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.
Context Engineering
The next evolution of AI isn’t just about smarter models, it’s about smarter context. While “prompt engineering” focused on crafting the perfect query for chatbots, “context engineering” is the art of automatically providing dynamic, relevant information to AI agents in real-time. Think of it as the difference between giving someone a task via sticky note versus briefing them with all the background, resources, and nuanced understanding they need to excel. As 2025’s models improve, the bottleneck shifts from raw capability to contextual awareness — even the smartest human can’t perform without knowing what they’re actually supposed to do. This isn’t just theoretical fluff. Companies like Anthropic are using context engineering for multi-agent research systems. But the security implications are real: when agents access private data, untrusted web content, and external services, poor context management creates “a lethal trifecta for security”. For team leaders orchestrating AI-powered workflows, mastering the flow of context is crucial — determining whether your AI agents are strategic assets or just expensive digital interns. (At Steady, you can skip the security concerns with SOC2 Type 2 -certified agents for context assembly.)
— Ben’s Bites, 4m, #ai-agents, #context-engineering, #team-coordination
Read to Write
Want to write better at work? Stop trying to reinvent the wheel and start studying what already works. A senior Meta engineer shares their opinion: to write well, you first need to read well. The secret isn’t grinding out more drafts, it’s developing taste by reverse-engineering the influential writing in your organization. Hunt down the docs, posts, and messages from senior folks that actually get engagement (look for those Slack reactions and comment threads). Ask your management chain and senior ICs to point you toward quality examples. Once you’ve collected your samples, dissect them like a forensic investigator. What made people stop scrolling? What drove them to share? Form theories, test them in your own writing, and iterate based on feedback. It’s not rocket science, but it requires the discipline to learn from the right sources rather than stumbling around in the dark hoping your next email will somehow magically resonate.
— Developing.dev, 3m, #communication, #leadership, #writing
Sideways Programming
Martin Fowler argues that LLMs represent the biggest shift in software development since we moved from assembly language to high-level programming languages like COBOL and Fortran. But while previous advances simply raised our abstraction level (think Ruby vs Fortran—more sophisticated, but fundamentally the same conversation with the machine), LLMs are doing something unprecedented. They’re not just lifting us higher up the abstraction ladder; they’re pushing us sideways into non-deterministic territory. When you compile a Fortran function a hundred times, you get the same bugs every time. When you prompt an LLM a hundred times, you might get a hundred different results. This is a fundamental change in how we think about programming itself. We have to learn to work with tools that don’t behave predictably, which means we need new mental models, new workflows, and probably new ways to debug when things go sideways (heh).
— Martin Fowler, 4m, #engineering, #ai, #leadership
System Takeover
The software world is splitting into two camps: those who help you run your business and those who actually do the work. In vertical SaaS, AI is creating a new battleground called the “system of action” — where the winner doesn’t just manage workflows but executes them. Tidemark Capital breaks down how this shift is happening through a fascinating lens: native AI startups are bypassing traditional control points by targeting “Hero users” (influential practitioners who can buy tools independently) with magical experiences that automate their most painful tasks. Take veterinary software: while incumbents gold-plate their practice management systems, scrappy AI transcription tools are winning over overworked vets who just want their documentation pain to disappear. These tools start small but use integration tactics to pull data from existing systems, gradually expanding to own entire workflows. The incumbents who survive will be those who abandon perfectionism, embrace product-led growth, and race to become the place where AI agents actually execute work rather than just facilitate it.
— Tidemark, 12m, #ai, #saas, #strategy
Reality Check
This guide cuts through the Silicon Valley hype to give engineering leaders practical tactics for managing wildly unrealistic AI expectations without torching relationships or burning out their teams. The core problem isn’t AI itself — it’s the sensational takes from tech celebrities claiming Microsoft’s code is 30% AI-written, creating a trickle-down effect of impossible demands from non-technical stakeholders. The solution starts with building unshakeable credibility and trust, because when business leaders hear “this AI tool will revolutionize everything,” you need the gravitas to push back with data, not desperation. Smart leaders focus on small experiments, measure actual impact with real metrics, and educate themselves on practical AI applications rather than getting swept up in the headlines. The key is positioning yourself as the voice of reason who can separate the signal from the noise, helping your organization adopt AI thoughtfully rather than recklessly chasing every shiny new automation promise.
— Engineering Leadership Newsletter, 6m, #expectations, #ai, #leadership
Teamwork for the AI Era
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