Refactoring to Recursive Thinking in Python

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

A plain LLM call says: "The answer to 6 × 7 is 42." An agent calls multiply(6, 7), gets 42, then says: "The answer is 42." One returns text. The other runs code. We put together langgraph-zero-to-agent at Theseus AI Lab. Free, open source, 4 modules. Python basics is all you need to start. Module 1: you wire the graph by hand. Every node, every edge, every tool binding. Module 2: same agent, rebuilt with create_agent(). You see what abstraction actually buys you. Module 3: a coding assistant with a local shell. It writes Python files and runs them on your machine. Module 4: OpenAI Responses API. Web search, code interpreter, reasoning built in. 🔗 https://lnkd.in/dpgwyqq8 #LangGraph #AIAgents #Python #OpenSource

🚀 Day 65 of My Python & DSA Journey Today’s problem was Word Pattern (290) — a great exercise in understanding mapping and relationships between data. 🔍 Problem Solved: Given a pattern and a string, determine if the string follows the same pattern with a one-to-one mapping (bijection) between characters and words. 💡 Approach Used: • Split the string into words • Use two hashmaps (dictionaries): • One for character → word • One for word → character • Ensure consistency in both mappings while iterating ⚡ Key Learnings: • Concept of bijection (one-to-one mapping) • Using hashmaps for efficient lookups • Handling edge cases like unequal lengths • Writing clean validation logic 📊 Complexity Analysis: ✅ Time Complexity: O(n) We traverse the pattern and words once ✅ Space Complexity: O(n) For storing mappings 🎯 Why This Works? Using two dictionaries ensures no duplicates or conflicts, maintaining a strict one-to-one relationship. Another step closer to mastering problem-solving patterns! Under the Guidance of: Rudra Sravan kumar and Manoj Kumar Reddy Parlapalli #Day65 #Python #LeetCode #DSA #Algorithms #CodingJourney #100DaysOfCode #10000Coders 🚀

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

🚀 Solved a great problem today: “Consecutive 1’s Not Allowed” At first glance, it looked like a simple binary string problem… but it quickly turned into a lesson in pattern recognition and dynamic thinking. 📌 What the problem was about: Count all binary strings of length n such that no two 1’s are consecutive. 💡 What I learned: Instead of brute forcing all combinations (which would be exponential), the key was to observe a pattern: If a string ends with 0 → we can add 0 or 1 If it ends with 1 → we can only add 0 This leads to a recurrence: 👉 dp[n] = dp[n-1] + dp[n-2] Which is basically the Fibonacci pattern in disguise. 🧠 Big takeaway: Many problems are not about coding harder… they’re about seeing the hidden pattern behind the problem. This was a reminder that: Brute force is rarely the answer Thinking in terms of state transitions is powerful Optimization often comes from observation, not syntax 📷 Sharing my solution screenshot below 👇 #DataStructures #DynamicProgramming #ProblemSolving #Python #LearningInPublic #DataAnalyticsJourney

Day 22 of my DSA journey Today I worked on the Sliding Window technique. It is a useful optimization approach for problems involving subarrays or substrings. Instead of recalculating values repeatedly, it helps reuse previous computations and reduces time complexity. Problem: Maximum Sum Subarray of Size K Given an array of integers and a number k, find the maximum sum of any contiguous subarray of size k. Brute Force Approach: Check all possible subarrays of size k and calculate their sums. Time Complexity: O(n²) Optimized Approach (Sliding Window): Calculate the sum of the first window of size k Slide the window by one element at a time Add the next element and remove the previous element from the sum Track the maximum sum Python Code: def max_sum_subarray(arr, k): window_sum = sum(arr[:k]) max_sum = window_sum for i in range(k, len(arr)): window_sum += arr[i] window_sum -= arr[i-k] max_sum = max(max_sum, window_sum) return max_sum arr = [2, 1, 5, 1, 3, 2] k = 3 print(max_sum_subarray(arr, k)) Time Complexity: O(n) Space Complexity: O(1) Learning: Sliding Window significantly improves performance in problems related to subarrays and substrings. I will practice more variations like variable window size and string-based problems next. #DSA #CodingJourney #SlidingWindow #Python #100DaysOfCode

Day 3 Mastering the logic behind the code. 💻 Today’s deep dive: Booleans and Logical Operators. It’s fascinating to see how complex machine decisions are actually just a series of simple True or False evaluations. I’ve been exploring the Boolean data type and how comparison operations drive decision-making in software. It’s not just about 'running code'; it's about structuring logic that scales. Progress over perfection. 📈 Moving through the 'Lesson Takeaways' today. There is something so satisfying about seeing a complex scenario broken down into a simple flowchart. What are you currently learning? Let's connect! #BuildInPublic #TechStack #CareerGrowth #ComputerScience #PythonProgramming #TechEducation #Python #LearningToCode #ContinuousImprovement

LLMs are not good at everything - when I asked it to rotate an image 180 degrees, it completely butchered the image. This is a question of using the right tools. If instead you say, "Use Python to rotate this board 180 degrees," it can write a deterministic script to do so and give you the right results. Maybe one day the LLM will be able to distinguish when it needs a deterministic solution from a generative solution but at this point it's still on the user to choose the right approach for the problem.

My thesis was stuck. A matrix had the wrong shape and I had no idea why. I could have printed the entire dataset to find the error. I did not. Instead I used Python's debugger. One breakpoint. One look at the intermediate state. Wrong dimensions. Found in seconds. That moment changed how I work. Not because debugging saved my thesis. But because it taught me something I still use every day: You do not need to see all the data to understand what is wrong. You just need to see the right data at the right moment. Since then, every time a pipeline breaks or a model behaves unexpectedly, I reach for the debugger first. Not print statements. Not guesswork. A breakpoint. An intermediate result. A clear answer. Debugging is not a last resort. It is the fastest way to understand what your code is actually doing. What is your go-to strategy when something breaks unexpectedly? #Python #Debugging #DataScience #MachineLearning #FreelanceDataScientist

𝐋𝐋𝐌𝐬 𝐝𝐨𝐧'𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨 𝐚𝐧𝐲𝐭𝐡𝐢𝐧𝐠.   They generate text. So when an “AI agent” queries a database, sends an email, or runs a command — something else is doing the real work. An orchestration layer most people never see. I wrote a breakdown of what that layer actually is — the 𝐑𝐞𝐀𝐂𝐓 𝐥𝐨𝐨𝐩 behind agent frameworks — built from scratch in ~40 lines of Python, with an interactive stepper to watch a full run end-to-end. Check out my blog post about this subject: → https://lnkd.in/e3K94--D #AIAgents #AgenticAI #LLM #AIEngineering #SoftwareEngineering