You’re reading The Steady Beat, a weekly pulse of must-reads for anyone orchestrating teams, people, and work across the modern digital workplace – whether you’re managing sprints, driving roadmaps, leading departments, or just making sure the right work gets done. Curated by the team at Steady.
Taste Over Output
The maker movement had Arduinos gathering dust in garages for years before anyone tried to sell a smart toaster. Vibe coding skipped that entire incubation period. Sachin’s analysis identifies what’s missing from AI-assisted development’s meteoric rise: “scenius” – that messy, low-stakes phase where communities develop judgment through playful experimentation before anyone expects commercial results. Without it, teams suffer from what he calls “evaluative anesthesia,” churning out code that feels productive without the instinct to know if it actually is. Here’s the parallel to 3D printing: it democratized prototyping but concentrated real value upstream in industrial manufacturing. Similarly, vibe coding commoditizes rapid iteration while model providers capture the durable advantage. So where does that leave your team? Sachin proposes four strategies that to reframe the conversation: develop taste by building many prototypes (most of which you’ll throw away), generate attention through public creation, contribute to gift economies that build social capital, and (most importantly) capture signals from what fails. Those failures compound into judgment that no model can replicate.
— Technically, 9m, #ai, #engineering, #strategy
Get Your Hands Dirty
Shopify CEO Tobi Lütke famously ships code with AI tools. Meanwhile, most engineering leaders are still delegating “AI exploration” to consultants and innovation committees. João Alves makes the case that if you’re leading a technical organization and haven’t personally built something with these tools, you’re flying blind at the worst possible time. His recommendation: build something in a greenfield environment first, where AI tools shine brightest, then try it inside your actual enterprise constraints with all the legacy systems, compliance requirements, and authentication layers that make real work hard. The delta between those two experiences is your strategic insight. No consultant’s slide deck will give you that. Alos, when leaders visibly engage with new tools, it signals permission and urgency in ways that memos and mandates never will. A VP of Engineering running four layers deep in a 200-person org can’t afford the luxury of abstract understanding, because the decisions about team structure, hiring profiles, and tooling investments that flow from AI adoption require firsthand intuition, not secondhand reports. Stop waiting for the perfect time to experiment.
— Terminal Prompt, 6m, #leadership, #ai, #management
Elite Amplifier
CJ Roth’s examination of engineering culture in 2026 lands on a harsh truth backed by data: while high-adoption teams complete 21% more tasks with AI tools, their PR review times balloon 91%, creating a bottleneck right where human judgment matters most. Senior engineers see nearly 5x the productivity gains of juniors, not because the tools are better for them, but because they have the taste to know what to build and the discipline to catch what AI gets wrong. The winning formula Roth identifies – taste times discipline times leverage – is multiplicative, not additive. Zero out any factor and the whole thing collapses. Linear’s zero-bugs policy, Stripe’s three-tier quality model, spec-driven development before anyone touches a prompt – these examples aren’t process theater, they’re the guardrails that make AI velocity safe instead of reckless. The takeaway for leaders isn’t “adopt more AI.” It’s that AI is about to make your existing culture problems deafeningly loud. Fix the fundamentals first, or watch the gap between your team and the elite ones widen faster than ever.
— CJ Roth, 14m, #engineering, #ai, #leadership
Stop Being the System
If you’re spending your mornings nudging tickets and chasing updates, you don’t have a discipline problem, you have a clarity problem. This piece nails a pattern most engineering managers recognize but rarely diagnose: when capable professionals consistently ignore your tracking process, it’s because the real status lives in someone’s head and surfaces in Slack threads, not in whatever tool you’ve decided is the source of truth. The author’s confession is the real gem: he accidentally trained his team to wait for his follow-ups before acting, building the exact dependency he was trying to eliminate. Every nudge you send is an implicit priority signal that competes with the last one, turning you into a human JIRA bot rather than a leader. The fix is to engage in ruthless narrowing. Two priorities, not ten. Explicit “why” statements connecting work to outcomes. Meetings redesigned to surface surprises instead of recite status. And here’s the litmus test that’ll keep you honest: take a full week off. If work stalls, you are the system. If things keep moving, your team has internalized what matters.
— Blog4EMs, 5m, #management, #coordination, #systems
Purple Chain
Jimi Hendrix wasn’t just the greatest guitarist who ever lived, he was a systems engineer who happened to express his work through music. IEEE Spectrum makes a compelling case that Hendrix’s real innovation was his understanding of how components interact within a complex system. He didn’t just plug in a distortion pedal and crank the volume. He designed integrated signal chains where each element – guitar pickups, fuzz boxes, wah-wah pedals, amplifier tubes – fed into the next, producing sounds that were literally impossible by treating any piece in isolation. Sound familiar? It should. The best engineering leaders think the same way about their organizations. Optimizing individual components – your CI pipeline, your sprint process, your hiring funnel – means nothing if you don’t understand how they interact as a system. Hendrix’s genius was recognizing that the connections between components mattered more than the components themselves. In other words, stop designing parts and start designing systems.
— IEEE Spectrum, 8m, #systems, #engineering, #leadership
Echo of the Week
Echoes are AI agents in Steady that automatically gather and deliver work context to teams on a schedule—answering recurring questions about progress, capacity, and coordination so you stop burning hours assembling the same information manually.
Automatic Changelog — Your team merges PRs all week, but translating that into something customers actually understand? That’s the part nobody wants to do. This Echo watches your merged GitHub PRs and automatically generates customer-facing release notes, organized into new features, improvements, and bug fixes — no technical jargon, no manual compilation. Product managers, engineering leads, and DevRel teams get a ready-to-publish draft every two weeks instead of spending an hour piecing one together.
The lightweight teamwork OS
Teams rely on two coordination loops to function: a big-picture loop connecting plans to progress, and a ground-level loop keeping teammates in sync.
Problem is, status quo approaches to running those loops are an incomplete, inconsistent, and inefficient tangle of meetings, emails, chat threads, dashboards, and manual toil.
Steady is the teamwork OS that runs both loops for you. Purpose-built agents continuously distill updates and activity into personalized intelligence that keeps everyone aligned and informed automatically.
The outcome: high-performing teams that deliver better work, 3X faster.
Learn more at runsteady.com.
