Engineering Problem-Solving Techniques

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas

The Right Way to Solve a DSA Problem! I’ve been solving at least one DSA problem on LeetCode almost every day for more than a year... hardly a day goes by when I don’t. And during this time, I realized there’s one common mistake that almost everyone (including me) makes. You start solving a problem. You get stuck. You check the first hint... then the second... then the discussion section. Still can’t figure it out. Finally, you open the solution and boom, it looks so simple. You copy it, submit it, feel great for a moment… and move on. But here’s the problem a month later, you see the same question again and get stuck in the exact same place. You vaguely remember the approach but can’t recall the logic. You open the solution again, try to memorize it again… and the same cycle repeats. Welcome to the DSA Trap... where you think you understood the problem, but can’t solve it on your own. 💡 How to Break Out of it! Start by reading the problem statement properly... understand what’s being asked and what the expected output is. Then, give it time. Think through multiple approaches and always begin with the brute force method. It builds confidence and ensures you’ve understood the problem correctly. Write pseudocode before you code. Pseudocode helps you visualize the logic and exposes the pattern behind the solution. If you still can’t solve it, look at others’ solutions, but don’t just memorize. Understand why the solution works. If it’s still unclear, watch a tutorial (I personally recommend this youtube channel @codestorywithMIK). After you understand the concept, immediately solve 2-3 similar problems. This is how you lock the pattern into your brain. One more practical tip: keep an Excel/Notion sheet or a notebook of problems you couldn’t solve on the first try. Revisit it weekly. Once you can solve a problem on your own, remove it. Over time, the list shrinks and your confidence grows. ✨ Hope this adds some value to your DSA journey! #DSA #ProblemSolving #Coding #LeetCode #Learning #Consistency #Programming

I am an Engineering Manager working at Google with almost 20 years of experience. If I could sit down with a Jr. Software Engineer, here are 50 cheat codes I would share with them that I learned from my experiences. [1] Ask why this system even needs to exist ➤ Before a single line is written, challenge the core purpose, “Is this a business problem or just a tech exercise?” Real systems solve pain, not boredom. [2] Redraw the lines, define what’s “inside” and “outside” your system ➤ Figure out where your service starts, stops, and how it talks to the world. 80% of future headaches come from blurred boundaries. [3] Don’t chase new tech for the resume, use what your org supports ➤ That AWS Lambda demo looks cool until your team tells you there’s a 10-year-old Jenkins server already scheduled to do that job. Proven > Shiny. [4] System design isn’t “one size fits all”, context is everything ➤ YouTube and interview videos show perfect worlds. Your system will live in mess, legacy, and compromise. Embrace it. [5] Optimize for “how easy to change?” not “how cool is this?” ➤ You won’t get it perfect first time. Make it so anyone (even you) can swap out parts later, with minimal pain. [6] Start with use cases, not tech ➤ Interview solutions start with “put Kafka here.” Real solutions start with “who will use this and how?” [7] Know your real users, not just your APIs ➤ Customers, PMs, even other devs, all are “users” with needs. If your “system” forgets one, it’s doomed. [8] Design for the traffic you have, not the traffic you dream of ➤ Every engineer who overbuilt for ‘Google scale’ at a 10K user startup has regretted it. Scale when you must. [9] Understand your company’s default tech stack, don’t fight it ➤ Don’t propose a NoSQL database if everyone else is running Postgres unless you have a bulletproof case. [10] Pick the boring solution if you want peace ➤ Every time I chased “the best tech,” maintenance bit me back. The system you forget about is the most stable one. [11] Get the team’s buy-in before you architect a “masterpiece” ➤ Don’t be a solo hero. Feedback from PMs, ops, QA, other engineers, all of it will expose what you missed. [12] Refactor and cleanup aren’t “nice to haves”, they’re your real job ➤ Every shortcut you leave will double your pain in 6 months. [13] Read logs and production metrics every week ➤ Production is where truth lives. Ignore at your own risk. [14] Test how things break, not just how they work ➤ Simulate failing databases, crashing services, weird user flows, assume chaos is coming. [15] You’ll be asked to fix code you didn’t write, embrace it ➤ Legacy code is half your career. Treat it with respect and curiosity, not blame. [16] Never let a diagram go out without clear boundaries ➤ Always show what’s external, what’s internal, and what’s a dependency, otherwise, no one will know what breaks what.

Too often, innovation gets associated with billion-dollar labs. What do you think about this one? Sometimes… it comes from a guy in a garage. Enter Colin Furze and his Magnetic Suspension Board. No springs. No traditional mechanics. Just raw engineering curiosity pushing boundaries. What looks like a wild experiment is actually something deeper: 👉 Replacing physical contact with magnetic force 👉 Exploring frictionless suspension concepts 👉 Challenging how we think about motion, stability, and control This is how real innovation starts. Not polished. Not perfect. But bold enough to question fundamentals. While enterprises debate roadmaps and ROI… people like Colin are testing the edges of physics in real time. And here’s the takeaway for leaders and builders: ⚡ Breakthroughs don’t always come from scaling what exists ⚡ They come from rethinking first principles ⚡ And having the courage to build what shouldn’t work Today it’s a magnetic skateboard. Tomorrow? New suspension systems. New transport models. New industries. The future doesn’t arrive fully engineered. It starts as something that looks a little crazy. #Innovation #Engineering via @realcolinfurze #FutureTech #Leadership #Startups #DeepTech #AI #Hardware #FirstPrinciples

I’ve been waiting a long time to show an example of what gets me up in the morning… Because in this world, failure isn’t the end — it’s the start of real insight! 💥 During hot-fire testing, an additively manufactured GRCop-42 combustion chamber failed — and with it, offered a powerful #FailureAnalysis case study on the critical role of process rigor in additive manufacturing, especially when builds are interrupted. We conducted a full failure analysis: reviewing test day data, manufacturing records, post-processing steps, and metallurgical characteristics of both the failed chamber and adjacent components. 🔬 Key findings: •⁠ ⁠Failure occurred at a build interruption location, witness line, with metallographic analysis revealing higher porosity than expected. •⁠ ⁠This localized porosity reduced tensile strength and elongation, triggering the failure. •⁠ ⁠Interestingly, test bars with emulated build interruptions showed no performance degradation — confirming that proper restart procedures preserve part integrity. Additive manufacturing offers incredible promise, but as this work shows, it also demands discipline. Especially when the stakes are rocket engines. 🔗 Full article: https://lnkd.in/ekg-t4MH Ben Williams, Colton Katsarelis, Will Tilson, and Paul Gradl, thank you for the collaboration in making this fun analysis and article! #AdditiveManufacturing #RocketEngines #FailureAnalysis #MaterialsScience #GRCop42

A huge mistake to avoid in pursuing "Shift Left Data" practices is assuming that upstream engineers will readily accept the additional work and constraints. At the end of the day, pushing data quality and governance upstream creates more processes for engineers. You will fail to launch any "shift left" approach if you push these constraints on engineers without context and buy-in. Many data teams realize this and then decide to stop here, as they feel like they can't ask for extra work. Why would they believe otherwise if every past interaction was upstream engineers deprioritizing their data requests? The data teams that succeed are the ones that instead ask, "How do we incentivize upstream engineers to take on additional constraints for data?" Think about it! Upstream engineers are not against MEANINGFUL constraints. Their whole paradigm around unit tests, CI/CD, version control, etc. are all constraints they not only accept, but demand for a stable code repo. Here are five approaches that we have found incentivize upstream engineers to start thinking about data: 1. Embed within their existing developer workflow so they don't have another tool to deal with (and be less likely to adopt)-- tests within the PR workflow are key. 2. Provide insights on their code repo that would require substantial manual work. This is why static code analysis has been a powerful tool for getting engineers to adopt contracts. 3. Treat alerts with the utmost care, as they can be the difference between engineers taking action and engineers losing all trust in future alerts (e.g., alert fatigue). 4. The moment you block an engineer from merging code AND you highlight how the blocked change prevented a major issue, is the moment you gain the engineer's trust. That's why context-heavy blocking alerts are critical. 5. Make sure the DevOps engineer's (or whoever oversees the CI/CD pipeline) job is as easy as possible to implement constraints, such as data contracts, as they are often the ones with first impressions that other engineers will look to. I hope this helps!

To become a top data analyst you need to be a strong problem solver! Follow this structure to find the real reasons behind business problems: 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Start by clearly stating the issue. For example, “We’ve observed a significant decrease in sales in the UK over the last few days.”   2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect relevant information such as order processing times, customer service interactions, inventory levels, and active marketing campaigns.   3. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: Use tools like SQL, Python, or Excel to analyze the data. Look for patterns, trends, and anomalies that could point to the root cause.   4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: Brainstorm all possible reasons for the issue. Use methods like the 5 Whys technique to investigate each potential cause more deeply.   5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Test your hypotheses against the data to see if they are supported. If not, refine your hypotheses and test again.   6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Once you’ve identified the root cause, support the business by showing possible solutions to address it. Monitor the results to ensure the issue is resolved. 𝗔 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗽𝗮𝘀𝘁: We notice an increase in customer lead time and here’s how we tackle it. 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: “Customer lead time has increased by 20% in the last three months.”     2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: We collected data on order processing, sales forecast deviation, and shipping times.     3. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: We found that the actual sales were in line with the forecast, and shipping times had remained constant. However, order processing times had increased significantly.     4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: We checked factors such as outages in warehouses, staffing issues due to high sickness rates, and process inefficiencies resulting from operating close to maximum capacity.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Data revealed that a spike in the sickness rate had reduced the available workforce.     6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: We proposed to increase capacity buffers by 5% to 10% during the winter and hiring additional temporary workers to address the situation in the short term.   Following this approach for your root-cause analysis, you will become a valued problem-solving partner for your stakeholders. How do you ensure you’re addressing the root cause of an issue and not just the symptoms? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #rootcauseanalysis #problemsolving #careergrowth

🎯 Why Most Business Problems Remain Unsolved (And How to Fix That) Last week, I had the privilege of facilitating a Problem Solving & Business Acumen workshop for our teams at L'Oréal Indonesia. 💡 The Problem We All Face (But Rarely Talk About) Here's an uncomfortable truth: we're wired to jump to solutions. In business, this looks like: ✔️ Launching promotions without understanding why sales declined ✔️ Hiring more people without diagnosing process inefficiencies ✔️ Copying competitor tactics without validating if they fit our context The cost? Wasted resources, frustrated teams, and recurring problems that never truly go away. According to the World Economic Forum's Future of Jobs Report 2023, analytical and critical thinking are the #1 and #2 most important skills for workers. Yet, most of us were never formally taught how to think critically or solve problems systematically. 🛠️ The Problem-Solving Process: A Step-by-Step Guide Step 1: Define the Problem (Don't Jump to Judgment!) 📝 Craft a Problem Statement with 6 components: "How can [responsible party] improve/reduce [reality] to meet [expectation] within [timeline] without [anti-goals], in order to fulfill [reason]?" Example: "How can the product team launch a new product on time in Q4 2024 without sacrificing key processes, in order to meet the sales target?" Step 2: Find Alternatives (Issue Tree + MECE) Once the problem is clear, break it down using an Issue Tree. For instance, if mascara sales dropped -14% YoY: 📦 Placement → Gondola compliance, visibility, signage 🎁 Promotion → BOGO mechanics, POS materials 💰 Price → Elasticity, perceived value 🎨 Product Claims → Content freshness, reviews 🔥 Competition → Share of voice, endcap presence ✅ Ensure hypotheses are MECE (Mutually Exclusive, Collectively Exhaustive)—no overlaps, no gaps. Step 3: Test Your Hypotheses Don't fall in love with your first idea. Run quick tests: 📊 For a skincare serum declining in pharmacies, we tested: ✔️ Hypothesis A: Reduced pharmacist advocacy is the issue → Micro-detailing pilot in 10 stores ✔️ Hypothesis B: Cold chain OOS drives lost sales → Warehouse SOP audit + temperature logs ✔️ Hypothesis C: Execution gaps suppress promo ROI → Endcap compliance audit Each hypothesis had clear KPIs and timelines—no guessing, just data. Step 4: Make the Decision (Impact vs. Effort Matrix) Not all solutions are equal. Prioritize: 🟩 Quick wins—do this! 🟦 Strategic bets 🟨 Fill-ins 🟥 Avoid Focus on low effort, high impact moves first. Build momentum, then tackle the big bets. 🚨 What Happens When We Skip These Steps? A mascara brand saw sales drop -14% YoY. The reaction? "Let's run a BOGO promo!" The result? Sales stayed flat. Why? Because the real issues were: ❌ Poor gondola compliance (only 68% correct facings) ❌ Weak influencer share of voice ❌ Competitor secured prime endcap space The lesson: Solutions applied to the wrong problem = wasted budget and missed targets.

Here’s the uncomfortable truth I’ve learned in a decade of cost engineering: Most teams get cost feedback after the design is frozen. By then, the decision that made a part expensive is already locked in CAD. You can't unmake it. You can only absorb it. The best manufacturers operate differently. They move cost visibility upstream, directly into the design process. Here’s what that looks like in practice: A designer finishes the first iteration of a bracket and loads the 3D model into their workflow. The system runs a cost check in seconds and flags: "This geometry costs 40% more than your budget allows." The designer then has three options: 🛠️ Simplify the geometry. ⚙️ Change the process. ✅ Accept the cost. They choose. They decide. They own it. The design moves forward with cost baked in, avoiding the dreaded quoting surprise. It's about shifting the moment cost becomes visible from production ➡️ to design. When cost moves upstream, three things happen: 1️⃣ Expensive decisions become reversible again. 2️⃣ Designers get feedback they can actually act on. 3️⃣ Estimates become predictable, not surprising. This isn't about adding process. It's about moving information to where decisions still have options. How is your team currently handling cost visibility during the New Product Development cycle? Let me know below. 👇 #CostEngineering #ValueEngineering #SupplyChain #ProductDevelopment #Manufacturing

A decade ago, the boundary between Product Management and Engineering was very clear. Product managers focused on requirements, roadmaps, customer conversations, and prioritization. Engineers focused on system design, architecture, and building software. There was some overlap, but it was thin and deliberate. That separation made sense at the time. In today’s AI-driven world, that boundary is fading fast. With modern AI tools and vibe coding workflows, getting a working POC no longer requires weeks of detailed handoffs. Ideas can move from concept to something tangible in days, sometimes hours. In the past, a typical flow looked like this. A product manager wrote a PRD. Engineers interpreted it. The first real output appeared after multiple sprints. Feedback loops were slow and expensive. Today, the workflow is very different. Using AI-assisted coding, agents, and scaffolding tools, I can explore ideas end to end. I can think through the customer journey, define feature behavior, prototype logic, and validate feasibility early. Many assumptions get tested before formal engineering cycles even begin. This is completely changing the nature of the role. Product managers are no longer limited to conceptual ownership. They are increasingly shaping solutions at a technical level. Engineers, in parallel, are deeply involved in product decisions from day one. This is how Product and Engineering roles are blending into a Product and Engineering role. From my own experience, the technical depth I can reach today in AI product work is far deeper than before. I still need to understand product vision, customer journeys, and core product management fundamentals. But I also need to engage with architecture, model behavior, orchestration patterns, and system-level tradeoffs. AI tools make this possible. They compress learning curves and shorten feedback loops, but they also raise expectations. Staying shallow is no longer an option. Looking ahead, I see the intersection of Product and Engineering growing significantly. Over time, we may end up with thinner layers of dedicated Product roles and dedicated Engineering roles, with a much larger core where both blend together. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar