Nelson Lai’s Post

In the rapidly evolving realm of technology, it’s no longer sufficient to rely on assumptions and anecdotes when it comes to measuring success. After all, we're in the era of measurable engineering, where every facet of our organization is quantifiable, and data-driven insights are shared openly across the team. In this article, Joey McCord shares some knowledge about the true power of numbers. https://lnkd.in/g43BayFc

Engineers are the driving force behind every product, yet much of their work stays behind the scenes. This naturally makes measuring an engineering organization tricky. Unlike more visible departments, engineering’s impact is often woven into the backend processes, infrastructure, and optimizations that keep everything running smoothly—work that isn’t always easy to quantify or even see. So, how do you capture the true impact of an engineering team when much of what they do is unseen? Which metrics can genuinely reflect their contributions, without reducing their work to mere numbers? And how can these measurements support your engineering team’s growth and highlight their value across the organization? Our latest blog tackles these questions, exploring how to identify and use meaningful metrics that highlight the true value your engineering organisation brings every day. Read the full blog to see how you can make the invisible work of engineering teams visible in ways that truly matter. Check the link in our comments to read more. 📖🔗 #EngineeringOrganisations #EngineeringTeams #DeveloperProductivity

Tune into this episode of The Engineering Roundtable to discover how cloud-connected engineering software is transforming the game for individuals and small firms, freeing them from the constraints of limited capital and a modest headcount: https://lnkd.in/eaDi5HRD

"Introducing Co-Pilot for Engineering Leaders" Itechgenic has built an engineering intelligence tool that collects data from multiple dev-ops tools and provides transparency across all the layers of hierarchy right from the CTO to the team lead level. The key difference of this tool is the Itechgenic patented Model ingests, cleans, normalizes and scores signals from disparate engineering tools and identifies and predicts risks including quality alert, process, team communication, delivery execution, resource utilization, Allocation and resource health. Not Only does it provide recommendations based on your past data and industry best practices on how to mitigate risks, but also has built a scenario planning analyser that aids software teams and decision makers in recognizing various potential outcomes and implications, assessing how to respond to changing priorities and preparing for positive and negative eventualities. Website: https://lnkd.in/dyePQ7Rx

The Two Hardest Problems Engineers often joke that there are only two intractable problems in engineering… 1. Naming things 2. Off by one errors If you know anything about engineering, you know that this isn’t far from the truth. However, the truth is also that… Oftentimes, half the battle for business and product leadership can be disambiguating and naming things well. Names are very, very powerful. Obviously, they are essential to creating easy symbolic handles that people can use to refer to complex concepts or objects quickly. However, names, if used precisely, can be far more potent than that. They can also be used to… 1. Clarify the utility of a concept or entity E.g. Movie Search Page 2. Clarify the distinguishing characteristics of the given concept or entity from other concepts or utilities (particularly those that could be confused or conflated together) E.g. General Movie discovery vs Movie Search 3. Motivate and animate everyone toward a shared vision E.g. Movie Search 2.0 - Featuring Next-Gen Ranking 4. Imply a namespace for adjacent concepts that fit together - helping to map the concept space E.g. Movie Search, Movie Recommendations, Movie Rankings 5. Help drive important and implicit understanding in the problem domain E.g. I never mentioned the company I'm referring to in the examples, but I bet you could guess which one would have such features/concepts in their product. These few examples clearly show that naming things carefully can be a superpower for product and business leaders. It can be a key technique to accelerate everything from internal discussions, consensus building, and team alignment to external product marketing and sales.

3 New Prompt Engineering Resources to Check Out Check out these 3 recent prompt engineering resources to help you take your prompting game to the next level.

Empowering individual contributors (ICs) with a clear engineering strategy can accelerate technical decision-making. At Carta, the "Navigators" model combines senior engineers with a well-defined strategy to bridge global goals and local decisions, ensuring alignment and reducing the need for consensus. This approach balances autonomy with accountability, helping teams make quicker, informed decisions while maintaining strategic coherence. #TechnicalDecisionMaking #EngineeringStrategy #TeamAutonomy Accelerating Technical Decision-Making by Empowering ICs with Engineering Strategy

Top Engineering Metrics I wrote a new article about the metrics I like to use to understand the capabilities of a new engineering team or project.

Engineers often care too much about technology. Why? They aim for perfection and forget that impact comes from solving real problems. Here’s the shift that makes all the difference: 👇 Instead of focusing on the tech itself... 1️⃣ Identify a problem. 2️⃣ Propose a solution. Perfection isn’t the goal here; progress is. 3️⃣ Iterate relentlessly. You’ll fail (a lot). That’s by design. 4️⃣ Showcase your results when you get it right—and move on to the next problem. Impactful engineering is about solving problems, not perfecting technology. Focus on the problem, not the solution 💡 - You don’t need the latest state-of-the-art tools. - You don’t need to overengineer every detail. Instead… 👉 Build things that don’t scale. Test them, break them, and when they work, then build for scale. The art of impactful engineering lies in knowing when to keep it scrappy—and when to go big. #engineering #dataengineering #machinelearning #dataanalysis #careeradvice

One kinda surprising good-ish metric for engineering maturity within an org is how they deal with "hard" issues (ie: something which cannot be easily diagnosed or fixed, and/or where the initial reported data is misleading in terms of root causes, and/or where cost/benefit to address may not make sense). Immature orgs don't really have these types of issues; when you're just trying to get something out, you tend to ignore anything complex, and just do workarounds. Also, less experienced devs don't really have the ability to fix complex issues anyway, so often workarounds will be the only solutions (and also often, these will be confused with actual fixes by people who don't understand the difference, and/or just "tweak until it works" without understanding root causes). For these orgs, achieving "no bugs" is perceived as an achievable goal. As orgs grow, one of two things tends to happen: you create cultural understanding and processes to handle these types of issues better, or people continue to assume that everything can be fixed quickly. The former is a sign of engineering maturity; the latter is usually indicative of knowledge gaps (at one or more levels), and will usually lead to recurring issues over time.