Behind Our Largest Subscription Revenue Increase
Description
Yash Poojary, a growth engineer at Every, dropped an idea for a campaign in Slack at 7 p.m. Instead of building it himself, Every’s head of growth Austin Tedesco took a screenshot of the Slack thread, dropped it into Codex, typed "Can you do this?", and went to the gym. By the time he got back, Codex had built four audience segments, drafted emails for each one, and pulled a social image that had worked before. It took Austin 10 minutes to make some tweaks and schedule the whole thing to send the next morning. Within a few hours, it generated more than $25,000 in revenue. That story came out of the launch week for All Access, Every’s new $625-a-year membership built around the Builder Pack. It includes $7,000 in credits and free usage from ten of the AI products Every uses every day, including Claude Max, Codex, Cursor Pro+, PostHog, Notion, Framer, Render, and Flora. On this episode of AI & I, four of Every's own builders—COO Brandon Gell, head of marketing Douglas Brundage, as well as Yash and Austin—sit down to show how they use AI, breaking down their personal stacks and giving insight into their own strategies and mindset for building. If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Timestamps for YouTube: 0:00 Intro 0:35 All Access Explained 3:01 Yash's Tech Stack and How He's Automating Testing Pipelines 8:02 The Idea to Execution Loop 10:25 How an Agent Turned an Idea into $25K 17:50 The AI Sandwich Workflow 22:03 Making AI Tools Accessible to Solo Builders 28:50 Douglas on Brand and Design 34:51 Tips on What to Build First 43:46 What's Next for All Access Links to resources mentioned in the episode: Brandon Gell on X: https://x.com/bran_don_gell Yash Poojary on X: https://x.com/poojary_yash Austin Tedesco on X: https://x.com/tedescau?lang=en Douglas Brundage on X: https://x.c
Summary
Generated by gpt-5.6-terraAt-a-Glance
- Verdict: Watch fully
- Core thesis: Every argues that the highest-leverage AI workflow is not replacing judgment but building connected, compounding agent loops that automate execution across specialized SaaS tools while humans choose problems, set direction, and review outputs.
- Why it matters: The launch is a concrete case study in agent-native GTM: a small team converted a Slack idea into segmented, drafted, scheduled campaigns that generated more than $25,000 in revenue with roughly 10 minutes of human revision.
- Best use: Watch for reusable patterns in agent harnesses, MCP-connected tool stacks, growth automation, and the operating-model shift from doing individual tasks to orchestrating systems.
Executive Summary
Every frames its Builder Pack launch as both a commercial success and a demonstration of how its team works. The $625 annual All Access tier bundles credits and benefits from AI products including OpenAI/Codex, Anthropic/Claude, Cursor, PostHog, Framer, Render, Notion, and Flora. The company says the launch delivered its largest subscription-revenue increase to date, though it does not disclose the total increase.
The strongest operational example is a launch-day recovery campaign. After a teammate proposed emailing high-intent non-converters in Slack, Austin fed a screenshot of the discussion into Codex, invoked a compound-engineering harness, and left for the gym. The agent created four audience cohorts in Kit, drafted tailored emails, applied Every's approved Spiral writing style, reviewed historical email performance, selected a previously effective image, scheduled the sends, and posted the plan for team review. After light edits, the campaign generated more than $25,000 in revenue within a few hours the following morning.
The team’s working model is the “AI sandwich”: humans define the problem and desired outcome at the top, agents execute across connected systems in the middle, and humans evaluate and steer the result at the bottom. Their broader claim is that automation should eliminate repetitive setup work—such as configuring experiments, managing dashboards, maintaining Notion, and assembling first-pass video edits—so skilled people can spend more time on hypothesis selection, creative direction, and high-value iteration.
The video is also a product pitch, and its advice is directional rather than a rigorous implementation guide. Still, it offers useful practical lessons: connect agents to best-in-class systems rather than rebuilding every category internally; codify repeatable processes as skills and harnesses; start with a personally meaningful build; deploy it; and use parallel agents and multiple models to develop model-specific judgment.
Key Takeaways
- Claim: Connected agent workflows can compress an end-to-end GTM task from an idea into executed, revenue-generating work without requiring a human to manually operate each SaaS interface. | Evidence: For the Builder Pack launch, Codex turned a screenshot of a Slack proposal into four Kit cohorts, four tailored emails, Spiral-style copy, a historically effective social image, scheduled sends, and a Slack review post; after approximately 10 minutes of human edits, the emails produced more than $25,000 in revenue the next morning. | Implication: Ken should treat AI GTM agents as a supervised execution layer for known playbooks—especially segmentation, drafting, asset selection, scheduling, and post-send follow-up—not as an unsupervised source of campaign strategy. | Caveat: The team explicitly notes that a "$25,000" result does not happen every time; the outcome depended on a time-sensitive early-bird offer, launch intent data, existing email history, and an established stack with integrations.
- Claim: The desired human role is to operate at the top and bottom of the “AI sandwich”: select and frame the problem, then review and redirect the agent’s work. | Evidence: Yash wants to automate A/B-test mechanics such as audience sizing, cohort progression from 10% to 50%, and dashboard configuration, while retaining decisions about what hypothesis to test, who should see a homepage takeover, and whether results justify implementation. | Implication: Prioritize automation candidates where execution is repeatable and criteria can be expressed, while reserving customer psychology, strategy, prioritization, and quality judgment for human operators. | Caveat: This model only works after the workflow has been sufficiently specified; Douglas says teams must first do the metacognitive work of identifying and codifying how they think and operate.
- Claim: The advantage comes from persistent, compounding operational context rather than from a single clever prompt. | Evidence: Austin describes months of incremental improvement to email automation, while agents increasingly understand the project through a compound-engineering harness. He says newer models improved at retrieving needed context across Notion, Slack, and PostHog and retaining it through long-running work and context compaction. | Implication: Ken should invest in durable project context, reusable skills, review loops, and telemetry before expecting agents to execute broad mandates reliably from sparse instructions. | Caveat: The transcript supplies no independent benchmark for the claimed model-version improvement or for the reliability of long-running agent context.
- Claim: Agent-native work favors using specialized systems through MCP/API connections rather than rebuilding commodity software from scratch. | Evidence: The speakers say they often interact with PostHog, Notion, Framer, Flora, Descript, and Kit through Claude or Codex rather than directly in each interface. They describe a failed instinct to build their own CRM, dashboards, and design systems before concluding that trusted category products should remain systems of record. | Implication: For Ken’s control-plane approach, retain authoritative tools for analytics, knowledge, CRM, deployment, and creative production; build orchestration and policy layers around them rather than replacing their core capabilities. | Caveat: This depends on the target systems exposing reliable MCP/API access and on the organization granting agents appropriately scoped permissions.
- Claim: Parallel cloud agents and model routing can turn model selection into a practical, personal evaluation process rather than a one-time vendor choice. | Evidence: Yash uses Cursor cloud agents to spin up isolated work, mentions running up to 10 agents, and recommends trying different models—including Cursor’s Kimi K2.5-based offering—to determine which model fits a person’s tasks and preferences. He distinguishes his Claude preference from colleagues’ Codex preference. | Implication: Set up repeatable internal eval tasks and allow model routing by workflow; the relevant question is not which frontier model is universally best, but which model-plus-harness combination performs best for each operation. | Caveat: The discussion is anecdotal and does not provide task-level evaluation criteria, cost comparisons, or quality measurements across models.
- Claim: AI expands creative and nontechnical operators’ scope by letting them orchestrate research, brand strategy, and production, but it does not remove the need for taste. | Evidence: Creative director Douglas uses custom marketing skills for competitive audits covering pricing, language, and visual identity; he uses Flora to analyze references and generate visual directions. Separately, Fable plus Descript MCP generated a transcript-derived storyboard, script, and rough edit that Austin estimates got video work about 70% of the way to usable. | Implication: Use agents to accelerate research, synthesis, first drafts, and production assembly, while keeping senior creative and strategic review accountable for the insight, aesthetic standard, and final release. | Caveat: The team says generated video was not publishable without human intervention, and Douglas says models do not consistently originate strong insights even if they can help validate one.
- Claim: The best onboarding path for AI-native building is a concrete project with personal pull, rapid deployment, and real user feedback—not abstract tool exploration. | Evidence: The team recommends duplicating a beloved product as an MVP, rebuilding a portfolio site, or making an app one would be excited to text friends about. One speaker built a live indie-film discovery app despite having no prior experience and learned deployment, OAuth, design, and iteration while trying to make it real. | Implication: For team adoption, give operators bounded projects tied to real usage and feedback, then layer production controls after they demonstrate a working loop. | Caveat: The advice is optimized for learning and motivation, not for production-grade security, reliability, or commercial validation.
Detailed Brief
Builder Pack launch economics and positioning
- Claims: Every positioned All Access as a $625 annual tier whose main value is the Builder Pack: 10 benefits from tools the team says it uses daily, plus office hours, unlimited Quora and Spiral use, and planned additions.; The sales argument is that individual builders face a meaningful access barrier because serious AI experimentation can be expensive without employer-funded credits.; The product is positioned as both discounted access and a guided path into Every’s preferred stack.
- Evidence: Benefits cited include $1,000 in Codex overage credits, a free OpenAI/Codex period, 12 months of Cursor access, and offers involving Anthropic, Gemini, PostHog, Framer, Render, Notion, and Flora.; A speaker recounts meeting an unemployed engineer who was not using AI and was discouraged after hearing that one engineer had spent $30,000 on AI in the preceding month.; Every plans to add partners, publish per-tool guides, and run camps; one speaker says the preferred consumption mode is to drop a guide into Codex and ask it to apply the guidance to the next project.
- Caveats: The episode is produced to promote Every’s All Access offering, so tool selection and value claims are not independent recommendations.; Benefit values, duration, and partner availability may change; the discussion itself contains a correction about whether a Codex-related benefit lasts one or three months.
- Implications: The commercial packaging insight is to sell a curated stack with activation support, not merely a collection of SaaS discounts.; If Ken evaluates similar ecosystem partnerships, measure activation into real workflows and retained usage—not nominal retail credit value.
Examples of work moved from manual interfaces into agent loops
- Claims: Yash sees manually setting A/B-test audiences, monitoring thresholds, and configuring analytics as "fake work" when agents can manage the mechanics.; Notion becomes more usable when its organizational upkeep is delegated to agents rather than imposed on every team member.; Automation can preserve deep-work time by preventing smaller valid ideas from becoming engineering interruptions.
- Evidence: Yash rebuilt Every’s Sparkle product roughly 15 times using Claude and PostHog, and uses custom skills to automate growth work.; Austin asks Codex weekly to reorganize Every’s growth hub and maintain database structure in Notion.; Yash cites quick experiments such as testing website headlines, paywall timing, and homepage takeovers as work that should be executable without hiring an engineer for each request.
- Caveats: The transcript does not explain the permission model, approval gates, rollback procedures, or observability used for agents making changes across production marketing systems.
- Implications: The missing layer in the case study is governance: production agent systems need explicit authority boundaries, pre-send/pre-deploy review, audit logs, and rollback paths alongside speed.
Notable Concepts & Terms
- AI sandwich: A division of labor in which humans define the task and review outcomes, while AI handles the execution layer between those two points.
- Compound engineering: An iterative approach in which agents work inside a growing project context, reuse prior lessons and skills, self-plan and review, and improve the workflow over time.
- Slash LFG: Every’s stated compound-engineering command or harness that instructs an agent to brainstorm, plan, execute, review, and compound its work.
- MCP: Model Context Protocol connections that allow a model to access and act through external products such as Framer, Descript, Notion, and Flora.
- Cloud agents: Remote, isolated coding agents that can run work in parallel without disrupting a user’s local development environment.
- Model routing / personal model taste: The practice of selecting models by task and operator preference rather than standardizing on a single model; Every’s speakers contrast Claude, Codex, and Cursor-hosted models.
- System of record: A specialized SaaS platform such as PostHog or Notion that remains authoritative even when users primarily access it through agents.
- Orchestration: Douglas’s metaphor for AI-enabled creative and operational leadership: directing many tools and capabilities toward an intended outcome without personally performing every specialized task.
Operator Notes / Why Ken Should Care
- Pilot a supervised revenue-recovery agent: ingest intent events, segment non-converters, draft variant-specific emails from approved voice and performance history, require Slack approval, then log outcomes for the next run.
- Define a small set of reusable agent harnesses for recurring work—research, campaign assembly, experiment setup, content production—and require each to include planning, tool permissions, review criteria, and a post-run learning artifact.
- Audit MCP/API-connected systems for least-privilege access before permitting agents to schedule communications, modify knowledge bases, or deploy changes.
- Create task-specific model evaluations using actual company work; test quality, cost, latency, tool-use reliability, and correction burden rather than relying on broad model rankings.
- Avoid rebuilding mature operational systems solely because agents can generate code; use agents to query and orchestrate trusted systems of record unless a proprietary capability is strategically necessary.
Source/Metadata
- Title: How Every's Team Used AI to Ship Its Biggest Launch Ever
- Transcript words: 8101
- Duration seconds: 2790
- Timestamp note: No timestamps or chapter markers were present in the supplied transcript.
Transcript
We generated the largest subscription revenue increase in Everest history last week. We did it by launching this thing called the Builder Pack. We partnered with a bunch of the AI tools we use every day, and subscribers to the Builder Pack get discounts and credits to those tools. So $1,000 in Codex credits, 12 months of free Cursor access, all that stuff. The Builder Pack is awesome, but I think the actual story behind it, how we did it, why we did it, how it generated so much revenue, is really interesting. For this episode, I had the team who worked on the Builder Pack. Everything from conception all the way to the full execution launch. Come to the show to share how we did it and what we learned. I hope you like it. Hello, everyone, and welcome to AI&I. This is a slightly different version of the pod today, where we've got myself, Yash, Austin, and Douglas here, and we're missing Daniel. We're going to be taking you on a journey today, introducing you to All Access and the Builder Pack. And this is a new tier at Every that we decided to launch recently. And we just want to talk about why we decided to build it, why it's important, how we use it, and what it means to be a builder today. All of us are builders and use these tools every day. So, hopefully by the end of this, there'll be one nugget that you can take out of this and use in your everyday life. So I guess to introduce us, or to introduce this pod, first I'm going to introduce All Access. We launched it Tuesday on the 14th. It was a big launch for us, a big endeavor across the whole company. All Access is a new tier. At Every, it's a $625 annual plan that gives you access to the Builder Pack, which is 10 benefits, exclusive benefits from all AI companies that you know and love that enable you to go build and adopt some of these tools in ways that you may not have been able to before. And All Access includes a couple other things. It includes access to office hours that we're hosting that are exclusive to All Access members, unlimited use of Quora and Spiral. And we have a couple other things up our sleeve that we're going to be launching in the next few months as well. But we really want to talk about the Builder Pack because that's probably the meat of All Access. And the Builder Pack is 10 soon-to-be-more exclusive benefits to AI tools that the Every team uses every day so that you can get access to these tools and use them for free for a while and get comfortable with using them in your stack. It's early access, exclusive access to the Every stack. This is the team that brought it to life. All right, Yash, how do you use the Builder Pack? Yeah. Context. I'm Yash. I run one of the... Let's just say... I'm just... Yeah. Who are you? Who am I? Well, I ask myself that every day. But I run one of the products at Every called Sparkle. And I also work on the growth team now. A large part of my work is using Claude. I know at Every we are Codex stands, but I'm still very Claude. I've been using Claude for the last one year. And this involves... I built out the entire product, I think 15, rebuilt Sparkle 15 times using Claude and PostHog. So those are the tools I reach out for the most. And recently, I joined the growth team at Every. And growth meant a lot of A-B testing, a lot of segmentation, and other stuff. And again, I started optimizing PostHog. But I find it really annoying to open dashboards and set a lot of these things up. And that's where my setup with Claude helps a lot, where I have custom skills built out. And essentially, my plan is in the next few weeks to automate the entire A-B testing pipeline. A lot of the work that I spent doing for the first month on the growth team, I'm like, this is very boring because it's very straightforward. I should be working on more interesting problems. And I should not be clicking buttons and figuring out what's the right audience size and stuff like that. Claude is so much better at that. I have to prompt it. I have to figure out, okay, what's the right audience size? When should an experiment go from a 10% A-B testing cohort to 50%? Why should I be doing that manually? I feel it's a lot of fake work. And we like to spend a lot of time observing these things, setting up an experiment every time. It felt so boring to me. I'm like, the only way I can do more experiments is by automating the entire pipeline. So I spend a lot of time... So what's interesting to you is less actually the work. The work is no longer setting up the pipeline to be able to do it. It's actually what should I test? Yeah. And then did it work? Great. Implement it. What's the next test? Yeah. I should be... I'm more interested in the ideas. Like, should we do a homepage takeover? Who should we show that homepage takeover to? Versus actually setting that up is not that interesting. We have so many experiments live right now, which is like, when should we cut off the paywall? When should we show the paywall? Those are way more interesting. And those are also more human psychology. And I can... I have more fun doing that. And I personally feel like a large part of our work at Every is everyone is very secure in what they do. And so we are okay with automating our jobs. I spent one month doing these things, and I'm like, okay, I'm done. I want to do other things now. And if it was some other organization, I'm going to try to stretch this out for a year, like what I did. You want to move on to the next thing that you can tackle. Yeah. Because that's fun. And that's why we are always at the edge of AI. We are like, yeah, this is done, guys. What's next? What's more interesting? Okay. Is there a better model for this? Yeah. So what in the Builder Pack do you use every day? Every day, it's Cursor. I use Cursor because they have cloud regions, which are really good. We did a bunch of Fable 5 optimizations because I was working on some other tasks. I did not want to mess with my setup. So cloud agents just spin off a cloud server and run there and get me the results back. And they have a really good open source model, which is really cheap, like QE Composer, which I love. So that's something I use. I use Framer a lot as well while I was building the Sparkle site. It has good MCP support. So I could change the headlines, experiment more directly without actually going into Framer and setting... Every time, it's very annoying to me. I'm setting a lot of these things up, the right font size for mobile and stuff like that. This is how you... I used to do this 20 years back. Today I should not be spending my time on these things. And I would say Claude Max is my go-to. I feel it's insane. Yeah, that's my stack. And PostHog. And PostHog. Yeah. I use a lot of these products, but it's so cheap. I feel that Claude Max subscription is really underrated. I'd pay way more for it. Right. I couldn't work without it. Right. Anthropic, I hope no one from Anthropic is watching this. Get the deal, guys. Get the Builder Pack. I like the way Yash is describing one potential approach to work, which is there's this approach to being a general manager in the NBA, which is just keep trading for future draft picks, to be like, we're on an eight-year time horizon for contention. And for someone as skilled as Yash at engineering, go-to-market engineering, and growth, you can use these tools, this stack that he has, to operate at the same level in a set-it-and-forget-it mode. And I would be like, well, Yash, I run the growth team. I look at what Yash is doing. I'd be like, Yash has another experiment, and it moves the needle by 2%. I guess he's on it. And he knows how to set up that Claude code and PostHog loop. But the way that he works instead, which is exciting, is that after he does that, he looks at how can we win right now? How can I be creative and competitive to find the next thing? To be, we're on an eight-year time horizon for contention. And there is, for someone as skilled as Yash at engineering, go-to-market engineering growth, you can use these tools, this stack that he has, to operate at the same level in a set-it-and-forget-it mode. And I would be, well, Yash, I run the growth team. I look at what Yash is doing. I'd be, Yash has another experiment. And it moves the needle by 2%. I guess he's on it. And he knows how to set up that Claude code and post-hog loop. But the way that he works instead, which is exciting, is that after he does that, he looks at how can we win right now? How can I be creative and competitive to find the next thing? And how can I use these always-on loop and architecture-based automations to make it so that Yash can do the thing that lights him up, which is I can see Yash and Slack being, oh, what if we made a three-hour time-constrained discount specifically targeted to these users using this video, building this thing in Framer? And this kind of stuff has always been the case, right? That work is the most fun way to work. It's how smart, creative people are able to differentiate themselves. But I think what the Builder Pack allows is that it allows someone like Yash to automate and defer a lot of the work to cloud agents. He would have otherwise had to manually click in post-hog or manually code himself, and then he can go be creative in the space where he has expertise. And then it also allows someone like me, who doesn't know what the fuck any of that stuff is, but is naturally more of an ideas person, to give the model and the SaaS tools in the Builder Pack my outcomes and ideas. And then just let it run, which is the craziest part for me. I let go— Oh, yeah. Can you tell that story of you had an idea for something, you went to bed, you woke up, you had four emails? Yeah. So it wasn't even my idea. We did the Builder Pack launch on Tuesday. It was going really well. I'm on the West Coast. Most of the team is on the East Coast. As they were getting ready to go to bed, Yash dropped a note in Slack saying, hey, we should send emails in the morning to a group of users who have shown some sort of checkout intent but not converted because we were running an early bird discount for the first 24 hours of launch. So it's, we should probably hit them again with some sort of urgency-backed approach. And then Yash said, this isn't actually my idea. It's Dan's idea, but I'm sharing it. And then they all signed off. And I think it was maybe 7 p.m. here. I had been in front of my computer all day. And I really wanted to go to the gym. And so I took a screenshot of the Slack because it was a bunch of different Slack messages. Usually I would just copy-paste the link to the Slack message and send it to Codex. I took a screenshot of the Slack. I dropped it in a Codex thread using 5.6 on Medium. It's usually what I operate in. And I said, can you do this? And then I did Splash LFG, which is the harness in our compound engineering plug-in to tell an agent to take itself through a brainstorm, plan, work, review, compound loop, which is, to me, the ideal way to work with agents. And then I went to the gym. And I came back from the gym. And what I saw in Codex was that it worked for, honestly, not that long. It probably finished by the time that I changed into my workout clothes at the gym because it didn't actually need to do that much. And it had created four different audience-based cohorts, targeted cohorts in kits, based on these people got to the checkout page. These people visited the BuilderPack website but didn't check out. These are our highest engaged users. There's four different types. It drafted specific emails for each of those audience types. It ran the text for the emails through the MCP for our AI-based writing app called Spiral. So it's using a writing style that we've approved. It looked through all of our previous sends in kit, both for our language style as well as what had driven click-throughs, what had driven opens. It grabbed a social share image that had driven click-throughs in a previous send and embedded it. And it had scheduled it for the next morning. And it's crazy because I wasn't, holy shit, that's so cool. I was, that's basically what it should do. My brain is so cooked on this stuff now that I'm, this is literally exactly what I expected. I think it did something. I think it then posted it in all emails. Yeah, I have an automation in there. So we have a Slack channel called All Emails. If you're a subscriber to every—I'm sorry, we send you a lot of emails. I swear we're working on it. One step toward working on it is having a channel where, one, we build all of our email sends through Codex or Cloud Code, and can have the agent push a Slack message saying, here's what's going to be sent to whom and why. And then they can get a review of, should we actually send this to this audience at this time? So it sends that as well, and the team can review it and know what's up. But I pulled up these four email sends, and I made very light tweaks, partially because I was, oh, I think I can write a slightly better headline, and also partially because I'm still a control freak who doesn't want to turn over every single thing to AI. I was, let me put a little bit of spice on this. But I maybe spent 10 minutes of extra work on it when I came back from the gym. And then we scheduled them, and we let it rip. And in a couple hours the next morning, those emails alone had driven more than $25,000 in revenue. It had driven multiple conversions. It suggested things we could do to retarget the audience that didn't convert. And two nice things happened. One, I got to go to the gym, which is great, because otherwise I would have sat there building these emails. And two, at the gym, not looking at my phone, not looking at Slack, I had other ideas for stuff we could do the next day, right? Disconnected from being in the weeds, I'm working out. And I'm, oh, yeah, we should do this experiment and that experiment, because I don't have to use my brain and my fingers to click around in post-doc, which I love using, but I don't have to click around in post-doc to find the exact right audience segment to target. So that's, it's just so crazy, to go from, here's an idea, to go do this idea using slash LFG, to actually executing on what it created, in this case, sending these emails, to generating $25,000 in revenue, which doesn't happen every time you do slash LFG, but that is a possibility, to then compounding what you learned and also recommending what to do next. It's just remarkable that 5.6 soul is expensive, but relative to the value that you got out of that, it's nothing. And best of all, you went to the gym and took care of yourself. And it's just remarkable that you can do that. If you're really harnessing these tools well for work and for life, I use it as my trainer at the gym, it can be very fulfilling and so effective. Yeah, I feel it allows you to get better at things you want to really get better at. It frees up more time. To give an example, when Austin and I, people are always pitching growth ideas to us. Hey, do this, do this. And to be really honest, those are good ideas, but I don't want to take away time from doing my main thing and spend time on that. And that entire thing should be automated because, A, the idea is good. We should not hire an engineer just to implement that particular idea. So, I feel we have these tool sets, if I have a workflow set up, hey, let's try this particular headline on our website. I use it as my trainer at the gym. It can be very fulfilling and so effective. Yeah, I feel it allows you to get better at things you want to really get better at. It frees up more time. To give an example, when Austin and I, people are always pitching growth ideas to us. Hey, do this, do this. And to be really honest, those are good ideas, but I don't want to spend, take away time from doing my main thing and spend time on that. And that entire thing should be automated because, A, the idea is good. We should not hire an engineer just to implement that particular idea. So, I feel we have these tool sets. If I have a workflow set up, hey, let's try this particular headline on our website. It should be automated. Yeah. And I feel that's the goal. I have more time to do deep work. Yeah. And be in my flow state without really compromising these things. Building that loop is me being in the flow state. Yeah. Versus, hey, try this, try this, try this. And I think the thing we're circling around is this is the best way to build right now, which is to, Kieran, our colleague, has described it this way, of you're at the top and bottom of the AI sandwich. You decide what the problem is, you frame the idea, and then you review the work. That's been the case for a while, but the exciting thing about this moment, the exciting thing about being able to use the tools in the Builder Pack, is that you can go from idea to execution quickly, easily, reliably, and at a very high level by being able to type or, with monologue, any speech-to-text tool like we use, just to be able to say the idea and have it come out. And the thing that I really like about this, the way I like working, is that whether you're primarily a Claude user like Yash or a Codex user like me, the tools all now speak together really, really well and connect to each other really well. Notion is a great example of this. I have hated Notion for years because my brain doesn't work in that way of, I'm going to organize this database and I'm going to make sure that the growth hub makes sense to other people. Yeah, yeah. I think up until a year ago, Notion had too much of a barrier to entry on organization and maintenance that anytime I try to use Notion with a team, we've churned because no one's around to maintain it. And all of that maintenance has now been pushed off to Codex and Cloud Code. Once a week, I just ask Codex, hey, can you reorganize our growth hub? And it does it perfectly. It's amazing. Or it's like, hey, make sure this database is set up well. My favorite example of this is while I was doing this in-the-weeds Codex work. When I work with Codex, I'm steering it and going back and forth with it. But in Cloud Code and the desktop app using Fable, I had the videos we're releasing attached to this launch all connected through Descript and the Descript MCP. And Fable is just running in a loop, building out the edits for all of those videos and reviewing its work and interacting with Underlord, the Descript AI tool. Because I'm not a video editor. I'm actually really bad at it. But sometimes that falls under my responsibilities. And that can run in a loop so that when I go to edit the video, by no means have Fable and Descript created a video that is publishable. But it has gotten me 70% of the way there while I'm steering Codex. Cloud Code and Fable and Descript have gotten it there. So that I can go and I'm like, oh, I know what to do now. I see the pieces. It does this crazy stuff where it takes the transcript, makes a storyboard, makes a script itself, and then pieces the video together. So I just have to do a few tiny things to get us to a final product. You already know how AI is changing how everyday work gets done, how much ground you can cover, and how fast a team can scale. To stay ahead, you need tools that give you a competitive advantage built for this new era. Adio is the CRM for the agent-native world. It meets you where you work, compounds every customer signal into context, and then acts on it across your pipeline to let you move at unmatched speed and scale. With agents and automations for every job, Adio orchestrates your work around the clock. We use it internally at Every, and we love it. It's built to handle the scale of your workloads. It's extensible with an API and MCP access, and is built with infrastructure to keep up with your most ambitious agents. It's loved by high-growth startups like Granola, Modal, Whisperflow, and Every. Every. How? Well, one is how, which is like, you should subscribe to Every, we will teach you how. But two is, this stuff is expensive. And the stack that you use, Austin, the stack that you use, Yash, Douglas, myself, it's very expensive. We spend a lot of money at Every on AI and credits. And last night we had an event in the office, and a friend of mine's younger brother, I invited him to come. He's an engineer. And he's trying to figure out how to get a job as an engineer right now. Even though he is an engineer, he's really struggling to get a job. And he's not using AI at all. Yeah. And I introduced him to some people that were telling him how they work. And then at the end of it, he was super excited. And then these people were telling him how much money their companies enable them to spend on AI. Some guy, he's a single engineer, spent 30 grand last month. And this guy, I could see him visually gulp. Or he was like, how am I going to discover this technology when it's prohibitively expensive? And I'm an individual. I'm not at a company right now. And the thing that I love about the Builder Pack is it is, I think the Codex, the OpenAI benefit is a great example of this. Because you're getting three months free of OpenAI and Codex and $1,000 in overage credits. But the three months free includes, I don't know what the number is anymore, $5,000 of value. These subscriptions, just like our Anthropic benefit, include so much spend inside of a small subscription. So, I mean, it's the same thing for OpenAI, Cursor, Gemini, and Anthropic, those offerings. Because they're model companies. So, okay, it's one month, excuse me, the one-month offering. You can use the Codex credit. You can use the Codex credit for three months. Yeah. Yeah. It's so much value that you then get to go experiment with the technology so that you can be the builder that you want to be. I use them all the time. My stack, I mean, the Builder Pack is basically everything that we use at Every. Personally, I'm in OpenAI, Anthropic. I'm more of a consumer of PostHog than I am a user of their AI credits. You're always in Notion. I'm a big Notion user because I do a lot of ops, framer user, render, and I really like experimenting with the Flora MCP. So, I mean, I'm all over the stack. My biggest use is obviously the model companies and just doing my work inside of Claude or Codex. I mean, ironically, a lot of these tools, almost all of these tools, if they aren't a model company themselves, I'm utilizing them through a model company. I'm never really going into their tool. Even Flora, which has a remarkable tool, I only ever use it through the MCP. Yeah. And it doesn't devalue those tools. It actually, to me, incentivizes those SaaS operators to be the very best at the thing they do. I think a lot of us went through a moment from January to March where we were like, let's go. We're building our own CRM. We're building our own data dashboards. We're going to do design all ourselves. And you start doing it and you're like, man, this fucking sucks. Right. And when you're able to sit there and be like, you know what? Ironically, a lot of these tools, almost all of these tools, if they aren't a model company themselves, I'm utilizing them through a model company. I'm never really ever going into their tool. Even Flora, which has a remarkable tool, I only ever use it through the MCP. Yeah. And it doesn't devalue those tools. It actually, to me, incentivizes those SaaS operators to be the very best at the thing they do. I think a lot of us went through a moment from January to March where we were like, let's go. We're building our own CRM. We're building our own data dashboards. We're going to do design all ourselves. And you start doing it and you're like, man, this fucking sucks. Right. And when you're able to sit there and be like, you know what? I really trust PostDog to both run and automate my analytics. I really trust Notion to have this source of truth for the entire business. And even if I never opened it up, even if my pages and my databases look incomprehensible to a human, it doesn't really matter. Because when I'm sitting in Cloud Code or Codex, which I do all the time, I'm like, hey, I need to make this plan. I need to do this thing. At Notion, at Slack, at PostDog, find the necessary information. That's one reason why this suite of tools is so powerful right now, is that the leap from 5.5 to 5.6 came with this improvement in the model's ability to go find the context it needs to do work well. And then to maintain that context through any amount of compaction that happens as it works over a long term, that happens through the way in which, if you looked at my prompts in Codex, you would be like, I think this guy doesn't know what the hell he's doing. Because I'm usually just like, hey, look at all this stuff. Create a goal for yourself and use compound engineering. And I'm like, go. It's barely any thought or work. The thought or work comes from, like, I've identified this as the thing to do. And I know that either I or my team have set up the harness that the agents can work inside of to be trusted. And I think, to a lot of people, that sounds tough. It sounds like a lot of work. But the reason that we've gotten there from Every is we've done it incrementally. Right. It's like, we did one thing and it started to work. Okay. I've been trying to automate these marketing email sends for months and it keeps getting a little bit better. And through compound engineering, the results keep compounding. So the project I work inside of keeps getting a better understanding of it. And it goes back to the best time to start doing this was a year ago. The second best time is right now. You can start this compounding flow right now with models that have really caught up to how quickly you can work yourself. Yeah. I'm curious to know, Douglas, what's your stack? Because you're the creative director behind this. And someone asked me, this branding is very different from Every's branding. So, why is it green? I'm like, okay, I'm going to ask you. Yeah. Yeah. Why is it green? Douglas. I think it's green partly because we haven't used green that much yet in our brand world. But I think what's interesting that all of you are describing, and that I've learned in my short few months I've been here at Every, is: I think language is very interesting. And I find the way the model companies name themselves and their new models very interesting. And a lot of it is very literary. Codex is a book. There's haiku, sonnet, fable, right? These are all stories or forms of putting words together. But the term people use for agents is orchestration. And it feels a lot more like music to me. And I'm not super musical, but I think everyone in their job played a few instruments. And it now feels like we can all take a step back without knowing how to play the rest of the instruments in the orchestra. We can conduct it. Right. And as long as we understand what the piece needs to sound like. And then, you take that and you compound it onto, you can be conducting a lot of different orchestras at the same time. And creative direction, I think, and brand strategy is a lot of famous designers have described it as being like a maestro. And you have color, type, shapes, symbols, forms, words, systems, photography a lot of the time, illustration styles. And how do you conduct all of these pieces together to sing a song that sounds beautiful? And I feel like this stack is that for working with AI. And it's applicable to a lot more than just the highly technical work. Right. So I certainly use it for knowledge work. And to answer your question, I do like Flora, so I can talk more about Flora because I'm probably the deepest in Flora. But I've learned here too, and I think Austin touched on this, what to use Claude for, what to use Codex for. And being able to use both of them through, I've also built out a set of marketing skills that I can use with compound engineering that really helps shorten the timeline on something like research. When you're trying to come up with strategy, you need to have data that backs an insight or a hunch that you have. Do I think that the tools come up with the insight particularly well? Not always. Sometimes they do. But you can then give it the insight and say, prove this out. What direction should we be taking this? Here's the competitive set. It's really good at competitive auditing, especially if you build a skill for it. And it can compound on that. And you can say, push more. I want pricing. I want language. I want what colors these brands use. And you take that, you can feed it into a system and have a really, really sharp strategic lens in hours or less, as opposed to what used to be a several-week-long process, frankly, with humans researching. And then from there, if you have an eye toward what you want to create, using a tool like Flora is really helpful. Because, again, similar to Austin, I feel I'm not a technical designer either. But I've been mood boarding for 15 years. You can now productize a mood board, especially if you have the right prompt. And you can use Claude to write the prompt for you. Put it into Flora, right? Or use the MCP as Brandon does. And have it take a set of reference images. I often have it analyze the images and say, what am I doing here? And it'll be interesting. It might say something like with BuilderPack, this is very high contrast. To your note about green, I think this is, in a way, our most explicitly technical-facing audience that we've been speaking to in terms of the BuilderPack. This is for people that are really ready to start experimenting with all these tools. And it feels a little bit more matrixy and digital than a lot of the more Greco-Roman and heritage-driven elements of the Every brand. So all of those were decisions that these tools helped us land on in terms of the aesthetic of everything. And also helped us design the website. Right? So it's pretty cool to be able to, it's a mindset shift for me of moving from playing the instrument to orchestrating. And when you learn how to get one group singing and playing, you move on to the next one and you can conduct lots of pieces at once. And that's what I find, I'm just beginning to use, have everything plug into Notion. Right? Super helpful. And to Austin's point, you can just be like, look in Notion, find this thing, do this for me, compound, whatever it may be. And it has all the relevant context because it's been updated, because someone else on the team has automated it to update itself. And I think the way you work as an organization also then just gets souped up to a crazy degree because we're all orchestrating at the same time. And it kind of pumps. So, yeah, it's been really exciting for me to work with these tools and apply them to, it is the sandwich. And when you learn how to get one group singing and playing, you move on to the next one, and you can conduct lots of pieces at once. And that's what I find. I'm just beginning to have everything plug into Notion. Right? Super helpful. And to Austin's point, you can just be like, look in Notion, find this thing, do this for me, compound, whatever it may be. And it has all the relevant context because it's been updated, because someone else on the team has automated it to update itself. And I think the way you work as an organization also then just gets souped up to a crazy degree because we're all orchestrating at the same time. And it pumps. So, yeah, it's been really exciting for me to work with these tools and apply them to it. It is the sandwich. You have to put a lot of work in at the beginning, is what I found, in terms of really doing some metacognition and figuring out, how do I think about this? What is my process? And then once you've codified that, these tools can run with it, and you can ask them for help if you don't know why something happened, which I find very revelatory. Yeah, it's remarkable when people are like, well, how do I use it? It's like, well, what are you trying to accomplish? And then they say that. And I'm like, great, just say that again. Yeah. I think a good final thing here is, if we're going to do this, if we're speaking directly to people who either just got access to the tools in the Builder Pack or are thinking about it, this has been a lot of higher-level thinking about how to work with a few examples. I think each of us saying, say you have access to all the stuff in the Builder Pack and you're like, what do I do now? Right? What would each of you recommend someone do, whether it's one specific thing to try or one mentality approach to take if someone is a brand new builder? I think we can go around and do that. If I had just got the Builder Pack and I'm just getting my feet wet right now with building, I think the first thing I would do is, if you're really early on, sometimes the hardest part about building is knowing what to build. And you can just sit at your computer and be looking for a problem. That is the worst way to find a problem. Going for a walk is also not an effective way to find a problem. That is a habit that you can build, where you can identify problems in your life well. Because usually we're just so numb to those problems. So if I'm new to building and I'm new to the Builder Pack, the very first thing I would do is pick something simple that I really, really love, like a product that I really love, and dupe it. I would just make it myself. The very, very simplified MVP version of that. And use as many parts of the Builder Pack as I possibly could. So I would launch it on Render and use the Render credits. I would plug in PostHog so that I could actually maybe build in a loop or use some of PostHog's traditional products where I can actually track how I use it, how my friends use it. I would build it using Anthropic, using Claude, using Codex, using Cursor. I might actually use some AI in that product. Even if it's a product that doesn't include AI, I might figure out how to do that. I might use Flora to actually create some unique icons or graphics. But I would just dupe something. Because what I find when I dupe things, at least early on when I was duping, I would get started with the process of making it, and very quickly I'd be like, I don't like that feature. I don't like that version of this thing. And very quickly, it's not duping. It's inspiration, and you're off making your own thing. So if I'm new to the Builder Pack, I would dupe something. And I would look through the Builder Pack and be like, which one of these things can I actually start using in this build? And not question yourself for a second. Just start doing it. That's the hardest part sometimes. It's just the first prompt. Yeah. Yeah. I find that everyone learns everything in different ways, but especially learns how to become an AI builder in different ways. I think some people really want to understand what's happening, and other people just want to go and ship and learn from what doesn't work. The thing that works best for me was, okay, I actually have a lot of ideas for what I might want to do. Of the ideas I might want to do, let me pick the one that I'm the most excited about, which for me was this movie app that's Fandango for indie movies. I was like, this doesn't exist. If this worked, this would make my life a lot better. I would be so stoked. One thing that I think is a good prompt to yourself is, what would you be the most excited to text someone, like, hey, I made this? What would make you feel really good to do that? And I would actually really encourage people to try to make, not necessarily a complex app for the sake of complexity, but something you don't think you can, and try to make an app to start because you're going to learn so much through that process. In doing that process to make this web app that is now live, that a lot of my friends use every week now, I've never done any of this stuff before. And then it's taking you through, oh, yeah, you need to connect Render. You need to go through Google OAuth, whatever. Because it's a thing that you're so passionate and excited about and you want to get to the end, it quickly starts to feel like you're playing a video game and trying to get to the next level. And I do think Brandon's point about, okay, to start, connect the agent, whether it's Cursor, Claude, or Codex, to the tools in the Builder Pack and any tools that you have access to, to be like, you can connect it all and be like, hey, you have all this stuff. The agent will figure out where to go. But I think driving toward that goal of, I'm going to make something great that I'm excited to text a friend, like, hey, go try it, and along the way, you'll realize, oh, it actually works. That's crazy. It's so exciting that it works. But the design looks like ass. How do I make the design better? And you can sit and work with that. You can read every for some inspiration for design. You can go use it to check stuff. But that would be my best recommendation. And that's worked really, really well for me. And then I've learned from seeing the V1 ship that the agent comes up with and being like, okay, that's not good enough. Let me figure out how to get it to a V2. I have a good specific one for marketers, writers, designers, creative technologists, product designers. I think, to Brandon's point, I know your problem, and it's probably your portfolio. And this is a wonderful stack to rebuild your portfolio. And it's something that I think everybody kind of hates, their portfolio site, but you need it. And you, to Austin's point, probably have a dream version of it in your head. You can use Brandon's duping strategy here, too, if you see a site that you really like and you want to try and mimic it. Or you can come up with something from scratch and write the brief for what you're looking for there. Put in the references and give it to Codex or Claude, connect it to all of these different tools, and take that on as a project. I think it's a really enriching thing to do because most people have so much work that you're not liking the way you've potentially merchandised it to the world. And maybe you want more interactivity or different types of visuals. You can use these tools to create something really, really striking that you feel good about. And that also potentially helps you get more work or a better job or whatever else it is you're looking for. And it kind of also proves out that you're AI native, right? You can use Brandon's duping strategy here, too, if you see a site that you really like and you want to try and mimic it. Or you can come up with something from scratch and write the brief for what you're looking for there. Put in the references and give it to Codex or Claude, connect it to all of these different tools. And take that on as a project. I think it's a really enriching thing to do because most people have so much work that you're not liking the way you've potentially merchandised it to the world. And maybe you want more interactivity or different types of visuals. You can use these tools to create something really, really striking that you feel good about. And that also potentially helps you get more work or a better job or whatever else it is you're looking for. And that also proves out that you're AI native, right? So I think that's a great project for anyone that's creative or has even a personal side hustle or small business that they're running. Try and do something like that that's impactful right away because you can also get people responding to it. To Austin's point, right, his app. Hey, his friends use it now, right? People can actually use this thing. They can see it. They can tell you how great it is. And that's very encouraging as you begin to work with AI, in my experience, at least. Yeah, that's cool. Yeah, to second that, or to close it out, what I would do is I would use Cursor with Render. And what I realized, my mind was blown when I used Cursor cloud agents with Kimi K25, which is an open source model, which Cursor improved. I would say dupe something, use Cursor cloud agents, and use different models. I feel right now, if this is what you're going to be doing, you need to find your favorite model. And with cloud agents, you can have 10 agents running. And we have the Pro Plus plan. This is above Pro. So you're good in terms of spend and stuff like that. And I would use Render to deploy it and actually see which model you like working with. I'm so picky about my models that even if a new model drops, I don't change to that because if I have some preference, I'll test it out. You need to have, we have evals for if a model is doing better or worse, you need to have your personal taste in terms of what you like and what you dislike. And I feel cloud agents are a very underrated way to find out what you like to build. And just start to feel that out. Yeah. Cool. In terms of what's next for all access, I think we have some cool stuff coming. In the next few days, week, we're probably going to drop two more partners that are pretty sick. Benefits. And I think that something that's really compelling for our readers, I've found, is now they have access to these benefits. You guys have access to these benefits. But the guides on how to use each one are—people love that. So I think that probably what we're going to do next is for each one of these benefits, you have amazing value that you can go capture by using them. But how can we pair that with a guide that you can probably just plug into one of your agents that leapfrogs you ahead with using whatever this net new technology is that you now have access to? So I feel like guides, some cool camps for each one of these partners, is probably something that we'll do. Yeah. To be clear, I've never read one of our guides. I dropped the guide into Codex, and I say, use this to help build the next thing we're doing. And I would recommend everyone do the same. And yeah, I think the best way to think about all access, but also how to best take advantage of an Every membership, is that we are going to keep adding partners to the building.