Boost Forecasting with 200+ Models via TimeCopilot & sktime

Every forecast I've posted recently (OpenClaw stars, US demographics, China demographics, etc.) was tested against multiple models before TimeCopilot picked one. The sktime integration makes this better with over 200 models to pick from right out of the box. Example: OpenClaw forecast tested Prophet, AutoETS, Theta, AutoARIMA, SeasonalNaive. AutoETS won. But I wouldn't have known that without testing all five... I now can do that with +200 models 🤩 Model selection rigor matters.

🚀 Day 55 of #100DaysOfCode Solved LeetCode 350 – Intersection of Two Arrays II today! 🔍 Problem Insight: Given two arrays, we need to return their intersection such that each element appears as many times as it occurs in both arrays. 💡 Approach Used: - Used "Counter" (hash map) to store frequency of elements from first array - Traversed second array and matched elements - Reduced count after every match to handle duplicates correctly ⚡ Why this works: Efficient frequency tracking avoids nested loops and reduces time complexity. 🧠 Complexity: - Time: O(n + m) - Space: O(n) ✅ Key Learning: Hashing + frequency counting is powerful for problems involving duplicates and intersections. 🔥 Consistency is the key — showing up every day! #DSA #LeetCode #Python #CodingJourney #PlacementPrep #SoftwareEngineering #100DaysOfCode

Why aren’t my Matplotlib tick labels behaving? Let’s ask Cameron Riddell! In this week’s Cameron’s Corner, Cameron digs into Matplotlib’s ticker system and shows how small choices can make your charts much clearer (or much more confusing). Learn: ✅ How major and minor tickers work ✅ When to use AutoLocator, MultipleLocator, and custom formatters ✅ Tips for clean, readable axes that communicate your message Read here: https://lnkd.in/g5hkw8ua Ever wrestled with cluttered tick labels? Drop your best Matplotlib tip below 👇 #Python #Matplotlib #DataViz #CameronsCorner #DontUseThisCode

Reducing 3Sum from O(n³) to O(n²): The Power of Sorting + Two Pointers Most developers first approach 3Sum with nested loops, hitting O(n³) complexity. The breakthrough: sort the array first, then reduce it to n iterations of the Two Sum problem. For each element as the fixed anchor, use two pointers on the remaining sorted portion to find pairs that complete the triplet. Sorting unlocks directional movement (move left if sum too high, right if too low), collapsing a dimension of complexity. The Critical Detail: Duplicate handling isn't just about correctness — it's about recognizing that sorted order lets you skip duplicates in O(1) rather than using a HashSet for deduplication. The while nums[l] == nums[l-1] skip after finding a valid triplet prevents processing identical combinations. Time: O(n²) | Space: O(1) excluding output #AlgorithmOptimization #TwoPointers #Sorting #ComplexityReduction #Python #CodingInterview #SoftwareEngineering

Day 11: Alpha feature showcase: redirect chain detection. You can now find every redirect chain in a crawl. Filter for chains over 3 hops. See exactly where crawl budget is leaking. One alpha user ran this on a ~50k page site. Time in the Screaming Frog GUI: around 20 minutes of clicking and exporting. Time in Python: a few seconds. Computed directly from the Derby database. This is one of those features that sounds small until you run it on a big site. Next up: canonical chains. If redirect chains are part of your workflow, I’d love to hear how you’re currently handling them.

𝗪𝗲𝗲𝗸 𝟰 of my 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 & 𝘔𝘓 journey with ParoCyber Here's what I explored: ✅ String Formatting: three ways to combine text and variables; concatenation, f-strings, and the .format() method ✅ Control Flow: using if, elif, else, loops, and ternary operators to make code think and decide ✅ Functions: writing reusable logic with def, lambda, and built-in functions like map(), filter(), and reduce() ✅ Pandas: working with Series and DataFrames to store, access, and combine real data Biggest lesson? Python doesn't use brackets to organize code; it uses indentation. One misplaced space can break everything. I also realized that what I learned in Week 2 (dictionaries) was quietly preparing me for Pandas DataFrames all along. The dots are connecting. 4 weeks in. Still showing up. Still learning. To see the full breakdown, it's all documented on my GitHub. Link below. 🔗https://lnkd.in/d_v-C3mW #DataScience #Python #MachineLearning #ParoCyber #LearningInPublic #CareerGrowth #WomenInTech

🚀 Day 35 of #60DaysCodingChallenge | ABTalks Today’s problem focused on the Sliding Window Technique (Variable Window) while solving the classic Longest Substring Without Repeating Characters problem. 🔍 Key Learning: Instead of checking all possible substrings (which would be inefficient), the Sliding Window approach allows us to dynamically adjust the window size while traversing the string. This reduces the time complexity significantly and helps build a more optimized solution. 💡 What I Practiced Today • Understanding the concept of Variable Sliding Window • Efficient string processing techniques • Optimizing brute force solutions into linear time algorithms ⚙ Approach Used Two pointers (left and right) maintain the window A data structure keeps track of characters inside the window If a duplicate appears, the window shrinks from the left Track the maximum window size without repeating characters 📈 Complexity Time Complexity: O(n) Space Complexity: O(n) Consistency is slowly building stronger problem-solving skills every day. The goal is not just solving problems but understanding the patterns behind them. Looking forward to Day 36 tomorrow. 🔥 ABTalksOnAI #ABTalks #60DaysChallenge #DSA #SlidingWindow #ProblemSolving #CodingJourney #Python #LearningInPublic

Day 5/100: Auctions and Stacks! 🚀 Today was all about mastering Python Dictionaries and diving into Data Structures! I built a Secret Auction Program that uses dictionary mapping to handle multiple bidders and logic to determine the highest bidder. It was a great exercise in loop control and data retrieval. On the DSA front, I officially started Stacks. 📚 Understanding the LIFO (Last In, First Out) principle and how it powers everything from function calls to the "undo" button in your favorite editor is fascinating. Key Takeaways: 1.) Implementing dictionaries for real-world data storage. 2.) The mechanics of push and pop operations in Stacks. 3.) Building cleaner, more modular code. Onward to Day 6! 💻✨ #100DaysOfCode #Python #DSA #LearningJourney #BuildInPublic #LPU

From Guesswork to Evidence This week I open-sourced a small CLI tool: Activity Reporter. It scans a directory within a given time window and generates a human-readable report of file activity. But the real focus isn’t the feature. It’s the design principles behind it: • One-shot execution • Read-only • Append-only logs • No automatic judgment Most tools try to decide for you. I prefer tools that generate evidence and leave decisions to humans. To me, engineering is about reducing uncertainty — not adding abstraction. Repo link in the comments. #SoftwareEngineering #OpenSource #DevTools #Python #EngineeringMindset

🚀 Solved another problem on LeetCode: Matrix Block Sum (2D Array Running Sum) Today I implemented a solution to compute the block sum for every element in a matrix. The task is to calculate the sum of all elements within a K-distance block around each cell while handling matrix boundaries correctly. My approach: I used a brute-force method where, for each element, I determine the valid block boundaries using max() and min() to avoid index overflow. Then I iterate through that block and accumulate the values. Key concepts used: • 2D array traversal • Boundary handling in matrices • Nested iteration over submatrices This problem strengthened my understanding of matrix manipulation and coordinate boundary control. It also highlights how prefix sums can further optimize the solution for larger inputs. LeetCode practice continues! 💻📊 #LeetCode #DataStructures #Algorithms #Python #CodingPractice #ProblemSolving