I just watched the Jake Van Cleef breakdown, and the biggest thing that stood out to me was the idea of a floor plan for the project. It clicked because the IA will always understand what I’m talking about, without having to load a whole lot of context. I also find that the probability of getting wrong answers can be mitigated to near-zero, achieving a high repeatability of a process, that is something I struggled a lot with AI. What I find interesting too was the lower consumption of tokens, due to a context that only loads what it needs.

a16z speedrun is one of the biggest accelerators in the world. We submitted our application a bit back on the recommendation of one of our friends. Our EdubaWare software has been in closed testing for a bit (get ready, next week thousands of you will get access to it and see our changes) We received and Email yesterday saying they want to hear more from us and to send them a video in under three minutes that explains: - Your backgrounds and greatest accomplishments, professional or personal - What makes you exceptional, and why you're the team to win this market - What you're building and what problem it solves - Why this is a big, underserved opportunity - What progress you've made so far  Now fitting all that into one video is challenging, but we did our best. I hope this gives you a view into the other side of my life which is my company Eduba!

Hi everyone, I have just reached level 2 and would like to make a first post of my own. I have always been interested in technology, economics, and human behavior, but only recently started writing about how they connect. How AI is used inside our systems is the focus here. It is what brought me here, and why ICM makes sense to me. I also like to explore how we humans behave with these systems. I am sharing an essay I originally wrote in Portuguese and translated into English for this community. It explores the social cost of asking another person for help, and how AI changes that choice. Asking a machine can spare us the embarrassment of admitting what we do not know, or the judgment that sometimes comes with an answer. I look at how that changes whom we turn to, what we trust, and how we relate to one another.

I just watched the Jake Van Cleef breakdown, and the biggest thing that stood out to me was using a simple agent.md file and clean folder structure instead of complicated AI frameworks. It clicked because having one central source of truth keeps things organized without unnecessary bloat. My main takeaway was that feeding the AI only the specific context it needs keeps its output accurate and focused. The experiment at the end helped me understand how quickly context overload degrades results. Going forward, I'm going to set up an agent.md file and structured project folders on my Mac Mini so I can leave the copy-paste loop behind and build smoothly.

The Automation Paradox: Are We Building Freedom or Our Own Digital Prison? 1/21 An AI agent creates a file and reports: “Task completed.” But did it actually do the job? A discussion about verifying AI-generated work led me to a much bigger question: Are we building automation to gain freedom, or systems we can no longer stop supervising? 2/21 First, requiring agents to provide verifiable results is absolutely the right direction. But notice the paradox. We automate work to save effort. Then we need additional work to verify whether the automated work was done correctly. Have we actually eliminated work? 3/21 So we add logs, tests, watchdogs, fallback agents, monitoring dashboards, and independent auditors. Eventually, we need to maintain those systems too. At what point are we no longer solving the original problem, but managing the complexity of our solution? 4/21 The real equation isn’t: Time saved = time the AI spent working. It’s closer to: Net benefit = useful work saved − supervision − corrections − maintenance − failures. If I save ten minutes of execution but create fifteen minutes of supervision, where’s the freedom? 5/21 This isn’t a new problem. In 1983, Lisanne Bainbridge described the Ironies of Automation. As machines take over routine operations, humans may be left supervising rare failures while gradually losing the practical skills needed to intervene. [1] 6/21 Research on trust in automation adds another problem. Trusting a system too much can be dangerous. Trusting it too little creates unnecessary work. The goal isn’t blind faith or endless checking. It’s knowing when reliance is justified. [2] 7/21 And productivity isn’t always what it feels like. In a 2025 experiment, experienced developers using AI tools took 19% longer on assigned tasks, despite believing they were faster. That finding applied to a specific setting, not every AI workflow. [3] 8/21 The picture is also changing. METR’s 2026 follow-up suggests newer tools may improve productivity, but selection bias makes the precise effect uncertain.