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: ]

☕️ AI-native documentation and Collections, now generally available. - Collections, group related Wiki pages under a single named home, so an onboarding handbook or a security playbook isn't scattered across loose pages. - AI block, a block you drop into a page that generates content from everything else on the page, could be a FAQ at the bottom of a long doc, a TL;DR at the top, action items pulled from a meeting note, anything - Documentation gets AI-assistance, an agent in the Page sidecar that drafts sections in real time, proposes changes you accept or reject, and can rewrite a single selected block without touching the rest. Day 4 of our caffeinated launch week. ☕️

Traditional software engineering optimized for creation velocity. AI-native engineering now requires understanding velocity. This is not “technical debt.” It is: comprehension debt You will have post-build operational comprehension issue.

Cournot AI Oracle Launches on #BNBChain# Mainnet: Bringing Verifiable AI Reasoning Onchain Cournot is building an AI-native oracle infrastructure on @BNBCHAIN, enabling applications to verify real-world outcomes through evidence collection, rule interpretation, and auditable reasoning. Supporting multiple verticals such as prediction markets, onchain collectibles/RWAs, parametric insurance and agentic commerce, Cournot is acting as an independent evaluator agent within workflows like the BNB Agent SDK (ERC-8183/APEX) to verify task outcomes and enable trustworthy automated settlement. Why BNB Chain? 🧵

Our AI infrastructure releases have focused on exposing wallet, exchange and onchain functionality through agent-compatible interfaces. Our repositories now include, 1. Agentic Wallet with TEE-secured signing 2. MCP integrations for AI-native workflows 3. CLI + Skills tooling via Onchain OS 4. Agent Trade Kit components for trading automation 5. Transaction simulation and risk grading before execution 6. Multi-chain support across Ethereum, Solana, X Layer and others 7. x402-compatible payment tooling 8. DEX routing, wallet operations and transaction broadcasting APIs The current architecture exposes these capabilities through, MCP servers, CLI tooling, Open APIs and installable Skills repositories This is the vision set by Star to develop AI infrastructure while preserving execution controls around signing, permissions and transaction risk. There is more coming in the near future.

GoblinCoin ( $Goblin ) is now live on Byreal. Inspired by AI-native internet culture, @GoblinSPL is a meme token shaped by goblin-themed AI memes. Trade or LP here:

Sentio just got a lot more talkative. AI Skills lets your coding agent speak Sentio natively — describe the processor, SQL query, alert, or dashboard you want, and it ships end-to-end. Two skills. Auto-activated: ➡️ sentio-processor — scaffold, write, test, upload across 8+ chains ➡️sentio-platform — SQL, alerts, dashboards from natural language This is what AI-native infra actually looks like. #AI# #Sentio#

The future of AI Agents needs #ICP# AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ @zCloakNetwork is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (@xiao_zcloak), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

Your AI agent, one minute from now: “Moutai closed at ¥1,482, down 0.6%.” “BTC funding rate is 0.012% on Binance perps.” “TSMC posted $23.5B revenue last quarter, +36% YoY.” Same conversation. Three markets. Zero API wrangling. Hubble Data Service Skill just went live on GitHub — the same data stack behind Hubble’s Research Analyst Agents, now open to every AI native traders out there. → A-shares · HK · US · Crypto → Quotes · K-line · Fundamentals · Financials → 27 technical indicators, cross-market Claude Code, OpenClaw, Hermes, anything that supports Skills — just say: “Install this for me: Your agent does the rest. Experience it today!

Sharon AI $SHAZ: Capacity Target Hits 100MW After Two Upgrades in 2026 as AI Cloud Demand Outstrips Supply $SHAZ just reported Q1 2026 results, highlighting the year's twice upgraded 2026 data center capacity target — from an initial 55MW, to 70MW, and now to 100MW. This consistent upward revision is a direct signal that customer demand is running ahead of the company's ability to deploy infrastructure. The 100MW target is backed by a previously announced US$350M convertible note, giving the company capital to continue scaling its customer base through 2026 and beyond. Combined with the pro-forma cash position of approximately $225M following its February 2026 IPO, Sharon AI enters the growth phase of its buildout with a well-funded balance sheet. Why Investors Should Be Watching: • Capacity upgrades as a demand signal: Two upward revisions in a single year suggest the pipeline of signed or committed enterprise contracts is growing faster than initial projections. • Supply-constrained market: Management explicitly flagged that demand materially outweighs available supply — a setup that supports contract pricing and utilization rates as new capacity comes online. • US$350M convertible note: A significant capital event that funds the 100MW buildout without relying solely on equity issuance. • Acceleration into Q2: CEO James Manning noted that business momentum has accelerated beyond Q1, pointing to continued commercial execution in the near term. • Multi-sector demand base: Enterprise, hyperscale, research, government, and AI-native customers are all active — a diversified demand base that reduces concentration risk. Sharon AI is executing against a customer-led deployment model where contracts precede infrastructure. If demand continues to outpace supply, the 100MW target may not be the last revision investors see this year. Read the full Q1 2026 report: $SHAZ $NVDA $NBIS $APLD $ORCL $CORZ $GPUS $MSFT $AMZN