Day 18: Tree Traversal Techniques and Patterns

The Habit Behind the Insight 🐍 The insight doesn't come from the tool. It doesn't come from the dataset. It doesn't even come from the analysis. 👉It comes from showing up curious. Every day. Asking why when the number looks fine. Looking one level deeper when the chart makes sense. Questioning the assumption everyone else accepted. That's not a technique. That's a habit. And like every habit — it's built quietly, on the days nobody's watching, when there's no obvious reason to keep going. 👉The tool is easy to learn. 👉The curiosity is harder. 👉The consistency is hardest. But that's where the insight lives. #DataAnalytics #Python #LearningInPublic #AnalyticsThinking

AI is not just about using tools anymore. Now it is about building systems that can do tasks on their own. This 14-day AI Agent roadmap shows a clear path from basics to building real projects. Very useful for anyone who wants to learn AI by doing, not just by watching tutorials. #AIAgents #GenerativeAI #Learning

Building an AI agent doesn’t have to take months. With the right steps, you can get started and build something in just 2 weeks. Simple plan, just stay consistent. #AIAgents #BuildInPublic #TechLearning

Building an LLM-based extraction service taught me one important lesson: Hardcoding prompts doesn’t scale. Moving from prototype to production, I realized the system around the model matters as much as the model itself. What actually helped make it usable across different use cases: • Decoupling prompts from code using a MongoDB-backed configuration layer • Supporting project-specific prompts instead of one fixed prompt design • Designing the service to handle varying extraction requirements without code changes • Using models like Mistral-7B for extracting structured outputs (JSON/fields) from unstructured documents The real challenge wasn’t prompt design — it was making prompt iteration safe, flexible, and maintainable in a production system. #GenAI #LLMs #SystemDesign #Python #AIEngineering

The Shortcut That Became Your Default A quick fix. Skipping validation. Hardcoding values. Copying old logic without questioning. A faster way to get results. The steps you skipped writing down never got documented. “I’ll fix this later,” you told yourself. It felt temporary. But you didn’t fix it and days later the logic was already forgotten. And soon, it became the default—quietly shaping your process. 👉 Shortcuts don’t fail in isolation. They quietly build a system that works—until it doesn’t. 👉 In data work, shortcuts rarely stay short-term. #DataAnalytics #Python #LearningInPublic #AnalyticsThinking

Most Python workflows rely on heuristics. They’re quick, intuitive, but usually not optimal. A simple greedy approach might get you a solution, but it often leaves efficiency, performance, and cost savings on the table. GAMSPy brings algebraic modeling into Python, so you can express constraints and objectives directly and solve for a true optimum. At PyConDE & PyData 2026, Justine Broihan and Muhammet Soyturk will walk through this using a classic operations example, and then extend it into machine learning. They'll cover: 🔸 How optimization compares to rule-based heuristics and 🔸 How it can be used to test ML models (e.g. minimal changes needed to trigger misclassification) 🔸 The Art of the Optimal: A Pythonic Approach to Complex Decision-Making 📍 April 14 · 16:30 📍 Platinum (2nd Floor) If you're building decision-making systems in Python, this is worth a look. More details 👉 https://lnkd.in/dyifGdVi #PyConDE #PyData #Optimization #GAMSPy #GAMS #Python

Solved the Continuous Subarray Sum problem on LeetCode using an optimized prefix sum and hashing approach. The solution checks whether a continuous subarray of size ≥ 2 has a sum divisible by k. By tracking remainders of prefix sums in a dictionary, the algorithm efficiently detects valid subarrays in O(n) time complexity. ✅ 102 / 102 test cases passed ⚡ Runtime: 46 ms (Beats 96.15%) 💻 Language: Python This problem strengthened my understanding of prefix sums, modular arithmetic, and hash map optimization techniques commonly used in algorithmic problem solving and technical interviews. #LeetCode #Python #Algorithms #DataStructures #CodingInterview #ProblemSolving

Tinkering with SKILLs and optimizing like crazy. Funnily enough, the optimizations are all about writing python scripts and minimizing the LLM's task to only the sliver that requires judgement. Also, I moved to running the SKILLs using Gemini 3 Flash instead of Claude Opus 4.6. So my SKILLs is running 5x faster and at 10-20% the cost. Conclusion: SKILLs is another layer for abstracting and democratizing coding, and the smaller models are good enough. #AI #Agentic #SKILL #Gemini #Claude #python

🚀 𝗗𝗮𝘆 𝟮𝟮/𝟯𝟬 — 𝗗𝗦𝗔 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Day 22 and today’s focus moved into graph problems. Graphs introduce a different way of thinking — instead of linear structures, it’s all about exploring connections between nodes. Traversal techniques like DFS and BFS start to play a big role here. Today was about understanding how to navigate through connected components and explore all possible paths systematically. 🔎 𝗗𝗮𝘆 𝟮𝟮 𝗙𝗼𝗰𝘂𝘀 • Understanding graph traversal concepts • Practicing DFS/BFS thinking • Solved: ✅ Number of Islands ✅ Flood Fill ✅ Find if Path Exists in Graph Graph problems can look intimidating at first, but once the traversal pattern becomes clear, the logic starts falling into place. Still learning. Still improving. On to Day 23 #DSA #Python #LeetCode #Consistency #SoftwareEngineering #ProblemSolving