Podcasts about MCPS

Produced by Foundersuite (for startups: www.foundersuite.com) and Fundingstack (for emerging manager VCs: www.fundingstack.com), "How I Raised It" goes behind the scenes with startup founders and investors who have raised capital. This episode is with with Matt Ober of Social Leverage, a San Diego-based venture capital fund that invests in FinTech and Vertcal AI startups. Learn more at https://socialleverage.com/. In this episode, Matt shares his journey from working at a quant hedge fund to becoming a VC, trends in FinTech and Vertical AI, tips for using Claude and MCPs for raising capital, how they use content to attract the best founders, advice for emerging VC managers, tips for founders, and more. How I Raised It is produced by Foundersuite, makers of software to raise capital and manage investor relations. Foundersuite's customers have raised over $21 Billion since 2016. If you are a startup, create a free account at www.foundersuite.com. If you are a VC, venture studio or investment banker, check out our new platform, www.fundingstack.com

Did you know that 82% of all intrusions don't involve any sort of malware, and AI-augmented attacks are up 89% year over year? If your security operations team is solely focused on reacting to known-bad SIEM alerts, you may be missing the silent breaches. In this episode, Ashish is joined by Damien Lewke, Founder and CEO of Nebulock, to discuss the critical shift toward a "Hunt First" mindset. Damien explains why moving away from alert fatigue and focusing on raw, normalized telemetry (across endpoint, identity, and cloud) is the only way to proactively surface unknown threats. We also dive into how AI is finally democratizing the elite skill of threat hunting, allowing even single-person security teams to investigate and attribute complex behaviors.From hunting down shadow AI (like unapproved MCPs) to challenging the notion that AI will replace threat hunters, this episode is a masterclass in modern security operations. Guest Socials -⁠⁠ Damien's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security PodcastQuestions asked:(00:00) Introduction: The Problem with Reactive Security Alerts(02:00) Damien Lewke's Background (DoD, CrowdStrike, Arctic Wolf, Nebulock)(04:00) Why Breaches Happen in Silence: The Value of Telemetry Over Alerts(05:30) How AI Democratizes Elite Threat Hunting for Small Teams(07:30) Defining the "Hunt First" Mindset and Methodology(11:00) Surfacing Active Intrusions Using Cross-Domain Context(13:30) The Importance of Transparency in AI Decision Making(16:30) When NOT to Use AI for Detections (The Power of Heuristics)(18:30) The Best First AI Security Use Case: Hunting Shadow AI & MCPs(23:00) Detecting Rogue AI Agents via Tempo, Breadth, and Automation Signatures(28:30) The Future of SIEM: Data Gravity vs. Purpose-Built Security Analytics(34:30) Disagreeing with Gartner: Why Threat Hunters Are More Vital Than Ever(40:00) The 89% Rise in AI-Augmented Attacks and Taking Action(41:30) The "You Laugh, You Lose" Cybersecurity Joke Challenge Resources spoken about during the episode:- Hunting MCP Server Exploitations- Using classical machine learning for threat hunting (and saving tokens in the process)- Your newest insider has legitimate access

Hoje o papo é sobre AI Gateways! Neste episódio, conversamos sobre os desafios de centralizar o acesso a modelos, MCPs e ferramentas para controlar custos, reforçar a segurança e ampliar a observabilidade nas empresas. A discussão passa por roteamento entre LLMs, guardrails e fallback, além dos desafios de tirar agentes do purgatório dos pilotos e levá-los à produção. Vem ver quem participou desse papo: Paulo Silveira, o host que já não sabe onde seus agentes estão rodando Vinny Neves, cohost, dev e professor na Alura Kleber Bacili, cofundador e CEO da Sensedia Fernanda Goulart, Head de Produto na Sensedia Murillo Godoi, Tech Lead na Alura  Links:  Gartner: Market Guide for AI Gateways O que é o Sensedia AI Gateway MCP: Model Context Protocol Nova especificação do MCP: protocolo sem sessões OpenRouter Vinny: O que service mesh tem a ver com o teu agente de IA APIs: Gerenciamento e Criação – Hipsters #57 Comente no Spotify: O que você tem usado para organizar o uso de LLMs, MCPs, e o custo de IAs na sua empresa? TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

Sathiesh Veera, a GenAI Solutions Architect at At&T, speaks with host Brijesh Ammanath about the data-protection guardrails required when using LLMs. The core issue is that LLMs sit outside the cloud tenant in most enterprise AI deployments, which means that data leaves the company's perimeter with every prompt, RAG retrieval, and tool call. Contractual agreements can restrict the data that LLM vendors are allowed to use for training and audits, but they don't stop prompt injection or unintended exposure as company data is often shared to LLMs via natural language queries, APIs, tool and function calls, and MCPs. Sathiesh discusses ways to employ security measures and data filtering at each layer to conform to data security policies and protect the data. 

If MCPs are the infrastructure, agentic AI is the workforce.Full show notes hereThe DisruptorsWith Liz FarrRandy Johnston has been in accounting technology longer than many of today's practitioners have been alive. With more than 50 years in the field, and a hand in designing everything from IBM ThinkPads to Excel's PivotTables, he has seen more technological revolutions than he can easily count. He even wrote AI code in 1975 in Lisp. Through it all, he has remained true to a singular ideal: “My personal mission has always been to help as many people as possible do the things they want to do with technology.”MORE DISRUPTORS: David Cristello: Growth Breaks Things – and That's Normal | The Disruptors | Blumer, Vacin: What Only 5% of Firms Get Right | Disruptors | Ira Rosenbloom: PE Forces Firms to Pick a Future | The Disruptors | Doug Slaybaugh: How to Define “Values in Motion” | The Disruptors | Nancy McClelland: Bookkeepers Need a Safe Space to Collaborate | The Disruptors | Chase Damiano: Good Operations Means Defining How the Hand-Offs Happen | The Disruptors | Jeff Seibert: Digits Software Moves Toward Real-Time Accounting | The Disruptors | MORE CPA Trendlines Streaming NetworkYet Johnston, executive vice president of K2 Enterprises and co-founder of Network Management Group, says the most significant shift is happening right now with Model Context Protocols (MCPs). MCPs are a technical layer that allows multiple systems to be connected and queried through AI. Though he's spent decades watching technology evolve through mainframes, databases, graphical interfaces, and SaaS, he says MCPs may be the most consequential development he's seen in years.“It's making me think we no longer need dashboards. It's making me think we no longer need user interfaces the way we did,” he says. “I think we're actually going to wind up talking to a lot of our systems in the very near term.”MORE > > > 

According to Gartner, worldwide AI spending is forecasted to increase 44% by the end of 2026. Companies are investing in AI, and they are investing heavily. But knowing where and how to invest isn’t easy, especially with what feels like a million different AI tools out there and a million more different ways to build your own. So how do you figure out what to build, what to buy, and which investments will help you move the needle for your business? Riley Rogers: Hi, and welcome to the Win/Win Podcast. I’m your host, Riley Rogers. Join us as we dive into changing trends in the workplace and how to navigate them successfully. Here to discuss this topic is Cody Sims, head of commercial brand at Cox Communications. Cody, thank you so much for joining us today. Super excited to hear your thoughts on this one. Before we dive into what is quite a loaded topic, could you tell us a little bit about yourself, your background, and your role? Cody Sims: Yeah. So, hi, I’m Cody. I’m the head of commercial brand for Cox Communications, and it’s kind of crazy how I came into this role. So I actually started my career when I was 15 and was installing phone systems for my dad’s phone company. And after that, I had actually had two parts of what I thought was what I wanted to go into, and that was either musical theater or physics, because those were two things I really had a passion about. And when I got into college and had musical theater as my major and physics as my fallback, I realized that both of them left a part out of what I really enjoy. And so I ended up actually landing in marketing because it’s both analytical and creative, and that has served me really well over the years. So, across Cox, I have done all kinds of things from product management to market development to pricing to competitive analysis, and now in the brand world. It’s given me kind of a 360 view of the entire business from a marketing lens. I would say that I’m pretty much a transformation leader. I really enjoy breaking things and building them up new again. So, AI is happening right at the right time for me. RR: I love that story, and I love that it’s taking you to a place that especially now is getting more and more technical, more and more analytical. I’m very excited to get into that transformation leader side of things. But before we do, can you paint a little bit of a picture of your sales environment? CS: Yeah. So when I first came to Cox, it was very similar to most of what you would call a CLEC, or a competitive local exchange carrier, which is primarily internet service, voice services, and obviously because it was Cox, some cable TV services that were the triple threat. That was kind of what in the early 2000s was kind of the way that they went to market. But over time, the team at Cox realized that in order to stay competitive, they had to add to the portfolio to make sure that they were providing value to their customers, and I’m sure many would understand that and have gone through similar transformations. And so we had acquired several different other companies that added to our portfolio, and developed some of our own products, and over time that turned into a lot of products. But it’s not just 70 products. It’s 70 products, it’s nine customer segments that we have from a segmentation perspective. It’s six distinct buyer personas, industry verticals, what’s serviceable at that address. So you take all of these different components and it’s almost like three-dimensional chess for the salesperson. The way that I like to think about it is that the sellers, what they really need and what their challenge is, is that they’re not looking for specs. They’re looking for what are the business outcomes that my customer is trying to achieve, and then what do I have from my portfolio that will help them to achieve those results? So it’s no longer a world where they can memorize everything and know every product in and out, and be the technical expert. They really do have to have tools and systems that help them to have the right knowledge at the right moment for the right person in the right place. RR: Yeah, there comes a point when the human brain just can’t contain the context and the expertise that you need. So when you can’t ask for expertise, what you can do is provide, to your point, that just-in-time support. And one of the things that you alluded to was that that’s where you kind of started some of that AI investment as a way to bridge that gap. You’ve given us a little bit of a taste, but what kind of motivated that early initiative? CS: Well, I would say that, over time what we discovered was that we couldn’t keep track of all of our marketing materials, collateral, all of the pieces of information in just files, formats, and putting it online into a here’s-an-accessible-library. Because the library just becomes bigger and larger and more difficult to manage. But I would say that we didn’t set out to do AI. We didn’t sit down and say, “Oh, hey, AI looks cool. Let’s make sure we’re doing it.” We needed to transform our go-to-market strategy so that we were more nimble, we were more competitive, and that we could deliver the kind of experience that our customers were asking for. And so AI was the mechanism that would help us to get there. But what really triggered this whole thing was what I mentioned earlier, was our segmentation. When we sat down and said, “Let’s rebuild the way that we look at our audience segments,” and we did that based off of what is the value to Cox of each of these customer profiles, and then what is the technology sophistication of that client, of that business. And that intersection allowed us to create our nine different segments that we were working on. And so when we did the math, when we looked at all of that information and all of the things that we needed to be able to provide to those segments, we realized this was quickly going to turn into something that was far beyond any marketer’s ability to do. But what we knew is that the Gartner information we were tracking said that personalization was having much higher returns on the way that people respond to information. And not only that, but if you do personalization and you get it wrong, if I call you and I say, instead of, “Hey, Riley,” and I say, “Hey, Jonah,” you’re like, “Hmm, nice try.” So personalization is really important to being successful, but getting it right is even more important. So we realized that we needed to have some radical partnership between our marketing, AI, and sales teams, that we needed to make sure that this was not just an IT project, that we were going to go and pull a bunch of requirements together and everybody would be like, “Oh, hey, here’s this new tool. Everybody figure out how to use it.” And it wasn’t necessarily about optimization. It was about transformation, the way that we go to market, the way we think about our customers, and the way we show up. So I would say that AI definitely was part of the solution set, but we had to look ourselves in the mirror and say, “It’s time for us to actually think about this in a completely different way.” RR: That distinction comes through very well, and I think is very important because oftentimes when you’re in kind of the scramble to be keeping up with the market, keeping up with your competitors, there is this urge to just tack on AI because we have to. But when it’s not strategic and it’s not built into the things that you’re actually doing, to your point, it’s we put together some specs, good luck using it. But instead, now it’s something that’s really built into the way that you work. I would love to hear a little bit more about that specific use case, especially given the fact that a lot of teams are running into that question of how do we use AI and can we just build what we need ourselves? Given that you’ve done the math, answered the question, I’d love to hear how it worked and kind of where you landed. CS: It’s very easy to fall into the trap of, “Hey, everybody, here’s AI. We put it on your computers, now go use it.” And so then everybody starts using AI to try to figure out, how does this help me in the job that I already do, in the role that I already do, in the processes that I already do? And so then it really limits the impact that it can have on the business and the performance because either, A, you have a handful of people who are really smart, and they go crazy with it, and they create their own thing, or you have a bunch of people who are looking at it going, “Okay, came up with some ideas, but I still have to do my work.” What you end up with is there’s no standard. There’s no flag running up the hill to say, “Everybody follow me. Let’s go do it this way.” So, it required both the yes, we had to make sure that the teams were bought into using AI, but we also had to have a standardized way of approaching how we deploy AI. And that brought us to the question of do we buy or do we build? And because there were so many different parts of what kinds of functionality we needed, it wasn’t the same answer for every one of those needs. So in some cases, we have a tool, we have a partner, they already have AI integrated into their platform, let’s go see how we can use that. In other cases, and I’ll give you an example, in the case of content generation, that is where we started with our AI journey about a year ago. We sat down and started interviewing and reviewing all of the different providers who can do content generation. Every one of them had a different approach to content development, content generation, which were all very good, and they attempt to make sure that they are covering as much of the marketplace as possible. And so sometimes when you buy that, you end up with features maybe that you don’t need, and you also have to still go through the process of integrating those platforms into your security posture. So us being a connectivity provider for governments, for major corporations, enterprise carrier grade, we have a very, very strict and strong security policy, which means that when we bring new vendors on, it takes a lot of time and a lot of effort and a lot of back and forth. And so what we found in certain cases, it was actually better for us and more beneficial for us to build the actual platforms that we needed for that particular use case. But like I said before, in other situations, we found that there was a partner who we had who already had AI integrated into their platform, and so they were already part of our security posture. They were already inside of our ecosystem. So the question of build versus buy really had to do with time, had to do with return, and it had to do with the security measures that we had to put in place. RR: Thinking about in addition to those factors, when you’re evaluating these things that you outlined, time, potential cost, security, how are you kind of doing that ROI math to say one is going to be better than the other? CS: There’s several different parts of that. And like I mentioned, we wanted to make sure that we were following our AI strategy foundation that said, we don’t want to introduce more and more vulnerable access points. And so it’s important for us to make sure that we are all coming together with everyone across the Cox leadership team according to who are the vendors that we feel the safest with, that we can go set up and make sure that we are pulling together the best of the breeds. The assessment, like I mentioned before, is what is the value that we’re returning to the business in terms of revenue generation, new customers, cost savings in terms of not necessarily just reducing people’s time, but redeploying people to doing other important tasks. And then what are the things that we are doing that help us to keep the system all working together? So, revenue generation, cost deferment, and then keeping a cohesive connection between all of the different platforms. So some of the things that we looked at from our comparing vendors versus doing DIY, is there a maintenance tail that goes in this? So if we build it, what does that look like in 18 months? How much more people do we have to have to support it? Governance and observability, do we have the permissions, the versioning, the audit trail, all of the parts for discovering what is needed and then able to see it and observe it as we go? Interoperability, as I mentioned before, really important between different platforms that we have, that those APIs and MCPs all work together. And then whose roadmap is this? Is this our roadmap? Is this the IT roadmap? Is this the vendor’s roadmap? If we know where we need to go, is there anything that’s getting in our way of being able to get there? And then of course, obviously the speed to value against the cost of being wrong. RR: And so hearing you outline this very comprehensive list of considerations, you can start to understand why it starts to feel complicated and really hard to tackle. To your point, it’s been a year of figuring it out since you started developing that very first use case. I’d like to go into a little bit of detail about the evaluation piece and deciding what vendors you felt safe with, that you were excited to partner with and continue to either use or build upon as you’re developing your AI strategy in alignment with your business transformation. One of those that you landed on was using Highspot’s MCP server to support some of the workflows you wanted to spin up. How did you make that decision and why did that feel like the way to go? CS: Well, as I had mentioned before, as we had gone through our history of, here’s a library of a whole bunch of stuff and everybody’s trying to find the right item, and it just was such a headache to make sure that we were always getting the right information to the right customers at the right time. And not only that, but we had no real clear feedback about how it was performing. And so at that time, which I believe was in the 2015 to 2017 timeframe, is when we had first started our relationship with Highspot to help us better catalog the library, make it more searchable and usable and referenceable for the sellers to be able to share information and track the information, make sure that it was the most relevant and recent, and then help us to understand what’s working and not working. So all of that was already in place before we even started the AI conversation. And so as we were doing our work around our go-to-market roadmap, we started with content because it was probably the easiest place for us to use AI to generate content, and that looked like a two-layered approach. We had what we called a knowledge base, which is formally putting into AI rules that can be read by AI around all of our standards for brand, for legal, for segment definition, for product information, for pricing and promotion information, industries, verticals. All of that was put at this knowledge base foundation layer. And then we built the content generation engine on top of that, where each of the agents within that tool would go find what it is that the marketer was asking to do, compare it against all the information in the knowledge base, the brand standards, all of those good things, and then produce the content piece that the marketer was asking for using that foundation layer. However, once we got that moving and going, we realized that that level of personalization for marketing could be even more valuable and even more specific when used by a seller. But in order for that to work properly, the seller had to have access to a large range of information all at the same time, including any of the buying signals or online signals that we had through some of our lead generation partners, any of our information that we have within our own systems, like when was the last time they called into billing or when was the last time that they had an outage or what is their general sentiment that the customer has right now. And then all of the information about their current services, their current products, all the things that are going on in their world. But then once we have all of that information, we have propensity to buy, propensity to churn, propensity all these modeling, now we need to be able to talk to them and provide a recommendation to the seller that says, “Here’s what we recommend you use, what you should say, how you should set it up.” And all of that was inside of Highspot. And so we realized again, we could look at this and say, “Are we going to go buy a new platform? Are we going to use a platform we already have or are we going to go build something new?” And obviously when we looked at the Highspot platform, the MCP servers, and the way that it was laid out and set up already, we knew that that was the right path to go. So what we had started with was the content engine, then we went into a sales enablement engine, and as part of that sales enablement engine, the only way for it to work properly was for us to bring in the Highspot MCP service. RR: And how has that been working so far for your sellers? As you’ve rolled this out, how has it been used? Any anecdotal feedback you’ve heard? CS: It’s pretty funny because we have done multiple rollouts of sales enablement platforms over the years, and as anyone who’s ever tried to roll out new sales items and new sales tools will say, it takes time, it takes consistency, messaging over and over. But in this particular case, when we went out and did our roadshow with all of the sellers and sat down and showed them how the new tool worked, there were so many positive responses, and the adoption was much faster than most of our previous releases of other types of products. And I think that the reason why is because it was bringing together all of those pieces of information that I mentioned before and bringing in the Highspot information that they were already very familiar with. And in our world, we call it the sales asset manager, SAM. And so they were very familiar with SAM and then this new tool with the AI capabilities built into it. Now it’s specifically just telling them, “Here’s what you should do. Here’s the way to lay it out, and here’s all the content to talk to the customer about in what order.” And it took a lot of the burden off of them to research, go find a piece, start to build a story in their head, try to build a deck, and then think about what are they going to share with them in what order. So it’s been a huge benefit to the sellers. They’ve loved it. RR: Yeah, that’s such a strong signal when adoption doesn’t feel like a push and more of a grab. Curious if there are any other AI or agentic connectors that you’re pairing with Highspot in another AI application that you think would be interesting to share? CS: We have basically six different programs or parts of our roadmap, and we’re calling them AI modules, and then they work together in different components for different functions that need to be done. So as I mentioned, we have the knowledge base that is the base. Then we have the content creation tool, which we call CAMI. So it’s Content Automation Marketing Intelligence, and that has everything that is needed to produce and create new pieces of content, and then those content pieces are either generated in emails or things like that. A lot of them actually are put into the Highspot tool. And then we have what we call SAMI, which is the Sales Automation Marketing Intelligence, and that is the tool that integrates directly with Highspot to make the recommendations to the seller based off of all of the other information, the 360 view of the customer. We also have what’s called Livia, which is the Lead Validation and Enrichment. The tool uses all of these multiple different access points and different vendors to pull information about that particular contact to validate that it’s accurate, so that by the time it gets to the seller and they’re going to go do a pitch, they have a lot more confidence that who they’re talking to, the business, and it’s at the right address, and prevents them from wasting time. And then, of course, Highspot is such a critical part of how that story all comes together because it’s capturing all the content that’s being created by CAMI, and then the AI that comes from Highspot is infusing into the SAMI tool that the sellers are using. It’s an interesting thing because somebody might say, “Well, you’re not really using Highspot, you’re using SAMI.” And the reality is, well, yes, I am using Highspot because Highspot is feeding all of that into the SAMI tool. There’s a whole bunch of other stuff we add into that for flavoring, all of the information about the customer so that the seller has a 360 view, but that just sets it up. The what do you do next is what’s coming out of Highspot. The next phase of this that we’re going to is a fully agentic approach to our marketing and sales engine. And what that means is that today, most of the work that’s being done is a marketer who is saying, “Here’s what I need to go get done. I’m going to use AI to help me go do it.” We’re going to flip that script, and we’re going to say, the agents that we create are going to do the work, and the marketers are going to instruct the agents on how to do that work properly and watch it and govern it. That will then accelerate for the sellers as well. RR: We’ve heard a little bit about what’s been built in the last year, but it’s, again, to your point, crazy that that’s one year of building, thinking, strategizing, and it’s come to this point. When you look across all of that, what has changed for your sellers and for the business? CS: Well, I would say the first thing is, is that sellers are now able to focus on what they’re really good at. What I mean by that is their confidence is shifted to focus on outcomes and value. They are now able to build trust and provide value, which is honestly what all of our customers, especially our business owners and decision makers are looking for. And then for the marketers, it’s no longer about building a queue, trying to figure out what is the message that’s going to hit the most people with the most response. This idea of efficiency for media or efficiency for marketing materials. It’s like, what is the one message I can send to a million people and have the most response? Well, now you actually can flip that on its ear and say, “I’m going to personalize it at scale.” So that is super exciting. And then the last thing that I would say is that consistency became structural. The same knowledge base, the same rules across every surface, making sure that our content is clean, correct, built on the same policies and rules, but is personalized. Doing those two things at the same time is very tricky, and being able to do it with AI is the only way we could get there. RR: Curious if you’ve seen any sort of measurable returns. CS: Our lead accuracy, like I mentioned before, moving from that 13 to 18% all the way up to the 95th percentile. We have campaign speed to market of improvement of 55%, meaning the amount of time that it takes us to get to market is cut in half. The marketing content teams are 40% more productive, which means they’ve been able to redeploy their time for 40% of the time that they spend at work on other projects, which is amazing. Our conversion rates are up, our driving net new revenue is up, and we have seen material improvement in click-through rates and conversion rates when we are more specific and personalized to the audience. So Gartner was right. Yay. So that’s been really good. And I would say that part of the reason why I think that, at least for part of what we did, doing it as a build ourselves was wise, is because we learned so much by going through the process of just banging our shins on the corners and running into cabinet doors that were open, and we’re just like, “Oh, wow, that was, I did not see that.” So it’s been a huge learning process, a very, very intense learning process, but we’ve all had a really good sense of humor and amusement and just, we are having a ton of fun. RR: And I think that’s one of the more encouraging things to hear. Is that nobody starts perfect, and you just have to build your way up to good. And once you get there, you start to see again, like those measurable improvements. But it is a process. So I guess the message there is stick with it. Which I think kind of feeds into that last question I have for you, which is for anybody who is running into this question, hitting their shins on all of these problems, how would you recommend they approach the question of building, buying, blending some things together when they’re thinking about their AI investments? CS: Well, I would say the first thing is you have to look in the mirror and be real with yourself and say, “Is my processes and workflows working? If I blew up my entire go-to-market, I blew up all my processes, what would it look like?” And don’t start with a tool. Start from a place of what would serve me best. The other part of it that I would say that Highspot did really well is because of the MCP product, I was able to look at it as how am I using this from a plumbing perspective, not just a judgment perspective. And what that means is that it worked well with the strategy and the AI strict rules that we had built for ourselves. Highspot, kudos to Highspot, built a platform that is trusted and that works well with all of the other components that we had flying around, whether it was Salesforce or AWS or even our Accenture development team being able to use the components and pieces to connect to the whole ecosystem. Then the other thing I would say is that even though we’ve been doing this for a year, a year is like eons in AI’s time. It was every other week there was something that changed, something new, something shifted. So you have to go into it with this idea of you have to prepare yourself that this is how I set it up now, but I might have to change it tomorrow, and just be okay with that. So my answer for build or buy, my answer is both. Build the things that make sense for you and where you have the resources and when it’s the right fit. But definitely buy when you are in a partnership or when you have someone that you already know that you can trust. RR: Very pragmatic. That’s kind of the only way to do it. One thing I’ll say, I know I am walking away inspired, and I can imagine our audience is going to as well. So Cody, thank you for the time. I really, really appreciate it. It’s been so wonderful to hear a little bit more about what you’re building. CS: No, I love it. And the reason why this is great for me is that it forces me to think back on this journey that we’ve been on for the last year and really consider what is it that has brought us to where we are, what are the things we’ve learned, and then, maybe how are my bruises doing? RR: Well, thank you for the time again. And to our audience, thank you for listening to this episode of the Win/Win Podcast. Be sure to tune in next time for more insights on how you can maximize go-to-market success with Highspot.

In this open discussion episode, the Monday Meeting community digs into Adobe's new After Effects AI Assistant, agentic workflows and MCPs, the booming market for interactive graphics in Rive, and how AI is reshaping specialization and career strategy in motion design.This episode covers:First impressions of Adobe's After Effects AI Assistant: The beta tool promises to build comps from text descriptions, but early reactions raise questions about speed, pricing, and when it's actually worth changing your workflow.How one agent can replace a stack of AI subscriptions: Why paying for isolated AI features inside each app might be selling yourself short—and how a single agentic workflow with good context can organize Illustrator files, Rive state machines, and more.Where AI helps and where it doesn't: The tasks worth handing off (labeling layers, hunting down broken expressions, batch settings) versus the ones where you'll spend more time prompting than doing it yourself.Rive's interactive graphics boom: How the "Flash on steroids" tool is powering live event graphics for NBA All-Star Weekend, Final Four broadcasts, and interactive games—and what that market looks like for freelancers.How to make developers love working with you: The handoff documentation approach that keeps clients coming back, plus a free open-source Rive player tool shared with the community (link in show notes).Spotting AI misrepresentation in freelance work: The growing problem of hired artists secretly passing off AI output as handmade—and where the community draws the line on acceptable use.Hyper-specialization vs. covering more ground: How AI lets artists go deeper into a niche or expand their range to deliver full products solo—and how to decide which path fits you.Do degrees still matter in an AI world?: A spirited debate on liberal arts education, critical thinking, and what to tell the next generation entering creative careers.Upcoming Events:Game Night date TBA—keep an eye on the website and Discord for the official dateGuest episode later this month: Nick and Sam Snyder from Snaggletooth TV on passion projects outside client work (date TBA)Next open discussion: September 14thOffice hours on Discord on non-podcast Mondays—portfolio reviews, reel reviews, troubleshooting, and hangoutsIlya is seeking beta testers for a new After Effects expression editor—post in the Discord to joinVisit MondayMeeting.org for this episode and other conversations from the motion design community!SHOW NOTES:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monday Meeting Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monday Meeting Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MondayMeeting LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MondayMeeting Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MondayMeeting Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MondayMeeting Newsletter⁠⁠⁠⁠IVG Tool KitIlya's expression editor plugin (needs beta testers!)

O maior banco da América Latina agora é parceiro oficial do podcast! Bem-vindo Itaú!Assine aqui a Product Arena com R$100 OFF e participe de todos nossos cursos e eventosQuase seis meses depois da segunda edição, Arthur, Aíquis e Efrem voltam pro episódio clássico do Papo na Arena: Como estamos usando IA no dia a dia?Spoiler: mudou menos de ferramenta e muito mais de comportamento.Neste episódio, cada um abre o próprio sistema operacional.Efrem vive ~12h/dia no Claude Opus Ultra High, migrou do terminal pra web e celular, e praticamente abandonou skills e MCPs: o modelo evoluiu tanto que skill demais vira legado e enviesa o resultado.Aíquis está no mix Claude + Codex, saiu do tokenmaxxing, testa modelos mais baratos e entrou em modo manutenção de skills. Arthur cancelou a assinatura do Claude e roda um setup multimodelo no Cursor, com Grok 4.5 como padrão, carrosséis e cortes 100% com AI, e o Start My Day conectado a Hotmart, Analytics, Meta Ads e até Wispr Flow.A conversa também entra no código como substrato universal, no abismo entre a bolha tech e quem ainda está no ChatGPT grátis, e na pergunta que já virou pressuposto: você ainda pergunta se alguém usa IA, ou já assume que usa, igual computador?

If you've ever felt like AI is something that's happening to your organisation rather than something you're steering, this episode is for you. James Drury spent a decade rising through the ranks of one of the Middle East's original e-commerce agencies, eventually running operations and AI strategy end to end. What he brings to this conversation is rare - a genuine understanding of how AI works at the enterprise level, and an equally sharp eye for how solo operators and small teams can use it to punch well above their weight.This is a practical episode. No hype, no hand-waving. Just how to actually build something useful.Three key areas we covered:✳️ The free thinker versus factory worker distinction - not everyone in your organisation will go down rabbit holes with AI, and that's fine. The mistake is expecting them to. Find your free thinkers, let them build the systems, and let your factory workers use those systems to produce.✳️ Context is the unfair advantage - what separates a mediocre AI user from a genuinely effective one isn't the tool, it's the context. James's approach of running a personal interview with Claude - talking for an hour or more about your goals, your business, your working style - and embedding that into your folder structure is one of those things that sounds obvious once you hear it and completely changes how you work.✳️ AI as strategist and operator - the goal isn't to use Claude for one-off tasks. It's to wire it into your actual systems via MCP connections and APIs so it can think alongside you as a strategist and execute as an operator. James does this with his accounting, his content system, his calendar - the lot.James's three amplifiers:✳️ Manage your internal feed - be ruthless about what you consume. The algorithm is not your friend. Curate what's coming in - business, gym, whatever fires you up - and protect your mental state the same way you'd protect your calendar.✳️ Decide on your trade-offs - if you want abnormal results, you have to make abnormal choices. That means being clear about what you're optimising for and being at peace with what that costs. Less scattered, more locked in.✳️ Learn AI, every day - not in a burst, not when you feel like it. One session a day, cap your tokens, build something. Do that for six months and you won't recognise how capable you've become.If this episode sparked something, follow or subscribe so you don't miss what's coming next. And if you've got a guest recommendation, I'd genuinely love to hear it.Timestamps00:00 - Introduction01:55 - James Drury's background: from junior analyst to COO and Chief AI Officer05:24 - Free thinkers vs factory workers: finding your AI heroes in a large organisation09:30 - Why AI amplifies what's already there - systems first, tools second11:14 - How to drive AI adoption through cultural pull, not top-down push14:32 - How personality shapes the way you use AI - and where to start15:50 - Setting up Claude Code: the context interview and building your AI brain19:28 - Daily strategy sessions with AI: how James uses Claude to decide what to execute22:36 - MCPs, APIs, and connecting your tools without the technical terror25:15 - Decision fatigue and letting Claude make the call27:00 - Building skills in the open: the video game analogy for AI learning30:31 - How to reorganise a messy Claude Code folder structure33:40 - Voice vs typing: what works for different kinds of AI users37:00 - AI slop vs AI-supported content - there's a difference38:30 - Building a content system: design documents, image generators, and automation45:35 - Hiring for what you're not good at: the case for a creative director47:13 - One tool recommendation: Motion AI for time blocking and project management50:58 - Image generators: James's pick and why55:16 - Amplifier 1: manage your internal feed58:14 - Amplifier 2: have fewer commitments, get obsessed with the right things01:01:15 - Discipline defined: how far you fall, how fast you climb back01:03:13 - Amplifier 3: learn AI, every single day01:05:40 - Where to find James Drury and Tattoos and Typewriters podcast----Get your copy of my Personal Brand Business BlueprintIt's the FREE roadmap to starting, scaling or just fixing your expert business.www.amplifyme.agency/roadmap----Subscribe to my Youtube!! Follow on Instagram and Twitter @bobgentleJoin the Amplify Insiders Facebook Community : www.amplifyme.agency/insidersPlease take a second to rate this show in Apple Podcasts. ❤ It will mean a lot to me.Mentioned in this episode:Signature StudioGet started today - signtaturestudio.me

How can content marketing in a tight niche build the audience that launches your book? And how do you decide whether to hand your self-published bestseller to a traditional publisher. Suzanne Smith shares what she learned in four years of going from blog to book deal. In the intro, how to stand out as a writer in the age of AI [Nathan Barry Show; Interview with Nathan Barry]; thoughts on asset maintenance; Goodreads giveaway on Bones of the Deep (Aug 5-20, 2026) This episode is sponsored by Publisher Rocket, which will help you get your book in front of more Amazon readers so you can spend less time marketing and more time writing. I use Publisher Rocket for researching book titles, categories, and keywords — for new books and for updating my backlist. Check it out at www.PublisherRocket.com This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Suzanne Smith is the founder of The Independent Landlord, and the bestselling author of The Good Landlord Handbook. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How a free blog in a tight niche built the audience for the book Rewriting the book from scratch when the law changed Why speed made self-publishing the only option Building a paid membership after one audience member asked for it Negotiating a Penguin Random House deal with no agent Using AI as a business sidekick, with a control room and an engine room You can find Suzanne at TheIndependentLandlord.com. Transcript of the interview with Suzanne Smith Jo: Suzanne Smith is the founder of The Independent Landlord, and the bestselling author of The Good Landlord Handbook. So welcome to the show, Suzanne. Suzanne: Thank you. Jo: Oh, there's so much to talk about today. But first up— Tell us a bit more about you and your background, and how you got into property and writing after a legal career. Suzanne: Well, I've always loved reading books. In fact, I recently did a French literature degree as a mature student. Being an author was never in the game plan at all. It's not something that I even thought about. I was brought up in New Zealand, so shout out to all the Kiwis and those across the pond in Australia. The thing about it is, Jo, you've lived there yourself. Kiwis are independent, self-reliant and have this great sense of fair play. So that was a very formative experience for me. We moved back to England when I was 16, and I have become thoroughly anglicised since then, but a Kiwi at heart. I always wanted to become a lawyer. New Zealand in some ways on television is quite American, and there was this American programme called The Paper Chase. It was about all of these students at Harvard studying law, and the professor said, “You come here with a skull full of mush and you leave thinking like a lawyer.” I thought, “Oh, I like the sound of that.” I didn't really know what a lawyer was, but everyone seemed to be very happy that I wanted to become one, and then that was it. Jo: So you went into law, and then how did you get into property? Suzanne: So I worked for 25 years as a solicitor. That's like an attorney if you're American. Started off in a law firm, and then I went into pharmaceuticals and I worked for big companies like what is now GSK, GlaxoSmithKline, and small companies as well. When I started out, it was before the internet, before Google. When you're in house, you're very much a generalist. You do a bit of everything. So you help companies grow their business. You're not business prevention, but you're still bound by the code of conduct for solicitors. You've got this role of keeping the company on the right side of the law. Then I had twins, who were born about five years after I became a lawyer, and I decided to work part-time for a while and did an MBA when they were little, part-time through the Open University. I know Jonathan is doing one at the moment. Jo: Yes. He's finished, so that's exciting. Suzanne: That was transformational for me, because I had probably been thinking a bit too much as a lawyer, and it helped me to broaden my view of the world and understand all sorts of things. Sso I continued going up the greasy pole, and then for my last job, in 2015, I joined a biotech company in Cambridge, England, as general counsel and company secretary. It was a long way from home, about two, three hours' drive from home. So I decided to buy a flat, an apartment, and to stay there in the week. I thought to myself, “Well, when I leave this company, I can let it out as a buy-to-let,” but actually as a landlord. So I stayed there for five years, and then when I left, I let out the property. The reason why I decided to leave law after 25 years, I had what I call a sliding doors moment, like in the film. I was 50. I was on holiday with my husband, and we'd probably had one too many rum cocktails. And he said to me, “Well, what do you want to be doing with your life? What would you do if you could do anything?” I was thinking, “Well, I've done law. I want to do something else now.” I didn't really know what that was, and I'd always been thinking about studying French properly, and that's when I left. So I decided, 18 months later, I left to do a French degree at King's College London, full-time. I was the only old person there with lots of 18-year-olds. When I did that, I was able to cash in my share options because I was a good leaver. I retired, and so I started buying properties to let out and became a landlord, without really thinking too much about it, and I used letting agents. They were fine to begin with, but I didn't really have a game plan or anything like that. What I realised is that when I tried to research things online, I couldn't really find anything that was terribly helpful. It was either quite general or it was very legal. So after a while… I became a landlord in 2019. I had the idea, why don't I set up a blog? And this is August 2022, so just four years ago. My husband came up with the idea of the name, The Independent Landlord, because it's that Kiwi spirit, being very independent. I thought, “Right, I'm not going to charge anyone for it. It's a hobby. It's not a business. I'm going to pay it forward and help, use my legal training, practical legal approach, and practical common sense, by doing this blog.” Almost exactly four years ago, I sent my first newsletter to 13 people. Jo: Woo-hoo. Suzanne: And I sent one last week to over 18,000. So it's been quite a journey. Jo: Wow, this is so great. I love this. There's so much in there. The turning 50 and then doing a degree. My master's in death is a little different to your French literature, but I like it. So I love this, and buying properties, starting it on the side, not a business at first, and growing the audience, and obviously you've put so much work in. Then you decide to write a book. So talk about that, because an online blog, although I'm sure your articles and everything were super useful, it's very different to write a blog than a book. So talk about your challenges in writing. Why did you decide to do a book in the first place? Suzanne: Again, I was an accidental landlord, is what they call it when you let a property when you didn't intend to buy it as a buy-to-let, which I did with my Cambridge flat. And I became, in many respects, an accidental author. So I was having a conversation with my husband again and I was saying I'd done this lead magnet to get people to sign up to my newsletter, and a big new law was going through Parliament at the time, called the Renters Reform Bill, that was going to completely transform the way landlords operate. I was saying to my husband, “Oh, I need to update my lead magnet, a little ebook, to explain the new law.” He looked at me and said, “Well, why don't you do a proper book? Write a book.” This was on the 29th of September, 2023. The reason why I mention that is that I thought, “Wow, what a great idea,” and my head was bursting. I went onto Google, and guess what I downloaded on the 1st of October? Jo: My blueprint? Suzanne: Exactly. I found you immediately, the Author Blueprint, and I downloaded it. I checked: on the 1st of October, 2023. Then I listened to almost… well, I think I went back several years on your podcast, just trying to understand. I'm like that. When I try and do something, I just try and learn everything that there is to know about it. So I started writing the book, and I guess the first challenge was I write quickly, and I'm used to writing for people who aren't lawyers, being in-house. So I thought I needed to have a structure. The structure was easy in many respects because, a bit of business at the end, and then you can go through a tenancy. I thought it was important to have a narrative thread all the way through it, just to bring it together. This is the literature degree coming in here. I thought that the mission for everything I do, the reason why I started doing this, is to help landlords, but also to help the experience of renting that people have in England. It's very specific for English law. And to help improve the private rented sector. So that's why I originally set up my blog for free, and I wanted, when people went onto Google, they could find something sensible and very detailed from me. My blog posts were… Well, I've now got over 400,000 words on my blog, so it's a substantial piece of work that is out there free of charge. So what I decided to do was to bring this narrative thread, I call it the good landlord ethos, to the book. Then I wrote very quickly, and I had a pretty good draft by April 2024, because we were all thinking that the law was going to change very soon. But then there was an election, and in the end the government changed and the legislation changed completely, so I had to rewrite the book and start again. So I think that my biggest challenge was that my subject matter, the new law, changed. Because I wanted to publish this book that explained to people practically what they have to do, and make it really straightforward, keeping out of politics, because it is a very politically charged area. I wanted to write it so it's a manual, somebody could literally follow it. So I used an editor, and I did write the book twice. I had a beta reader who is another lawyer, and a landlord as well. Then I got to the get-the-damn-thing-done stage. The really tedious bit of all the typos at the end. Jo: Yes, the finishing energy to get it out there. So at that point, obviously you'd found my blueprint, so you were learning about the indie way of doing things. Did you always decide to self-publish? How did you think about publishing? What were your challenges in publishing? Suzanne: It never occurred to me not to self-publish, because the new law came into effect on the 1st of May, 2026. The law and the details that I needed for the book were finalised in January, and I published on Amazon on the 5th of March, so I had to go so quickly. Even though I'd got a lot of it written, the last bit came in January, and so I needed speed. I knew that for landlords to be able to have something that they can use straightaway to help get them ready for it, and then use as a manual afterwards, I had to be first. Jo: Sorry, just on the year. Was it '24? You said '26. You meant May— Suzanne: 2024? No, no, because I actually published it this year. What happened in 2024, I had the first draft ready, but then I had to do another draft because the law changed when the Labour government came in. Jo: Right. Suzanne: The Renters Reform Bill turned into the Renters' Rights Bill. So I had to rewrite the book. So I finished however many drafts at the end of January 2026. Then it went to an editor, et cetera, et cetera, and I managed to get the book ready for a proof, to get the proof printed, towards the end of February. So it was really quick to go from the law being sufficiently finalised for me to write a book in January, and then having it ready in just over a month. There is no way that I could have done that if I'd gone to a traditional publisher. It didn't even occur to me to go, because I didn't want to be going touting around my book and, “Please publish me,” et cetera. It's just not me. I'm the independent landlord, and that moved very easily to being the independent publisher. So I learnt how to do all the publishing. And a huge thanks: I joined your Patreon and I was a very good student. I went through everything systematically and followed your playbook, and used Vellum and BookFunnel and all the other tools. So I decided to go on Amazon as well as have my own Shopify store, which just about killed me. Jo: I was going to say, you are an excellent student. You really like learning, but you also put this into practice, which is why I also wanted to talk to you. You haven't just talked about all this. You've literally done everything. Suzanne: Sometimes it was like my head was going to burst. Luckily, Claude upped his game earlier this year when we got the Opus 4.5. I didn't use AI really until this year. I decided I need to do exercise all the time, and have that as a have-to-do, because my head was spinning all the time with all these different things. So I would go to the gym, go to a spin class, and then I would walk out with my phone on, with the Claude app, and dictate a stream of consciousness into it. “Oh, I need to do this, or what about that? Oh, I just remembered about this. Oh, I've had this idea, blah.” And then said, “Make sense of it for me, Claude.” So it was very much as a thinking partner, because when you're writing your first book, it's bad enough, but when you're learning how to publish… Even, like, when I got the first proof of the book back from BookVault, I realised that all the footnotes—I have 114 footnotes in my book, and that, again, is the recent degree there—and the formatting had gone skew-whiff. Apparently it was an issue with Vellum, and they were really lovely and they sorted it straight out for me. So it shows: always get a proof of the book. They were able to sort that out very quickly, and BookVault were very quick in getting me another proof, because you can shortcut it and just pay to get a very quick delivery. Amazon, on the other hand, was really slow. It took a week. So I actually published earlier on my Shopify store for my members, of my membership, and I gave them a discount. Then I finally got it onto Amazon on the 5th of March. There are all these different skills you're having to learn. The Shopify store I found very hard, and there was all the tax, because I'm VAT registered. So I think I'm still recovering. Jo: You're still recovering. I wouldn't normally recommend a Shopify store for someone with their first book, doing first of everything. But, as you say, you're someone who learns a lot, puts it into practice, and— I think you were pretty determined to do that because you had a community as well, right? Suzanne: Exactly, yes. The big subscriber list. I think that's why the book did so well. So in the first week, because I met you at the Indie Author Lab put on by— Jo: Yes, London Book Fair, yes. Suzanne: Yes, the Alliance of Independent Authors. I met you there, and it was just my first week, and I had 1,000 sales in the first week. That was because of my audience. I'd been going on about the fact that I'm writing this book for two and a half years, because that's how long it took me to do. So I had a wait list for it, and I had a thing on my website, a landing page on my website, saying how good the book was and why it's the best thing for the Renters' Rights Act. Then I went onto Google, and I think I sent you a screenshot of this at the time. I put into Google, “What's the best book for the Renters' Rights Act for landlords in England?” And it came up with me as a featured snippet, and I hadn't even published it at that time. It was just about there. So the blog really helped, because I'd become an authority on the Renters' Rights Act. Even though I'm not a practising solicitor any more, I spent all my time reading the damn thing, and it is a very complicated bit of legislation. Funnily enough, I have ruffled a lot of feathers. People have even said about me behind my back, “What does she know? She's only got four properties.” But I just took no notice. I thought, “I'm going to try and use my legal brain and my understanding of what it's like being a landlord, there with the rubber gloves cleaning an oven when people have moved out, and try and write something that's not trying to sell anything else, and to help people.” And then it got picked up. Jo: Yes. Wait, let's just slow down. Slow down, because we will get onto that in a minute. But let's just come back to that launch. So as we talked about, you've had a blog for five years— Suzanne: It was three and a half by then. Jo: Three and a half years you've been blogging, but hundreds of thousands of words of useful information. So you've essentially done content marketing. You've attracted people. You had a lead magnet. You got them on your email list. You told them that you were writing a book. You got a sort of pre-sales list up. So that's an email list. You've got a blog. Did you do anything else in terms of marketing? Suzanne: I had YouTube, a big YouTube channel. I'd only set it up at the end of 2024, and I'd had half a million views. And again, just very straightforward advice, and without all the scaremongering and politics. I deliberately keep out of it all. A lot of people joined my newsletter as a result of that. Also a year ago, exactly today, I was running a Facebook group, which was a lot of hard work. There were a few thousand people in it, but there are often a lot of people going in there trying to sell things: insurance, eviction specialists and things. And there was also a lot of people just being unpleasant to other people. I was getting fed up with it. It was taking me a lot of time, and I was doing a lot of speaking events and trying to explain what this new law was doing, and wearing myself out. I'm an extrovert, but even I find speaking events absolutely exhausting, because it's like everything gets sucked out of you. It's strange. Then somebody came up to me in July last year and said, “Suzanne, can you set up a membership?” I said, “Well, landlords aren't going to pay for that.” And they said, “Yes, they will. You build it and they will come.” I asked ChatGPT and thought about it. I asked ChatGPT, who I was dating at the time, now exclusively with Claude, but I know Claude has other people in his life. But I'm very much set with Claude Fable at the moment. So I asked ChatGPT, how can I go about setting up a membership? And I mentioned your one and said, “Should I do it on Patreon?” And then he came back with: go for Circle. So I set up a membership on Circle, exactly a year ago. In fact, it's the anniversary of my first member yesterday. hTe rules I had were, no selling. So I don't sell, no affiliate links, no one else can sell anything, and we have to be supportive. No negativity, no politics. So what it's become, it's like the senior common room of the private rented sector, with landlords, lawyers, letting agents. There's a fantastic forum in there. It's not me doing it, it's peer-to-peer. I have twice-monthly live streams where people can ask me questions. I wonder where I got that from. No, I very much modelled it on your Patreon, but on a different platform. I have courses in there as well. So that has really grown. I launched it in July, and by September, October, I'd gone past the VAT threshold, which has complicated everything, but it means my business now is this membership. I really enjoy doing it, and there hasn't been all the negativity that you have in a Facebook group. So I had them as… talk about your thousand fans. There are about 1,500 in the membership, and their support really helped the launch of my book, as well as the wider people who get my free newsletter. Jo: Yes. Suzanne: So it's all different types of content marketing. Jo: Y, but I do love this. And of course, if people are wondering, I joined Patreon back in 2014, I think it might have even been before that, and there weren't too many places back then to run communities. It wasn't even really a community at the time, it was a sort of, almost a “give me a bit of support for the podcast.” So things have changed a lot in terms of communities, and obviously you went with Circle, which is great. Patreon is slightly different now, and some people are using Substack for something similar. So that's just on the platform, but on the business: early on in our conversation you said, “I wasn't going to have a business. It wasn't a business. It was just putting stuff out there, helping other people,” and then your audience asked for this membership. And so now it is a business, right? Suzanne: Yes, it is. Jo: And you've got a book and all of this. So are you happy with the change to a business? Because obviously you have to treat it quite differently. Suzanne: Yes, I am, because I think to begin with, I was just doing it one or two days a week. I was actually studying a master's in French literature part-time, and I then found that I was enjoying the blog more than the master's, so I dumped the master's after the first year. But after getting 88% for one of my dissertations, which interestingly was on the translation of a Simone de Beauvoir book into English, and the publisher who's got that now is Random House, but that's another thing. Anyway, so I decided to give up my master's and double down and work full-time on the blog. People were paying to help me with all the big fees and things, the big tech stack, Buy Me a Coffee. I was doing a little bit of consulting and things. I was working six, seven days a week. I was treating it like a business in terms of quality and my effort, but it wasn't a business in terms of revenue. Then it just all came together, and this person said, “Set up a membership,” and I thought, “That's what I'm going to do. I'm now going to put it on a business setting.” I've got an MBA, I know how to do it, and people thought I planned it, but I didn't. It just happened. So now I do very much treat it as a business, but I still don't advertise. I don't allow people to advertise with me, because I want to be independent. If I recommend something, I want people to believe it's me recommending it, not just because someone's paying me, which can be a big issue in the landlord area. Jo: Oh, in any industry. I get pitched every day with loads of random things that people are like, “Oh, a dollar a click or whatever, if you send this to your list.” And it's like, seriously? Just stop it already. I did just want to add there: somebody asked you, they said, “You should have a community,” and that sparked that idea. I just wanted to acknowledge that my Patreon came from Jim Kukral. Some of you will remember, who've been around a long time. Jim Kukral came on my blog around sort of 2013. Amanda Palmer had just put out a book called The Art of Asking, and I was doing a lot of unpaid work on the podcast at the time, and I was either going to give it up or I had to fund it somehow. Jim said, “You should do a Patreon.” And I was like, “Oh, no, I hate asking for money.” So at the time I just felt, oh, weird. Then I was like, “No, I do all this work,” as you were saying. Now the Patreon has changed so much in terms of what it is, but it is the backbone of my business, too. So I love that you listened to one of your fans who said what they wanted, and I love that I've listened as well. Sometimes we just have to listen to those urges, don't we, to take things on? Suzanne: Yes, absolutely. In some ways I didn't really back myself before. I thought, “No one's going to pay for this.” Then the more you give, the more they want. Jo: Yes. Suzanne: What I've been really working on now is having boundaries, because there were two big kind of mottos that I picked up when I was working in pharmaceuticals. One was from a head of the business. He was Canadian, and he was always saying, “You've got to skate to where the puck is heading.” Jo: That's Wayne Gretzky, is it? Suzanne: Exactly. Yes. He would always say it, and so that's what I've done with my blog and my book. When I write things, I don't pay for any tools. I don't do keyword searches and all that. I just think, I do one blog post per topic, and I'm going to guess what people are going to be searching for soon, and I build up all this content around it. That's why most of my blog pages are top five. I've had no advertising. I haven't asked for any backlinks. I don't do it. People backlink to it because it's useful. So that was the first thing, is skate to where the puck is heading, and that was my approach with the book. I knew people would need this book from around May, and they'll need it forever, because it is so complicated and regulated, the rules for being a landlord in England. So that was the first one. The second thing was: when you take something on, you've got to let something go. One in, one out. I found that I was taking on so many different things, and I've just been cutting back, because I can't be doing all the speaking, I can't be answering people's emails. So I now don't do emails. If people want my advice on something, they ask me in the hub, at the twice-monthly live streams. Sometimes I answer in the forums, but I don't have time. When there are 2.4 million landlords in the UK, and even with our 18,000 on my newsletter, I could spend, and I did, I used to spend all my time replying to emails. So anyway, there are the things. Oh, and there was a third one, which is: attract, don't chase. One of my friends gave me that advice and that's exactly what my approach has been. I just don't chase for anything. I just put the stuff there and then build it and they will come. Jo: Yes, and I think another thing is the power of the niche. It's so clear that what you write about, the people you are aiming at, you have an extremely tight target market. That is both a strength and obviously a weakness, because they're the only people. But as you say, there's more than enough of those people for a community, for the book you have. From my own perspective, that's the same for me, the power of the niche. That's how I have a successful podcast, for example, because of that reason. I think you're like a poster child of what a non-fiction author should do. What I like is that you didn't go, “Oh, where's a niche where I could make money?” and then jump in. You've gone about this in a kind of slightly accidental way, but now you're leaning in and this uses all your skills. So this really is a great example of the power of the niche and then making the most of it. But let's move on to what then happened, and— What happened with the book deal? Suzanne: Wow. So you and I met each other on whatever day that was in March at the Indie Author Lab, and the following day I got an email, via my website on a contact form, from Penguin Random House saying, “We love the book. We love the mission, its values,” all this kind of thing. And I was thinking, “Oh, it's another one of those. Must be an—” Jo: AI spam bot, right? Suzanne: Yes, and I remember I sent you a screenshot of it, and then I checked her out on LinkedIn and thought, “Okay, there is somebody with that name there.” You're always saying, and Orna Ross and everyone are always saying, “Watch out for scams.” And in fact, Penguin Random House even this weekend on Instagram put out something saying, “There are lots of people impersonating us.” So I didn't take it too seriously, and it was something like, “Oh, would you be interested in us publishing your book?” And I thought, and I laughed. It was like, no, this is too good to be true. So I replied and said… Oh, I said, “Well, thank you so much. The Renters' Rights Act…” And so this is like the second week in March. “The Renters' Rights Act comes into effect on the 1st of May. If you want to publish it, you're going to need to get your skates on.” I literally did say that. Then she arranged a meeting with me the next day, on the Friday. I still was very dubious about it, and I had a think about it. What helped me, and I have the little booklet here: at the Author Lab, we did some work at the beginning, and Orna said, “Put your phones away.” And it was like, “What? Put my phone away?” Then we had to do this definition of success, and our passion, and our mission, and our purpose. I wrote down things like, I want to help landlords, and in so doing, help improve the private rented sector. I get pleasure from helping people. I want to improve standards and use my legal and practical skills, et cetera. So I thought, “Okay, what is my purpose of doing this book?” It isn't really to make money, because going with Penguin, you wouldn't do that for financial reasons, because you'd make very little money. So I thought, what is my why? My why is I want as many people to read this book as possible. And I've managed to sell a few thousand copies, but there are 2.4 million landlords, and they all need to understand this book, and the only way that I can get it out there, apart from doing ads, is to get it out in bookstores. So I thought about it, and then said, “Yes, I will do it, because I want to get the book out there.” So it's distribution. It's going to be published on the 6th of August, which is really quick, bearing in mind they contacted me in the middle of March. It's exactly the same book, it's just got different copyright wording and different blurb, different paper. Same cover, because I managed to find a fantastic cover person to do it. So they've kept everything the same. So we negotiated that book. I have no agent. They came to me. It's the attract, don't chase. I just put my lawyer hat on, and because one licence is very much like another one… I did turn down their first offer. Jo: Well done. Good negotiation. Suzanne: My daughter said to me, who's an adult daughter, she said, “But it's Penguin.” And I said, “Well, no, but it doesn't work for me.” So I had a call with them, and then they came up with something that worked for me a bit more. I did have to concede on a few things, like I can't sell it in my Shopify store. But in some ways, that was a blessing in disguise, because it means I don't get any more “Where's my book?” emails. Jo: Yes, exactly. Pros and cons of everything, basically. Suzanne: I have very clear rights to get it back. If I want it back, I can get it back and I don't have to give a reason. They're lovely. They have been really very wonderful. When I went up there a month or so ago, they gave me this book bag, and it's got on it, “I'm published by Penguin,” and I burst into tears. Jo: Aw. That's nice. Suzanne: I don't know, it just seemed like such a big deal. Because up until then I was just being all very lawyerly and task-orientated. Then I thought, “Oh my goodness,” and then it dawned on me. So I'm now in this interim period where I've taken it off Amazon and off my Shopify store, and I feel very maternalistic towards the book because, you know, it took me two and a half years, which is longer than a pregnancy. Obviously it's not a child, but it's like my book child. I've sent it off with a backpack and a drink and some snacks, and I hope that they look after him, my book. The day I took it off Amazon it was still number one. And a big shout-out to Publisher Rocket, by the way. Jo: Yes. Very, very useful for niche publishing. Suzanne: Very. It helped me choose the right niche categories. So it was number one on at least one category, often six, all the way through. I thought, “Well, it's over to them now.” They're very lovely people. They've given me some marketing assets, as they call it, some swanky graphics and things to use. We'll have to see what we do in terms of marketing. I don't mind doing marketing. I'm on LinkedIn quite a bit, and my whole blog is marketing. What I've been doing is updating my blog to include one of these graphics and to mention the book, and I got Claude to help me draft the code so it looked right. So I've been going through all of my blog posts and sending people to Amazon rather than to my Shopify store. It is mixed feelings, because I care about my book. I put a lot of effort, a lot of love, a lot of tears. No, not tears, but I put a lot of effort into it, and it's out of my control now. Jo: Yes, you said it's over to them, but obviously you will still be creating content around this topic, so you'll probably still be the biggest driver of book sales. Suzanne: Yes. Jo: Are they also suggesting, for example, a podcast tour, like pitching for podcasts? Are they going to assign you some PR? Because, also if people don't know, as we are recording this, we have a new prime minister who wants to do various things. You said no politics, but this is obviously a political thing. So you have the potential to go on a lot of different podcasts, media, talking about this, becoming almost a talking head in this kind of area. So are you angling for all that, and is that in your contract, or is it literally just going to be whatever you want to do? Suzanne: That's not in the contract. What's in the contract is very minimal. I think I've already done what I'm supposed to do. They are pitching for me to go on podcasts and things. I'll tell you a really funny coincidence. So we now have a new Prime Minister, Andy Burnham, and when he was Mayor of Greater Manchester, he set up something called the Good Landlord Charter. I actually talk about it in the book, and I quote him in my book saying that good landlords mean people trying to do the right thing, or something like that. And I coincidentally came up with the same name, The Good Landlord Handbook. I'd already had the book title for a long time. So this idea of good and landlord coming together, the adjective good as opposed to criminal or rogue, and the cover being green. I'm wanting to change the narrative so it's the norm to have a good landlord, and to help people become good landlords. Or if they're good landlords, help them to understand the new rules, because the new rules are very complicated. So what I don't get involved in is this right or wrong. Is it right that landlords can't do this or have to do this? Because as an in-house lawyer, it doesn't really matter what I think about the law. GDPR, goodness me. Jo: Oh, dear. Let's not start on GDPR. Suzanne: No, exactly. Because we've just got to suck it up. I liken it to the grief cycle, that people have been going through so much change and you have the anger, the depression— Jo: Denial. Suzanne: Bargaining, the denial, and then you get to acceptance. For some people, the acceptance means they want to stop doing it. If you want to accept it and stay, you need to understand the rules. So I've deliberately just kept very practical and have kept out of all the politics of it. I have, funnily enough, become involved because I'm now seen as an expert on the Renters' Rights Act. I've worked behind the scenes with the government to help, and give comment on government guidance for landlords. I was even invited to a reception to mark the passing of the Renters' Rights Act at Downing Street with the previous prime minister, all whilst staying apolitical. I won't let anyone make me be a mouthpiece for their political view. It's more, we just have to do this if we want to continue doing it. I've been very clear on that. Jo: It's interesting you mention the grief cycle there, and you've also mentioned Claude and ChatGPT. I wonder if you might also just comment on use of AI for authors and for marketing and all this. Also with legal stuff, because for me now, if I'm looking at a particular legal thing, I tend to ask Claude. I'm like, “Can you just explain this?” or upload a contract or whatever. Although it is not legal advice, it can be quite useful. So give us your thoughts on using AI as a sidekick in your author business and also for wider life. Suzanne: I now struggle to think what life would be like without Claude. I don't use Claude to write, at all, because I have a very particular voice and a turn of phrase, and if ever Claude writes something for me, it doesn't sound like me. It flattens me, and it makes me sound a bit American. So I don't do that. I've used it in the back end of the business. For instance, my blog was down, and there was something called a recursive bot, which I don't even know what it was, and Claude helped me fix it for free. I went through, I did screenshots. When I did an ElevenLabs audiobook and did it all myself, I was literally, for every screenshot, showing it to Claude. Claude said, “Do this, press this, press that.” So I have all these different projects set up. One is the control room, where it's for my strategic thinking. If I have an idea, I want to think about something, I put it in there. I have the engine room, which is for everything techy. Like when I had the recursive bot, or if I'm wanting to have some code on the website to make it look a particular way. Then I have other things for different subjects, and I put all the resources in there, and I use it a lot as a thinking partner. I've noticed that Fable doesn't hallucinate as much, but the Opus used to. There's something called rental discrimination, and it was proofreading and said, “No, it's not rental discrimination, it's rental income discrimination,” and that was just a load of rubbish. So I would never let it go and change things without me looking at it. I went on one of your webinars a month or so ago about MCPs and all the connectors, which is fantastic. It can go into my community and pull out all the questions for one of my live streams and put it into a document in order, by theme, for instance. It can look at my MailerLite, because that's where my newsletter is with, and analyse the different open rates and click rates and things. It's so good for analysing everything, all the book sales. It helped me with my negotiation with Penguin, and it is pretty good on law. It has sometimes hallucinated things, but not so much now. I think with anything, you've always got to go back to the primary source, and this is what we learn in academia: you have to check the primary source yourself. I have a bit of a magpie brain. I'm very much a discovery writer, like you, and things occur to me as I'm doing it. I think that Claude, at the moment, is incredible. I've been quite open about it on social media that I have Claude as a business partner. I'm a solopreneur, or whatever the word is. I have quite a big business now, and lots of different things, and it's just me doing it, because I can ask Claude how to do this, and how to do that. Claude can go and check my emails and tell me, is there somebody I've not replied to, which helps a lot. Jo: Yes. I think it's empowering as a solopreneur as such. You talk there about the fixing the tech stuff. I have my web host come to me and say, “Look, you're getting so much traffic and bot stuff, and we need to put this thing in, and it's going to be $120 extra a month.” I was like, “Can you just give me an hour? I'll get back to you.” And then I just had Claude code up, and I was like, “Analyse this and tell me what we can do.” It was like, “No, you just need to flip this switch and do that.” And I'm like, “Okay, fair enough.” Then the guy said, “Oh, no, okay, actually you don't need it.” Just stuff like that. As a solopreneur, you're either going to pay somebody technically quite a lot of money, or you can get Claude or ChatGPT. We should say, the ChatGPT Sol is very good, like the Claude Fable, for example. So, yes, using it as a sidekick. I love your control room and your engine room projects as well. That's a great way of doing it. Suzanne: I wouldn't be without it now, and I would have published the book a lot later without Claude, because Claude was helping me with the Shopify store and all the many steps of things. It saved me real time. It is just fantastic. I think, like now when I'm updating my blog, I have a connection between Claude and my blog. Claude can go in, I can give it my Google Search Console results for the page: what should I change, are the headings right? All this kind of thing. And it will give me a view on every single page, which is incredible. Jo: And YouTube, and just everything. Just super useful for that business sidekick. That's what I want authors to think. I feel like authors get so obsessed with the creative side with AI, whereas actually, people like you and me, we're using it as that engine room for the solo business, which is what I love. So we're out of time. I did want to ask one more thing, which is, one of the biggest issues with a specific book like yours is when they change the law again. So do you have a plan in place for if, say, a new government changes the law again? Will you just be updating the book over time? Suzanne: I think that there'll need to be a new edition of the book in three years' time, and I've spoken to Penguin about it. Not all of this new law has been implemented, and there's going to be case law and things. So I expect that I will update the book every few years. I have some other ideas for books as well, but for the moment, I'm just taking a bit of a break. You always say we've got to refill our creative well. I really feel like that at the moment. Recently I've just got myself a personal mobile phone so that I can turn off my work one when I'm on holiday and actually take time off. Because for all the time that I was doing the book, basically from Christmas until May, I didn't have one day off. That is not good. So I'm just trying to be a bit more balanced. I had an idea to write another book for summer, but I've just decided not to, and I'm going to leave it until I feel the urge again. Jo: Oh, well done. Suzanne: Which will come. Jo: Yes, well done. Suzanne: I think there's nothing wrong with that. We just need to think what's right for us. I'm 58. So I want to be able to have time to enjoy things and not be working all the time. Jo: No, that's great. It's a sustainable business. So where can people find you and the book and your community online? Suzanne: The easiest way to find me is theindependentlandlord.com. Or if you put Suzanne Smith and landlord into Google, you'll find me as well, and there's a link on there to the book, The Good Landlord Handbook. In the community, there's a link to that on my website as well. Jo: Brilliant. Well, thanks so much for your time, Suzanne. That was great. Suzanne: Thank you.The post From Blog To Community To Book: A Non-Fiction Author's Journey With Suzanne Smith first appeared on The Creative Penn.

Every employee at your company probably has ChatGPT, Claude, and Gemini installed, and nobody's tracking what data goes where. Xia Hua, co-founder and CEO of Traceforce, came back a year after her first appearance to show us what that looks like from the inside. Her team's open source scanner, MCP X-Ray, found a prompt injection flaw in Playwright, one of the most widely used MCPs, and she triggered it live with a single sentence. We also get into Anthropic's report on the espionage campaign that used Claude and a set of MCPs against about 30 organizations. And the bigger problem underneath it all, that data and instructions are now co-mingled, so any tool that reads text can be told what to do by that text. Xia: www.linkedin.com/in/xia-hua-ph-d TraceForce: www.traceforce.ai MCP X-Ray: www.github.com/traceforce/mcp-xray Jon: www.linkedin.com/in/jon-mclachlan Sasha: www.linkedin.com/in/aliaksandr-sinkevich YSecurity: www.ysecurity.io

How do you prove AI is shipping more features? Amos Haviv leads the Developer Workflow teams at Booking.com, supporting 4000 engineers operating 8000 repos.Everybody is burning through their AI budget right now and almost nobody can answer what it bought them. Amos can, because his team spent four years building an event system to debug their own SDLC before AI upped the urgency.In this video, we cover:Why verification is the bottleneck right now, and where it moves nextBuilding an event store that separates KTLO from real feature deliveryWhy static dashboards create the metric they measure, and the cobra story behind itAgent cost, model routing, and why Booking ignores token maxing entirelyRunning a developer survey with a 92% response rate across 3k+ engineersWho should own skills and MCPs: a central platform team or the domain experts?For platform engineers, engineering leaders, and anyone being asked to prove ROI on AI tooling this quarter.Timestamps:00:00:00 - Everyone is burning through their budget00:00:32 - Verification Is the Bottleneck Every Team Hit00:03:35 - 4,000 Engineers and 8,000 Repos at Booking.com00:06:48 - Why Copying Google and OpenAI Will Break You00:09:21 - Verification Is a Stack of Agents, Not One Review00:13:27 - Cost Is Becoming a Bottleneck of Its Own00:17:14 - Was the Internet a Bubble? What That Teaches Us00:25:32 - What Working With the Frontier Labs Looks Like00:28:26 - Debugging the SDLC With Four Years of Event Data00:30:24 - Do Engineers Using AI Actually Ship More Features?00:37:13 - Where to Start If You Measure Nothing Today00:45:01 - The Cobra Effect: When a Metric Becomes a Target00:52:23 - Everyone Is a Builder Now, and Everything Needs Support01:01:21 - Is AI Turning Every Engineer Into a Manager?01:03:46 - The Developer Survey With a 92% Response Rate01:10:09 - Who Owns Skills, MCPs, and the Enterprise Harness01:17:46 - Great Developer Experience Is High VelocityMentioned in the episode:High Output Management by Andy GroveThe Sovereign Individual (1997)The story of General MagicViews expressed are Amos's own and do not represent Booking.com.#AI #SoftwareEngineering #DeveloperExperience

This week, the crew digs into one of the biggest AI security stories of the year: how an OpenAI autonomous agent accidentally compromised a Hugging Face environment during testing and what the incident reveals about the growing risks of agentic AI. They examine how AI models behave in offensive security scenarios, discuss emerging attack surfaces around MCPs and AI agents, explore the challenges of AI red teaming, and debate what organizations should be doing today to secure AI-powered workflows. The episode also covers AI safety initiatives, model behavior, and where defensive security is struggling to keep pace with rapidly evolving AI capabilities.Join us LIVE on Mondays, 4:30pm EST.A weekly Podcast with BHIS and Friends. We discuss notable Infosec, and infosec-adjacent news stories gathered by our community news team.https://www.youtube.com/@BlackHillsInformationSecurityChat with us on Discord! - https://discord.gg/bhis

Orgs need to be able to use agents, MCPs, and LLMs in ways that don't lead to unexpected actions and undesirable outcomes. The OWASP Agent Security Regression Harness project is an approach for defining customizable scenarios and testing whether those systems fail against known security threats. Mert Saltimaz talks about the background of the project, how orgs can use it as they bring more LLMs into their environment, and how the project intends to grow. Importantly, we also talk about the security controls and designs that orgs can build around the systems and data that models interact with in addition to evaluating the security of the agents and agent harnesses themselves. Segment Resources: https://github.com/OWASP/Agent-Security-Regression-Harness https://youtu.be/6DWs5EwbFQ0?si=r0IJ_F0SZnkPzzYg -- "What Trading Systems Taught Me About Breaking (And Defending) Infrastructure" Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-393

Hoje o papo é sobre novos protocolos agênticos! Neste episódio, conversamos sobre como o Model Context Protocol (MCP) busca padronizar a integração entre agentes e ferramentas, além das diferenças e complementaridades entre MCPs, skills e APIs tradicionais. Vem ver quem participou desse papo: Paulo Silveira, o host que ainda acha automação uma bagunça Vinny Neves, cohost, dev e professor na Alura Mikaeri Ohana, Staff Developer Relations Engineer no Google Sulamita Dantas, Database Engineer e professora na FIAP Marco Antonio da Silva, Diretor de Engenharia do Conta Simples  Links:  Anthropic apresenta o MCP em 2024 MCP MCP Apps Registro oficial de servidores MCP Vinny: MCP tá onde o npm tava em 2014. e isso não é elogio Conta Simples + MCP MCP Tools Agent Gateway ADK: Agent Development Kit A2A: Agent-to-Agent UCP: Universal Commerce Protocol AP2: Agent Payments Protocol Google: Agent Identity Agent Skills Repositório oficial de Agent Skills da Anthropic  Plugins no Antigravity skills.sh, o npm de skills Criador de skills da Anthropic Roadmap do MCP Boas práticas de segurança para MCP Toda revolução tecnológica começa com quem antecipa o futuro e transforma ideias em soluções de alto impacto. Conheça os cursos da Alura + FIAP Skills & Go: Agentic Engineering, Building AI Products, e AI Data Strategy. Saiba mais sobre o Skills & Go. Vá para o Vale do Silício com Paulo Silveira, Marcell Almeida, Fabrício Carraro e Marcus Mendes na “Imersão IA Sob Controle e Alura no Vale do Silício“! Vagas limitadas, corra para reservar a sua. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

Orgs need to be able to use agents, MCPs, and LLMs in ways that don't lead to unexpected actions and undesirable outcomes. The OWASP Agent Security Regression Harness project is an approach for defining customizable scenarios and testing whether those systems fail against known security threats. Mert Saltimaz talks about the background of the project, how orgs can use it as they bring more LLMs into their environment, and how the project intends to grow. Importantly, we also talk about the security controls and designs that orgs can build around the systems and data that models interact with in addition to evaluating the security of the agents and agent harnesses themselves. Segment Resources: https://github.com/OWASP/Agent-Security-Regression-Harness https://youtu.be/6DWs5EwbFQ0?si=r0IJ_F0SZnkPzzYg -- "What Trading Systems Taught Me About Breaking (And Defending) Infrastructure" Show Notes: https://securityweekly.com/asw-393

Orgs need to be able to use agents, MCPs, and LLMs in ways that don't lead to unexpected actions and undesirable outcomes. The OWASP Agent Security Regression Harness project is an approach for defining customizable scenarios and testing whether those systems fail against known security threats. Mert Saltimaz talks about the background of the project, how orgs can use it as they bring more LLMs into their environment, and how the project intends to grow. Importantly, we also talk about the security controls and designs that orgs can build around the systems and data that models interact with in addition to evaluating the security of the agents and agent harnesses themselves. Segment Resources: https://github.com/OWASP/Agent-Security-Regression-Harness https://youtu.be/6DWs5EwbFQ0?si=r0IJ_F0SZnkPzzYg -- "What Trading Systems Taught Me About Breaking (And Defending) Infrastructure" Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-393

Orgs need to be able to use agents, MCPs, and LLMs in ways that don't lead to unexpected actions and undesirable outcomes. The OWASP Agent Security Regression Harness project is an approach for defining customizable scenarios and testing whether those systems fail against known security threats. Mert Saltimaz talks about the background of the project, how orgs can use it as they bring more LLMs into their environment, and how the project intends to grow. Importantly, we also talk about the security controls and designs that orgs can build around the systems and data that models interact with in addition to evaluating the security of the agents and agent harnesses themselves. Segment Resources: https://github.com/OWASP/Agent-Security-Regression-Harness https://youtu.be/6DWs5EwbFQ0?si=r0IJ_F0SZnkPzzYg -- "What Trading Systems Taught Me About Breaking (And Defending) Infrastructure" Show Notes: https://securityweekly.com/asw-393

Databox is an easy-to-use Analytics Platform for growing businesses. We make it easy to centralize and view your entire company's marketing, sales, revenue, and product data in one place, so you always know how you're performing. Learn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreIn this episode, Rick and Pete break down exactly why: the semantic layer, the metric definitions, and the standardized math that make an AI's answer trustworthy instead of a guess. If you've ever wondered why connecting five random MCP servers to Claude doesn't give you the same results as a purpose-built data layer, this is the episode.What you'll learn:Why raw data connected directly to AI can actively mislead youThe three things a system needs (semantic relationships, metric definitions, consistent statistical math) before AI can safely draw conclusionsReal examples of AI skills built on Databox MCP — sales pulse, content performance partner, weekly growth dashboardWhy "just hook up your MCPs" burns through AI credits without getting you a real answer

Nicole Patten is the founder of Elevate Online and Claude Training, businesses helping founders, teams, and enterprises actually adopt Claude rather than simply pay for it. A former Senior UX Engineer at Google, she left in 2025 to focus full time on Claude adoption, and now runs her consultancy solo, with zero employees, generating over $30,000 a month. Her client roster includes multi-billion dollar teams alongside fast-scaling AI studios, and her recent Claude Code hackathon at NY Tech Week drew over 100 attendees.AGENDA:- 00:00:30 - Meet Nicole Patten, Ex-Google UX Engineer- 00:03:22 - Where Are We on the Claude Adoption Curve?- 00:05:22 - The #1 Misunderstanding About Claude- 00:06:05 - Claude AI vs Cowork vs Code, Explained- 00:07:00 - Where Non-Technical Founders Should Draw the Line- 00:07:54 - Running a $30K/Month Business Fully on Claude- 00:09:35 - Agents, Skills, MCPs, and Directives, Decoded- 00:11:47 - Skills vs Agents: Where Should Beginners Start?- 00:13:28 - Human in the Loop vs Full Automation- 00:14:26 - Enterprise Teams vs Solopreneurs, Same Claude Playbook- 00:15:37 - Why Every Employee Needs the Same Claude Skills- 00:16:32 - Why AI-Native Companies Will Outrun Everyone Else- 00:18:03 - Inside the NYC Tech Week Hackathon Project- 00:21:38 - The 60-Day Playbook for Company-Wide AI Rollouts- 00:22:52 - What Actually Proves Your Team Adopted AI- 00:23:44 - Where to Learn More and Final Advice

Future Finance host Paul Barnhurst and co-host Glenn Hopper are joined by Alex Brower, co-founder and CEO of QFlow.ai, to unpack why forecasting often breaks down even when teams have plenty of data. The discussion focuses on how finance and go-to-market teams can work from shared inputs, improve planning accuracy, and use AI without losing the structure needed for reliable decisions.Alex Brower is the co-founder and CEO of QFlow.ai, helping finance and go-to-market teams connect data, improve forecast accuracy, and boost analyst productivity up to five times. He previously led finance and marketing at high-growth tech firms like AppTelligent (acquired by VMware) and Cloud Academy (acquired by QA).In this episode, you will discover:Fix data plumbing before system consolidation.Use data dictionaries for consistent definitions.AI helps, but semantic logic is still needed.Vibe coding can create hidden costs if untested.Start with key unanswered questions, then plan solutions.Alex shares lessons from leading finance, operations, and marketing teams at high-growth companies, including what he learned from living the forecast tension on both sides of the table.Follow Alex:Website: https://qflow.ai/LinkedIn: https://www.linkedin.com/in/alexbrower/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.ai:Website - https://bit.ly/4i1EkjgFuture 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:[00:00] – Trailer[03:44] – Finance-to-marketing journey & QFlow's solution[07:10] – Data plumbing vs. alignment[11:37] – MCPs, AI, and logic layers[14:50] – Risks of vibe coding & maintainability[21:56] – How QFlow handles data, planning, and storytelling[30:57] – Alignment before strategy & reducing fact-hunting[35:39] – Where CFOs/FP&A should start modernizing[44:03] – Closing & sponsor sign-off

MCPs are everywhere—but what are they, really?This week, Chris Savage is joined by Sasha Friedman and Sam Balter to break down one of AI's fastest-growing technologies, share the real workflows it's unlocking for marketers, and discuss why connecting AI to your tools may fundamentally change how teams work together.Links to Learn More: Follow Sasha Friedman on LinkedInFollow Sam Balter on LinkedInFollow Savage on LinkedInSubscribe to Talking Too Loud on WistiaWatch on YouTubeFollow Talking Too Loud on InstagramFollow Talking Too Loud on TikTokLove what you heard? Leave us a review!On AppleOn Spotify

Dan Mosher is the CEO of DealGround, an AI-native platform helping commercial real estate brokers and investors find deals faster by organizing property data, identifying opportunities, and connecting directly with property owners. A veteran Silicon Valley entrepreneur, operator, and investor, Dan previously built and led the Merchant team at Postmates through its acquisition by Uber, served as President of Presto, and helped scale BrightRoll tenfold before its acquisition by Yahoo. Live from ICSC+Proptech in Las Vegas.(01:09) AI Survey with FirstAmerican (03:07) How DealGround Helps Brokers Find More Deals (06:08) Why AI Adoption Outpaces Trust (10:14) MCPs, Integrations & Fitting Into Broker Workflows

AI agents are now standard in enterprise workflows but most organizations have no idea they've created a governance time bomb. When agents connect to Salesforce, Snowflake, Tableau, and a dozen other systems through separate MCPs, the result is a fragmented audit trail with no single record of who accessed what data, under whose authorization, or how an answer was generated. Under GDPR, HIPAA, SOX, and the EU AI Act, that's big regulatory exposure. Join Shawhin Mosadeghzad and Terrence Sheflin as they examine why multi-MCP architectures create compliance gaps that traditional data governance tools weren't built to address, and why the fix has to happen at the access point. We'll discuss how a single governed semantic layer acts as the sole MCP for AI data access, enforcing row- and column-level security, logging every query with full user attribution and deterministic lineage, and satisfying the individual attribution requirements regulators are actively closing in on without sacrificing architectural control or LLM flexibility.

Matt Putra runs Eightx, a fractional CFO firm for consumer goods companies. He is not a marketer. He is not technical. And in about 60 days, using Claude, he took his site from roughly 3,000 impressions a day to 76,000 — indexing 20 blog articles a day and beating "old school" SEO agencies who don't even know it's happening.In this episode, Matt screen-shares the exact marketing hub he built and walks Jordan through the whole stack: the DataForSEO audit loop, the 40+ MCP data connections, the parallel-agent writing pipeline, the Pinecone vector database of three years of client calls that clones his voice, and the PR/backlink play he's running next. If you own a marketing agency and you're not doing this yet, consider this your wake-up call.The uncomfortable takeaway for agency owners: a CFO with no marketing background is out-executing full SEO agencies — because he's willing to spend the tokens and do the volume no agency will.What You'll LearnWhy AEO/GEO (answer/generative engine optimization) is a wide-open gold mine right now, and why most SEO agencies are still playing the old gameThe exact starting point: rebuild the site with Claude, hand Claude a DataForSEO key ($200/mo), and knock out the audit listHow to run competitor audits and 45-day SEO sprints that finish in 9 daysThe technical-SEO checklist Claude gave him: llms.txt, llms-full.txt, robots.txt, and schema (FAQ, datasets, breadcrumbs) as "formatting for AIs"How to index 20 articles a day without getting flagged as thin/templated contentThe content pipeline: 40+ MCPs into free data sources (SEC EDGAR, census.gov, World Bank, OECD, national stats) for genuinely valuable postsDynamic workflows / parallel agents — up to 50 agents writing and reviewing at once (~5–6M tokens per 10 articles)The voice engine: a Pinecone vector DB of 3 years of Fireflies call transcripts (~10–12M words), with oversized chunks for richer retrieval, so Claude "interviews" his own past words for every articleWhy PR/backlinks (Forbes, WSJ) are the next authority moat for GEO — and how he's using HARO/Connectively to startThe real economics: ~$400/mo in tokens (Claude Max 20x) against $6–8K CFO retainersChapters / Timestamps — Intro: who is Matt Putra and what is Eightx — The "marketing hub" and why visibility is the whole game now — Two months in: crushing competitors in the LLMs and GEO — Tour of the hub (PR, blog, news signals, trends, Reddit, newsletter, socials) — The #1 ROI channel: the native blog — How a non-technical founder learned SEO from scratch (a Hampton tip + Claude) — DataForSEO for $200/mo, and a 45-day sprint finished in 9 days — The results: April to July, ~3K to 76K impressions/day — Capturing the traffic: RB2B, Apollo, and the NoLeadLost widget — Technical SEO: llms.txt, robots.txt, and schema for AIs — Indexing 20 articles a day (and why it takes ~30 days to start) — Getting Claude to write content Google actually indexes — The writing pipeline and 40+ MCP data sources — Show-and-tell: Claude Code in VS Code — The cost: Claude Max 20x, ~$400/mo in tokens — Building skills and reviewing the early (bad) drafts — Dynamic workflows: up to 50 agents in parallel — CFO retainer economics that justify the spend — The voice engine: a vector DB of 3 years of client calls — Chunking theory and RAG, explained simply — Channel #3: PR and GEO via Connectively (HARO) — The next play: backlinks and a Forbes/WSJ authority moat — Where to find Matt + wrapTools & Resources MentionedClaude Code (in VS Code) — the primary build environment for the whole systemClaude Max 20x — the subscription tier covering ~$400/mo of token usageDataForSEO — pay-per-call SEO data ($200/mo) handed to Claude via APIPinecone — vector database storing 3 years of call transcripts for voice/RAGFireflies — call recording/transcription source for the voice databaseNetlify — site hosting (migrated off WordPress)Connectively (formerly Featured / HARO) — PR question sourcing for backlinksApollo / RB2B — visitor identification and enrichmentNoLeadLost.com — the ~$99 site widget Jordan recommends for booking callsMCP data sources — SEC EDGAR (10-Ks/8-Ks), census.gov, World Bank, OECD, national statistics officesConcepts worth Googling: AEO, GEO, schema markup, chunking, RAG, dynamic workflowsConnect with Matt PutraFirm: Eightx — fractional CFO for consumer goods companiesBlog (called a "gold mine" in the episode, with a chatbot trained on all his content): eightx.co/blogWant help building this or something like it for your agency?Go to 8figureagency.co to book a call

Shownotes STF ajusta tese sobre responsabilidade de redes sociais Segurança Legal – #395 – A inconstitucionalidade do art. 19 do MCI ConJur, “Decretos das plataformas digitais: o que muda, o que avança e o que ainda preocupa” Após alerta do ChatGPT, FBI avisou polícia brasileira sobre plano de pai matar filho para não pagar pensão no ES The Hacker News — Fake AI Agent Skill Passed Security Scans The Next Web — Fake AI agent skill bypassed every scanner Cybernews — Researchers hijack 26,000 AI agents The Register — Mythos discovers ‘Squidbleed’ The Hacker News — 29-Year-Old Squid Proxy Bug ‘Squidbleed’ Cyber Press — Squidbleed discovered with Claude Mythos Preview Imagem do Episódio – Composição VII – Kandinsky

Live at Cannes, Ari Paparo sits down with René Raiss, Founder of SQREEM, to discuss how the company's Large Behavioral Model (LBM) analyzes real-world behavior instead of language to predict intent, improve advertising performance, and power AI-driven insights across industries. Takeaways- Why SQREEM built a Large Behavioral Model instead of a Large Language Model.- How behavioral data provides deeper consumer insights than keywords alone.- Why AI models, not proprietary data, are becoming the competitive advantage.- How brands use SQREEM to improve targeting and lower customer acquisition costs.- Real-world applications of behavioral AI across media, healthcare, finance, and government.- How AI libraries and MCPs are replacing traditional software platforms.- - - Why understanding intent is the future of marketing and advertising. Chapters 00:00 Introduction to René Raiss and SQREEM00:49 The Story Behind the SQREEM Name01:20 What Is a Large Behavioral Model (LBM)?02:11 Why Consumer Behavior Matters More Than Keywords04:21 How SQREEM Collects and Models Behavioral Data05:44 AI Libraries vs Traditional Marketing Platforms06:48 Using Behavioral AI for Better Ad Targeting07:31 Campaign Performance and Lower CPA Results08:11 Applications Beyond Advertising and Marketing09:11 MCPs and the Future of AI Workflows09:43 SQREEM's Growth Strategy and Competitive Advantage10:24 Why the Model Matters More Than the Data11:14 Lightning Round and Closing Remarks Learn more about your ad choices. Visit megaphone.fm/adchoices

This Episode is Sponsored by StayFi Your ultimate tool for Vacation Rental WiFi marketing allowing you to collect guest emails automatically via custom captive WiFi login splash pages. Drive repeat direct bookings and convert your OTA bookings to book direct for their next visit. Visit https://stayfi.com/vrsuccess/ and use code VRSUCCESS for 50% off 3 months of StayFi service. _________________________________________________________________________________________________________ Jodi Bourne is back for the latest installment of the new regular segment with Heather, built around a simple premise: AI is moving fast, and the two of them are going to keep working through it together, out loud, for listeners who want to come along. This conversation goes deeper into the practical mechanics of working with Claude. Heather and Jodi talk through connectors (MCPs that link Claude to tools like Gmail, Google Drive, Asana, and accounting software), skills (saved, reusable instruction sets that replace the old habit of copying and pasting prompts), and what both of them call their AI business brain - a structured foundational document that teaches Claude who you are, what you sell, and how you sound, before you ask it to produce anything. You'll come away with a clear starting point: build the foundation first, connect the tools you already use, and create one simple skill - Jodi's suggestion is a daily "morning coffee" briefing - before trying to do anything more ambitious. Key Takeaways AI output defaults to generic. The fix isn't a better prompt - it's a structured foundation document (Jodi calls hers the Hospitality Brand Bible; Heather calls hers her Business Brain) that teaches the model your business, voice, and audience before you ask it to create anything. Building that foundation properly is not a five-minute job. Heather recommends setting aside the better part of a day and using reverse prompting - asking Claude to interview you, question by question, until it has a full picture of your business. Connectors (MCPs) link Claude directly to the tools already in use - Gmail, Google Drive, Google Calendar, Asana, accounting platforms - so requests can be carried out end to end instead of copying information back and forth manually. Skills replace the old habit of maintaining a library of saved prompts. A skill is a reusable, named instruction set that automatically pulls in the right reference documents and brand voice without being told to every time. The recommended first skill for anyone starting out is a daily briefing - a "morning coffee" routine that summarizes email, flags anything unanswered, and reviews the calendar - because it is simple, immediately useful, and teaches the basics of how skills work. AI will hallucinate and occasionally get things wrong with total confidence. Both hosts were emphatic that nothing goes out the door - a guest bio, an email, an Instacart order - without a human checking it first. ________________________________________________________________________________________________________________________________________

In this episode of Business Brain, we dig into the question of who really controls AI. We trade notes on Anthropic’s new Mythos model and its Fable guardrails — including a jaw-dropping account of how relentlessly capable these tools have become — and we wrestle with the bigger issue lurking underneath: when AI decides what we can and can’t do, who’s holding the keys? We talk search-engine parallels, data retention, the push for government oversight, and why locally run, private AI might be the move for protecting our business data while still tapping the power. Then we get practical with MCPs — Model Context Protocol — and why this might be the easiest upgrade we can make to how we work. No fussy API tokens, no burning through credits letting AI drive a browser. We share real wins: connecting analytics dashboards, newsletter platforms, and entire email inboxes so our AI can summarize, draft, and act on our behalf. It’s platform-agnostic, dead simple to set up, and a genuine game-changer — exactly the kind of leverage that keeps us building the Charmed Life. 00:00:00 Business Brain – The Entrepreneurs' Podcast #763 for Casual FridAI, June 19, 2026 June 19th: Juneteenth and National Martini Day 00:01:44 AI Censorship Fable/Mythos is relentlessly good! Dave (unintentionally) hit Fable's cybersecurity guidelines 00:12:45 SPONSOR: Bitdefender. Keep your small business safe with Bitdefender Ultimate Small Business Security. Save 30% when you go to https://bitdefender.com/BRAIN 00:14:26 SPONSOR: OneSkin. Born from over a decade of longevity research, OneSkin's OS-01 Peptide is proven to target the visible signs of aging, helping you unlock your healthiest skin now and as you age. Get 15% off OneSkin with the code BRAIN at https://www.oneskin.co/BRAIN  #oneskinpod #ad 00:16:38 David-China turns on underwater datacenter 00:17:32 MCPs – Model Context Protocols Claude Cowork is becoming my primary email agent Fastmail Official MCP 00:21:44 This Episode's Big Takeway: MCPs are easy to implement, connect your AI to more things than you realize 00:23:00 Business Brain 763 Outtro Check out Business Brain Blueprints Tell Your Friends! Business Blueprints Review Business Brain Subscribe to the show feedback@businessbrain.show Call/Text: (567) 274-6977 X/Twitter: @ShannonJean & @DaveHamilton, & @BizBrainShow LinkedIn: Shannon Jean, Dave Hamilton, & Business Brain Facebook: Dave Hamilton, Shannon Jean, & Business Brain The post FridAI – AI Censorship and MCPs – Business Brain 763 appeared first on Business Brain - The Entrepreneurs' Podcast.

The skills problem isn't going anywhere — it's just wearing new clothes. In this episode, I unpack how the lessons we learned decades ago (limiting work in progress, the theory of constraints, test-driven development) are coming roaring back as the fundamentals that will carry you through the agentic shift. The bottleneck has moved, and knowing where it went changes how you should work. A lot of what we're learning about building with agentic tooling isn't new at all — it's a re-emphasis on lessons software engineers learned twenty years ago, just arriving in a new form. In today's episode, I walk through why the fundamentals are becoming more important than ever, why so many of us feel scattered despite having the most powerful tooling we've ever had, and where the real bottleneck in software delivery has quietly moved. My goal isn't to convince you that your job is now babysitting AI — it's to show you which parts of the work are still squarely yours, and how older principles can make you faster and more confident right now. Limiting Work in Progress Is Back: Just because you can spin up fifty agents doesn't mean you should split your focus across fifty things. Orchestrated fan-outs are powerful, but a human juggling agents across hiring, on-call, and a project all at once still pays the same old context-switching tax — and the quality drops while the speed never improves. Work Deeper, Not Wider: Instead of spreading yourself shallowly across more tickets, run multiple sessions on the same domain. Write a competing or adversarial version that critiques your assumptions, develop better documentation, or capture what you're learning as a reusable skill. Depth beats breadth. The Scattered-Engineer Epidemic: Engineers are burning out faster, not slower. We have the capacity to push more through the pipeline, so we're getting handed (or choosing) more than we can carry. Reducing parallelism often holds your delivery speed steady while dropping your cycle time and raising quality. The Theory of Constraints, Revisited: Treat your software development lifecycle as a pipeline with a bottleneck — and if you can't find one, you've optimized one part too far. Writing code used to be the choke point, so we spent enormous energy de-risking work before it ever reached an engineer. The Bottleneck Has Moved: When production gets cheap, it's no longer worth heavily de-risking upstream — which is why engineers are picking up more experimental, proof-of-concept, discovery work, and product folks are prototyping with these tools too. The new constraint isn't writing the code; it's verifying the agent didn't ship something broken. Verification Scales With Your Effort: The more an agent produces, the bigger the pile of PRs, MRs, and outputs waiting on human review. That backlog is the new bottleneck — and skepticism is creeping in because we're not even sure our tests are sufficient to verify what the agent built. Why TDD Fits This Moment: The honest question isn't "Can I trust the agent?" — it's "What verification loop do I need to build so I can trust it more?" Clear requirements feed a clear testing loop: write the failing test, let the agent write the code to turn it green, and you bridge the gap between requirements gathered and requirements met. It's not as simple as "go write a test," but it's a strong fit for where we are right now. Episode Homework: Go dig into the fundamentals — limiting WIP, the theory of constraints, test-driven development. Find the old lesson that still applies to your workflow today, bring it to your team's flow, and email me about what you discover.

In this episode, we explore the functionalities of MCPs and how they enhance the capabilities of AI tools like Claude and ChatGPT. We also discuss the differences between MCPs and APIs, share practical use cases, and highlight some of the most effective MCPs available for maximizing your AI integrations.Chapters00:00 Introduction to MCPs02:00 Understanding APIs vs. MCPs03:59 Setting Up MCPs Easily09:58 Top MCP Tools and Recommendations15:01 Benefits of Using MCPs Show LinksGet the AI Box MCP: ⁠⁠https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we explore the functionalities of MCPs and how they enhance the capabilities of AI tools like Claude and ChatGPT. We also discuss the differences between MCPs and APIs, share practical use cases, and highlight some of the most effective MCPs available for maximizing your AI integrations.Chapters00:00 Introduction to MCPs02:00 Understanding APIs vs. MCPs03:59 Setting Up MCPs Easily09:58 Top MCP Tools and Recommendations15:01 Benefits of Using MCPs Show LinksGet the AI Box MCP: ⁠⁠https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the functionalities of MCPs and how they enhance the capabilities of AI tools like Claude and ChatGPT. We also discuss the differences between MCPs and APIs, share practical use cases, and highlight some of the most effective MCPs available for maximizing your AI integrations.Chapters00:00 Introduction to MCPs02:00 Understanding APIs vs. MCPs03:59 Setting Up MCPs Easily09:58 Top MCP Tools and Recommendations15:01 Benefits of Using MCPs Show LinksGet the AI Box MCP: ⁠⁠https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Montgomery County, MD, pauses the processing of data centers permits for six months, Student journalists in MCPS protest a new rule that requires prior review of all school-sponsored publications. We have crowd-sourced data on MCPS schools affected by HVAC failures. And Delegate Kris Fair from Frederick County has an explainer for what happens with the replacement ballots Maryland had to send out. And more. Music from Seth Kibel's brand new album, Clarinet Without A Net.

The skills that survive every industry shakeup aren't the ones you can Google — they're softer, harder to name, and far more durable. In this episode, Jonathan explores principle-oriented thinking: the practice of stripping away the labels we attach to tools, roles, and even ourselves to see what something actually does at its core. It's the difference between handing your coding off to an agent and rethinking your entire workflow around what these new materials are truly capable of. If you've been following along with our recent focus on durable skills, you know we've been hunting for the abilities that translate beyond this month, this year, or whatever AI does to our industry next. Today's skill doesn't have a tidy name you can search for — it's softer than that. Jonathan calls it "principle-oriented thinking": the habit of deconstructing the labels we put on things to understand their core components, properties, and capabilities. It's how NASA engineers turned a sock into a water filter on Apollo 13, and it's how forward-thinking engineers are reframing what AI can actually do rather than jamming it into a predetermined slot. Labels Are Useful Shortcuts — Until They Aren't: Every label, from "software engineer" to "sock," carries baggage, heuristics, and presupposition. That's not a flaw — labels are how we move through the world quickly. But when a label is the only lens you have, it quietly caps how much value you can get out of the thing you're looking at. The Apollo 13 Sock: When the crew needed to fix a life-threatening problem with mismatched parts, the engineers on the ground had to forget what a sock was for and ask what it actually is — a piece of cloth with tensile strength, flexibility, and filtering properties. Strip the assumption that it goes on a foot, and a whole new set of uses opens up. Stop Slotting AI Into Old Roles: The common move is to take one responsibility — coding, debugging, refactoring — hand it to an agent, and keep everything else the same. That works, but it's low-leverage. The more powerful approach starts by asking what the agent is fundamentally capable of, then rebuilding the workflow around those raw materials. See Things as Materials, Not Fixed Functions: When you deconstruct out from under a label, tools and concepts start to look like craftable raw materials. You can then combine them in new, valuable ways they haven't been combined before — alloying old methods with new capabilities to create properties neither had on its own. Reason From Properties, Not Personas: Ask what the actual properties of an LLM are. Non-determinism isn't a bug to apologize for — it's a property you can exploit. The existence of many different models is a property too, which is exactly what makes adversarial review possible. That's principle-oriented thinking applied to agents. Extend the Latticework: Charlie Munger talked about a latticework of mental models that weave together rather than sit in isolation. The durable skill isn't quarantining your concept of "AI" off to the side — it's grafting a new section onto the existing tapestry and letting it reshape everything you already understood. Episode Takeaway: Look at how you spend your time and ask new questions of it. What is the material here? What kind of thinking does the agent actually do? What can a human do that an LLM can't — and the other way around? That's how you avoid believing a sock is only ever good for a foot.

I've been seeing a recurring pattern with companies selling APIs, MCPs, data feeds, and other developer-focused AI products. While the technology is often sound if not impressive, sales momentum sometimes slows when prospects have to imagine how the product will create value in their own environment. My perspective on this is that the flexibility that makes these tools powerful can also make them harder to evaluate. Flexibility can adversely increase the Invisible Intelligence Gap, and I think certain types of AI-based solutions (LLM) may actually increase this because the boundaries of the product are often so much wider than ever before (if not invisible to the buyer). So, how to close this gap? Well, one way is to build a visual UI that showcases what's possible with your API/feed/data solution. You take the buyer out of the conceptual space and make things concrete. So today, that's what we dig into: when to consider adding a UI, how far you need to go with it, how you can use Copilot/AI agents to help customize these example implementations, and the benefits you might see.  Highlights / Skip to: The challenges of selling API-based analytics and AI products (0:56)  Why this topic matters right now (2:48) The Invisible Intelligence Gap that may be slowing your sales (3:34) Strategies for bridging the Invisible Intelligence Gap with a UI (user interface) layer (7:01) Client case study: the impact and results you may see adding a UI on top of your technical product (14:05) Signs that you should consider adding UI to your technical product (18:23) Leveraging humans' highly developed visual system to help potential customers see the full value of your product (26:24) Conclusion (27:32) Links Invisible Intelligence Gap Azeem Azhar's Exponential View (6/4/26 episode)  

DMV Hoops Podcast – Episode 107

DMV Hoops Podcast – Episode 109

Interest in macrocyclic peptides (MCPs) continues to grow, which means manufacturers are facing mounting pressure to develop production methods capable of supporting commercial-scale demand of these molecules. While they offer a unique combination of potency, selectivity, and drug-like properties, the structural complexity of MCPs has historically made them difficult and costly to manufacture using traditional peptide synthesis techniques. As a result, new manufacturing approaches are emerging that aim to improve efficiency, scalability, and sustainability while expanding access to this promising class of therapeutics. In this episode of Off Script, we spoke with David Thaisrivongs, executive director, head of biocatalysis at Merck, about research recently published in Science detailing a biocatalytic manufacturing process for enlicitide, an investigational oral macrocyclic peptide designed to lower LDL cholesterol. The conversation explores the limitations of conventional solid-phase peptide synthesis, how Merck leveraged enzyme-driven manufacturing and crystallization strategies to significantly reduce process complexity, and why minimizing chromatography can be critical for commercial-scale peptide production. He also discussed the broader implications of biocatalysis for manufacturing increasingly complex therapeutic modalities and how the technology could help shape the future of pharmaceutical production.

We dedicate an episode to catching up on appsec news with Kalyani Pawar. We see parsing problems that led to the BadHost vuln, which exposed lots of LLMs, MCPs, and agents to potential compromise. We wonder where to look for security education and practice as the camaraderie of the CTF community becomes infiltrated by LLMs. We talk about the tradeoffs in trust between using public packages vs. having agents write replacements from scratch. And we examine some of the appsec details that the Verizon DBIR reveals about how orgs are being attacked -- and how orgs might use that information to protect themselves. Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-385

DMV Hoops Podcast – Episode 103

Affordable Maryland PAC's ad attacking Will Jawando's record on education leads to push back. PAC Chair Jonathan Robinson joins us. The Montgomery County Board of Education is going to vote on a long list of position cuts this week and MCPS school psychologist Alli Jacobus and parent Rachel Singer join to talk about the impact in one department. MCPS College and Career Navigator Sarah Kessler (whose position is also on the chopping block) joins to talk about University of Maryland's Fall 2026 admission numbers and clear up some common misconceptions about who is admitted and who is not. Music by Silver Spring rock musician MYSTR Treefrog.

Right now, the questions we have about our careers feel existential. We keep coming back to the same theme: how do you prepare for an industry that's changing this fast, and what mindset actually works in this new reality? One skill keeps surfacing as the answer — your ability to update your own mental models. In today's episode, I want to push on that further and put some of software engineering's most beloved thinking models under scrutiny. Some of these models served you well for years. Some of them now deserve to be challenged, replaced, or thrown out entirely — and learning how to tell the difference is itself the skill that will determine whether you hit a ceiling. Move Past "So What" Questions: The typical engineering objection to agentic coding is that it produces quality issues. But the people deciding to adopt these tools already accept that. Our job is to stop arguing the surface-level point and start asking the real one: so what do we actually do about this new economic reality? The Economics of Acceptable Loss: Abstraction always leaves something to be desired. An agent's code may not match what a staff engineer produces by hand over months — but that gap is usually an acceptable trade against shipping something two, three, or four times faster. Understand the cost-benefit picture instead of pretending the cost doesn't exist. Abstraction Has Always Done This: This isn't new. The calculator dissolved the specialization once required for complex math. Spreadsheets commoditized ledgering and accounting. Agentic coding is the same pattern arriving for our work — making something that required deep specialization suddenly far more accessible. Roles Are Blurring: As these generic tools raise everyone's ability to abstract, the boundaries soften. You're already seeing product managers open pull requests and engineers making product decisions. The neat lines around "what an engineer is" are not as fixed as they used to feel. Why Your Hard-Won Wisdom Is the Target: If you've spent years in this industry, your models were bought with blood, sweat, and failed projects. That experience is real wisdom — and it's exactly what I'm asking you to be willing to challenge, because the thing that always worked for you is the thing most likely to become a ceiling. This Skill Survives Either Way: Even if you think AI is mostly hype and I've been infected by it — fine. The ability to challenge your pre-existing models is a critical skill regardless. It's how you keep growing as you get more senior instead of repeating what used to work. Models Are Approximations: The whole point of a model is to approximate the reality around us. That's their value and their limitation. When the underlying reality shifts this dramatically, holding tightly to an old approximation stops being wisdom and starts being a liability.

In this sponsored bonus episode, NLW is joined by Atlassian co-founder and CEO Mike Cannon-Brookes for a conversation about how to build AI native teams. They discuss what separates enterprise AI leaders from laggards, why context is becoming a critical layer of AI adoption, how agents and MCPs are changing the way people work with software, and why 2026 may be the year AI moves beyond chat into more natural product experiences. This episode is presented in partnership with Atlassian, and includes a companion quiz to help you find out what kind of AI team you are.Sponsored by Atlassian https://www.atlassian.com/Find our what kind of team you are: The AI Native Team Quiz - https://play.aidailybrief.ai/episodes/ai-team-archetypes/