Josh Hurst

How do you know if your developer is giving you a good deal? Most teams don’t — not because they lack expertise, but because developer incentives aren’t always aligned with the owner. A pattern I’m seeing: Operator brings their “preferred developer.” No RFP. No competitive pressure. Just: “Trust us.” 12-18 months later… Hidden fees surface. Terms shift. A termination fee appears out of nowhere. When the developer controls the process instead of the owner, surprises aren’t a fluke—they’re predictable.

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Meetings are where technical writers build visibility and influence. 📅 Here's how to make every one count: 1. Sprint Planning Catch undocumented features early. Flag docs dependencies before code freeze. 2. Design Review You're the user advocate. Stop bad UX before it ships. 3. SME Interview Ask task-first questions. Share the recording with stakeholders after. 4. Doc Review Get signoff. Push back on vague feedback. 5. Postmortem Own your part. Document lessons learned in your knowledge base. 6. Stakeholder Sync Show impact with metrics. Ask for resources. 7. All-Hands Listen for product shifts. Follow up 1:1 after. Show up prepared. Speak strategically. Ship better docs. This is how senior technical writers build influence. Which meeting type needs your attention most? Drop your thoughts below. 👇 Save this for your next sprint. Share it with a TW drowning in meetings. 📰 Want weekly insights on technical writing? Subscribe to my newsletter (link in the comments). Want more career insights for writers: 1. Follow Joshua Gene Fechter 2. Like the post 3. Repost to your network

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The AI productivity conversation has me wondering about a potential 'Big Uh-Oh' hiding in plain sight. Not taking sides here... just surfacing a tension I've seen in debates across QA, dev, and exec teams (customers as well as groups here on LinkedIn). Curious what y'all think (I know, a New Englander using "y'all"... cut me some slack)? The Root Tension: Production just moved to machine speed. Verification is still running at human speed. That's not acceleration... that's a high-velocity bottleneck. Your AI is generating, say, maybe 10x the output. But every unit still needs a human to determine if it's trustworthy. The review queue doesn't shrink because the tool is faster. It explodes. AI is the accelerator... a reinforcing loop driving volume up fast. Human verification is the balancing loop... the system's natural stabilizer. When those two forces fall out of balance, the system doesn't just slow down. It fails. The "Teenage Son" Analogy: My colleague's sage wisdom about his son comes to mind: "I trust him & I raised him right. But he's a teenage boy... they're inherently untrustworthy. So I love him, I trust him... and I verify the big things. Not because he's bad. Because he's unproven." To me it's a near perfect analogy. AI is exactly the same... well ... for now. Both will mature over time, but unproven systems require discipline, not just hope. QA Discipline: In my QA life, I run regression tests even when I expect zero bugs. You change the system, you validate the system. That's not distrust. That's discipline. The Balancing Loop Challenge: Corporate response right now seems to be cutting their "verification people" indiscriminately. Removing the "Human Hardware" exactly when production load is spiking. That's not tuning the balancing loop. That's disabling it. and before you say "just use a second AI to check the first one"... that's not balancing. That's two accelerators agreeing with each other. One machine validating another's "plausible enough sounding" output isn't verification. It's an echo chamber. AI can tell you if the code is well-formed. It can't tell you if it's right for the business. The machine knows the prompt; the human knows the consequences of the prompt being wrong. My Questions: Is the goal of sustainable AI productivity with certainty mean fewer humans or more? Lowering unit verification cost? Better craft? Smarter tooling? Architectural constraints that reduce what needs reviewing? Is anyone actually investing in the balancing loop (and How?)... or just riding the accelerator hoping nothing breaks? I am honestly seeking clarity & discussion here. I'm not against the accelerator. I just want to be cautious about a car with no brakes (every LinkedIn post needs a cliché, right?).

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Nate's one unique trick for leaders to leverage AI One opportunity that feels missed today is the tremendous availability of quality engineers in between positions right now. I have a number of former peers and friends that can be solid contributors to your codebase. AI makes the onboarding process so much easier than it has ever been. Look through your team's backlog, find a feature or service that doesn't touch any PII, doesn't add too much operational risk to your team. Give a candidate a two week contract. See what they can do. Maybe it doesn't work, but I bet there's a good chance you move the ball forward for your company. We worry so much about false positives in the hiring process, but we never test that theory. This could be a low stakes way to test that assumption. #oneteamoneheart

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