SynapseKit v1.4.7 & v1.4.8 Released with 5 New Loaders

💡 Data analysis workflows have become increasingly complex. In practice, they often require combining multiple tools: notebooks, scripts, AutoML frameworks, databases, and now AI assistants. We think this fragmentation slows things down. 𝐌𝐋𝐉𝐀𝐑 𝐒𝐭𝐮𝐝𝐢𝐨 𝐢𝐬 𝐨𝐮𝐫 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐲𝐢𝐧𝐠 𝐭𝐡𝐢𝐬. 𝐀 𝐝𝐞𝐬𝐤𝐭𝐨𝐩 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 𝐭𝐡𝐚𝐭 𝐛𝐫𝐢𝐧𝐠𝐬 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫: 🚀 Python notebooks 🚀 AutoML 🚀 AI-assisted data analysis 🚀 database connections — in a single, consistent workflow. The goal is not to replace Python 🐍 . It’s to reduce the overhead around using it. We’ve recorded a short introduction showing how these pieces fit together in practice. Less time on setup. More time on insights. Try MLJAR Studio today: 👉 https://mljar.com

If your CI pipeline is slow, start by looking at how your test suite is being split. 𝗽𝘆𝘁𝗲𝘀𝘁-𝘀𝗽𝗹𝗶𝘁 is a pytest plugin that does one thing well: it splits your test suite into equally timed sub-suites, not equally sized ones. Most naive approaches split by test count. 𝗽𝘆𝘁𝗲𝘀𝘁-𝘀𝗽𝗹𝗶𝘁 stores actual execution times in a .test_durations file and uses that data to balance wall-clock time across groups. Run --store-durations once, commit the file, and your CI groups will finish at roughly the same time. New or renamed tests are handled gracefully by falling back to average durations. No need to re-run --store-durations after every change. 🔗 Link to repo: github(.)com/jerry-git/pytest-split --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

Software isn't just math; it's business logic. Day 9/90 of my AI Product Engineer sprint: Python Conditionals (If/Else). This isn't just basic syntax. It is the exact mechanism that routes users in an application: If user is premium -> Route to Gemini Pro. Else -> Route to Flash. I built a raw terminal script today forcing user inputs through a basic decision tree. It’s unpolished, but the underlying logic is exactly how companies route data automatically. You cannot architect complex AI pipelines if you don't control the basic flow of information.

🚀 Efficient Duplicate Detection with Hash Sets | LeetCode Today, I tackled the Contains Duplicate problem. While the brute force approach is often the first instinct, optimizing for time complexity is where the real fun begins! 💡 The Problem: Given an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct. ⚡ My Approach: I utilized a Hash Set to track elements as I traversed the array. This allows for near-instantaneous lookups compared to nested loops. 👉 The Logic: Initialize an empty set seen. Iterate through the array once. For each number, check: "Have I seen this before?" (Is it in the set?) If Yes → Return True immediately. If No → Add the number to the set and keep moving. 🔥 Complexity Analysis: ⏱ Time Complexity: $O(n)$ – We only pass through the list once. 📦 Space Complexity: $O(n)$ – In the worst case (all unique elements), we store all $n$ elements in the set. 🏆 The Result: ✔️ Accepted: All 77 test cases passed. ✔️ Performance: 9 ms runtime, beating 73.44% of Python3 submissions! 📌 Key Takeaway: Using a Set turns a potential $O(n^2)$ search into a sleek $O(n)$ operation. Choosing the right data structure isn't just about passing tests; it's about writing scalable, "production-ready" code. 💻 Tech Stack: #Python | #DataStructures | #Algorithms #leetcode #dsa #coding #programming #softwareengineering #100DaysOfCode #pythonprogramming #tech #growthmindset

Today's topic is a tool combo breakdown focusing on three exciting combinations that can revolutionize your workflow and save you time. Whether it’s integrating Claude Code with Obsidian for a seamless knowledge management system or harnessing n8n combined with the Claude API to automate complex tasks, these tools offer specific benefits. Let's dive into one of our options: using Python along with the Claude API. This combo allows developers to leverage AI capabilities directly within their existing workflows. Here’s how you can set it up: 1. **Setup**: First, ensure you have Python installed on your machine. You'll also need n8n and Claude Code plugins for n8n. 2. **Write Your Script**: Start by writing a simple Python script that uses the Claude API to process text inputs. For example: ```python import n8n from claude import ClaudeAPI # Initialize Claude API cl = ClaudeAPI() # Function to get AI generated response def get_response(prompt): response = cl.get_completion(prompt) return response # Example usage of the function result = get_response("What's the weather like in New York today?") print(result) 3. **Integrate with Obsidian**: Next, you can integrate this script with Obsidian using n8n to automate tasks. This setup can save significant time and effort, reducing manual processing and allowing for more efficient workflows. Would you be interested in exploring further AI integration opportunities like this one? Let us know your thoughts or challenges in the comments below. #ClaudeCode #AIAutomation #AITools #BuildWithAI #loopfeedai

Struggling to improve your ML pipeline? Looking for new feature ideas that actually help your model? We built features_goldmine — a Python package designed to automate feature engineering for tabular data. 👉 https://lnkd.in/d_VzuKMb Instead of manually trying random transformations, it: generates a wide range of candidate features, applies different feature engineering strategies, removes weak or redundant ideas, keeps only features that show predictive value. It works directly on raw tabular data and integrates easily into existing ML workflows. The goal is simple: improve model performance with minimal code changes and less manual feature engineering. If you work with tabular datasets, give it a try — and let me know what you think.

New blog post. You've finished developing an ML model with {tidymodels}, and you're ready to automate it in Dagster. You hand things off to data engineering. Their reply: "Sorry, we need this rewritten in Python to deploy." But the model pipeline code is solid. It's wrapped in an R package; there's good test coverage, a {pkgdown} website documenting everything, the works. It's just written in R. Do we really need to do all of that work all over again? Not anymore. I built the R package {dagsterpipes} to solve this problem. It implements Dagster's Pipes Protocol for the R language, allowing you to run R code inside of Dagster without losing its logging and observability features. Walkthrough with a working example in the post: https://lnkd.in/gfxjadQy #rstats

𝐈𝐟 𝐘𝐨𝐮 𝐃𝐨𝐧’𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐓𝐡𝐢𝐬 𝐏𝐫𝐨𝐛𝐥𝐞𝐦, 𝐘𝐨𝐮 𝐃𝐨𝐧’𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐒𝐭𝐚𝐜𝐤𝐬 Today I tackled a fundamental problem that looks simple at first — but really tests your understanding of logic and data structures. 💡 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Given a string of brackets () { } [ ], determine whether it is valid. 🧠 𝐌𝐲 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Instead of checking everything at the end, I used a stack (𝐋𝐈𝐅𝐎 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞) to validate each step in real-time. • Push opening brackets • On closing bracket → match with the last opened one • If mismatch occurs → invalid • If everything matches & stack is empty → valid 🔥 𝐊𝐞𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: This problem taught me how powerful simple data structures can be when used correctly. 🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 👇 📌 Consistency in solving such problems is helping me build strong problem-solving skills. #Python #DSA #FullStack #AI #Logic #LeetCode #AIDriven

I built a complete 𝗨𝘀𝗲𝗱 𝗖𝗮𝗿 𝗣𝗿𝗶𝗰𝗲 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗼𝗿 from scratch, creating a full end-to-end pipeline that handles everything from raw data to a live application. Instead of relying on a pre-built dataset, I identified a unique problem and built my own data source using web scraping. My goal was to move beyond tutorials and mimic a real-world 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 workflow.  • 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴: Automated data collection to get real-time market prices.  • 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Cleaning messy web data into a machine-learning-ready format.  • 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Training a robust regressor to find the patterns.  • 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: Building a Flask web app to make the model accessible to anyone. The Workflow: 𝗦𝗰𝗿𝗮𝗽𝗲 𝗗𝗮𝘁𝗮 → 𝗖𝗹𝗲𝗮𝗻 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 → 𝗧𝗿𝗮𝗶𝗻 𝗠𝗼𝗱𝗲l → 𝗗𝗲𝗽𝗹𝗼𝘆 #MachineLearning #DataScience #Python #Flask #WebScraping #PortfolioProject Check out the full documentation and code on GitHub: https://lnkd.in/gAZp4iKq