Fixing DLL Hell with TORCH_LIBRARY and PYBIND11_MODULE

I used to think tuples were just “lists with stricter rules”… but today showed me they have their own vibe. 🐍 Day 06 of my #30DaysOfPython journey was all about tuples, and this topic made one thing really clear: sometimes the best data structure is the one that stays put. A tuple is an ordered and unchangeable collection of different data types, created using round brackets (). Today I explored: 1. Creating tuples with tuple() 2. Accessing items using positive and negative indexing 3. Slicing tuples with positive and negative indexes 4. Checking whether an item exists using in 5. Counting items with count() 6. Finding item positions with index() 7. Joining tuples using + operator 8. Converting tuples to lists with list() 9. Deleting the whole tuple using del What stood out to me today was how tuples are built for stability. They are not meant to be edited over and over again — and that actually makes them really useful when you want data to stay consistent. One more day, one more topic, one more layer of Python making sense. Github Link - https://lnkd.in/gHwugKTU #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

Built my first Python node in ROS 2 this week — a follow-up to my earlier post on core concepts. 𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝘁: A CircleDrawer node that publishes velocity commands to TurtleSim, making the turtle draw an expanding spiral. 𝗦𝘁𝗲𝗽𝘀 𝗳𝗼𝗹𝗹𝗼𝘄𝗲𝗱: 1️⃣ Created a ROS 2 Python package with rclpy dependency 2️⃣ Wrote the node — a Python class inheriting from Node, using create_publisher() and create_timer() 3️⃣ Updated package.xml and setup.py (entry_points) 4️⃣ Built with colcon build → sourced the workspace → ran the node 𝗞𝗲𝘆 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝗱: • OOP-based node structure in ROS 2 • Timer callbacks for periodic publishing • Building & running a custom package end-to-end • Visualizing node-topic connections with rqt_graph Going from theory to working code in ROS 2 is a big step. Colcon, entry points, sourcing the workspace — it's a lot of config, but once it runs, it's very rewarding. Next up: Services & Actions.

Today I worked on a classic string manipulation problem that looks simple but tests your understanding of logic and edge cases. 🔍 Problem: Given a string and a substring, count how many times the substring appears in the main string. ⚠️ The catch? 👉 You must count overlapping occurrences as well. 💡 Approach I Used: Instead of using built-in shortcuts, I applied a sliding window technique: ++Loop through the string ++Extract a substring of the same length ++Compare it with the target substring ++Increment count when matched This ensures we don’t miss overlapping patterns. 🧠 Key Learning: Sometimes, simple problems reveal powerful concepts. This one reinforces: ++String slicing ++Loop boundaries ++Sliding window logic 📌 Example: "ABCDCDC" → "CDC" appears 2 times (including overlap) 💻 Check out the visuals: 🖼️ Problem breakdown 🧑💻 Python solution 🔥 Why this matters? This pattern is widely used in: --Text processing --Pattern matching --Data parsing #Python #HackerRank #CodingPractice #DataStructures #ProblemSolving #100DaysOfCode #LearningInPublic #SoftwareEngineering

I bet your advisor has already told you to make your "code faster"! Want to solve the problem? Then use an emulator, my friend! We have released PyGLAM 0.2.2 (beta version), a Python framework for emulating probability distributions based on Generalized Lambda Distributions. Our implementation is based on the paper "Replication-based emulation of the response distribution of stochastic simulators using Generalized Lambda Distributions" by Professor Bruno Sudret. Thanks to the partners Prof. Ketson dos Santos, Prof. Marcos Luiz, and the students Victor Hugo Moreira and Renata Maria Pensin. Simple to install: pip install pyglam Learn all about it at: https://lnkd.in/gutJNpDj Feedback is more than welcome! 👇 #Python #DataScience #Math #Coding #Reliability #Reliability #Statistics #Mechanics #CivilEngineering

🚀 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

🚀 Stop killing your CPU with Python loops. I recently refactored a data transformation pipeline that was crawling because it processed 5 million rows using a standard row-by-row iteration. Moving from native loops to vectorized operations changed everything. Before optimisation: results = [] for i in range(len(df)): val = df.iloc[i]['price'] * df.iloc[i]['tax_rate'] results.append(val) df['total'] = results After optimisation: df['total'] = df['price'] * df['tax_rate'] Performance gain: 45x faster execution time. Vectorization offloads the heavy lifting to highly optimised C code under the hood. When you use Pandas or NumPy native methods, you stop fighting the interpreter and start leveraging memory alignment. If you are still writing loops for data manipulation, you are leaving massive amounts of compute time on the table. It is the easiest performance win you can claim this week. What is the biggest speed boost you have ever achieved by swapping a loop for a built-in vectorised function? #DataEngineering #Python #Pandas #Performance #Optimization

Here is an event scheduler I made in python that can handle creating a new event to be scheduled, cancel one by ID, update the priority, peek at the next one, pop the one ahead, outputs the events that are in an array, load a sample data. The point of this was to create a program where we learn about the implementation of heaps, hash table and ordered structure in an algorithmic setting.

Day 43 of LeetCoding everyday until I get a J-O-B: Check If a String Contains All Binary Codes of Size K. If K = 2, the possible codes are 00, 01, 10, 11. We need to prove every combination exists inside our string. The Noob Trap: Generating all $2^k$ combinations upfront to cross them off. If K = 20, you need over a million strings. You just blew up your RAM. The Senior Fix: Math & Sliding Windows 1. The Math Filter: For a string to even physically fit all combinations, its length must be at least 2^k + K - 1. If it's shorter? Instant return False. Just like my job applications. 2. The Set: We slide a window of size K across the string, dumping every slice into a Python Set (which automatically vaporizes duplicates). 3. The Check: At the end, we check if len(seen) == (1 << k). (We use a Bitwise Left Shift because we are elite). See more: https://lnkd.in/gUki2imQ #LeetCode #Python #DataStructures #Engineering #TechHumor

Built a Python-based Directory Sync Tool to compare and synchronize files between two directories with reliability and control. Instead of relying only on file names or timestamps, the tool uses a combination of metadata and SHA-256 hashing to accurately detect new, modified, and missing files. Key highlights: • Recursive directory scanning with structured metadata (name, extensions, size, hash) • Efficient change detection using size-first filtering followed by hash comparison • Memory-efficient hashing using chunk-based file reading (handles large files) • Synchronization support with metadata preservation using shutil.copy2 • Safe cleanup by optionally removing extra files from the destination While building this, I focused on moving beyond a basic script and treating it like a real tool, structuring the code into clear components, improving output readability, and adding validation and error handling to make it more reliable in real use. GitHub:https://lnkd.in/gt-Ec3rF #Python #CLI #GitHubProjects #SoftwareDevelopment #LearningByBuilding #SystemsThinking

Vector databases are great, but they aren't always the right tool for complex document intelligence. 🧠📉 If you are tired of context fragmentation and untraceable LLM hallucinations, it is time to look at Vectorless RAG with Page Index. By swapping out mathematical embeddings for a reasoning-based, hierarchical document tree, you can achieve upwards of 98% accuracy on complex Q&A tasks with perfect citation traceability. I wrote a complete guide on how this architecture works, including a full Python code implementation. Read it here: https://lnkd.in/gRuXiSxK #ArtificialIntelligence #RAG #PythonDeveloper #MachineLearning #AIEngineering