AOS

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers. Further, small teams who work in the same office, where they can communicate face-to-face, can move incredibly quickly. Because we can now build fast, a greater fraction of time must be spent deciding what to build. To deal with this project-management bottleneck, some teams are pushing engineer:product manager (PM) some teams are pushing engineer:product manager (PM) ratios downward from, say, 8:1 to as low as 1:1. But we can do even better: If we have one PM who decides what to build and one engineer who builds it, the communication between them becomes a bottleneck. This is why the fastest-moving teams I see tend to have engineers who know how to do some product work (and, optionally, some PMs who know how to do some engineering work). When an engineer understands users and can make decisions on what to build and build it directly, they can execute incredibly quickly. I’ve seen engineers successfully expand their roles to including making product decisions, and PMs expand their roles to building software. The tech industry has more engineers than PMs, but both are promising paths. If you are an engineer, you’ll find it useful to learn some product management skills, and if you’re a PM, please learn to build! Looking beyond the product-management bottleneck, I also see bottlenecks in design, marketing, legal compliance, and much more. When we speed up coding 10x or 100x, everything else becomes slow in comparison. For example, some of my teams have built great features so quickly that the marketing organization was left scrambling to figure out how to communicate them to users — a marketing bottleneck. Or when a team can build software in a day that the legal department needs a week to review, that’s a legal compliance bottleneck. In this way, agentic coding isn’t just changing the workflow of software engineering, it’s also changing all the teams around it. When smaller, AI-enabled teams can get more done, generalists excel. Traditional companies need to pull together people from many specialties — engineering, product management, design, marketing, legal, etc. — to execute projects and create value. This has resulted in large teams of specialists who work together. But if a team of 2 persons is to get work done that require 5 different specialities, then some of those individuals must play roles outside a single speciality. In some small teams, individuals do have deep specializations. For example, one might be a great engineer and another a great PM. But they also understand the other key functions needed to move a project forward, and can jump into thinking through other kinds of problems as needed. Of course, proficiency with AI tools is a big help, since it helps us to think through problems that involve different roles. Even in a two-person team, to move fast, communication bottlenecks also must be minimized. This is why I value teams that work in the same location. Remote teams can perform well too, but the highest speed is achieved by having everyone in the room, able to communicate instantaneously to solve problems. This post focuses on AI-native teams with around 2-10 persons, but not everything can be done by a small team. I'll address the coordination of larger teams in the future. I realize these shifts to job roles are tough to navigate for many people. At the same time, I am encouraged that individuals and small teams who are willing to learn the relevant skills are now able to get far more done than was possible before. This is the golden age of learning and building! [Original text: deeplearning.ai/the-batch/issu… ]

USD1 Trading Campaign is Dead. Who’s Holding the Blame? If @BNBCHAIN actually cares about its ecosystem, it owes everyone a real explanation for the USD1 Trading Campaign. Some winners didn’t build a community, didn’t spark discussion, they just… existed. Builders with real communities, vibes, and cultures, on the other hand, got ignored. Trust in USD1 is bleeding. We’re done waiting. We’ll be shifting our energy to building the $U ecosystem instead. But before we do that, we must figure out who killed the game. Welcome to Blame Exchange 🏛️ Who really caused USD1 Campaign failures? Vote for the truth — numbers don’t lie. Weak Sponsors? @BNBCHAIN , @worldlibertyfi , @Aster_DEX , @fourdotmemezh ? Unsatisfactory winners? #egl1, #memes, #一? People who failed our hope? @0xDylan_ , @heyibinance , @cz_binance , @nina_rong ? Vote now. Let’s see what the community thinks. ➡️ big-don.net

USD1 Trading Campaign is Dead. Who’s Holding the Blame? If @BNBCHAIN actually cares about its ecosystem, it owes everyone a real explanation for the USD1 Trading Campaign. Some winners didn’t build a community, didn’t spark discussion, they just… existed. Builders with real communities, vibes, and cultures, on the other hand, got ignored. Trust in USD1 is bleeding. We’re done waiting. We’ll be shifting our energy to building the $U ecosystem instead. But before we do that, we must figure out who killed the game. Welcome to Blame Exchange 🏛️ Who really caused USD1 Campaign failures? Vote for the truth — numbers don’t lie. Weak Sponsors? @BNBCHAIN , @worldlibertyfi , @Aster_DEX , @fourdotmemezh ? Unsatisfactory winners? #egl1, #memes, #一? People who failed our hope? @0xDylan_ , @heyibinance , @cz_binance , @nina_rong ? Vote now. Let’s see what the community thinks. ➡️ big-don.net

56/3.42B At a certain point, it's going to explode with insane speed. #USD1

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We have open-sourced our new 𝕏 algorithm, powered by the same transformer architecture as xAI's Grok model. Check it out here:  github.com/xai-org/x-algo…