engineering problems of the next few years.We discuss:* Why agentic coding went from controversial to the default in less than a year* Why prompting is still one of the highest-leverage skills for working with Claude Code* How expert users build a mental model of Claude and what it can reliably one-shot* Why discovering your “unknown unknowns” matters more as agents become more capable* Artifacts as persistent, generative interfaces between humans and agents* How Claude could split into a cloud-based “brain,” local or remote “hands,” and dynamic interfaces* Claude Tag, Projects, and multiplayer agents and how collaborative agent workflows could evolve* Why spending more time on the initial prompt can dramatically reduce wasted agent work* When to use low, medium, high, or max effort for different engineering tasks* Why frontier models may eventually outperform smaller models on both intelligence and token efficiency* Why implementation notes can expose decisions the model considered but chose not to make* Why Claude.md may eventually disappear — and why starting without one can sometimes be better* Claude Mods: customizing the execution loop, UI, subagents, routing, and behavior of Claude Code* Model routers, forked agents, and supervisor agents that automatically improve agent workflows* Why Claude Mods may be an early preview of “mutable software”* The bitter lesson of harness engineering and why agent architectures go out of date so quickly* How Claude Tag is becoming an organizational harness for multiplayer work* Why giving agents access to company data creates an enormous new security surface* The Exploit-Bench incident where agents discovered ways to communicate and collaborate* Why agents hacked Hugging Face for scorer code rather than benchmark answers* How agents chained sandbox and infrastructure vulnerabilities in unexpected ways* Why increasingly capable agents make traditional security assumptions harder to maintain* The argument behind Anthropic's “Pacing the Frontier” proposal* Why software engineers are increasingly doing two jobs: engineering and keeping up with AI* Constitutional classifiers, probes, and fallbacks and what interpretability looks like in production* How Auto Mode checks whether an agent's actions actually match the user's permissions* Why Thariq can see serious AI risks while still having a relatively low p(doom)Thariq Shihipar* X: https://x.com/trq212* LinkedIn: https://www.linkedin.com/in/thariqshihiparTimestamps00:00:00 Introduction00:04:12 Ask User Question and the Future of Agent Interfaces00:08:29 Artifacts, Projects, and Multiplayer Agents00:15:37 Prompting as the Core Claude Code Skill00:21:52 Context, Effort, and Smarter Model Usage00:28:10 Is Claude.md Going Away?00:32:49 Claude Mods: Customizing the Claude Code Harness00:36:35 Model Routing and the Rise of Mutable Software00:44:40 The Bitter Lesson of Harness Engineering00:50:49 Claude Tag as an Organizational Harness00:55:59 Pacing the Frontier and Autonomous Agent Security00:58:22 Agents Hack Hugging Face for the Scorer01:05:34 What Happens When Agents Need More Compute?01:10:32 AI Coding Is Changing Faster Than Engineers Can Keep Up01:17:17 Probes, Fallbacks, Interpretability, and Auto Mode01:28:32 AI Risk, p(doom), and Closing ThoughtsTranscriptIntroduction: Life at Anthropic and the Pace of ChangeSwyx [00:00:00]: We're here in the studio with our friend Thariq from Anthropic, and I guess generally the Claude Code, I-- there's, there's so much, merging of boundaries and you've been so on top of everything since you joined Anthropic. You have been early to Claude Code itself, but then also, and you've told that story in other podcasts, and you've also been talking about seeing like an agent. Most recently you did the top AIE World Tour talk, Field Guide to Fable, which obviously you guys launched Fable, so that was-- that's cheating. And mostly you most recently also launching Claude Tag, and we're also gonna be talking about Pacing the Frontier. There's a lot going on in Anthropic. I guess top of the question is, what's it like being at Anthropic when there's so much going on?Thariq Shihipar [00:00:48]: I think that It is, like. I think you can get whiplash sometimes. I think, like, going. When I joined Anthropic, I joined because of Claude Code. Like Claude Code had just come out and I was like, “This is so good.” And Opus 4 to me was like just, I could not imagine, like, how good it was? And that was, like, a real moment for me. But I was, like, trying to convince, like, my startup friends to use agentic coding, and they're like, “Oh, no, like, our engineers don't think it's good enough,” or something. And I was like, “That's insane.” and now you, like, fast-forward, 12 months, less, and, like, it's just like, yeah, the default way that everyone codes, right? And I think that, like, just having to go from, like, selling it to, like, now, teaching people how to be. make the most use of it and be more efficient and things like that is just like a big, like big change. And, yeah, I think, like, it's just hard to stay on top of everything as a human? Like, I think things happen so fast and likeSwyx [00:01:51]: You just throw more agents at it.Thariq Shihipar [00:01:52]: Yeah, like that's like the agentic stuff scales much better than the, like, human stuff where it's like, oh, like, there are three things happening right now and, like, they're all emergencies and, like, how do you, like, respond to it? Yeah.Teaching People to Use Claude CodeVibhu [00:02:05]: What do you split your time on? You do a lot of technical writing, engineering work.Thariq Shihipar [00:02:10]: Yeah, so I think that, like, when I joined the Claude Code team, I wanted to teach people how to use Claude Code and I think that, like, that has been something that, like, I thought, like, maybe I would spend a little bit of time on it or, like, I'd, like, do. I was spending some time on the agent SDK first, and I wasn't exactly sure, like, how the bitter lesson would go, when it comes to, like, harnesses, right? Like, I think sometimes we were like, “Oh, like, what's after Claude Code?”? And so initially I was like, I just wanna teach people how to use Claude Code and make it easier to use Claude Code. And I think that has just, like, as the harnesses have gotten better and better, that's like the dominant problem now is, like, how do you use the agents, right? Like, it's like such a high skill expression thing. So I do that and then I do engineering work. I give talks, but I think, like, when I'm doing engineering work, my goal is to take that feedback that we get from users and also, like, then be able to talk about, like, hey, how to use Claude Code to do engineering. So there's like a good loop there. Yeah.Swyx [00:03:07]: Yeah. I'll-- For listeners, we'll attach, the talk that you did with Sarah for the Dev Writers, meetupThariq Shihipar [00:03:13]: Oh, yeahSwyx [00:03:13]: Which we talked a little bit about, well, first you do the work and then you talk about the work.Thariq Shihipar [00:03:16]: Right.Swyx [00:03:16]: Something like that.Thariq Shihipar [00:03:17]: Yeah.Swyx [00:03:17]: It's sow and reap orThariq Shihipar [00:03:19]: Yeah, reap and. Sow and reap.Swyx [00:03:21]: Something like that. Something like that. Yeah, so, and then just to preview a little bit, we are gonna talk about the evolution of the harness. It has come a long way from just being a CLI. We're gonna talk about, Claude Mods, which is starting to leak today, because you couldn't keep it secret.Thariq Shihipar [00:03:36]: Yeah. yeah.Swyx [00:03:39]: Yeah, there's, there's a lot, there. I think you started off with, like, adding ask user question tool, which people love and hate.Thariq Shihipar [00:03:48]: Yeah.Swyx [00:03:48]: Like, I thought it was, like, very innovative, and then now I have, like, my own version. You have your Interview Me version.Thariq Shihipar [00:03:55]: Yeah.Swyx [00:03:56]: And, yeah, everyone just has, like, their own stuff. And, like, it no longer matters ‘cause now you're supposed to, write prompts that create other prompts and loops and all these things.Ask User Question and Human-Agent InteractionThariq Shihipar [00:04:05]: Sure, yeah.Swyx [00:04:06]: So what's the state of the art, today? Like, what are people. what are you, like, telling people to do today?Thariq Shihipar [00:04:12]: Yeah, ask user question was the first time that the model was good at elicitation. I think this was, like, an emergent behavior that I, like, wanted to see if the models could do. I have, like a human-computer interaction background, so I, like, did that in undergrad and grad school. And so this was like. I think it's like human-agent interaction to me, like, trying to figure out, like, how can the agent communicate with you and extract, the requirements, right? I think that, like, one of the things about, like, that's difficult as Claude Code has gone broader and broader is that everyone has, like, their own way of using it, and it's very hard to, like, change the default behavior. So for example, like, if someone asks Claude Code to do something,Thariq Shihipar [00:04:59]: Sometimes they just want them to do the work, ‘cause they're, like, maybe a very good prompter, and sometimes they want. like, are not good at prompting? And you need. like, the agent needs to, like, clarify? And so that's, like, a good split. Like, and the ask you the question tool like, splits along that side where, like, are-- do you feel like you're good enough to instruct the agent as it is, or is the agent able to, like. does the agent need to, like, pull out more requirements and, like, collaborate with you more and really understand your preferences?Thariq Shihipar [00:05:27]: I, on the whole, believe that pretty much everyone is more on the latter than the former, that they, like, have more ambiguity and they know less than they want, than they, like, think they know about the problem. but, like, it's like a interface design problem to make that easy? And so, like, if you're designing a problem, like, or if you're going through a problem, like, things like what's the schema or, like, what's the call stack and things like that are really important. like, the details in the design are important. Ideally, you want to figure out some of these, like, hard problems ahead of time before starting implementation. And yeah, that's why they call, like, unknowns, right? And so I think that this will forever be, like, a skill in agentic coding is, like, figuring out your unknowns. So, like, because even if the model is, like, super intelligent- It, like, needs to know what you want? And, like, you have preferences. like, you need to like, pull the, pull that out. and so that's, like, I think how I'm, what I'm pushing. the question then is, like, how does the agent interact with you? And I think that has been HTML, has been, like, the big way of doing that. And we've recently added artifacts, right? And artifacts, I think we've done a bad job of, like, or, like, I've done a bad job of, like, explaining how to use them fully. We have a lot of property capabilities. They have a database associated with them? And so every artifact can store and write persistent data. They can, like, feed back into Claude? And so, like, one thing that, like, people are not doing yet that I'm trying to, like, encourage is, like, this idea of a dashboard artifact. So you have, like, Claude working on a project long-term. Maybe it's like a kanban or something. it can store that kanban data in its database. Multiple Claudes can access that data via, like, the artifact MCP, and, like, that artifact can, like, talk to those Claudes as well. And so, like, the. We're building the primitives for you to be able to have this, like, generative interface via artifacts that will, like, let you surface more of that rich detail from the agents. And I think that, like, almost everything with agents right now is, like, this problem of, like, you think what you want, but you don't really know what you want, and, like, the agents need a lot of detail, and collaborating with them in the loop is really important. And so artifacts are, like, the, like, way that we're trying to evolve there. But there's a lot of work to do because it's so much more complicated than, like, a multiple-choice question? there's a lot more, like, detail in terms of, like, diagrams and code snippets and schemas or, like, whatever it is for that problem. But, like, artifacts is, like, the mo-more AGI-pilled way of, like, doing ask user question. So yeah.Artifacts as the Interface to the HarnessSwyx [00:08:15]: I think one thing that's unclear to me about these, the artifact stuff is, like, what feedback should go in through the artifact and what feedback should go through a Claude, a chat? Because the more AGI-pilled one is to just feed everything to the Claude.Thariq Shihipar [00:08:29]: I think the more AGI-pilled one is to go through the artifact. Like, and I think that, like, we imagine in the limit, I think that artifacts will be your interface into the harness? You can, like, comment on this, like, live, like, document of your plan, of the work. you can see maybe, like, multiple agents and different agents are doing this, and that artifact is built for the current work that you're doing, right? And so, like, each one has, like, slightly different. I think we're still, like, getting there from, like, an infrastructure perspective. But yeah, I think, like, on-the-fly interface for your harness is probably where things are headed.Vibhu [00:09:03]: Is there a version of it that's an abstraction from CLI or chat and you. Because right now, a lot of it is, okay, you're interfacing with Claude Code, you're having HTML given back for a mockup. It's pretty rich. There's diagrams. Artifacts are ways to connect these together. Why not just do everything that way?Separating Brain, Hands, and Surface UIThariq Shihipar [00:09:22]: Then it becomes, like, separating out, like, where is the inference happening? Where is the intelligence happening? Where is the work happening? like, I think this is like, difference between, like, or, like, some of the distinction between local and cloud, right? And so, I think right now, if you use Claude Code, it's, like, local and, like, you can spin off remote control, for example, to get some cloud behavior, or you can spin off Claude Code in the cloud, right? We're moving towards a place where instead of Claudes, like, you message a local Claude, it starts a session locally and it executes, to more like you have a Claude that you message that's in the cloud that's running. it can run, like, local, or, like, cloud sessions. This is how Claude Tag works. But, like, over time, we'll add, like, local hands as well. And so, like, local hands will be the ability for that agent to access your computer if it's online, and be able to, like, work there. And so it can spin off many different subagents. It can, like, commu- those subagents can communicate with each other, and that's where the artifact comes in to display all of that work. So you can imagine, like, the. You're separating out these things. So there's, like, the surface UI display that's an artifact and hosted somewhere and has a database and everything. There is the inference intelligence, right, that's happening on the cloud, and you don't have to worry about shutting off your computer or whatever, right? and then there's the, like, hands. Like, and it can be local, it can be in, like, a remote sandbox or wherever you need your work to be done. That's like unpackaging, like, the Claude Code experience right now where, like, right now it all happens in one place, right? So.Multiplayer Agents, Claude Tag, and ProjectsVibhu [00:11:00]: How do you see, like, the multiplayer side of that? So say teams want to work in this way. Right now it's very individual, but how do you see the future of multiplayer? Like, right now, I guess there's Claude Tag, which is a version, but.Thariq Shihipar [00:11:12]: We're launching projects. And so projects is the, like, this abstraction that's like Claude Tag, but on our Claude products, right? So you can message it and, like, it will do the Claude Tag-like stuff, like spinning off subagents. So We think with multiplayer. Like, Claude Tag is, like, a little bit more native multiplayer because it's just, like, in your Slack and the permissions are all figured out and stuff like that. But I do think multiplayer is, like, an important part of the story and, like, that will need to get tied together more. Like, you can imagine how complicated it gets when you're like, oh, you have hands, but now you have other hands in other people's computers too, and, like, you need to, like, permission them or, like, you have, like, your MCP and someone else's MCP, and how do you figure out how to use them, right? It gets, like, quite complicated. And Claude Tag does a good job of, like, sanding down all of these issues, right? So that, like, when you have, yeah, Google Docs, how does it access Google Docs, right? Like, it accesses through the shared Claude MCP, or it can access through your local credentials as well if it doesn't have access. But yeah, I think Claude Tag is our multiplayer, product, and it's really useful for these, like, things that are inherently multiplayer. Like, okay, like on-call, for example, incidents are inherently multiplayer. You want to tag Claude, you want multiple people to log in, you want it to be able to find context. I think whenever I'm, like, working on something and I want, like, privacy or security or, like, I want other people to review it's really nice to, like. I'll have a channel per project and I'll, like, at legal, for example, be like, “Hey, like, I want to ship this. Can you, like.” Like, here's. Like Claude knows everything, just chat with it. And that way legal gets precise answers, on like what exactly is shipping into the code, and I don't need to be in the loop, right? So I think like multiplayer is getting like more and more like, yeah, everyone can participate with Claude. I think Claude Tag is like that product and like projects will start off single player and will like, expand.Swyx [00:13:14]: I think there's a question about like maybe dual questions about identity and the unit of isolation.Identity, Permissions, and IsolationThariq Shihipar [00:13:20]: Yeah.Swyx [00:13:20]: Claude Tag, you specifically chose to make it its own identityThariq Shihipar [00:13:26]: Yes.Swyx [00:13:26]: Which is like, a controversial choice. There's, there's other ways to do it.Thariq Shihipar [00:13:30]: Yeah.Swyx [00:13:30]: Claude Projects probably it sounds like, if it's anything like ChatGPT Projects, it is, the isolation is that artifacts, that cloud instance, everyone's collaborating on this. It'll. It sounds like, it should be like if you're, if you're collaborating with legal on a thing, like that channel should be a project, right? Like it's not yetThariq Shihipar [00:13:50]: Yes.Swyx [00:13:50]: But it. that's the natural next step.Thariq Shihipar [00:13:53]: Yeah, like I think in Claude Tag, it's effectively. Like Claude Tag, you have to do your own arrangement. And so Claude Tag, yeah, each channel is like you can name it as you want, and I nameSwyx [00:14:04]: Yeah.Thariq Shihipar [00:14:04]: Like each featureSwyx [00:14:06]: Yeah.Thariq Shihipar [00:14:07]: As a channel.Swyx [00:14:07]: And, but I think like there is some trans- like it's unclear when there is transference, because let's say it is. if you have a coworkerThariq Shihipar [00:14:14]: Yeah.Swyx [00:14:14]: Who is tagging on all these things, yes, there is transferThariq Shihipar [00:14:16]: Yeah.Swyx [00:14:16]: Because it's the same person. but with Claude, it's unclear if it's like necessarily like, well, no, you don't know any of. you don't know about the other stuff. You should only use this stuff.Thariq Shihipar [00:14:25]: It's like the tip of the iceberg meme, right, where you can like. This is what we spend so much time onSwyx [00:14:31]: Yeah.Thariq Shihipar [00:14:31]: Is like there is like infinite surface area of like, okay, you want Claudes to. Not infinite, but like there's like surface area, a lot of like, surface area to figure out of like permissions and visibility and like how can you let Claude operate as well as you can, as safely as you can? And obviously, this is very important to us because like security for our code base is very important. And so we've put a lot of time into this. Yeah, there's so many like edge cases you can figure out where it's like, oh, like, yeah, this Claude in this channel has different permissions, but it can message another channel, and can't it exfiltrate data that way? Or like can you like. What if it uses your MCP and then messages someone else? Like there's like so much, and we've like really put a lot of work into sanding it down.Swyx [00:15:14]: Yeah. Lots of work. okay. Fable?Fable and the Meta-Skill of PromptingVibhu [00:15:18]: Fable, you wrote two good articles. you've written many good articlesThariq Shihipar [00:15:22]: Yeah.Vibhu [00:15:22]: But on, Field Guide to Fable, Building Claude Code. I'm curious from what you've seen, is there any common patterns that you see in like top users at Anthropic externally? Like what are best practices for getting the most out of Claude Code?Thariq Shihipar [00:15:37]: The like meta skill I say is like prompting is like very important? And like that. Like I think this is like not trivial to say because I think a lot of people are like, “Oh, prompting doesn't matter. It's just like I can just say a sentence and Claude will do it.” And I think prompting is really this like, this. It's like public speaking, like, or writing or something, and for a specific audience, and that audience is Claude. And you need to like build a mental model of Claude and how it thinks and how it works, right? And so that's like the most important skill in working with Claude Code is like having this mental model, right, of Claude and like what it can do well, what it can one-shot, what it can't. And so many people when you see prompting, they're just like, they're short prompts, but they have such a good mental model of Claude and of like the code base and things like that like it's effortless? But it's like high skill ceiling. So like that work of like, spending a lot of time prompting and building mental models of how, and intuition for how the agents work is really important. And then I think like the next thing is like the unknown stuff we talked about earlier, where it's like being able to find out like your, what you don't know or what you haven't written down, learning about like different things. I think as Claude can do more and more things, the likelihood of you doing something out of distribution for you and like you have low domain knowledge on is very high? And the more you can like learn the vocabulary to be able to prompt Claude, it becomes really important. And so like I think the most important unknowns are the unknown unknowns, where you're like, I just like don't even know that this exists, right? Yeah, exactly. I think that's like a illustration of like the map and the territory, right, where you're like, “Okay, this is my prompt,” and the territory is like the actual like work that the agent needs to do, right? And if you are like very precise, you can give more precise things, right? So like for example, in design, I'm not very precise. I'm not a designer, so I say like, “Give me like eight different mock-ups.” But if I was a designer, maybe I'd be like, “Oh, hey, here are some reference sites.” Like, “I want this type of font and this type of like look to it, and here's like a few different components to like visualize. Here's a Figma MC board to bring in,” like. And so you can just be so much more precise with that language. And if you're not a designer, you just need to like try and learn the language or learn the unknown unknowns. And this is true of like everything, I think. Like the more, like you can work with Claude to learn like how things work, the better your prompting will be. I think another good example of this is like game design, like where a lot of people are like, “Oh, like I can vibe code a game now.” And they're like, “It's not fun.” And like it's just like the thing about game design is like every one of these choices has like a lot ofTaste, Domain Knowledge, and Learning the VocabularySwyx [00:18:25]: Variations.Thariq Shihipar [00:18:25]: A lot of like craft to them. So it's like, oh, okay, like when you're making a flying game, the feel of the plane and the like, way it responds to your controls has a lot of like. Like, a game designer would spend like days on that. Do? and likeSwyx [00:18:44]: To me, that's what taste is, right?Swyx [00:18:45]: Like it is like from the possible space of one thousand mathematically valid answersThariq Shihipar [00:18:49]: Yeah.Swyx [00:18:49]: Here's the one that is the humans will like.Thariq Shihipar [00:18:51]: Yes. Yeah.Thariq Shihipar [00:18:52]: I think with taste, I'm like torn on this word ‘cause I think you're right, but everyone has different definitions, and it sounds kind, sounds like low skill or like elitist almost, where you're like, oh, like there are certain people with taste?Swyx [00:19:06]: It's like taste is what I call taste.Thariq Shihipar [00:19:07]: Yeah, exactly.Swyx [00:19:08]: And it's like these guys don't have taste.Thariq Shihipar [00:19:09]: Yeah, exactly. Oh, like an engineer doesn't have taste. Like I, the like founder, have taste.Thariq Shihipar [00:19:14]: ? And I think that's not true. Like I think like the engineers have a lot of taste for these particular like problems? And I think everyone has taste for particular problems. I think like Jason Liu, like say like in order to, yeah, have taste, you have to eat?Thariq Shihipar [00:19:32]: And I really like that, where it's like, okay, you have to like do a lot of things. You have to like iterate and figure out what you want, what you like, and, like build that like domainSwyx [00:19:41]: YesThariq Shihipar [00:19:41]: Domain vocabulary. And then when you're prompting, you're like synthesizing all of that for a product.Swyx [00:19:46]: Isn't it annoying when someone else says it better than you?Swyx [00:19:48]: It's just like, f**k, I have to quote this guy forever.Vibhu [00:19:51]: Having to quote Jason Liu forever.Vibhu [00:19:53]: He's gonna love this.Thariq Shihipar [00:19:55]: So I get prompts, more than that.Vibhu [00:19:57]: And sometimes it's not even that. Sometimes it's just intuitive, right? Like you don't realize you even want something till a model puts it out, and you're like, “Oh, this just feels immediately better,” right?Voice Prompting and Information DensityThariq Shihipar [00:20:07]: Yeah, exactly.Swyx [00:20:09]: One thing I go back and forth on is I feel like the way I prompt half the time, let's say I use voice.Swyx [00:20:16]: Did I say voice? Other people have voice. that is the opposite. That is just like me rambling for like two minutes Pressing down the function key and then let go, and then like hopefully it figures it out. And oftentimes it does.Thariq Shihipar [00:20:26]: Yeah.Swyx [00:20:26]: But it's not as thoughtful as like a structured prompt with like Well-run communication as though it's a PRD or a memo. Is that in line with how people do this? There's like bimodal prompting where there's some prompts where you spend a lot of time upfront and other prompts you just dash it off?Thariq Shihipar [00:20:43]: I don't think the voice is necessarily low. Like I think it's like more like how much information is in the prompt. like the model can. Like you can and like add some sentencesSwyx [00:20:53]: RightThariq Shihipar [00:20:53]: And be like, “Oh, like I changed my mind,” like in the middle of the prompt, and it will be able to follow that perfectly? So I think the like actual format of the text is less important, but then like the ability to. Like how much information is in it, right? And I think for voice, a lot of times, going back to like human-agent interaction and like for a lot of people, it's just way easier to talk than to like type? and I. If that gets more information out of you, like that's better.Vibhu [00:21:21]: At some level, it feels like just giving the model as much contextThariq Shihipar [00:21:24]: YesVibhu [00:21:24]: Over prompting before you kick off is a best practice. I don't know. A lot of the times, like when I was first trying out Fable, I spend a solid 30 minutes like really crafting a long prompt. This, I think, is a response of models running for longer and longer, right? It's still a little difficult to nudge them as they're in like, in the loop, but I just like intuitively spend more time kicking off that first prompt and working with it a lot.Spend More Upfront, Iterate LessThariq Shihipar [00:21:52]: My personal opinion is that if I was a software engineer, if I was like, just running my own startup, for example, I think I would mostly fit, stick to a max 20x? like maybe verification and so code review are like separate things. But I think like what I see a lot of times is people hit rate limits when they're doing this like, oh, like it did a lot of work and you're like, “Oh, I don't like this.” Like, “Can you like undo this and redo it?” And then you're like iterating on this like thing that the model could have done if you had like spent more upfront time or given it better context? And instead it's like you're like, “Nope, don't like that design. Try this.” Or like, “You messed this up,” or something like that. And then that just eats up so much more of like, your usage. And so that's like, I think maybe like a key like tip both for like efficiency as well, right? And yeah, I think like context, and not just like context on like what the goal is good, right? Like are you building a prototype or is it like a production thing? Like where can you spend compute or when, where can you not spend compute? Like I think you have to give the model permission or like not permission to do things sometimes where, like it doesn't know intuitively how much you want to spend on this task, right? And you can use effort for this. So I did-- I'm working on a blog post about that where it's like, if you want. For like we see that effort scales with the complexity of the task. So for security, effort gets like way more results. Like high effort versus like low effort gets, like changes the evals a lot. But for software engineering, it doesn't change it a huge amount because effort is mostly spent on the verification and the like edge case testing and things like that. And so like being able to like give the model that guidance of like, “Hey, this problem is something that I think I want you to spend a lot of time verifying and edge case testing,”?Effort, Model Choice, and VerificationVibhu [00:23:43]: How about model in the mix? So, there's Opus and Fable with effort.Thariq Shihipar [00:23:47]: Yeah.Vibhu [00:23:48]: There's also Haiku in there.Thariq Shihipar [00:23:49]: Yeah. It's not quite true yet, but it's very close where I think the frontier models will be Pareto dominant over like almost everything. like maybe. And sometimes I think Opus might be Pareto dominant. Do? Like I think depending on like how things, like shake out if it's like a newer version of Opus. But I think that like increasingly it's just going to be like the smart model is going to be able to like do the simple task for less tokens than the like the other models because of verification. With verification, in the limit, your model doesn't need to verify, right? If it's a perfect model, it just does the work once and it's like, okay, like you, I did it? And increasingly with Fable, I'm like, I'm like, “Dude, you don't need to spin up Chromium and screenshot all of these things.” Like I see it. Like you did it, right? And so a lot of the. At higher effort, you spend more of those tokens verifying. But if you're working on simpler problems, and a lot of software engineering is like well, like in Fable, like low and medium stability, it can spend less tokens verifying. And as the models get smarter and smarter, they will just be able to like, “All right, done.”? Like, I can run the lint for sanity's sake, but, like, I, like, know it lints? Like, you don't even need to do that. And that will be so much more token efficient than, like, the smaller models. Yeah.Swyx [00:25:15]: Is there a good, practice on our side that we can use to see if we're using too much effort? Like, I freakingThariq Shihipar [00:25:23]: YeahSwyx [00:25:23]: Hate wasting time on that stuff.Thariq Shihipar [00:25:24]: Yeah. I know what you mean. I think, like, so in this blog post, my rough distribution is, like, code review and security should be, like, high or max and, like, software engineeringSwyx [00:25:37]: You said recommend mix settings per domain.Thariq Shihipar [00:25:37]: Yeah. I think, like, if you're doing, like, UI or something like that, like low and medium, I think is you're building, like, an API and you want to make sure, like, you cover enough edge cases? And so I think building, like I said, that mental model of, like, how things work across these distributions is, like, yeah, part of the job.Implementation Notes and Decision LogsVibhu [00:25:56]: This is more intuition-driven or eval? Because I'm guessing this would change as you go.Swyx [00:26:00]: He has evals.Thariq Shihipar [00:26:01]: Yeah. So what I did in the blog post is I go over all of the terminal bench evals. So there are, like, 70 problems and I'm show that, like, okay, like, in the security problems it does more. and then I also, like, look at some of the transcripts just in terms of, like, how-- what does it answer, what does it forget or something. And a lot of times, this is another prompting tip I have, is, like, asking it to make decision notes or implementation notes because, in every eval problem that it faces, it thinks about the correct solution, and decides not to do it. it's like, oh, like, here is the answer. What if I did this? And then it's like, oh, probably not? and then keeps going. And this is, like, the majority of the failures, at, like, a higher max level. It's very rare that the model just doesn't know how to do something. If you just have these implementation notes, then you can review and you can be like, “Oh, I want you to do this thing that you didn't do.” The models are getting better at surfacing that overall. Like, I see in the transcripts of Fable 5.1, like, when it does this output, it will call out its decision-making as well. but making this more explicit in the harness is better. And now we're, allowing ways of you modifying the harness so you can, like, add someVibhu [00:27:23]: Ooh.Thariq Shihipar [00:27:24]: Calculate with there. Yeah.Swyx [00:27:25]: Yeah. So I do wanna call out two things that you mentioned that I think exist outside of prompting. One is like, let's, let's call it the prompt that is so important that it shouldn't be in a prompt. It is in Claude.md or Agents.mdThariq Shihipar [00:27:38]: YeahSwyx [00:27:38]: Which is like goals, right? Like your situation, your goals, the things that you want, the thing. and then second of all is the decision log or the experiment log or whatever log of traces that you might want to survive the current session to do those things. Those are, like, externalities that there's no standard. There's no-- It's not like skills. It's not like MCP. There's no standard. It's, it's just like it's a markdown file. first of all, is that right? Is Claude.md going away? You have a documented dislike of, Agents.md, but you're gonna do it?Claude.md, Agents.md, and Model-Specific InstructionsThariq Shihipar [00:28:10]: Yeah. Okay. So Agents.md, yeah, like, we're, we're gonna do it. I think it's just, like, different models are very different from each other? But I realize that it's, like, such a pain to, like, maintain different ones? And yeah, like, as the models get better and better, the floor of how they accomplish the simpler task is better. And so I do think in the limit, Claude.md goes away, and maybe not even, like, that far. Like, I think, like, I think that right now it might be better to start a new project without a Claude.md.Swyx [00:28:44]: Yes.Thariq Shihipar [00:28:44]: I think that, like, maybe if you see very repeated failure modes, you add them to your Claude.md. The really tough thing is that this changes per model. And so, like, if you've added a bunch of failure modes or, like evenSwyx [00:28:57]: So you need Fable MD, you need Opus MD.Thariq Shihipar [00:28:59]: Or well, even Fable 5.1 versus Fable 5.Swyx [00:29:03]: Yeah.Thariq Shihipar [00:29:03]: Like, it is annoying. Like, I'm not like,Swyx [00:29:05]: YeahThariq Shihipar [00:29:05]: Like, we don't, like, do this on purpose? It's just, like, how the models work, right? And so, like, maybe, like, Fable 5 had this, like, failure mode that Fable 5.1 doesn't. And if you keep this context, this running log of a bunch of different failure modes, they will probably over constrain Claude? And so this is like. we just added evals plugins for skills.Swyx [00:29:28]: Yeah.Thariq Shihipar [00:29:29]: And so now you can eval if a skill is better. I think Daisy on our team did this. And so, yeah, this is like we're trying to work on this. We know it's, like, you still have to spend tokens on it and, like, it's not, it's not perfect, but it's, like, we're trying to help out with this problem.Swyx [00:29:44]: And so, and as far as prompting goes, the one tip I wanna offer is, something I have told people a lot is sufficiently advanced prompting is indistinguishable from sufficiently advanced executive communication. So I've referred to-- This is an executive comms workshop from Heavybit that is the best I've ever seen in my career. And they teach this thing called the SCQA model. Just Google it. It's a, it's a thing. Like, people have done prompting for decades. It's just called executive communication. It's like when one person has to communicate to thousands of people down the org chart, this is what you do. so situation, complication, question and answer, is how you write the memo. but obviously sometimes you don't have the answer, but you can at least list out the SC and Q, and then they have some examples in there. So just leaving breadcrumbs for people if they want to explore.Underrated Prompting Patterns and ELI5Vibhu [00:30:31]: Before we move on, I wanna ask you, any other underrated tips, ways people could get a lot of value from Claude Code that they're not using?Thariq Shihipar [00:30:41]: Yeah, I think a lot of them are in the, this unknowns, like, doc. Like, I give a bunch of example prompts, like, using it for brainstorming, using it to quiz you after. we added this, like, explain it like I'm five skill which is a very short prompt. And it doesn't even say explain it like I'm five. It's like the key word of this prompt is big pictures, few words. like, that's like the main thing. And it is shockingly good? Like, you, like, I think I tweeted about this and it's like /eli5, and, like, you can install it as a plug-in. But yeah, it's, like, way better at just cutting through the BS and being like, yeah, exactly right here. So the diagrams are, like, quite clear. I think one of the things that is true with artifacts is, like, they put too much text in and people are not reading the artifacts? And so, like, this simplifies it a lot more. And, yeah, this came out of, like, just people at Anthropic, like, going through very complicated incidents and being like, “What is happening?”? So, this one I think is great, yeah.Swyx [00:31:47]: My version of this is the, it's like test your understanding. Give you a few choices and then, like, if you get it wrong, you have a mismatch between what you think is happening versus what's happening.Thariq Shihipar [00:31:58]: Yeah. I think this is one of those things that everyone loves talking about, and then very few people really do. Like, I thinkSwyx [00:32:05]: Really helpful.Thariq Shihipar [00:32:07]: Yeah. But most people just don't want to get quizzed about something? Unfortunately, I think this is one of the, like, things that we need to, like.Swyx [00:32:16]: What's the opposite of ask you the question or ask you the question before the thing?Thariq Shihipar [00:32:19]: Yeah.Swyx [00:32:19]: This is after the thing.Thariq Shihipar [00:32:20]: Exactly. Yeah.Vibhu [00:32:21]: It's a good way to stay grounded of, like, do you even know what you're doing, right? The worst case is when people send you slop and they haven't understood what they're asking for or what the output is, and it's like, “Dude, I don't wanna read this. Do you even know what it is?” So, you make it a rule for yourself that before you send stuff, you should at least know what's implemented.Claude Mods: Customizing the HarnessThariq Shihipar [00:32:41]: Yes, but so you could make this a mod and you could build your own mod to, like, make sure you test it. So yeah, you can do that.Swyx [00:32:49]: All right. Let's get right into it. What is Claude Mod, and what is this diagram showing?Thariq Shihipar [00:32:54]: Yeah. Okay, so Claude Mods is you can customize the entire Claude Code harness, and we're going to. If you have requests, we will, like, let you, like, please let us know. We'll add more and more. This works for CLI, it works for desktop. maybe it will work for Claude Tag in the future. I don't know. Like, we're trying to make this very extensible. You can see this reference sheet. I don't want people to get overwhelmed by it? At a high level, you can customize both the execution of the harness, and the UI of the harness. And so, like, you say on that Tetris example from Boris, that's like customizing the UI, right? Like showing, like, Tetris in the game.Thariq Shihipar [00:33:35]: But, like, let's say that you wanted to do this thing where you had. you tested your assumptions or, like, tested your understanding after every project, right? What you would do is you would ask Claude to make this plug-in. It would spin a classifier after every prompt. And so, like, at the end of each turn, you would spin off a sub-agent or, like, a forked agent. A forked agent is, like, maintains the prompt cache, right? So it's like a, like one of those unintuitive things where you can fork and do, like, a little request, and it'll be very cheap because the entire prompt cache is, like, done. And so you can be like, “Has this task been completed?” likeSwyx [00:34:18]: This is how you do BTW and all those.Thariq Shihipar [00:34:20]: Yeah. The underlying forked agent, yes. But so you can, in the f-fork sub-agent, you can say, like, “Has this task been completed? If so, return true.” And then in your hook, or in your, like, plug-in mod, or sorry, like, in the sub-agent probably, you would say, like, “If true, give me a quiz.” give me questions and answers, and then, like, in a JSON format, and then you'd parse it, and then you display above the prompt input, this list of questions, right? And so this is something that's, like, slightly token-intensive because, like, you have to do it after every end of the assistant turn. But it's, like, a lightweight classification, and then you can, like, get this quiz, and then you'll see, like, Claude will always do it for you. You don't need to remember to do it. There are lots of these, like, tips that we've talked about, right, where it's like, oh, implementation notes. You can also add a tool for implementation notes now. And so, like, this tool that I'm adding is, like, register, like, I think assumption is what I'm calling it, but, like, maybe I'll change it around. And this is a mod. And so, like, you give it a register assumption tool, and then it will keep a list. It'll. Every time it does it'll keep a, like, add to the list, and then at the end it will display those assumptions? Another mod I'm working on is a model router. And so, like, internal, like, Claude model routing, right? So it's. This is, I want to say the reason we don't do model routing by default is, like, it's a hard problem? And likeForked Agents, Assumption Tracking, and Model RoutingSwyx [00:35:51]: You will get it wrong.Thariq Shihipar [00:35:52]: Yeah, you, like, yeah, you will, like, accidentally use, like, Fable for a hard problem or Sonnet forSwyx [00:35:57]: Yeah, if you have auto approve, but you don't have auto mode.Thariq Shihipar [00:36:01]: Well, you will have auto. Like, you don't have, like, auto routing or something.Vibhu [00:36:04]: You don't have auto mode for model picker.Thariq Shihipar [00:36:06]: Yeah, exactly. SoVibhu [00:36:07]: I'm getting the rough question of, like, how much do you open this up and how much do people have to think about this? Like, when you talk about prompt caching and building a router, it seems like you could easily build a mod that routes per query, and I'm just killing my plan very fast, right? I guess my question is more so, like, what is, like, a product talk like this look like, right? Who is it for? Is it for power users? Is it everyone should be able to go throughSwyx [00:36:33]: Oh, definitely power users, right?Thariq Shihipar [00:36:35]: Yeah, I think it is power users, but, like, the nature of Claude Code is that so many people are power users? Because it's easy to share things, like you can. Like, one person can make a good model router thing that doesn't break prompt cache all the time, and then you can, like, compose them. Another cool thing about the plug-ins is that they can hook into and compose with each other. And so I have, like, a mod that will, like, create a mode selector at the top, and any plug-ins can register to be a mode. And so, like, the auto router can be a mode, right? Or, like, you can have a mode that's, like, artifact mode, where it's like it primarily talks to you in artifacts. like, you can toggle between plan mode? And so, like, you can create more and more of these modes. But the ability to create modes is in it itself a mod? And so there's a lot of richness here, but we do want to make it fairly easy. We want to be-- make it so that you can just, like, install someone else's. You can ta-- you can chat with Claude and, we'll, like, make sure that it understands the nuances of things like prompt caching and stuff, so it can, like, warn you. This is, like, not extremely complicated behavior for Claude, I think, but we should have just a good skill on how to make mods. and yeah, we'll see how we go. But I do think that this is, like, a preview of, like, mutable software, and, like, how, like, generative software, just like you can customize safely. If enabled, you could customize any piece of software. And I think that more and more apps ideally do something like this?Power Users, Modes, and Mutable SoftwareSwyx [00:38:13]: And by the way, you, we have, you have another cool tweet about how, there's the infinite money button, which is like make your SaaS, consumable by agents. I think mutable software is interesting and, other people have also tried to do it. I think the hurdle comes when you can do everything, then people, users get, tend to get confused. So usually the stuff that works is just like one opinionated flow. This is in the side of less opinionation. It's just like, well, more power to power users. And I think probably unlocked by AI, where, like, you can just prompt for whatever the thing is.Thariq Shihipar [00:38:47]: Yeah, or there can be a skill that gives the opinions?Mods vs. Hooks vs. ArtifactsSwyx [00:38:50]: Yeah.Thariq Shihipar [00:38:50]: And then, yeah.Swyx [00:38:51]: So knowing a little bit about, like, TypeScript and build systems and all these things, the closest-- I'm very curious that the team who worked on this, if, I don't know how close you were to them, if they drew any inspiration from build systems like Babel, Webpack, all these, like, old school things. Because it sounds very similar, like the plug-in ecosystem of those things where they can compose with each other.Thariq Shihipar [00:39:11]: Yeah, I'm not deep in the technical details, but I do know it was a collaboration with someone on the Bun team and someone on the Claude Code team.Swyx [00:39:17]: Yeah, it's a build system mecca.Thariq Shihipar [00:39:19]: Yeah. Exactly. It's, it's very exciting. But yeah, like, agents can just do this very complicated like, extensibility into your software now. And so, yeah, like, another reason to, like. If you run a startup, like, you can just prompt Claude and be like, “Hey, like, could we make an extension system? Like, what would that look like?”?Swyx [00:39:37]: Yeah.Swyx [00:39:38]: And I just really wonder, like, you had hooks in the past and plug-ins, all these things. So what specifically will mods be able to do that those things could not do?Thariq Shihipar [00:39:47]: Internally, we were originally calling this function hooks. And so, like, that's, like, gives you a little bit of an idea where, like, hooks register a, like an event to happen and then, like, a script to call. And this inside of the, like, TypeScript runtime is running things. And so, like, you get some benefits of just, like, it has a bunch of things in the Scope with, like, for example, like how many turns is in this conversation, right? Like, how many tokens have been used? Like, et cetera. Like, what are the messages? Things like that. So it has a bunch of messages that can be used. And then it's just, like, a lot more hooks. So we have, like, or a lot of, lot more, like, things you can register on. And then you can do because of the. because it's all happening in process, you can, spawn sub-agents, with four contests and contexts and stuff. And, like, that will return. You can parse the results of those. You can use structured output to like, return them. and then you can modify the UI, which you can never do in hooks. So, yeah.Swyx [00:40:50]: Yeah. Yeah. So modify UI, this is why you showed the Tetris example. Does it also ex-extend to artifacts? I assume it does.Thariq Shihipar [00:40:57]: You-- Like, artifacts are like a different way of customizing it. like, you can definitely. One of the mods I'm working on is, like, this dashboard mod, which will, like, prompt Claude to maintain a dashboard, that's an artifact. But they're like, slightly orthogonal, or not orthogonal. They compose with each other in different ways. Like, mods are, like, a little bit more, like, in your Claude Code harness, changing the agent loop? And, like, the UI is, like, an added benefit. and then artifacts are just like you want to, see things at a high level, very inter- highly interactive. like, the affordances can be a lot bigger than, like a TUI or even in our desktop.Next Steps, Supervisors, and Persistent GuidanceVibhu [00:41:40]: I'm guessing you'll have a good blog post on the differences, because right now you can also, make a loop that outputs to an artifact that's an interactive dashboard, but you can also do it with a mod. There's just some thinking about making a hacking on a harness when we don't know much about the harness, right?Thariq Shihipar [00:42:00]: Well, something I'm excited about with mods is, like, there's so much things with Claude Code that you just have to remember? You're like, “Oh, like, let me do this, and then let me call the dashboard skill that does the loop,” and things like that. And, or like, “Let me test my assumptions afterwards.” And I think, like, if you do all of these things using these little classifiers and stuff, and you're like, “These are the things I care about. This is what I want to do,” you can, like. You don't have to remember as much. One more, like, mod I'm working on is a next steps mod thatSwyx [00:42:28]: I have-- I was gonna say, I have a next step skill. I always run next steps.Thariq Shihipar [00:42:32]: And does it have access to your skills? Like, this is one of those things where I'm like.Swyx [00:42:37]: I think so.Thariq Shihipar [00:42:38]: Okay. Yeah, probablyVibhu [00:42:39]: Do skills need specific access toThariq Shihipar [00:42:41]: Well, I think there'sSwyx [00:42:41]: Don't they always haveThariq Shihipar [00:42:42]: I think there's, like, specific prompting, I guess, to, like, know your skills. Like I think Claude forgets them sometimes throughout, like, the thing. But anyways, the idea of, like, yeah, next steps that also are like, “Oh, hey, this has happened. Use the explain skill to explain to you what happened because this seems, like, quite complex,”? Or, like, yeah, “Use your unknown skill. It looks like you are, like, asking the model to, like, iterate on these small changes. It seems like you could prompt better.” like, “What if you did this?” Right? So, I think, yeah, like spending more compute there. Yeah.Swyx [00:43:20]: And it should always come out as multiple choice. we have, I haveVibhu [00:43:23]: We have his skill.Swyx [00:43:24]: My next step skill is like this.Thariq Shihipar [00:43:26]: Okay, perfect. Yeah.Swyx [00:43:27]: You can steal it.Thariq Shihipar [00:43:28]: Yeah.Swyx [00:43:29]: Like, but like, for me, it's all-- I think models really always need to be reminded, what are you trying to do here?Thariq Shihipar [00:43:35]: Yeah.Swyx [00:43:35]: Look at the whole transcript and go like, oh, was this original goal? Did your solution solve it? Were you lazy? If you're lazy, maybe there's a reason. Maybe you needed approval from me. Maybe you needed, there's two things you wanna suggest. So it's, it's a little bit like the modification of the ask user question or interview me skill. so it's next steps.Thariq Shihipar [00:43:55]: Yeah, exactly. And again, the benefit of doing it with mods is you can do it as a fork sub-agent, and so it doesn't remain in the context afterwards. So you have this, like, idea of like, okay, the model is doing its execution and you have this almost like supervisor, like, that is like making sure that you can do like the next steps well. So yeah.Swyx [00:44:15]: Yes. I do have two panels and like I often try to have a supervisor thing, keep the high-level context and then the implementationThariq Shihipar [00:44:21]: YeahSwyx [00:44:22]: Detail in another agent.Vibhu [00:44:23]: I feel like a lot of this abstracts away as models change? The, like, half an hour ago you said bitter lesson of harness engineeringThe Bitter Lesson of Harness EngineeringThariq Shihipar [00:44:31]: YeahVibhu [00:44:31]: And we're on the other extreme right now, I feel.Swyx [00:44:33]: Well, so yeah, exactly. If everything's customizable, what is Claude Code, right?Thariq Shihipar [00:44:37]: Yeah.Swyx [00:44:37]: And which I talked to you about last night.Thariq Shihipar [00:44:40]: Yeah, I think that this is. I think the bitter lesson is unintuitive? In terms of like. Also, like we're misusing a little bit of the bitter lesson here where it's like, it's more about like scaling and compute and stuff. But like, I think there is something where it's just like. I think I use it as an approximation here to say that harnesses go out of date very quickly? And like how, but how they change is unintuitive? And so like the big obvious example is like from chat to like agents where you had to give them entirely new tools, right? But like, I think this new version of like, oh, it can modify its own harness, right? This is like, an own harness loop is like a way of using its capabilities, right? Or like it can build an artifact. And like, I think the way I think about it is like the models have more and more intelligence, and they're like so much more intelligent now than like the average software engineering task. Like, you look at the like terminal bench ones and they're like solve like the Jacobian conjecture. Not really, but like, it's like they're, they're quite complex. Like, I would not have been able to do this really as a software engineer.Swyx [00:45:42]: And you said TB4 or TB2?Thariq Shihipar [00:45:43]: TB3. TB3.Swyx [00:45:44]: TB3.Thariq Shihipar [00:45:44]: Yeah. They're quite complex, but the goal is still to deliver user value, right? And like you said, there's like this infinite space of things to do. And so the ways like you spend compute are to keep the user in the loop and make sure that like you're getting to the right decision in the end of the day and like the right output. And artifacts and mods are this way of like spending that intelligence. and I think that's like, yeah, the next step. And so, yeah, I think Claude Code is like, has the core things of agent loop which are, have gotten more complicated. It's like, it needs a sandbox to operate safely. It needs auto mode to like make sure like the permissionsVibhu [00:46:21]: Approvals.Thariq Shihipar [00:46:21]: Yeah, approvals. it needs computer use and MCPs and like all of these like ways of accessing your data, and it needs web search and web fetch. And like, so the-- as the models can do more and more, the core harness has to be like quite complex and very secure. But then like how you interact with it can change quite a lot.Vibhu [00:46:42]: What other harness engineering best practices have you, from the Claude Code team itself? I feel like, there was a phase of plan mode, which is not as used. We now have auto mode. at a point you cut the majority of the system prompt, you got rid of examples. What other best practices are there for harness engineering?Core Harness Primitives and Managed AgentsThariq Shihipar [00:47:02]: I think there is like a forking path where at some point, eventually, yes, the model will just be able to like vibe code the exact version of Claude Code, even describing all this complexity that I've talked about, right? Like auto mode and computer use and stuff. Eventually, the models will just be able to do that in one shot. But I think they can one shot simpler harnesses? And so like, I think some people. Sometimes you don't need this full, like if you don't need computer use or like all this like more complicated stuff. I think before we, you had to use things like the agent SDK, which was like Claude Code wrapped, in order to like. And I would, like suggest people do that because there was so much complexity into building a harness. And now as that's got more abstracted, we have like, Claude managed agents, which lets you have that complexity, but still like, right, like a very bare bones like harness that's scoped to your task. Yeah, I think there's like this barbell effect where like for like very complex, for like coding task and like these like complex things, you should use our harness. And then for like a lot of like simpler or like, more domain-specific things, you can build your own harness because Claude has gotten better at building harnesses, and we have these harness primitives like managed agents. So yeah.Swyx [00:48:18]: Yeah. Is there a general progression? Let's say chapter one was ultra code dynamic workflows, then chapter two was cloud mods. Where is this going?Swyx [00:48:29]: Where you're, you're, you can customize the thing on demand.Thariq Shihipar [00:48:36]: Yeah. I do think that like this evolution of projects and like artifacts and splitting out like brain and hands and, surfaces is like where things are going more. And like, I think it's like not all quite there. partially it's like a, it's just like more token expensive? And like, I think likeProjects, Local Hands, and Cloud-to-Local HandoffsSwyx [00:48:59]: Why would projects be more token expensive? I understand mods would be slightly more token expensive. No, not something I'm worried about.Thariq Shihipar [00:49:06]: Yeah.Swyx [00:49:06]: But whatThariq Shihipar [00:49:07]: You're asking Claude to do. It's like creating loops. Like you're asking Claude to do more work for you. And so like it's managing the sub-agents and reviewing it, versus where you would be doing that work normally. And so that's like gonna be a little bit more intensive, like. Outputting to an artifact is gonna be a little bit more token-intensive than, like, outputting normally. I don't think it's too much more, but like, it's like combining all of these together well, like I think we're, we're still working on like local hands and things like that, I think is like, yeah, where things are headed, yeah.Swyx [00:49:37]: Yeah. Claude and local is, handoff is very interesting. I was thinking about this as reverse cloud remote.Thariq Shihipar [00:49:44]: Yeah.Swyx [00:49:45]: Because it's like remote, it's you're handing off to cloud, but here the cloud is handing off to local, right?Thariq Shihipar [00:49:49]: Yeah, exactly. Yeah, remote control is also another way of doing it. And I do want to say this is like how I think about it and like what the things that I'm most excited about this, but like there are, just like lots of different ways to work with Claude. Like some people use remote control a lot, some people use Claude Code on the web a lot. Obviously, like at Anthropic, we use Claude Tag a lot, and like what's great about Claude Tag is we set up all this stuff for our own execution. And I do think if you're an enterprise, that's still the best way to go. but if you're like an individual, Projects is this way of like, getting some of that like niceness of Tag, which has like that like supervising agent and yeah, adding artifacts and stuff, but like without having that whole like admin setup. And so there will be many ways to use Claude, I think. I think it's probably not just one like single.Claude Tag as an Organizational HarnessSwyx [00:50:36]: You had the multiplayer thing here. Let's, let's just check in on Claude Tag. it's been about two-plus months. Lots of, public, adoption and trying it out.Thariq Shihipar [00:50:45]: Yeah.Swyx [00:50:45]: What's new? What's, what have you found since the launch?Thariq Shihipar [00:50:49]: Like, Claude Tag is how we useSwyx [00:50:51]: It's like 80% of your

Nate Sokolić started and sold companies in college, spent three years in executive search at Russell Reynolds, then led the firm's AI strategy, where he brought Findem in and rolled it out globally. He now works at Findem, helping HR leaders and talent teams adopt AI. Shane Driggers has spent close to 30 years in human resources, starting in Bay Area startups during the dot-com boom and most recently serving as Chief Talent Officer at T-Mobile. He now advises growth-stage companies.Most companies are already using AI. Employees report that they feel more productive, and budgets for tools and tokens keep climbing. Yet very few leaders can point to the result in product innovation or revenue. Nate and Shane argue that the problem is rarely the technology. Companies are adding AI on top of workflows and org structures that were designed for a different era, and they are letting the tools set the strategy instead of starting with the problem they want to solve.In this episode, Jessica sits down with both of them to talk about what it takes to make AI deliver measurable outcomes, why the people function has one of the biggest opportunities in the business right now, and how recruiting is changing from searching keyword profiles to getting verified, finished work back from AI agents.In this conversation, Jessica, Nate and Shane discuss topics such as:◼️ Why "tech wags the tail of strategy" and how that derails AI programs◼️ Why employees feel more productive while the business sees no gain◼️ How to redesign work from a blank page instead of rebuilding the past◼️ Why AI is creating a new wave of tech debt inside large companies◼️ How to decide which work should be human-led and which should be AI-led◼️ Why HR leaders must be human thinkers, business thinkers and systems thinkers at once◼️ How the CHRO role quietly became the company's chief AI strategist◼️ Why the best AI adoption starts small, proves ROI, then expands year by year◼️ How recruiters can search for real experience, like a CFO who has taken a company public◼️ Why shifting from SaaS tools to AI agents changes how companies buy and use software◼️ How Findem Studio produces market maps, succession plans and talent inflow/outflow reports◼️ Why every AI output needs to show its work before a leader can trust it◼️ Why transparency is the most valuable currency a leader has in an anxious workplaceThis episode is sponsored by Findem. Findem is the AI infrastructure for people decisions. Its People Intelligence platform turns fragmented people data into context teams and AI can reason over, and act on. The 3D People Graph connects billions of data points across individuals, companies, and time. Expert labeling translates that data into consistent, evidence-backed signals about experience, capabilities, and relationships, with explainability behind every insight so teams can act with confidence. Findem's agents and enterprise applications run on this foundation, and partners can build on this infrastructure and bring the same intelligence into their own products. Findem is trusted by FedEx, Intuit, Nutanix, and Emirates.Learn more about Findem: https://www.findem.ai/platformTruth Works is hosted by Jessica Neal, bringing honest conversations with the leaders shaping the future of work.

Siege Media CEO Ross Hudgens flies solo for a case study episode on Zapier, one of the most cited domains in LLMs and a name almost anyone in B2B or SaaS will recognize from their own citation reports.Drawing from a longer chapter in his upcoming book on GEO with Wiley, Ross breaks down why Zapier shows up in roughly 80% of the bottom-funnel prompts it cares about, and why it ranks fifth among cited domains in B2B, behind only G2, Facebook, YouTube, and Reddit.Ross walks through the pieces behind that visibility: an integration library that lets Zapier write about thousands of tools without competing with them, a deep bench of editorially rigorous "best of" and "versus" articles, digital PR built on survey-driven data studies, and an actively curated Reddit community. He also takes on the big objection, whether citations actually drive business, with Zapier's own numbers: 40% of visitors now find them through LLMs, and 25% of signups are attributed to AI.Get the book to read more about Zapier: https://www.siegemedia.com/geo-bookShow Notes0:00 Why Zapier as a case study 0:36 Zapier's bottom-funnel AI visibility 1:05 Top 20 most cited domains, 5th in B2B 2:02 The integration library advantage 2:56 Best-of listicles and versus articles 4:42 Why versus content drives LLM visibility 5:32 Premium design and editorial standards 6:58 Digital PR and data studies 7:28 Building a Reddit community 9:10 Do citations actually drive pipeline? 10:48 The book and final thoughtsShow LinksPreorder "Generative Engine Optimization: The Definitive Guide to AI SEO" by Ross Hudgens: https://a.co/d/0a4oRiI5Subscribe for weekly episodes: https://bit.ly/3dBM61fListen on Apple: https://podcasts.apple.com/us/podcast/content-and-conversation-seo-tips-from-siege-media/id1289467174Listen on Spotify: https://open.spotify.com/show/1kiaFGXO5UcT2qXVRuXjsMFollow Ross on X: https://twitter.com/rosshudgensFollow Siege Media on X: https://twitter.com/siegemediaEmail Ross: ross@siegemedia.com Subscribe today for weekly tips: https://bit.ly/3dBM61f Listen on iTunes: https://podcasts.apple.com/us/podcast/content-and-conversation-seo-tips-from-siege-media/id1289467174 Listen on Spotify: https://open.spotify.com/show/1kiaFGXO5UcT2qXVRuXjsM Listen on Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9jT3NjUkdLeA Follow Siege on Twitter: http://twitter.com/siegemedia Follow Ross on Twitter: http://twitter.com/rosshudgens Directed by Cara Brown: https://twitter.com/cararbrown Email Ross: ross@siegemedia.com #seo | #contentmarketing

Your software might be the system of record, but if someone else owns the money movement, your growth is capped. Greg Myers sits down with Jane Podbelskaya, Founder of Charge Forward, and Jake Colognesi, Founder of Mamba Growth Equity, to dig into what separates “we added payments” from a truly investable embedded finance strategy for vertical SaaS.We get specific about why embedded finance can feel almost unfair when it's done right: revenue scales with your customers' transaction volume, one financial product naturally unlocks the next, and a platform can build multiple monetization streams across payments, lending, payroll, and insurance. Jane and Jake share why the best roadmap starts with customer interviews and real pain points, not trends, and how niche markets can suddenly have far more runway once you add a financial services layer.Then we shift into the scorecard. Jake breaks down what investors look for in the numbers like payments revenue mix, attach rate, and net take rate and Jane adds the “second-order” metrics that can make or break enterprise value: net dollar retention lift, CAC payback improvement, and the stickiness that comes from being mission critical. We also cover the operator reality: CEO alignment, true product ownership, compliance and go-to-market planning, plus the tracking and reporting infrastructure that turns embedded finance into a managed business line.If you're building in vertical SaaS and want embedded finance to drive valuation, not complexity, listen now, and share this with a founder who needs a clearer embedded finance playbook.

Ian and Aaron discuss Opus 5.5, Aaron's HomeOS, Ian's token usage, what's new with Aaron's office, and so much more.Sponsored by Svix, Bento, PostShiba, SerpAPI, Laracon AU, and Typesense.Interested in sponsoring Mostly Technical?  Head to https://mostlytechnical.com/sponsor to learn more.(00:00) - Ian Loves Developers! (05:16) - I Have Become Aaron, Destroyer of Worlds (12:11) - Update on Aaron's Office (22:18) - Orbin & Mogging (26:36) - Update on Jev (31:19) - HomeOS (48:23) - Opus 5.5 (55:48) - The Best Time Ever To Raise Prices Links:Jesse HanleyClaude DesignOpus 5.5RMFGShapeokoIan's tweet on his token usageJevAaron's tweet about Home OSTillerPlaidYodleeCloudflare Durable ObjectsJason Lemkin

Jeff Mains sits down with Carl Lenocker, a 30-year enterprise software veteran who started in Silicon Valley in the '80s, survived the dot-com bust, and now consults founders on what actually creates durable companies. Carl delivers a sobering assessment: more than 90% of the AI companies being built today could be reproduced by a larger competitor in six months or less. The conversation covers why distribution matters more than product, why "get acquired" is a hope masquerading as a plan, the disappearing apprenticeship pipeline and what it quietly breaks inside organizations, how AI "second brains" may replace tribal knowledge transfer, and what software might look like in 10 years when bespoke AI-generated tools could replace the SaaS model entirely. Carl also shares lessons from his book Success Plan for Life, his contrarian investment philosophy, and why he'd rather put money in apartment complexes than most small AI startups right now.Key Takeaways[4:18] — 90%+ of AI companies being built today could be reproduced by a larger competitor in about six months.[7:08] — Distribution matters 100x more than the product when anyone can build something.[8:35] — Rumors of SaaS being dead are completely overblown — AI is amplifying software roles, not eliminating them.[11:41] — Entry-level jobs have fallen off a cliff, and the loss of mentorship-style apprenticeships may cost companies in 5–10 years.[16:44] — Enterprise clients are greenlighting 8–10 AI platforms but expect to consolidate to 1–2 by 2027 — value and outcomes will decide who survives.[19:13] — Carl would rather invest in apartment complexes right now than small AI software companies, because most lack a defensible moat.[22:35] — Fundamentals matter: companies without a path to profitability, like pets.com, fail regardless of the hype surrounding them.[24:40] — Executive presence without a successful product is putting the cart before the horse — build the business first, hire the presence later.[30:49] — "Get acquired" is not a plan; most founders don't respect how hard acquisition actually is.[34:57] — Splunk's T-shirt marketing campaign is a masterclass in creative distribution and brand-building.[37:19] — Human-to-human relationships and sales skills are the most AI-proof skills you can invest in right now.[39:13] — In 5–10 years, software could become bespoke — AI agents building custom, self-maintaining solutions tailored to each company.Tweetable Quotes[7:02] Carl Lenocker: "If you could vibe code it in your basement, a major firm could probably have what you've built in six months."[7:28] Jeff Mains: "Distribution mattered way more than the product, and I think that is 100 times more true today than it's ever been."[8:35] Carl Lenocker: "The rumors of SaaS being dead are completely overblown."[19:13] Jeff Mains: "You'd rather put money in apartment complexes right now than a small AI software company."[26:08] Carl Lenocker: "People who put executive presence in front of having a successful product and a path to profitability are putting the cart before the horse."[32:25] Carl Lenocker: "Getting acquired is hard, and most people do not give it the respect it's due."[37:25] Carl Lenocker: "Everything good in my life came from having a plan. Second to that, everything good came from relationships."[37:55] Carl Lenocker: "If you're young and want to prevent your job from being taken by AI, invest in relationship building and sales skills."SaaS Leadership Lessons1. Distribution is the real moat. When anyone can build a product — and AI makes that faster every day — the companies that win are the ones that own distribution. Carl notes he could build a million-dollar company with one good SDR, one closer, and a product person, regardless of what the product actually is. If your go-to-market strategy is an afterthought, your company will be too.2. Tie every customer investment to a measurable outcome. Carl's 15+ years in customer success taught him that renewals live or die on value realization. Whether a client is writing a $50 million check or a $50,000 check, the question is always the same: did they see 2x, 3x, 4x the value of what they're paying? In the AI gold rush, companies theorizing future value will eventually have to prove it — and the ones who can't will be cut.3. Build a path to profitability from day one. The pets.com cautionary tale still applies. Hype without fundamentals is a time bomb. Carl's contrast between pets.com (no shipping infrastructure, no plan to ever make money) and Amazon (Bezos building distribution centers while everyone laughed) is the exact lens founders should use on their own AI startups today. Growth at all costs is no longer a viable strategy.4. "Get acquired" is not a plan — build like you're running it for a decade. Doug Merritt, former CEO of Splunk, said it best: people don't understand how hard it is to get acquired. Many founders take VC money, face mounting dilution, miss their growth apex, and end up sold to a hedge fund that fires 80% of employees. Build a company you'd want to run for 10–20 years. If someone wants to acquire it anyway, that's a bonus — not a strategy.5. Executive presence is hireable; product and revenue are not. Don't put charisma in front of fundamentals. Alex Karp at Palantir isn't charming — but he has a product that works and investors trust the results. Steve Jobs was known to be difficult. Bill Gates, same. Elizabeth Holmes had the presence but not the product. You can always hire a seasoned executive to sit across from clients. You can't hire your way out of a product nobody wants.6. Invest in human relationships — your most AI-proof skill. As AI writes emails, sends IMs, and soon handles calls, the ability to take someone to dinner, build genuine trust, and navigate a human-to-human conversation toward business outcomes becomes increasingly rare and valuable. Carl's advice to young professionals: relationship building and sales skills are where you should invest, because AI can fake empathy but it can't build real trust. Everything good in his career came from having a plan and, second to that, from relationships.Guest Resourcescarl.lenocker@gmail.comSuccessPlanforLife.comRockstarCSM.cominstagram.com/SuccessPlanforLifeEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/https://jeffmains.com/books/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Every product team is shipping AI. But the dirty secret is that, for many companies, very few users are adopting. The feature launches, the people who try it love it, and adoption stalls anyway. Our guest today thinks that's because teams obsess over building AI and barely think about adoption. Charanya Kannan is VP and GM of Navan Anywhere, which puts Navan's AI travel and expense tools inside Slack, Teams, and Gemini, where people already work, instead of asking them to open another app. She joined Navan when the company was still called TripActions and doing under $50 million in revenue... now it's well on its way to a billion. Her take: if customers have to work harder to use your AI, it isn't actually better. In this episode, Charanya shares: How Navan Anywhere was built distribution-first, bringing booking and expenses into the tools people already use every day Why Navan's margins went up in the AI era, while the rest of SaaS braces for compression And why she believes PMs who mostly manage process and Jira tickets will fade away, while those who can actually drive user benefit and business outcomes will matter more than ever Links LinkedIn: https://www.linkedin.com/in/meetcharanya/ Navan: https://navan.com/ Chapters 00:00 Introduction 04:17 Why AI features don't always get adopted 06:30 Building Navan Anywhere distribution-first 10:13 When to use AI vs. deterministic systems 12:01 Conversational cognitive load and why good UI still matters 15:57 Evals, distilled models, and the AI reliability pyramid 20:14 Product owners vs. product managers in the AI era 26:10 How Navan is growing margins while SaaS faces AI compression 31:40 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Charanya Kannan.

Kako je moguće da maleni softverski projekt u manje od godinu dana postane vodeći sustav u svojoj branši širom Balkana pa sve do Švicarske? U ovoj novoj epizodi podcasta ugostila sam Dunju Deket, IT poduzetnicu čiji se rad nalazi na samom spoju tehnologije, razvoja biznisa, prodaje i marketinga. Dunja se bavi građenjem digitalnih proizvoda i biznisa: od prepoznavanja prilika na tržištu i razvoja brenda, do prodaje, rada s klijentima i skaliranja. Jedan od projekata iza kojih stoji je upravo Andrea 360, regionalna SaaS platforma i specijalizirano CRM rješenje za fitness, wellness i beauty industriju. U razgovoru smo se dotaknule toga kako su fitness centri, wellness centri i spa centri prepoznali vrijednost ove platforme, ali i kako je Dunja, koju najviše pokreće proces stvaranja - od prve ideje i testiranja na tržištu do izgradnje proizvoda koji imaju svoje korisnike i prostor za rast - prebrodila vlastite blokade prema prodaji te kroz kontinuiranu edukaciju i promjenu mindseta ostvarila strelovit poslovni uzlet.Osim tehničkih i strategijskih aspekata koji direktno utječu na prodaju i održiv rast prihoda, otvoreno smo razgovarale i o izazovima koji prate naglo skaliranje poslovanja. Dunja je sa mnom podijelila svoje uvidje o reorganizaciji osobnih prioriteta, upravljanju svakodnevnim izazovima bez žrtvovanja privatnog mira, promjeni odnosa s okolinom te važnosti nagrađivanja same sebe na tom putu. Ako te zanima kako vrhunsko CRM rješenje pomaže da wellness centri, spa centri i fitness centri unaprijede svoje poslovanje, ili tražiš konkretnu inspiraciju kako da prodaja i rast prihoda postanu prirodan rezultat tvog predanog rada, sigurna sam da će ti ova epizoda donijeti neprocjenjive lekcije.Ukoliko želiš planiranje pretvoriti u svoj najsnažniji alat za osobni rast i napokon ostvariti ciljeve bez odgađanja vlastitog života, klikni na link i pridruži mi se u edukaciji Mentor pri ruci https://saraperanic.com/mentor-pri-ruci/U epizodi ćeš saznati:Kako je nastala i skalirala platforma Andrea 360: Put od studentskog projekta do vodećeg sustava koji transformira poslovanje za fitness centre, wellness centre i spa centre širom Balkana i Švicarske.Koje su 3 ključne stvari preokrenule Dunjinu prodaju: Kako prijeći put od početnika u prodaji do zatvaranja velikih klijenata kroz izlaganje, edukaciju i novu percepciju prodajnih razgovora.Zašto je prodaja tvoja dužnost prema klijentu: Kako promjena fokusa sa vlastitog srama na potrebe idealnog klijenta izravno donosi rast prihoda.Kako se nositi s promjenom okoline pri skaliranju biznisa: Istina o filtriranju prijateljstava, postavljanju zdravih granica i prihvaćanju podrške obitelji bez traženja njihove poslovne potvrde.Metodu silaznog planiranja za očuvanje unutarnjeg mira: Kako svakodnevno „gašenje požara“ promatrati iz ptičje perspektive i izbjeći sagorijevanje dok gradiš veliku tvrtku.Važnost nagrađivanja same sebe na putu rasta: Zašto je materijalna nagrada važan podsjetnik na to koliko si napredovala i koliko dopuštaš sebi da zaslužuješ uspjeh.Dunju možeš zapratiti:https://www.instagram.com/andrea360_app/?hl=hr

What if your customers were your cheapest media channel? Ryan Martin talks with Mike Haywood, Co-founder of BrandPay, about "customer media". BrandPay rewards everyday customers, not influencers, with store credit when they post about brands they already love. Mike explains how a $1-per-like experiment at his AirBnB retreat grew into a world-first build with Meta. He also covers why AI search runs almost entirely on third-party content, and how brands like LSKD now get a new piece of customer content every 33 minutes. On average, the cost per click comes in at about half that of paid media.Mike explains how the idea started. During COVID he ran a hinterland retreat and paid guests $1 per like in wine-cellar credit for posting content. Six months later it was named best holiday home in Australia and featured in Vogue. He also talks about building a world-first integration with Meta, and the psychology that makes customers post (time and effort, not likes). He shares hard numbers on cost per click, spend-back rates and category performance.Why BrandPay separates customer media from UGC and creator contentHow a $1-per-like guest experiment led to a Vogue feature and a new startupBuilding with Meta: a world-first API use case and the challenge of fraud preventionWhy fixed rewards beat per-like rewards: everyday users think in time and effort, not engagementHow the BrandPay wallet works: store credit in Apple Wallet, worth half its value at other brands, with the other half returned to the rewarding brand#ad disclosure, and why customer content doesn't feel like sellingAI search and third-party content: Google lowered its follower threshold for showing third-party content in search from 300k to 35k to 10kThe review flow: 48-hour auto-approval for reels (24 hours for stories), and 98% approval ratesPricing: a 25% fee on top of rewards, with no SaaS or setup feesAttribution: first-degree clicks at about 50% of paid media cost per click, plus the "BrandPay halo effect" measured via Triple WhaleWhy brands with real community (Facebook groups, Klaviyo lists) win first85% of rewards are spent back with the brand, on average within about 5 daysWhich categories perform best: fashion, beauty, skincare, nutrition, pet food, sunnies and furnitureFirst-mover advantage and the "infinite half-life" of third-party contentProduct launches: building customer media groups by postcodeBeyond e-commerce: distributor brands, service businesses, and TikTok coming soon

Justin Butlion turned excess profits from his BI agency into a portfolio of 10 SaaS apps across five acquisitions, all completed through ⁠Acquire.com.He started by looking for simple, low-maintenance businesses with predictable cash flow. But each acquisition refined what he valued, from churn and customer quality to seller financing, payback periods, and the operational cost of owning multiple small apps.In this episode, Justin shares how his acquisition strategy evolved across five deals, what a $97K purchase taught him about seller financing, and why his next acquisition will likely look very different.You'll hear:How agency profits funded his move into SaaS acquisitionsWhat he looks for before making an offerWhy seller financing can change the real payback periodWhat owning 10 apps taught him about scaling a portfolioWhy he now wants to pursue larger deals with more growth potential3 Lessons from a Serial SaaS Buyer:Churn Changes the Deal: Customer quality can reshape years of projected cash flow.Model the Financing: A smaller upfront payment does not mean faster returns.Cheap Apps Still Add Complexity: Low multiples do not eliminate support and operational risk.For buyers and sellers, this episode offers a closer look at what happens after a deal closes: how buyers evaluate businesses, how deal structure affects returns, and what operating acquired companies can reveal about the qualities that matter in an acquisition.Follow the Guest:LinkedInX (Twitter)

Bain estimates a $100 billion market for software that reduces cross-system labor, the manual work that links multiple applications. The opportunity spans integration platforms, workflow automation, robotic process automation, and AI assistants. Examples in the space include MuleSoft, Workato, Zapier, ServiceNow, UiPath, Automation Anywhere, Salesforce, Slack, and Atlassian. Adjacent data platforms like Snowflake and Databricks centralize analytics but do not execute operational actions, which remain in systems such as SAP, Oracle, Workday, NetSuite, HubSpot, and Zendesk. Buyers prioritize security, governance, auditability, and measurable ROI, with pricing models ranging from per seat to per workflow. Execution risks include brittle integrations and rate limits, pushing vendors to invest in observability, access control, and human-in-the-loop approvals.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Very Important Links!Support the show at Patreon! - https://patreon.com/gogOther ways to support the show! - https://gog.show/donateJoin our Discord! - https://discord.gg/r4ZmSHBBuy some merch! - https://shop.gog.show/Recorded on Wednesday, September 23rd, 2026.Jason, Brian, and Dave head to the Dark Side for a deep dive into vibe coding—and, against all odds, Jason may finally be cracking. After Salesforce decided to sunset Quip, Jason used AI to rebuild the collaborative show-notes system the guys have relied on for a decade, adding more features while cutting the monthly cost to almost nothing. Then he took up Dave's challenge to modernize an aging open-source ham-radio application, turning it into a native-looking Mac app in a matter of hours. The catch: after decades of programming, Jason knows exactly what to ask for, what to watch for, and when the AI is confidently doing something stupid.That leads to the bigger question: what happens when software that once took months and tens of thousands of dollars can be built in an afternoon? The guys get into value-based pricing, the disappearing entry-level programming ladder, whether AI makes people lazy or gives curious people superpowers, and how custom software could start eating away at expensive SaaS subscriptions. Jason also lays out what he's learned from actually building with these tools: keep everything portable, use Git from day one, make the AI document its work, and never build something you can't eventually maintain yourself. Turns out the future may not belong to vibe coders after all—it may belong to people who already know what the hell they're doing.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

a16z's Ben Horowitz and Martin Casado sit down with TypeSafe AI founder Diogo Almeida to ask a simple question: AI has become remarkably capable, so where is all the automation?Diogo argues that coding agents may help us write software faster, but the software they produce still largely works the way software always has. TypeSafe is taking a different approach with Jev: putting intelligence inside software itself, so developers can build programs that reason about intent and make probabilistic decisions rather than simply generate text for a human to interpret.They discuss why reliability is the key to making AI genuinely programmable, how this could open a new era of probabilistic software, and why established SaaS companies may be particularly well positioned to benefit. Ultimately, Diogo's goal is straightforward: technology that can reliably “do what I mean.”Resources:Follow Diogo Almeida: https://x.com/CompleteSkepticLearn more about TypeSafe AI: https://typesafe.ai/Follow TypeSafe AI: https://x.com/typesafeaiFollow Ben Horowitz on X: https://x.com/bhorowitzFollow Martin Casado on X: https://x.com/martin_casado Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Rob Ferguson is the VP of Technology and Strategy at Fireworks AI, a company that makes frontier AI infrastructure accessible to any organization that wants to own its own models rather than depend on someone else's. He started studying machine learning before it was called AI, worked on Pro Tools and AI systems for a music streaming service, helped design Amazon Go's grab-and-go technology, and built startup programs at AWS and Microsoft. Now he is at what he calls the front row of AI, sitting between applied research and go-to-market and translating what the technology can actually do into things companies can actually buy and build on. In this episode of Product Talk, iDonate VP of Product and Engineering Nacho Andrade sits down with Rob to talk about why speed is the AI feature no one asks for but everyone needs once they see it, how AI training went from lab-only to cheap as a cup of coffee, and his hot take that we are at the end of SaaS and entering an era he calls workwear. In this episode, we cover: (0:48) Rob's background from music and machine learning to Pro Tools, Artio, Amazon Go, and Fireworks (3:43) What it means to sit at the intersection of applied research and go-to-market (11:41) What Fireworks AI actually does: inference infrastructure and model training in one place (14:11) Who Fireworks serves and how the customer has changed as training costs have dropped (16:52) Speed as a hidden AI feature: why nobody asks for it and everyone needs it once they see it (20:18) The shift from AI as talkers to AI as doers, and what that means for product (21:24) AI independence vs AI dependence: why owning your model changes what you can build (23:27) The most common problem Rob sees: agents that work but cost thousands of dollars to run (26:25) Drawing the line in the sand: why doing less deeply beats promising everything (27:41) Hot take: we're at the end of SaaS and entering the era of workwear (31:55) Why domain experts and liberal arts thinkers become more valuable as surface-level software commoditizes (33:04) What the AI industry gets wrong about itself and the case for openness and independent scrutiny (38:24) The voice tech stack Rob is obsessed with and uses every day Blog and detailed workflow walkthroughs from this episode: https://productsthatcount.com/why-speed-is-the-ai-feature-no-one-asks-for-but-everyone-needs/

How was Puma India built, how did Virat Kohli's one8 come about, and what is Agilitas trying to build in India's sportswear market?Abhishek Ganguly spent 17 years at Puma, joining when the brand was virtually unknown in India and helping build it into the country's largest sportswear brand. Puma India went on to cross ₹2,000 crore in revenue, ahead of Nike and Adidas combined in India. Then, after nearly two decades in the industry, Abhishek chose to start again.Today, as Co-founder & CEO of Agilitas, he is pursuing a much bigger idea: can a sports brand from India compete with the best in the world?In this episode of the Prime Venture Partners Podcast, Amit Somani speaks with Abhishek about the journey from Puma to Agilitas and one8, how Indian consumers have changed over the last two decades, why past success can become a disadvantage, and what it takes to build a brand people genuinely care about.Timestamps00:00 Introduction00:59 Building Puma India From Scratch05:29 How the Indian Consumer Has Evolved12:06 Staying Close to the Consumer16:36 Why Consumers Don't Need More Products18:33 “Made in India, Unfinished on Purpose”21:19 Why Start Agilitas?24:20 The Risk of Past Success25:12 “Disruptors Don't Disrupt Again”28:16 Building With Virat Kohli32:09 Why India Needs a Global Sports Brand34:16 Why Virat Kohli Backed Agilitas36:45 Building a Brand From India37:32 Virat Kohli Rejected 17 Prototypes39:28 The Future of one841:35 Advice for EntrepreneursA major thread in the conversation is Abhishek's long-standing relationship with Virat Kohli. He goes back to 2016, when he proposed that Virat should build a brand of his own rather than remain only a brand ambassador. That idea became one8.Years later, when Abhishek left Puma to start Agilitas, Virat became one of the company's early believers, investing in the business and merging one8 with Agilitas. The conversation also explores why Abhishek believes Indian consumers are evolving too quickly for old global playbooks, why founders need to stay close to the consumer, and why experience can sometimes become baggage. His own reminder to himself is simple: “Disruptors don't disrupt again.”At the heart of Agilitas is a larger ambition: to build a sports brand from India that can compete with the best in the world, not on price, but on product, performance and brand.Connect with Abhishek GangulyLinkedIn: https://in.linkedin.com/in/abhishekgangulyInstagram: https://www.instagram.com/abhishekganguly/?hl=enX: https://x.com/gangulyabhishekFollow Amit SomaniX (Twitter): https://x.com/amitsomani?lang=enLinkedIn: https://www.linkedin.com/in/thesomani/Read the transcript for the entire podcast here: https://bit.ly/Abhishek-Ganguly-Agilitas About Prime Venture PartnersPrime Venture Partners is an early-stage venture capital firm backing exceptional founders building category-defining technology companies across SaaS, fintech, AI, healthcare, consumer internet, and enterprise software.Learn more: Prime Venture PartnersFollow Prime Venture Partners:LinkedIn: https://www.linkedin.com/company/2780448/admin/dashboard/X (Twitter): https://x.com/Primevp_inInstagram: https://www.instagram.com/primevp_in/Learn more about AgilitasWebsite: https://agilitas.com/LinkedIn: https://www.linkedin.com/company/agilitas-sport/home/Instagram: https://www.instagram.com/agilitas_sports/?hl=en#SportsEntrepreneurship #BrandBuilding

Megan Huber has spent five years teaching client success and far longer doing the role, and she comes on to make the case for the most overlooked profit center in a real estate business. Most investors obsess over stuffing more leads into the top of the funnel while a mountain of money leaks out the bottom, from the customers they already have.Megan breaks down why client success is a strategic role, not glorified customer service, and how it quietly oversees four of the five types of sales any company makes. She and David cover the four Rs of back-end revenue, the 90-day audit that tells you where to focus, why this person should be a profit center tied to KPIs rather than a cost, and when a growing investor should hire for it. If you're leaving repeat and referral money on the table, this one is for you.Timeline Summary[2:18] – What client success actually means outside of tech and SaaS[3:34] – The two jobs of client success: did the client get the result, and did they have a great experience[5:01] – How the principles apply to sellers, buyers, and tenants in a real estate business[5:24] – Why you still need a human to intervene even with automation and AI doing the tracking[7:19] – Is client success a role or a philosophy, and why the answer is both[8:04] – Why the client success person is the most undervalued seat and knows your customer best[9:23] – Why a client-centric culture has to start with the founder[9:50] – The five types of sales and why client success owns four of them[10:35] – Reactivating past customers as one of the most forgotten income streams[12:11] – The real difference between client success and reactive customer service[14:18] – The operational and psychological layers of designing the client journey[14:45] – Getting ahead of buyer's remorse in the first 24 to 48 hours[17:46] – Designing the journey all the way through to advocacy and referrals[19:16] – Building an upsell machine that doesn't feel salesy[20:26] – Why client success boils down to relationship management through the whole funnel[21:14] – Why repeat business still requires asking for the sale[21:50] – The 90-day audit of retention, renewals, reactivation, upsells, and referrals[23:39] – How to pick which of the four Rs to attack first based on your business type[26:47] – Why this role is what justifies its own cost[28:03] – Why the position owns about 80% of possible sales and is a profit center, not a cost[29:11] – When a newer investor should hire for client success[31:23] – Megan's own first hire at just ten hours a week, and why it freed her to grow[33:05] – Why the role should be tied to KPIs and still responsible for in-house sales5 Key TakeawaysClient Success Owns 80% Of Your Sales — Marketing and sales bring in new customers, which is just one of five sale types. Reactivation, renewals, repeat buyers, and referrals are the other four, and client success owns all of them.It's Not Customer Service — Customer service is reactive, putting out fires with frustrated clients. Client success is proactive and strategic, designing the entire client journey from the sale through to advocacy and referrals.Run The 90-Day Audit — Every quarter, audit your four Rs and find the one with the biggest, easiest revenue opportunity. Go all in on that single area for the next 30 to 90 days, then reassess.Treat The Seat As A Profit Center — This role should be tied to KPIs and a bonus structure, bringing in hundreds of thousands a year. Aim for any team member to generate about three times what you pay them.Hire When Clients Pull You From Growth — If taking care of existing clients is stealing the time you need to drive revenue, hire for this, even fractionally. Megan's first hire worked ten hours a week and handled everything her clients needed.Links & ResourcesClient Success Alliance — https://www.clientsuccessalliance.comConnect with Megan Huber on Facebook (Megan J Huber)Simple CFO — https://simplecfo.comProfit First for Real Estate Investing Free Workbooks — https://pfreiworkbook.comProfit First for Real Estate Investing by David Richter — https://profitfirstrei.comEnjoyed This Episode?If Megan made you realize how much repeat and referral money is sitting untouched in your past client list, that's the profit worth chasing this quarter. Share this episode with an investor who only ever focuses on new leads, and follow the show and leave a rating and review so more real estate investors can keep more of what they've already earned.

The structural mechanism highlighted in this episode is the gap between optimism for artificial intelligence (AI) in IT operations and the actual integration of AI as a core operational tool among MSPs. The conversation centers on Auvik's market activity, including data from its 2026 IT Trends Report and the launch of its Aurora AI agent suite, which aims to operationalize AI in network management. This shift surfaces a reliance on vendor-developed automation tools to address efficiency constraints, while simultaneously raising questions about operational accountability, documentation quality, and risk management as automation expands.Auvik's report found that 67% of IT professionals are optimistic about AI, but only 5% report AI as core to daily operations. According to the company, true integration of AI requires that it consistently deliver repeatable value and autonomy in network troubleshooting, rather than partial assistance that still requires escalation to senior staff. The Aurora release focuses on embedding troubleshooting agents within alerts to enable lower-tier technicians to resolve incidents that previously required escalation. Auvik claims this approach can reduce troubleshooting time for network issues by around 50%—though actual results will vary by use case and organizational readiness.Secondary developments discussed include the expansion of Auvik's platform to server, endpoint, and SaaS management in response to customer demand for greater visibility and tool consolidation. Shadow IT, particularly unauthorized AI and SaaS usage, emerged as an ongoing governance and security challenge, with Auvik detecting over 100,000 shadow AI applications across client networks in 2025, and 60% of IT teams discovering unauthorized SaaS monthly. The discussion also examined the need for up-to-date documentation, and the ongoing tension between adding more monitoring tools versus the operational burden and alert fatigue those tools can introduce.For MSPs and IT service leaders, these trends increase dependence on vendors to supply both the automation and the context necessary for safe and efficient operations. Effective AI integration requires accurate network documentation, clear governance, and jointly developed client policies for shadow IT management. As tool sprawl grows, the sector cannot rely solely on more visibility; instead, the actionable quality of alerts, tool interoperability, and operational discipline will be key to managing ticket loads and avoiding inefficiency or compliance risks. Vendor pricing shifts and platform lock-in further reinforce the need for informed procurement, benchmarking, and contingency planning. Supported by:TimeZestScalePad

Harvey's gross margin fell from roughly positive 50% at the start of 2026 to negative 50% by June, so every $1 of revenue cost $1.50 to deliver. Sam Jacobs, CEO of Pavilion, AJ Bruno, CEO of QuotaPath, and Asad Zaman, CEO of STA, go hosts-only on when AI growth justifies margins like that, why open weight models are no substitute for frontier intelligence, and what happens to the application layer if the exuberant market funding it goes away. Plus, AJ's 100x raise from 2021, a 68% jump in tech M&A and what it means for employee equity, and a Bulls and Bears round on AI catastrophe before 2031. Key Takeaways: - Negative gross margins can be a phase if there's a way out. Sam's straw man: "Above 200% growth, I can tolerate bad margins temporarily if you can show me you have a path to a good business." Between 100% and 200% he wants "a clear improving trajectory," and below 50% the margins have to be good. - Cheaper models fix the margin line but can break the product. Asad likened the switch to open weight models to "going from hiring people only from Harvard to then going and hiring people from the worst university you can find or some mid-tier university and saying it's the same thing." That leaves application companies "completely at the behest of these model companies." - The 2021 hangover is still on cap tables. AJ raised QuotaPath's Series B "at a 100x valuation in '21," but with SaaS multiples at "1x or less than 1x," he said, "If I wanted to go sell QuotaPath today, I really couldn't find a buyer." Asad countered that secondaries changed the math: "The founders are making a lot of money along the way." Connect with the Hosts: Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/ Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at STA - https://www.linkedin.com/in/azaman1/ Topline is more than a podcast: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Check us out on YouTube for the #1 video podcast for founders, operators, and investors in B2B tech: https://www.youtube.com/@TOPLINE-Media Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters:  00:00 The Math Of AI, Deals, And Equity 02:03 SaaS Margins Were A Law Of Nature 03:24 Harvey's Margins Went To Negative 50% 04:18 Sam's 200% Growth Straw Man 05:46 Open Weight Models Aren't Harvard 14:13 Open Models Vs. Frontier Lab Economics 19:01 What Would You Spend $1M On? 30:08 Bullish Or Bearish On The AI Trade? 34:15 Walrath's Warning And A 100x Round 36:37 Secondaries Changed The Founder Math 42:35 Most Companies Never Get The Choice 47:32 Corporate M&A Is Surging 52:04 Should Equity Vest Monthly? 57:40 AJ's Case For Performance Equity 1:02:49 Bulls And Bears: AI Catastrophe

This week: Amazon is blocking Meta's new Muse AI assistant from shopping on Amazon.com, saying the agent doesn't identify itself and violates the site's conditions of use. We explain why the standoff is an early sign of a much bigger fight over whether AI agents can use websites and apps on people's behalf, and why Amazon has so much riding on the answer. For the definitive word on the issue, we ask Muse what it has to say for itself, and get an impressively sophisticated and evenhanded answer. We recorded this week's show live at Daniel's Broiler in downtown Seattle, in front of a room of startup founders and CEOs, at a dinner hosted and sponsored by Northern Trust. Mike Fridgen, a longtime Seattle entrepreneur and Madrona venture partner now working on a stealth startup, joins us to talk about whether Seattle is still a good place to build a company. The conversation covers the region's talent, its history and the debate over taxes including Seattle's JumpStart payroll tax, which Mayor Katie Wilson's new budget holds flat. Then we take questions from the room. Ridge AI CEO Ellie Fields asks how business leaders can engage productively in building the region, Latch CEO Stefan Kalb asks why Seattle has less venture capital than the Bay Area and ACME Brains founder Mary Jesse asks how to get past polarization. We close with a satellite-themed GeekWire Trivia Challenge and an unexpected announcement from Starfish Space co-founder Austin Link. Related links Amazon blocks Meta's Muse AI assistant in new standoff over agentic shopping Facing 'seismic shifts' in tech sector, Seattle mayor freezes JumpStart tax rates in new budget 'The warning signs are flashing': New regional partnership calls for cohesive Seattle-area tech strategy Data visualization all-stars unveil Ridge AI with $2.6M to fix the analytics problem for SaaS apps With NASA's backing, Starfish Space is getting ready for the orbital debut of its flagship Otter spacecraft With GeekWire co-founders John Cook and Todd Bishop. Music by Daniel L.K. Caldwell. See omnystudio.com/listener for privacy information.

 EPISODE DESCRIPTION I sat down with Rafsun, founder of Sahaj Mobile, at Money2020 to unpack one of the most exciting fintech stories I've come across in a long time. Rafsun went from managing billions in asset-backed securities at Guggenheim to building a phone financing company in Bangladesh , a market that has been starved for credit for decades. With over 58,000 customers and millions deployed entirely from their own capital, Sahaj Mobile is proving that emerging markets can outperform the West on default rates. We dig into how they used merchants, OEMs like Samsung and Xiaomi, and TikTok influencers to scale fast, how AI powers everything from fraud detection to Bengali-language customer service, and where they are headed next , including a fascinating cross-border financing model for South Asian migrant workers in Saudi Arabia and the UAE. If you are a founder or investor looking at where the real opportunities are, this conversation is one you cannot afford to miss. DISCLAIMERNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/ CONNECT Sahaj Mobile Website: https://www.sahajmobile.org/Sahaj Mobile LinedIn: https://www.linkedin.com/company/sahajmobile/home/Sahaj Mobile Facebook: https://www.facebook.com/SahajMobileBDWeb3 with Sam Kamani: https://www.web3pod.xyz/ KEY POINTS WITH TIMESTAMPS • [00:00] Rafsun introduces Sahaj Mobile's core model , financing phones in Bangladesh with no credit score required• [01:00] Sam introduces the episode and explains why Sahaj Mobile stands out among fintech startups• [03:03] Rafsun's background: UC Berkeley, Kiavi (acquired by Figure), and managing $10B in ABS at Guggenheim• [05:24] How Sahaj Mobile works: down payment, device locking, and repayment via bKash and Nagad wallets• [06:16] Growth story: from a 100-phone pilot to 58,000+ customers since launching in 2024• [07:09] How they got initial traction: partnering with merchants, then OEMs like Samsung, Oppo, Vivo, and Xiaomi• [08:57] TikTok influencers and social media as a key growth channel in Bangladesh• [12:07] How AI runs 24/7 , from loss reserve calculations to NDA reviews , augmenting Rafsun's workflow• [13:30] AI in underwriting: ID fraud detection, face matching, and machine learning on alternative data• [14:55] AI-powered customer service responding in Bengali using voice models and Hugging Face• [16:49] HeyGen for AI-generated marketing videos , cutting production time to under 30 minutes• [20:18] The SaaS and legal disruption debate , and why AI tools are actually creating more lawyer work, not less• [27:44] Fundraising update: Series A equity round with a lead investor and a separate debt round from an emerging market credit fund• [29:06] Expansion into MENA: financing phones in Bangladesh for migrant workers paying from Saudi Arabia, Dubai, and Malaysia• [30:06] Next product: the Sahaj wallet with EMI discount incentives for customers who pay through it

LIVE from the Palace of Fine Arts in San Francisco…The Gap just adopted a teenage boy band that toured summer camps… don't rent a star, raise one.GrandmaSlop is the most contentious AI in America… Boomers use AI least but love it most.PE rolled up pool installers, then the cleaners… It's SaaS: Swimming-as-a-Service.Plus, San Francisco has 489 billboards… and half of them sell AI.$GPS $POOL $SFNEWSLETTER:https://tboypod.com/newsletter OUR 2ND SHOW:Want more business storytelling from us? Check our weekly deepdive show, The Best Idea Yet: The untold origin story of the products you're obsessed with. Listen for free to The Best Idea Yet: https://wondery.com/links/the-best-idea-yet/NEW LISTENERSFill out our 2 minute survey: https://qualtricsxm88y5r986q.qualtrics.com/jfe/form/SV_dp1FDYiJgt6lHy6GET ON THE POD: Submit a shoutout or fact: https://tboypod.com/shoutouts SOCIALS:Instagram: https://www.instagram.com/tboypod TikTok: https://www.tiktok.com/@tboypodYouTube: https://www.youtube.com/@tboypod Linkedin (Nick): https://www.linkedin.com/in/nicolas-martell/Linkedin (Jack): https://www.linkedin.com/in/jack-crivici-kramer/Anything else: https://tboypod.com/ About Us: The daily pop-biz news show making today's top stories your business. Formerly known as Robinhood Snacks, The Best One Yet is hosted by Jack Crivici-Kramer & Nick Martell. Hosted on Acast. See acast.com/privacy for more information.