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robertohluna

@robertohluna

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We just turned down a $100K AI client this week. The check would have cleared. The work was inside our wheelhouse. The founder was responsive and serious. We still said no. Here’s why, They wanted to build the wrong thing. They came to us asking for an AI agent that would automate a workflow that, when we broke it down, didn’t actually need automation. It needed a process redesign and a hire. AI wasn’t the answer. We told them that. They asked us to build it anyway. We declined. Most AI agencies would have taken the check. Built the wrong thing. Cashed the invoice. Moved on. Six months later the client’s stuck with a system they don’t use, blaming AI for being hyped, and the agency has another logo on their case studies page. We don’t run our agency that way. The check matters less than whether the build actually solves the client’s real problem. If it doesn’t, we say no. Here’s what we look for before we sign any AI client: The problem they describe is the actual problem (most of the time it isn’t, and we have to dig before we’ll commit). AI is genuinely the right tool for it (sometimes it’s a process fix, an org change, or just better tooling). The founder is willing to change their business to fit the system, not just bolt the system onto a broken process. The team has the maturity to actually use what we build (no point shipping a system nobody’s ready for). If those four aren’t there, we walk. Even if the check is big. That’s why our builds ship. Because we only take builds that should be built in the first place. Comment “fit” for the qualification framework we run on every potential AI client. by @robertohluna
16
3 months ago
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We just signed a new AI client and instead of opening laptops to start building, my business partner and I sat down to run our day-one playbook. Here’s why most AI builds fail, agencies treat day one like a kickoff call. Get the requirements, schedule the build phase, start writing code. By week six the client looks at what they built and says “this isn’t what I asked for.” Our day-one playbook exists to make sure that conversation never happens. The playbook has two halves and we run both at the same table. One of us focuses on business analysis. Breaking down the client’s company from the discovery call, every department, every workflow, every handoff, every gap. Mapping how the business actually runs vs. how the client described it. Finding the broken processes nobody documented. Identifying which problems are real and which the client doesn’t even know they have yet. The other focuses on technical translation. Taking everything the business analysis surfaces and turning it into system architecture. What can AI actually solve. What needs a process fix instead. What modules need to exist. How they connect. What the build phases need to be. Both halves at the same table is the unlock. Most AI agencies have one side or the other, business consultants who can’t translate to systems, or engineers who can’t read the business. The gap between those two roles is where 70% of AI projects die. By the end of day one we have a complete map of the client’s business, the real gaps (not the ones they pitched us), a clear scope of what AI will and won’t solve, a first-draft architecture document, and a phased build plan. Then the client gets a document that shows them their own business clearer than they’ve ever seen it, with a system designed to actually solve their real problems. That’s the playbook. That’s why our builds ship. Comment “playbook” if you want the full day-one onboarding methodology we run with every new AI client. by @robertohluna
16
3 months ago
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Most people run one AI agent to write code. I run 9 at the same time, like a real engineering team. Here’s the shift nobody’s talking about yet. One Claude Code running makes you a user. 9 running on the same codebase makes you a manager. And managing a team of agents looks a lot like managing a team of engineers. Here’s how it works. You don’t write prompts. The system does. I give it the goal — “fix these bugs, add this feature.” A planner agent reads the whole codebase and writes a full sprint plan. I review it, tweak it, approve it. Then it generates the exact instructions for each of the 9 agents — what to build, what files they own, what patterns to follow. I paste each set into each terminal and they start working. My job is to refine the instructions before dispatch. Make sure each agent follows our standards. The system writes the plan. I make sure it’s right. Each agent has a specific role. Database, backend, frontend, design, testing, security, adversarial review, merging. Same structure as a human engineering team. They work in waves, not all at once. Database first. Backend after that. Frontend after that. Security reviews everything before it ships. Sequenced work ships a product. Random parallel work ships chaos. Nothing merges unless it passes review. Build runs, tests pass, security agent signs off. If anything’s broken, the merge gets blocked. It’s not software. It’s a folder of templates you drop into any repo. Works with Claude Code, Cursor, Codex, or whatever AI tool you already use. The era of one-agent-at-a-time is ending. The people who figure out orchestration first ship 10x faster than everyone else. Comment “agent” and I’ll send you the repo. by @robertohluna
109
3 months ago
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This is what day 3 of a real custom platform build looks like. Empty chairs, 7 monitors, agents still running, dining room nowhere to be found. Here’s the part nobody teaches you about building custom AI platforms, because most “AI builders” skip it entirely and try to piggyback of the clients data with no real framework in mind. Day 1 isn’t building. It’s requirements gathering. Not “what do you want the AI to do.” Real requirements. How does work actually flow through their business. Where are the handoffs. What decisions does the team make and what criteria do they use. What’s institutional knowledge stuck in someone’s head that needs to become system logic. What’s broken that they’ve worked around for so long they forgot it’s broken. Most builders skip this and wonder why their platforms break the second they hit production. Day 2 is architecture, not code. Every module, every data flow, every integration point. What lives where, what talks to what, what the failure modes look like. This happens on whiteboards and in conversations, not in cursors. The architecture decisions you make on day 2 determine whether the platform scales or collapses six months from now. Most builders skip this too. They open Claude Code and start vibing. That’s how you ship a platform that demos great and breaks at scale. Day 3 is the modular build. Now you build. Every component does one thing, has clean inputs and outputs, can be swapped or extended without breaking the rest. The client’s team is in the room catching things live — “we don’t do it like that,” “add a step here.” 30 seconds of feedback instead of 3 weeks of Slack threads. That’s why the room looks like this. Because real builds need this much surface area to translate a subject matter expert’s knowledge into a system that actually runs their business. The dining room isn’t a dining room anymore because we’re doing the work most people pretend doesn’t need to happen. Comment “build” and I’ll send you the full implementation guide. by @robertohluna
17
3 months ago
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Your agents don’t have a home right now. Neither do your apps. They’re scattered everywhere. n8n workflows over here. Zapier zaps over there. A Lovable app you built last week. A v0 prototype that kind of works. Random Python scripts. Claude Code running in a terminal somewhere. Every tool lives somewhere different. None of them share context. None of them know what the others are doing. That’s not a system. That’s a graveyard of one-off solutions with a monthly bill. BusinessOS is what gives all of it a home. One interconnected layer where your agents, your apps, your automations, and your tools actually work together. Run Claude Code inside it. Run any agent inside it. Drop your Lovable apps in. Connect your existing stack underneath it. All running locally. All private. All on your data. It doesn’t replace what you’re already using — it becomes the connective tissue between everything. The orchestration layer that lets your agents reason across your apps, your apps share context with your automations, and your data stay yours instead of scattered across 14 different SaaS dashboards. n8n was built to connect apps. Lovable was built to generate apps. Neither was built to be a home for an interconnected system where everything talks to everything and your agents can actually think across the whole thing. This is. And later, when you’re ready, the interface itself can become your new operating environment. But that’s optional. Step one is giving everything you’ve built a place to live together. I’m releasing the free version so you can build on top of it. Same core architecture we use when building custom platforms for clients. Comment “OS” and I’ll send you the repo link. by @robertohluna
84
3 months ago
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Your automation layer is probably n8n, Zapier, or Make right now. Those tools were built for connecting apps. They weren’t built for orchestrating agents. That’s the gap. Agents need a different kind of orchestration layer — one that handles context, state, decision logic, and the kind of complexity that breaks workflow builders the second things get real. n8n can fire a webhook. It can’t reason about what should happen next based on the actual context of your business. That’s what BusinessOS is. The orchestration layer that powers the agents running behind your existing stack. It doesn’t replace your tools — it powers the intelligence layer underneath them. Your CRM stays. Your email stays. Your databases stay. BusinessOS becomes the brain that orchestrates the agents working across all of them. You don’t have to rip anything out. You don’t have to migrate. You just get a real automation orchestration layer instead of duct-taped workflows that break every time an API changes. And later, when you’re ready, the interface itself can become your new operating environment. But that’s optional. Step one is just having a real foundation for your agents to run on. I’m releasing the free version so you can build your own. Same core architecture we use when building custom platforms for clients. Comment “OS” and I’ll send you the repo link. by @robertohluna
51
3 months ago
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You spent 24 billion tokens turning your clients knowledge into a real platform. Here’s what the industry isn’t telling you about “building with AI.” The client is a subject matter expert. They know their business better than anyone — every workflow, every edge case, every unwritten rule their team runs on. That context is worth more than any model. But subject matter experts can’t translate their knowledge directly into code architecture. And AI can’t either. You can prompt ChatGPT all day with what you want — it’ll build you something that demos well and falls apart in production. That’s vibe coding. AI building whatever’s easiest in the moment with no regard for architecture, scalability, or the edge cases you haven’t thought of yet. That’s the gap the whole “just use AI to build your company” narrative skips over. You need someone who can sit with the expert, pull the real context out of their head, and translate it into a modular system architecture that actually holds up. That’s what 24 billion tokens actually buys when it’s done right. Not prompts firing off into the void. Business requirements. Process mapping. Architecture decisions. Then modular builds where every component has a clear job, clean inputs and outputs, and can be extended without breaking the rest. The client isn’t watching me type prompts. They’re watching me convert 20 years of their expertise into a system that’ll run their business. Their team catches things live — “we don’t do it like that,” “add a step here” — and the architecture adjusts in real time. AI builds what’s easy. Engineers build what’s right. Subject matter experts know what’s needed. You need all three in the room. That’s why we’re still up at 2am. That’s what 24 billion tokens looks like when it’s real. Comment “build” and I’ll DM you how I architect these platforms from requirements to deployment by @robertohluna
55
3 months ago
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24 billion tokens. Day 2. Client’s dining room at 2am and nobody wants to stop. Here’s what’s actually happening on this build, because the industry’s lying to you about what AI can do alone. The client is a subject matter expert in their field. They know their business better than anyone. Every workflow, every edge case, every unwritten rule their team runs on. That context is worth more than any model. But here’s the problem nobody’s talking about: subject matter experts can’t translate their knowledge directly into code architecture. And AI can’t either. You can prompt ChatGPT all day with what you want, it’ll build you something that demos well and falls apart in production. That’s vibe coding. AI building whatever’s easiest in the moment with no regard for architecture, scalability, or the edge cases you haven’t thought of yet. This is the gap the whole “just use AI to build your company” narrative skips over. You need someone who can sit with the expert, extract the real context out of their head, and translate it into a modular system architecture that actually holds up. That’s what day 2 at 2am looks like. Business requirements. Process mapping. Architecture decisions. Then modular builds where every component has a clear job, clean inputs and outputs, and can be extended without breaking the rest. The client isn’t watching me type prompts. They’re watching me convert 20 years of their expertise into a system that’ll run their business. Their team catches things live, “we don’t do it like that,” “add a step here”, and the architecture adjusts in real time. AI builds what’s easy. Engineers build what’s right. Subject matter experts know what’s needed. You need all three in the room. That’s why we’re still up at 2am. That’s why it’s 24 billion tokens deep. That’s what real implementation looks like. Comment “build” if you want to see the process. by @robertohluna
21
3 months ago
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20 billion tokens in 2 days. Custom platform getting built in their dining room. This is the part of “Al implementation” nobody on LinkedIn shows you. Because most of it doesn’t exist online. Most “Al builders” stop at the discovery call and the pretty deck. They send proposals, schedule follow-ups, push timelines. The actual building happens weeks later, alone, on Slack, with a client who’s already half-checked out. I do it the opposite way. Fly out, post up, build in front of them. Here’s why that matters: When the client sees the platform forming in real time, they catch things in 30 seconds that would take 3 weeks of back-and-forth over email. “Wait, that field shouldn’t be there.” “Actually we don’t do it that way anymore.” “Can we add a step here?” Instant feedback. Live iteration. The architecture adjusts as they talk. You can’t get that over Zoom. You can’t get it from a Notion doc. You definitely can’t get it from a “phase 1, phase 2, phase 3”timeline. The workflow they described in discovery is never how they actually work. You only find that out by being in the room while they use the thing. 20 billion tokens isn’t the flex. The flex is the 200 micro-decisions that got made correctly because the client was standing right there when the system was being built. In-person is about to matter as much as online presence. Because it’s the only place left where the work and the feedback happen at the same time. Comment “build” if you want to see how I build these custom platforms from scratch. by @robertohluna
266
3 months ago
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20 billion tokens in 2 days. Custom platform getting built in their dining room. This is the part of “AI implementation” nobody on LinkedIn shows you. Because most of it doesn’t exist online. Most “AI builders” stop at the discovery call and the pretty deck. They send proposals, schedule follow-ups, push timelines. The actual building happens weeks later, alone, on Slack, with a client who’s already half-checked out. I do it the opposite way. Fly out, post up, build in front of them. Here’s why that matters: When the client sees the platform forming in real time, they catch things in 30 seconds that would take 3 weeks of back-and-forth over email. “Wait, that field shouldn’t be there.” “Actually we don’t do it that way anymore.” “Can we add a step here?” Instant feedback. Live iteration. The architecture adjusts as they talk. You can’t get that over Zoom. You can’t get it from a Notion doc. You definitely can’t get it from a “phase 1, phase 2, phase 3” timeline. The workflow they described in discovery is never how they actually work. You only find that out by being in the room while they use the thing. 20 billion tokens isn’t the flex. The flex is the 200 micro-decisions that got made correctly because the client was standing right there when the system was being built. In-person is about to matter as much as online presence. Because it’s the only place left where the work and the feedback happen at the same time. Comment “build” if you want to see how I build these custom platforms from scratch. by @robertohluna
22
3 months ago
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Flying across the country to build a client’s platform in front of them isn’t a flex. It’s the new filter. Because anyone can sound smart on a Zoom call with Claude open in another tab. The internet’s is flooding with “Al experts” who have notetakers feeding them answers in real time. Every call sounds sharp. Every follow-up is perfectly worded. Every objection handled by a model, not a brain. None of it means they can actually build. So how do you tell who’s real? You put them in a room. No script. No Al safety net. No “let me get back to you.” Just the work, happening live. In-person is about to matter as much as online presence. Because it’s the last place left where you can’t fake the skill. You either know the architecture or you don’t. You either can map their workflow live or you’re full of it. Online gets you the meeting. Showing up is what separates the real from the rest. Comment “build” if you want to see the process & stay tuned for day 2 by @robertohluna
19
3 months ago
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