The power of subtle code changes: a 2-line fix that saved weeks of downtime

Your engineering teams are failing to meet deadlines. Again. Your first instinct? Demand better estimates, stricter accountability, more detailed planning. You're making the problem worse. Full stop. After 20+ years building software teams, I've watched this same cycle destroy otherwise brilliant organizations. The harder you squeeze for predictability, the more unpredictable your teams become. The more you demand accurate estimates, the less accurate they get. Software development isn't manufacturing. It's R&D work. Every project involves unique combinations of people, technology, and constraints. When you treat complex work like an assembly line, you get estimate inflation, corner-cutting, and innovation paralysis. The companies outperforming you aren't chasing predictability through estimates. They're building adaptive capacity through frequent delivery, probabilistic forecasting, and outcome-focused accountability. Real predictability comes from shipping value consistently, not hitting arbitrary dates. Your turn: What's more important to your business, teams that look predictable on spreadsheets, or teams that consistently deliver value to customers? Link to the full article in the comments. #softwaredevelopment #predictability #estimationtheatre #agility

Refactor, Replace, or Rewrite? A Framework for Engineering Leaders Sometimes we face the painful question: ➡️ Do we refactor what we have? ➡️ Do we replace with something off-the-shelf? ➡️ Do we rewrite from scratch? Here’s a simple framework I use: 🔧 Refactor — When the architecture is solid, but code quality is slowing development. 🛒 Replace — When the problem is solved better (and cheaper) by existing tools. 📝 Rewrite — When technical debt, outdated tech, or architecture bottlenecks block future growth. ❌ The mistake? Jumping straight to rewrite without validating if refactoring or replacing could deliver the same business value faster. ✅ The right call aligns with: Current pain (speed, cost, stability) Future vision (scalability, adaptability) Business priorities (not just engineering elegance) 👉 Curious: Have you been part of a rewrite that failed or one that succeeded? What made the difference?

Most CTOs can't answer this question: "Where are we actually spending our engineering hours?" And that's a $10M+ blind spot. I was talking to a CTO recently who thought his team was spending 80% of their time on new features. Reality: They were spending 45% of their time on new features and 55% on technical debt, bug fixes, and unplanned work. That's not a developer problem. That's a business problem. When you don't have visibility into how code quality impacts your engineering investment, you can't make strategic decisions about where to focus. Here's what engineering leaders are starting to track: → Investment Hours by Category: How much time goes to features vs. debt vs. maintenance → Change Failure Rate Impact: What percentage of deployments require immediate fixes → Cycle Time Trends: How code quality affects your ability to deliver features quickly → Developer Focus Time: How much uninterrupted time developers get for strategic work The teams that measure this stuff are making data-driven decisions about technical debt prioritization. Instead of arguing about whether to "slow down and fix things," they're showing exactly how much fixing specific quality issues will accelerate future delivery. Quality isn't the opposite of speed. Poor quality is what makes you slow. But you can only optimize what you can measure. What metrics do you use to connect code quality to business outcomes? #EngineeringIntelligence #InvestmentHours #TechnicalDebt #EngineeringMetrics

🔥 Chaos Engineering in Practice: Breaking Things to Build Resilience 🔥 Downtime is inevitable — but how prepared are your systems and teams when things fail unexpectedly? That’s where Chaos Engineering steps in. Instead of waiting for real-world outages, organisations are now deliberately injecting failures in controlled environments to uncover hidden weaknesses. Here’s what practical Chaos Engineering looks like: 🔹 Start Small – Introduce minor faults (like killing a pod or throttling network) in staging before touching production. 🔹 Define a Hypothesis – Example: “If service A fails, service B should still respond within 200ms.” 🔹 Automate Experiments – Use tools like Gremlin, LitmusChaos, or Chaos Mesh to continuously validate assumptions. 🔹 Measure with SLOs – Align chaos experiments with Service Level Objectives to know if resilience meets customer expectations. 🔹 Iterate Safely – Expand experiments gradually, involving not just infra but also incident response teams. 👉 The goal is not to create chaos, but to build confidence that systems can survive chaos. 💡 In practice, Chaos Engineering transforms outages into learning opportunities, making both systems and teams more resilient. Would you run chaos experiments in production, or keep them in lower environments only? #ChaosEngineering #SRE #DevOps #Reliability #Observability #CloudNative

📊 Engineering Teams Missing 65% of Q3 Commitments - Here's the $2.4M Hidden Cost Remember your Q3 roadmap from June? How many features actually shipped on time? If you're like most teams at 35-50% predictability, the answer hurts.   𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 July 1st: "We'll deliver 12 features this quarter!" September 2nd: 4 shipped, 3 delayed, 5 haven't started. Your board meeting is in 3 weeks. The story writes itself:   • "Unexpected technical challenges"   • "Resource constraints"   • "Scope creep from customer requests" 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻: We have no idea why we can't deliver predictably. 𝗧𝗵𝗲 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁 A client CEO told me: "I'm tired of apologizing for my engineering team. They're brilliant. Something else is broken." He was right. The people weren't the problem. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘄𝗮𝘀.   𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 ✅ The Predictability Health Audit™ uncovered the truth in 2 weeks: ✅ Requirements changed 𝟰.𝟯 𝘁𝗶𝗺𝗲𝘀 per feature ✅ Testing bottleneck added 𝟴 𝗱𝗮𝘆𝘀 to every release ✅ "Priority" meant nothing - everything was P1 We built their "Path to 80%" roadmap based on data, not opinions.   𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 Next quarter (with the same team): • 𝟭𝟭 𝗼𝗳 𝟭𝟮 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 shipped on time • Sprint velocity became predictable within 𝟭𝟬 - 𝟮𝟬% • Customer complaints dropped 𝟲𝟳%   The kicker? We spent $𝟬 𝗼𝗻 𝗻𝗲𝘄 𝗵𝗶𝗿𝗲𝘀.   📍 Be honest: 𝗪𝗵𝗮𝘁 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗤𝟯 𝗰𝗼𝗺𝗺𝗶𝘁𝗺𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗵𝗶𝗽 𝗯𝘆 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟯𝟬𝘁𝗵? Stop guessing. Start measuring: 📅 Schedule your free 30-minute assessment consultation now! https://lnkd.in/gtMFqADh 🔔 Ring the bell if you like it. ♻️ Repost to assist others turn Q3 goals into reality! 🎯Follow The Process Mechanic™ for more like this. #PredictabilityHealthAudit™ #EngineeringLeadership #ProcessOptimization #TechDebt #DeliveryExcellence #TheProcessMechanic #SoftwareDevelopment #CTOInsights #ScalingTech #OperationalExcellence

𝗧𝗵𝗶𝗻𝗸 𝗕𝗶𝗴: 𝗔 𝗛𝗮𝗯𝗶𝘁, 𝗡𝗼𝘁 𝗮 𝗩𝗮𝗰𝗮𝘁𝗶𝗼𝗻 Are you being asked to Think Big and wondering how to do it? "𝘚𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘵𝘢𝘤𝘵𝘪𝘤𝘴 𝘪𝘴 𝘵𝘩𝘦 𝘴𝘭𝘰𝘸𝘦𝘴𝘵 𝘳𝘰𝘶𝘵𝘦 𝘵𝘰 𝘷𝘪𝘤𝘵𝘰𝘳𝘺. 𝘛𝘢𝘤𝘵𝘪𝘤𝘴 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘪𝘴 𝘵𝘩𝘦 𝘯𝘰𝘪𝘴𝘦 𝘣𝘦𝘧𝘰𝘳𝘦 𝘥𝘦𝘧𝘦𝘢𝘵." - Sun Tzu This quote perfectly captures the engineer’s dilemma. We're great at the tactical grind—writing code, fixing bugs. But how do we find time for the strategic work of "thinking big"? I've learned that you don't block out a day to think; you build it into your daily work. Here’s what one of the approaches I follow: 1. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗔𝗹𝘄𝗮𝘆𝘀. Big ideas are born from deep awareness. I spend time learning customer pain points, developer frustrations, and our long-term vision. I look at what competitors (in and out of the company) are doing to understand the strategic landscape. 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗡𝗼𝘁𝗲𝘀, 𝗡𝗼𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀. In meetings, if a short-term fix is discussed, I make a note of the long-term problem that still exists. When I fix a high severity issue, I ask if my changes could be a tool for others to solve similar bugs. These are just small observations—I collect the dots without trying to connect them yet. 3. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗗𝗼𝘁𝘀. Every week, I take a 1-hour break to review my notes. This is where I find patterns in the problems I've faced. That short-term fix I noted down? It becomes the seed for a major architectural proposal. My bug-fixing script? It’s now the start of a widely-used internal tool. Thinking big isn't a single event; it's the consistent practice of paying attention to the details and connecting them over time. It's a habit, not a vacation. #softwareengineer #thinkbig #strategicthinking #engineering

Chaos engineering: breaking things on purpose to build resilience It sounds counterintuitive: why would you intentionally break your own system? Because in complex distributed systems, failure isn’t a possibility — it’s a certainty. The question is whether you’ll discover those weaknesses on your terms, or during an outage at 2 AM. What chaos engineering is Chaos engineering is the practice of deliberately injecting failures into your system — network delays, service crashes, resource exhaustion — to see how it responds. It’s not about causing chaos. It’s about building confidence in resilience. Why it matters ✅ Uncover hidden weaknesses – Find cascading failures before your customers do. ✅ Improve incident response – Train teams to react quickly under real conditions. ✅ Strengthen architecture – Force systems to degrade gracefully instead of collapsing. ✅ Build trust – Leadership and customers gain confidence that you’re prepared for the unexpected. The risks if you skip it ⚠️ Single points of failure stay invisible until they break. ⚠ Incident response becomes guesswork under pressure. ⚠️ Outages cost more in downtime, lost trust, and revenue than prevention ever would. The takeaway: Chaos engineering isn’t about breaking things for fun. It’s about creating controlled failure so your system — and your team — are stronger when real chaos happens. Would your team dare to break production on purpose to test resilience? #ChaosEngineering #SystemDesign #Resilience #DevOps #DistributedSystems #SoftwareReliability

How often do we truly measure what matters in engineering? Time and again, leaders fall back on vanity metrics that look like progress but say little about value, quality, or sustainability. Take velocity. It was designed as a planning tool, but in many organisations it’s used as a performance metric. The result? Pressure to inflate estimates, rewards for volume over impact, and teams optimising for motion rather than outcomes. Velocity is the most visible culprit, but it’s only one example of a wider trap. 𝗥𝗲𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗰𝘀. If you want to measure what matters, focus on metrics that reflect: • Flow of work (cycle time, lead time for changes) • Delivery of value (deployment frequency) • Reliability (incident recovery, change failure rate) • Sustainability (team health and engagement) These measures don’t just track activity. They reveal effectiveness. They build trust, highlight systemic issues, and connect engineering to customer impact. I’ve written more on why vanity metrics like velocity mislead us and how to replace them with indicators that truly matter: https://lnkd.in/dcfJg72m Because 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝘀𝗻’𝘁 𝗳𝗮𝘀𝘁𝗲𝗿 𝘀𝗽𝗿𝗶𝗻𝘁𝘀. 𝗜𝘁’𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀.

Most teams measure engineering in sprints, tickets, uptime. Useful, sure, but when you’re running at real enterprise scale… Those metrics stop telling the full story. I love the shift Steve Jang talks about here: cost per customer interaction. Suddenly, engineering performance isn’t just about system health; it’s about business value. It connects what your teams build to how the business grows. For me, that’s where clarity comes from. Complexity is unavoidable when you’re processing billions of signals and interactions. The trick is finding the one metric that cuts through the noise and makes sense to everyone, from engineers to the board. That way, decisions stop being “how fast can we ship?” and start being “is this the best use of our resources to serve the customer?” If you had to pick one metric that ties engineering directly to business value in your world, what would it be?

Everyone's doing prompt engineering wrong 😬 After analyzing thousands of successful prompts, I found most people skip the fundamentals and jump straight to "clever tricks." Here's the 7-Layer Prompt Framework that actually works: 🎯 Layer 1: Role Definition Don't just say "you are helpful." Be specific: "You are a senior product manager at a B2B SaaS company with 5+ years experience in user research." 📋 Layer 2: Clear Task Directive Lead with action. "Analyze these user interview transcripts and extract the top 3 pain points affecting onboarding." 📄 Layer 3: Output Format Be exact. "Present as bullet points, each under 25 words, with confidence level and supporting quote." 🚧 Layer 4: Constraints Prevent hallucinations. "Base conclusions only on provided transcripts. If unclear, state 'insufficient data.'" The difference? Night and day results. Which layer do you think most people mess up? My guess is Layer 4 - constraints. Everyone wants creative output but forgets to set boundaries 🤷♂️ Link for the Complete guide in the comments section below