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.
If a Team Ships and No One Hears It…
In the dark forest of modern work, if your team ships something but no one knows—did it even happen? Gregor argues that not communicating your team’s impact is functionally the same as not having any. Engineering teams, in particular, are guilty of this stealth-mode behavior, often mistaking internal clarity for external visibility. The solution? Treat communication like a feature, not an afterthought. Write the dang changelog. Celebrate progress. Translate commits into context. He offers a dead-simple checklist: tell people what’s going on, why it matters, and who made it happen. And repeat yourself more than you think you need to—because most people are distracted, busy, or just plain forgetful. Visibility isn’t vanity; it’s how you earn trust, secure resourcing, and build momentum.
Editor’s note: If you’re using Steady, here’s an agent you can share that sends out an automatic, jargon-free changelog from your PRs every Sunday night. Go forth and brag!
— Engineering Leadership Newsletter, 4m, #leadership, #communication, #engineering
The Agent Will See You Now
Cal Newport predicts the future of work won’t revolve around copilots that autocomplete your thoughts, but autonomous AI agents that take over entire tasks—like scheduling, email triage, or basic planning—without your constant oversight. This shift won’t just accelerate workflows; it’ll rewrite them. Forget speeding up busywork—think redefining what “delegation” and “ownership” mean when bots handle whole responsibilities. This isn’t about AGI; it’s about practical, domain-specific agents already on the rise. Leaders should stop asking what AI can do faster and start asking what it can do instead.
— Cal Newport, 7m, #futureofwork, #aiagents, #coordination
Liar, Liar, Neural Net on Fire
AI hallucinations aren’t a glitch in the matrix—they’re a feature. That’s the unnerving takeaway from New Scientist’s latest on why LLMs continue to fabricate facts with such eloquent confidence. Despite the hype cycles and headlines, hallucinations aren’t going away anytime soon. Why? Because the same probabilistic guesswork that makes these models so creative and fluent also makes them prone to conjuring nonsense. The bigger and more powerful the model, the more it hallucinates—especially when asked to do complex reasoning or synthesize across domains. Even guardrails like retrieval-augmented generation (RAG) or fine-tuning only do so much, especially when the underlying training soup is opaque or outdated. Experts argue that until we rethink the architecture of these systems, we’ll be stuck with helpful liars. For now, enterprises are best off treating LLMs like charismatic interns: dazzling, fast, occasionally brilliant… but always needing a fact-check.
— New Scientist, 6m, #ai, #risk, #decisionmaking
Not So Fast, HAL
A software architect put four leading LLMs (GPT-4, Claude, Gemini, and Mistral) through a battery of real-world architecture challenges to answer the burning question: Can AI replace software architects? Spoiler: not yet — and maybe not ever in the ways that matter most. While the models could spit out decent high-level designs and throw around buzzwords like “scalability” and “microservices” with the confidence of a bootcamp grad on day one, they flailed when it came to nuance, tradeoff thinking, and real contextual understanding. Claude led the pack (with GPT-4 close behind), but all of them lacked the judgment and intuition seasoned architects bring to the table. The biggest gap? Strategic communication. These AIs couldn’t tailor solutions to stakeholders or articulate why a particular design mattered in a given business context.
— LevelUp, 9m, #ai, #architecture, #softwareengineering
AI That Doesn’t Drift
In a world racing to adopt AI, the real winners are those who actually figure out how to use it. In this sharp dispatch from the front lines, Ethan Mollick distills what he’s learned by running hundreds of leadership and AI adoption workshops. Spoiler: most orgs fail not because the tech doesn’t work, but because they don’t. Mollick outlines a “Leadership Lab” framework—a structured, short-term sprint where teams actually try AI, then report back and evolve their playbook. The idea is to close the yawning gap between potential and practice. The piece gives practical tips for making AI stick: assign a “red team” to find failure modes, pair every experiment with a metric, and never assume what worked in one role will translate to another.
— One Useful Thing, 9m, #aiadoption, #leadership, #orgdesign
Teamwork for the AI Era
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