How accurate are your coding assessments?

Hiring decisions are only as good as the data behind them. 🚀 If your coding assessments treat a 75% like a 75%, you might be missing the bigger picture. At Codility, we’ve reimagined how engineering skills are scored - with Skills Intelligence Scoring that goes beyond percentages to deliver real, trustworthy insights. 👉 Discover why accuracy matters: https://bit.ly/4lRohoP 👉 See it in action: https://bit.ly/3JLeWS6

Does your organization have its own skills taxonomy? Our newest #CodilitySkillsIntelligence feature lets you upload your skills framework and map it to our Engineering Skills Model (2.0). Then when you create assessments and analyze individual & team skills, you'll see your organization's terminology throughout the platform. No manual translation. No replacing your framework. It just works! 📖 Read more: https://bit.ly/3ISRHFM 🧠 Learn about the Engineering Skills Model (2.0): https://bit.ly/48RDYti

Ever wondered how your organization’s skills taxonomy could plug directly into Codility? 👀 I’m really excited about our latest #CodilitySkillsIntelligence update - you can now upload your own skills framework and map it to our Engineering Skills Model (2.0). That means when you’re building assessments or analysing individual and team strengths, you’ll see your organisation’s own language reflected throughout the platform. - No manual mapping. - No changing your framework. - Just a seamless connection that works out of the box. 💡 📖 Read more about the update: https://bit.ly/3ISRHFM 🧠 Explore the Engineering Skills Model (2.0): https://bit.ly/48RDYti

🎯𝑷𝒓𝒂𝒄𝒕𝒊𝒄𝒆. 𝑫𝒆𝒃𝒖𝒈. 𝑺𝒐𝒍𝒗𝒆. 𝑹𝒆𝒑𝒆𝒂𝒕. 🔁 💡𝑴𝒂𝒔𝒕𝒆𝒓𝒊𝒏𝒈 𝑷𝒓𝒐𝒃𝒍𝒆𝒎-𝑺𝒐𝒍𝒗𝒊𝒏𝒈, 𝑶𝒏𝒆 𝑪𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒆 𝒂𝒕 𝒂 𝑻𝒊𝒎𝒆 ⚡ Problem solving isn't just about writing code — it's about 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐥𝐨𝐠𝐢𝐜𝐚𝐥𝐥𝐲, 𝐝𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐬𝐦𝐚𝐫𝐭𝐥𝐲, 𝐚𝐧𝐝 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐬𝐭𝐫𝐨𝐧𝐠 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬. Here’s a fun way to remember some key topics while keeping your practice streak alive! 🔁💡 𝐀𝐫𝐫𝐚𝐲𝐬 📚 – keep your data organized, fixed and neat 𝐀𝐫𝐫𝐚𝐲𝐋𝐢𝐬𝐭𝐬 🔄 – grow or shrink dynamically, as needed 𝐋𝐢𝐬𝐭𝐬 👥 – manage collections efficiently 𝐒𝐭𝐫𝐢𝐧𝐠𝐬 📝 – line up characters clearly 𝐒𝐭𝐫𝐢𝐧𝐠𝐁𝐮𝐢𝐥𝐝𝐞𝐫  ✏️ – lets you edit fast 𝐒𝐭𝐫𝐢𝐧𝐠𝐁𝐮𝐟𝐟𝐞𝐫 🛡️ – ensures thread-safe operations 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 ⏳ – helps analyze performance 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 🔍 – finds elements efficiently 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡 🧩 – splits the range for faster lookup 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 🏷️ – Merge Sort ⚡ is stable, Quick Sort 🔥 is fast 𝐀𝐫𝐫𝐚𝐲𝐬 & 𝐒𝐭𝐫𝐢𝐧𝐠𝐬 – your trusty problem-solving tools Practice, test, debug ⚡🏆💡 — sharpen your skills every day. 𝐂𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐢𝐧𝐠 𝐚 𝐋𝐞𝐞𝐭𝐂𝐨𝐝𝐞 𝐬𝐭𝐫𝐞𝐚𝐤: 35 𝐃𝐚𝐲𝐬! 🎯 #ProblemSolving #DSA #Debugging #TechnicalSkills #KeepLearning #CodingJourney #SoftwareEngineering

Turning IT Storms into Strategic Wins: Lessons from DSA ⚡ Ever feel like IT issues hit harder than a surprise debugging test? Don’t worry you’re not alone, and yes, there’s a method to this madness. In IT, problems aren't just occasional they're part of the daily grind. Slow systems , unexpected errors, network crashes … it feels like you're trapped in a loop of Problems - Stress - Pain, just like in this comic! 😤 Here’s the secret: solving IT problems is a lot like solving a DSA problem. Every challenge has a methodical solution you just need the right strategy.   Step 1 – Break it down: Just like a complex algorithm, divide the problem into smaller, digestible sub-problems. Complexity shrinks, clarity grows.   Step 2 – Stay calm: A stressed mind is like a stack overflow nothing works. Patience is your memory optimization.   Step 3 – Use the right tools: Debugging software, automation scripts, or even the right database queries think of them as your hash maps, stacks, and queues. Every challenge is a puzzle. Solve one piece at a time, and the bigger picture becomes clear. Chaos shrinks, confidence grows, and "impossible" problems start feeling like stepping stones. 🧩 Remember IT problems don’t define you—your approach does. Embrace each challenge like a coder debugging their first tricky recursion. Learn, iterate, and grow because that’s how mastery happens. 💪 #ITSupport #ProblemSolving #TechLife #DSAInRealLife #Debugging #ProfessionalGrowth #KeepLearning #TechCommunity #CodingLife

💡 Today’s Insight: Counting Character Occurrences Today I explored a simple yet surprisingly insightful problem — counting the number of occurrences of characters in a string. At first glance, it looked basic. But as I broke it down, it became a great reminder of how even small problems can sharpen logical thinking. 🔍 Key Learnings Simplicity can be deceptive. When you start considering consecutive duplicates or distinct sequences, the logic becomes more interesting than it first appears. Clarity before code. Writing the logic in plain words before jumping into code made the implementation almost effortless. Thought > Syntax. Small steps, strong foundations. Even a small exercise like this improves your ability to think in terms of patterns, data flow, and structure — skills that scale with experience. 🎯 Takeaway Sometimes, growth hides in the basics. The more you practice thinking deeply about simple problems, the better you get at solving complex ones. #CodingJourney #DeveloperMindset #ProblemSolving #CPlusPlus #TechLearning #ContinuousImprovement #100DaysOfCode #SoftwareEngineering

𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐢𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐟𝐢𝐱𝐢𝐧𝐠 - 𝐢𝐭'𝐬 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 The longer I work in engineering, the more I see debugging as rediscovering how the system really behaves, not the spec, but the truth under load. Bugs are feedback from reality, they expose hidden assumptions, design drift, and gaps in observability. The real fix is often beyond code: the model, the telemetry, and the team's shared mental picture. 𝐖𝐡𝐚𝐭 𝐠𝐨𝐨𝐝 𝐝𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐥𝐨𝐨𝐤𝐬 𝐥𝐢𝐤𝐞:  • Reproduce reality, not the spec; make the failure repeatable.  • Instrument first: logs/metrics/traces that answer 𝘸𝘩𝘺, not just 𝘸𝘩𝘢𝘵.  • Form a hypothesis, change one variable at a time, measure the effect.  • Close the loop: capture learning in tests, runbooks, and docs. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 .𝐍𝐄𝐓 𝐡𝐚𝐛𝐢𝐭𝐬: ✅ Structured logging (Serilog) with correlation IDs and request scopes. ✅ Use Activity/TraceId and OpenTelemetry to connect logs, metrics, and traces. ✅ "One-change diff" with a failing test before the fix. ✅ Turn incidents into dashboards/alerts so the next signal is visible. 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 Make learning cheap: strong observability, fast feedback loops, and blameless reviews. Invest in the team's mental model as much as the codebase. How does your team capture learning from tough bugs - tests, runbooks, dashboards, or something else? #SoftwareEngineering #TechLeadership #Observability #dotnet #Architecture

Source: https://lnkd.in/d9aqPZRq 🧪 How do we know our code works? Mark Seemann’s “Epistemology of Software” reframes TDD as a scientific method. By writing failing tests first, developers create falsifiable predictions, turning software verification into an empirical process. 🚀 This approach ensures tests and code evolve together, reducing defects and aligning with user goals.💡 Why it matters: Empirical testing (not just code coverage) is critical for reliable systems. TDD isn’t about writing tests—it’s about validating assumptions through experimentation. 📈 Engineers and managers should prioritize this rigor to avoid hidden bugs and misaligned features.🔧 Call-to-action: Reflect on your testing practices: Are you using TDD to validate hypotheses, or just checking boxes? Let’s embrace science in software!

Day 98 of 180 — How Online Technical Tests Actually Work (and what to practice) 🧩💻 Today I learned how online coding tests for technical interviews are run end-to-end — and what that means for how you should prepare. What happens in the platform: • Most platforms ask you to implement a single function (not a whole program). • The platform runs your function against hidden test cases (unit tests) automatically. • Your code is judged on correctness, edge-case handling, time complexity, and memory usage. • Common constraints: large input sizes, boundary values, nulls, and tricky corner cases. • You won’t get to rely on interactive input/output — focus on the function signature and contract. How to practice effectively: • Start with brute-force to understand the problem, then improve to a more efficient approach. • Always think about and handle edge cases (empty inputs, single-element inputs, negative values, duplicates). • Write code in clean function format with clear variable names and small helper methods. • Practice tracing your solution on paper and estimate complexity before coding. • Learn the common patterns: two-pointers, sliding window, hashing, binary search, DFS/BFS, DP, greedy. Recommended resources: • Books: Cracking the Coding Interview, Data Structures Made Easy • Online: LeetCode, HackerRank, Codeforces, InterviewBit, GeeksforGeeks 📝 Notes 🔗[ https://lnkd.in/d59EBEuh ] Thank you algorithms365 and Mahesh Arali sir for guiding the preparation strategy — small, consistent practice + pattern recognition makes a massive difference. #day98 #alvas #VeereshLearns #180DaysOfCode #DSA #interviewprep #leetcode #codinginterview #algorithms365