Define AI Code with Inputs, Return Type, and Post-Conditions

Python vs Mojo: Evaluating the Future of AI Development Python vs. Mojo: Is It Time to Switch for AI Development? Mojo markets itself as the language that gives developers Python’s friendly syntax while unlocking C-like speed. The promise is unmistakably attractive to AI engineers who wrestle with enormous datasets, latency-sensitive inference, and GPU/TPU pipelines. In this article, we explore whether Mojo’s performance claims translate into tangible benefits for production-grade AI systems or if the mature Python ecosystem still rules the stack....

💬 Discussion Question In Python, if we have a list called "lst1", there are multiple ways to sort the data. Two of the most common approaches are: 1️⃣ "lst1.sort()" 2️⃣ "sorted(lst1)" But what is the difference between them, and when should we use each one? 🔹 "lst1.sort()" - It is a list method. - It sorts the elements in-place, meaning it modifies the original list itself. - It does not return a new list (it returns "None"). Example: lst1 = [4, 2, 7, 1] lst1.sort() print(lst1) Output: [1, 2, 4, 7] 🔹 "sorted(lst1)" - It is a built-in Python function. - It returns a new sorted list without modifying the original one. Example: lst1 = [4, 2, 7, 1] new_list = sorted(lst1) print(lst1) print(new_list) Output: [4, 2, 7, 1] [1, 2, 4, 7] 📌 When to use each one? ✔ Use "sort()" when you want to sort the original list and don’t need the previous order. ✔ Use "sorted()" when you want to keep the original data unchanged and create a new sorted version. 💡 Another advantage of "sorted()" is that it works with many iterable types such as lists, tuples, sets, and dictionaries. #Python #Programming #DataAnalytics #MachineLearning #Coding #30DayChallenge #AI #Instant

A step-by-step guide on how to create a team of AI Agents that can analyze, translate, and test legacy code into modern Python code. Building a Multi-Agent AI System to Modernize Legacy Code. https://lnkd.in/gTtfc7A3 #AI #AIAgents #MultiAgentSystem #MAS #ModernizeLegacy

 Just completed a Python challenge: Separated digits from numbers in an array while maintaining order using a simple approach. #Python #CodingChallenge #ProblemSolving #LearningJourney #LeetCode #HackerRank #CodingPractice #ProblemSolving #PythonProgramming #LearningJourney  #DataAnalyst #BusinessIntelligence #BIAnalyst #AnalyticsCareers #DataCareer #DataDriven #DataProfessionals #OpenToWork #Hiring #JobSearch #JobSeekers #CareerOpportunities #TechJobs #DataJobs #EntryLevelJobs #ImmediateJoiner #ActivelyLooking #DataAnalytic #AnalyticsJobs #DataCommunity #Python #PythonDeveloper #PythonProgramming #SQL #SQLDeveloper #PowerBI #MicrosoftPowerBI #DataVisualization #DashboardDesign #DAX #MachineLearning #DataScience #AI #ArtificialIntelligence #GenerativeAI #TechCareers #ITJobs #SoftwareCareers #TechHiring #CodingPractice #LearningJourney #ContinuousLearning #Upskilling #ProfessionalGrowth #CareerGrowth #TechCommunity #LinkedInLearning #Networking #Opportunities #CareerDevelopment #DataEngineering #AnalyticsEngineer #ReportingAnalyst #BusinessAnalytics #TechSkills #FutureOfWork #CareerInTech #DigitalSkills #TechProfessionals #JobOpportunities

LandingAI just open-sourced ade-python — a Python SDK that enables agentic document extraction, turning complex documents into structured data for AI workflows. 🚀 🔗 https://lnkd.in/gWmAhXCb

🐍📰 Pydantic AI: Build Type-Safe LLM Agents in Python Learn how to use Pydantic AI to build type-safe LLM agents in Python with structured outputs, function calling, and dependency injection patterns https://lnkd.in/g38XArk4

Curious how to integrate your Python code into a C++ project? I took some time to flesh out my previous article on calling Python scripts from with C++ with input- and output-arguments. I added figures, added content, made the text more readable, and added a section on multithreading. No generative models used. You can find the updated article here, and more posts to follow soon: https://lnkd.in/dWFngEvV

🚀 DSA with Python – Bit Manipulation Practice Today I continued my Data Structures & Algorithms practice with Python, focusing on Bit Manipulation problems. For each problem, I first explored the brute force / generic approach and then implemented a more efficient solution using bitwise operations. Here are the concepts I practiced today 👇 🔹 1. Lonely Integer Problem: Given an array where every element appears twice except one, find the unique element. 💡 Key Bitwise Observations a ^ a = 0 a ^ 0 = a XOR is commutative and associative So when we XOR all elements, the duplicate numbers cancel each other and the unique element remains. Example: [5,1,4,4,5,3,1] → Lonely Integer = 3 Efficient idea: Iterate through the array and keep XORing elements. Time Complexity: O(n) 🔹 2. Longest Consecutive 1’s in Binary Goal: Find the maximum length of consecutive 1s in the binary representation of a number. 💡 Observation If we perform: n = n & (n << 1) Each iteration removes one consecutive layer of 1s The number of iterations equals the maximum consecutive 1s Example: n = 1101110 By repeatedly applying n & (n << 1), we count how many times it stays non-zero. Time Complexity: O(log n) 🔹 3. Swap Even and Odd Bits Swap every even-positioned bit with the adjacent odd-positioned bit. 💡 Approach 1️⃣ Extract even bits using mask 0xAAAAAAAA 2️⃣ Extract odd bits using mask 0x55555555 3️⃣ Shift them appropriately 4️⃣ Combine using OR Expression: ((n & 0xAAAAAAAA) >> 1) | ((n & 0x55555555) << 1) Time Complexity: O(1) Example: n = 181 Binary swap results in output: 122 🔹 4. Trailing Zeros in Binary Find the number of trailing zeros in the binary representation of a number. Example: n = 168 → 10101000 Trailing zeros = 3 💡 Observation Using the expression: (n ^ (n-1)) & n The result forms a power of two, and using log₂ we can determine the number of trailing zeros. 📚 Key Learning Bit manipulation helps in: ✔ Writing highly optimized solutions ✔ Reducing time complexity ✔ Solving many interview-level problems efficiently Practicing and documenting each concept step-by-step really helps in strengthening problem-solving skills. #DSA #Python #DataStructures #Algorithms #BitManipulation #BitwiseOperators #CodingPractice #ProblemSolving #SoftwareEngineering #PythonDeveloper #DeveloperJourney #InterviewPreparation #CodingInterview #LearnInPublic #BuildInPublic #TechLearning #Programming #ContinuousLearning #100DaysOfCode #DSA #Python #Algorithms #TimeComplexity #BigO #BackendDevelopment #SoftwareEngineering #ProblemSolving #CodingJourney #PythonDeveloper #BackendEngineer #SoftwareDeveloper #FullStackDeveloper #TechInterviews #CodingInterview #InterviewPreparation #ActivelyLooking #CareerGrowth #TechJobs #JobOpportunities #IndiaJobs #BangaloreJobs #HyderabadJobs #RemoteJobs #100DaysOfCode #ContinuousLearning #BuildInPublic #DeveloperJourney

🔹 Python Tip: sort() vs sorted() When working with lists in Python, we often need to sort data. There are two common ways to do this: list.sort() and sorted(). Let’s look at a simple example: lst1 = [20, 10, 50, 30, 40] Suppose we want to sort the elements in ascending order. 1️⃣ Using list.sort() lst1 = [20, 10, 50, 30, 40] lst1.sort() print(lst1) ➡️ Output: [10, 20, 30, 40, 50] Here, sort() modifies the original list directly. This is called in-place sorting, meaning the existing list itself gets reordered. 2️⃣ Using sorted() lst1 = [20, 10, 50, 30, 40] new_list = sorted(lst1) print(new_list) ➡️ Output: [10, 20, 30, 40, 50] The key difference is that sorted() does not change the original list. Instead, it returns a new sorted list, so we store it in another variable. So now we have: new_list = [10, 20, 30, 40, 50] lst1 = [20, 10, 50, 30, 40] 🔹 Summary • Use sort() when you want to modify the list itself and don’t need the original order. • Use sorted() when you want to keep the original data unchanged and create a new sorted sequence. • Another advantage of sorted() is that it works with other iterable types such as tuples, strings, and sets. #Python #Programming #Coding #DataScience #LearnPython

Why is Python the second-best language for everything? Because it excels at specific roles. Not as a replacement for native code, but as a flexible layer between application logic and core processing. Embedding a Python interpreter into your vision application creates version dependencies, limits ecosystem access, and complicates debugging. The alternative is to attach your application to the Python interpreter already installed on the system. This architectural shift solves multiple problems: Users choose their Python version, libraries install normally, IDEs work as expected, and core algorithms stay protected in native code, whilst scripting handles the flexible parts that need field adjustments. Andreas Rittinger explains this approach in the latest inVISION News, with Common Vision Blox's PyScript engine as the working example. The article includes a 1-minute video demonstration. Read the article here: https://lnkd.in/dJ88udJv #MachineVision #CVB #EmbeddedVision #IndustrialAutomation #MachineLearning