LangGraph Reducer Simplifies Parallelism in Python

Efficient grid navigation often requires a clear understanding of how different components or "tiles" connect to one another. A common challenge in pathfinding is determining if a continuous route exists from a starting point to a destination based on specific connection rules for each cell. In this approach, I utilized a transition table to map how an incoming direction (Top, Right, Bottom, Left) translates to an outgoing direction based on the tile type. By defining these movements as a fixed set of rules, the algorithm can traverse the grid in constant space, O(1), excluding the input itself. The logic follows a simple "follow the pipe" strategy: Start from the initial cell and check both possible exit directions. Update the current position based on the permitted transition. If the next cell does not support the incoming direction, the path is invalid. The process continues until the target coordinates are reached or the path breaks. This method ensures high performance by avoiding complex recursion or auxiliary data structures while maintaining strict adherence to the grid's connectivity constraints. #Algorithms #Python #CodingEfficiency #ProblemSolving #DataStructures

Geospatial Technologies essential keywords, daily Tips 🌎 : Keyword : Xarray Category :Programming Xarray is a Python library that extends NumPy’s ndarray to support labelled, multi‑dimensional arrays with dimension, coordinate, and attribute metadata, enabling intuitive indexing, broadcasting, and operations on spatial data grids. Its DataArray and Dataset objects mirror the structure of NetCDF files, making it ideal for climate, remote‑sensing, and geospatial workflows that require efficient handling of large raster datasets and seamless integration with libraries such as pandas, dask, and xgcm. By preserving semantic information through coordinate labels, X #TechGeoMapping #EssentialKeywords

LCA of BST: Exploiting Order Property for O(1) Space Generic tree LCA needs full traversal. BST ordering enables iterative solution — LCA is where paths diverge. Both values smaller? Go left. Both larger? Go right. Otherwise, current node is split point = LCA. BST Advantage: Ordering enables O(h) iterative solution without recursion stack. Divergence point is guaranteed LCA by BST property. This demonstrates how data structure properties simplify algorithms. Time: O(h) | Space: O(1) #BST #LCA #OrderProperty #IterativeSearch #Python #AlgorithmDesign #SoftwareEngineering

Interest in the gastric rhythm and its relationship to brain function is growing rapidly. Today I’m happy to share the early beta release of our Python package for electrogastrography and stomach-brain coupling analyses. GastroPy brings together tools from our own work and several leading labs into one streamlined toolkit. It is still an early beta and needs wider testing, but over the coming months we’ll be working with groups across the field to refine and expand it. The package already includes the main building blocks for filtering, cleaning, visualizing, and analyzing electrogastrography data, as well as tools for estimating stomach-brain coupling in fMRI. M/EEG extensions are underway. Preprint: https://lnkd.in/eJBhT6HM Code: https://lnkd.in/eV4Ubuax Docs and interactive tutorials: https://lnkd.in/eNU2ziH4

Learn about numpy sorting with real time example, In this video, we cover: 00:07 Basic Sorting in ascending order 00:50 Descending sorting 01:43 argsort explaning 04:04 partition 05:36 2d dimensional array. #numpy #python #datascience #machinelearning #coding https://lnkd.in/gqnjPFhs

A 10 million document RAG dataset occupies 31 GB of RAM at float32. turbovec fits it in just 4 GB - and now it searches it faster than FAISS. I just shipped a new release of turbovec: a Rust vector index with Python bindings, built on Google Research's TurboQuant algorithm. Data-oblivious 2-4 bit quantization that matches the Shannon lower bound on distortion - zero training and no rebuilds when the corpus grows. What's in the box: → Hand-written SIMD kernels - 12–20% faster than FAISS FastScan on ARM; match-or-beat on x86. → O(1) stable-id delete and save/load. The corpus is live and mutable, not a static snapshot. → Drop-in integrations for LangChain, LlamaIndex, and Haystack. → Published benchmarks (recall, speed, compression) at d=200/1536/3072 — every number reproducible from the repo. If you're building RAG where memory, latency, or privacy matters, give it a spin. GitHub: https://lnkd.in/e5M4dVRk Paper: https://lnkd.in/eHRmpYms #RAG #VectorSearch #OpenSource #Rust #Python #𝗟𝗟𝗠 #𝗢𝗽𝗲𝗻𝗦𝗼𝘂𝗿𝗰𝗲 #𝗚𝗲𝗺𝗺𝗮4

𝐌𝐚𝐩𝐩𝐢𝐧𝐠 𝐌𝐲 𝐂𝐚𝐫 𝐓𝐫𝐢𝐩𝐬 – 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐚 𝐘𝐞𝐚𝐫 𝐨𝐟 𝐓𝐫𝐚𝐯𝐞𝐥 𝐏𝐚𝐭𝐡𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 Day 4 of the 2025 challenge comes with our own personal data this year, where I decided to visualize my own mobility data. In the tutorial, I will teach you how to: - Convert addresses to coordinates with OSM geocoding, - Generate road paths using a simple route planner, and - Visualize the results with vivid yellow lines on a dark basemap - a design that almost looks like a glowing electronic motherboard. 𝐖𝐚𝐥𝐤-𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐨𝐧 𝐘𝐨𝐮𝐭𝐮𝐛𝐞: https://zurl.co/AeBfV 𝐂𝐨𝐝𝐞 𝐨𝐧 𝐒𝐮𝐛𝐬𝐭𝐚𝐜𝐤: https://zurl.co/E0J9c 𝐅𝐮𝐥𝐥 𝐜𝐨𝐮𝐫𝐬𝐞: https://zurl.co/vFaS3 #30DayMapChallenge #Python #GeospatialData #DataVisualization #Mapping #OSM #MobilityData #Commute #GIS

Excited to share my latest project: An AI-driven Dynamic Wumpus World Agent! 🏹🤖 I recently developed a Knowledge-Based Agent that navigates a hazardous grid using Propositional Logic and Resolution Refutation. Instead of simple pathfinding, this agent "thinks" by maintaining a Knowledge Base (KB) and proving cell safety through logic before making a move. Key Features: ✅ Logic Engine: Custom-built Resolution Refutation algorithm (CNF-based). ✅ Dynamic Environment: Real-time percept generation (Breeze/Stench) in a randomized grid. ✅ Web Interface: Built and deployed a full GUI using Python and Streamlit. ✅ Performance Tracking: Real-time metrics dashboard for inference steps. 🔗 Live App: [http://localhost:8501/] 💻 Code: [https://lnkd.in/dW53kwdw] #ArtificialIntelligence #Python #Logic #WebDevelopment #Streamlit #MachineLearning

A Renko forecasting pipeline was tested on EURUSD using MetaTrader 5 tick/time-bar data converted into 11,578 Renko bars with ATR-derived brick size 0.00028. Training used 60 days of M5 history and 14 engineered features covering direction sequences and volume stats. CatBoost delivered 59.27% test accuracy. Feature importance was led by volume: last_volume (18.36), avg_volume (14.23), volume_ratio (12.81), followed by consecutive-move metrics, challenging price-pattern-heavy rule sets. Implementation stack: Python, numpy, pandas, MetaTrader5 API, catboost. Modules cover data ingest, Renko conversion, feature generation, cross-validated training, and probability-based signaling with a 75% confidence threshold. #MQL5 #MT5 #AlgoTrading #AITrading https://lnkd.in/dAGfEC7K

🚀 Day 17 of #DSAStreak 📌 Problem: Longest Common Prefix 🧠 Problem Summary: Given an array of strings, find the longest common prefix shared among all strings. If no common prefix exists → return "" 💡 Approach: Vertical Scanning 🔥 Instead of comparing entire strings (slow ❌), we optimize using: ✔ Take first string as reference ✔ Compare characters index by index ✔ Check across all strings ✔ Stop immediately at first mismatch ⚙️ Key Logic: Take first string → iterate over each character Compare with same index in other strings If mismatch or index out of bound → return prefix till that point 📌 Example: Input: ["flower","flow","flight"] Output: "fl" Explanation: First mismatch at index 2 → "o" ≠ "i" ⏱ Time Complexity: O(N × M) 💾 Space Complexity: O(1) 🔥 Key Learnings: 👉 Nested loops ≠ always O(n²) 👉 Early stopping saves time 👉 Compare character-wise, not string-wise 👉 Prefix must be common in ALL strings #Python #DSA #Strings #CodingJourney #100DaysOfCode #ProblemSolving #LeetCode #Tech