Ayinampudi Siva Rama Raju on LinkedIn: yard management system (2024)

5. Share what you've learnedThroughout the development journey of our BMI and Healthy Body project, I've gained invaluable insights and experiences that have enriched my understanding of software engineering and personal growth. Here's a reflection on what I've learned:Technical Take-Aways:API Integration: Working on this project has honed my skills in integrating external APIs into web applications. I've learned how to effectively communicate with APIs, handle requests and responses, and parse data to extract relevant information.Database Management: Managing user data and BMI calculations in a database has deepened my understanding of database systems and SQL. I've learned how to design efficient database schemas, perform CRUD operations, and ensure data integrity.Front-end Development: Collaborating on the front-end aspects of the project has improved my proficiency in HTML, CSS, and JavaScript. I've gained insights into creating responsive and visually appealing user interfaces that enhance user experience.What I Might Do Differently:Reflecting on the project, I realize the importance of thorough planning and prioritization from the outset. In future projects, I would allocate more time for comprehensive requirements gathering and project planning to ensure smoother development and minimize unexpected challenges along the way.What I Learned About Myself as an Engineer:Working on this project has highlighted my adaptability and problem-solving skills. I've learned to navigate through technical challenges, collaborate effectively with team members, and persevere in the face of obstacles. Additionally, I've discovered my passion for creating software solutions that positively impact users' lives, driving me to continuously strive for excellence in my engineering endeavors.Future Engineering Path:This project has reaffirmed my commitment to pursuing a career in software engineering. It has provided me with practical experience in developing real-world applications, reinforcing my confidence in my abilities and shaping my aspirations for future projects. Moving forward, I aspire to explore new technologies, deepen my expertise in areas of interest, and contribute to innovative projects that push the boundaries of technology.Confirming or Questioning Beliefs:Prior to this project, I held a belief that simplicity is key in software development. While this belief still holds true, I've come to appreciate the complexity that can arise in implementing seemingly simple features, such as BMI calculations and personalized nutrition recommendations. This project has reinforced my understanding of the importance of thoughtful design and meticulous attention to detail in creating user-friendly and reliable software solutions.In conclusion, the BMI and Healthy Body project has been a rewarding journey filled with learning opportunities and personal growth. As I continue my engineering path,

1

30% of Engineer Leads Use a Spreadsheet as a Service Cataloghttps://lnkd.in/gfTxqiYrFor reporting or analytics, spreadsheets get a bad rap. They’re considered old school — and quite frankly, dated. Whether you’re an accountant or a software engineer, there’s always a conflict between the flexibility and usefulness of spreadsheets and the need to use dedicated reporting and analytics tools that aren’t manual.Even in the software engineering space, the same conflict arises, such as in the case of using spreadsheets as a way to track microservices. In Port’s 2024 “State of Internal Developer Portals” report, 30% of engineering leaders stated that they used a spreadsheet as a microservice catalog. This is despite virtually all respondents (99%) reporting that they’re embracing platform engineering in their organizations, which is all about empowering developers with the tools, resources and processes they need to promote productivity.Can Spreadsheets Be Service Catalogs?Our survey aimed to get a better understanding of the platform engineering space, and particularly the core interface of platform engineering: the internal developer portal. We surveyed 100 engineering leaders from companies using a microservices architecture in production.When asked if the respondents were using an internal developer portal, the majority (85%) said they were either already using one or were planning to do so in the next 12 months.Figure 1We then asked what type of portal they were using. Some 35% said they were using spreadsheets with microservices data. More than half (53%) use some kind of developer portal (in-house portal, Backstage or commercial products), and 12% use some sort of CI/CD developer self-service that isn’t a portal.As internal developer portals are still a new product category, it’s unsurprising that the market is unclear about what it entails, but it is also natural that engineering leaders are using all kinds of tools and functions to try to deliver portal functionality to their developers. Those capabilities can be broadly categorized as developer self-service, as a catalog, and as an ability to ensure software standards are met. Often organizations will combine a CI tool like Jenkins, which provides a self-service interface — a service catalog of sorts — and a solution to ensure standards such as production readiness are met to create a homegrown portal of sorts. These highly customized portals tend to work well initially, but are difficult to maintain and adapt as the needs of the organization grow.This is perhaps where the confusion to pick spreadsheets in the survey may have stemmed from. Leaders in eEngineering may be using them as a catalog in their organizations to track which microservices exist in the first place, as well as dependencies, software ownership and on-call personnel — all things you can do with an internal developer portal’s software catalog.The main drawback, of course, is that updating...

🔗 The Unsung Heroes of Software Development: Data Structures 🔗In a world where technology is constantly evolving, the fundamentals remain constant. Investing time in learning and mastering data structures is an investment in your future as a software developer.Data structures are the backbone of efficient software design. They provide the means to manage and organize data, enabling developers to write code that is both optimized and scalable. Here's a brief overview of some common data structures:Arrays:Definition: A collection of elements identified by index or key.Use Cases: Storing fixed-size sequential data, efficient access by index.Example: Storing a list of student names.Linked Lists:Definition: A linear collection of data elements where each element points to the next.Use Cases: Dynamic memory allocation, ease of insertions and deletions.Example: Implementing a stack or queue.Stacks:Definition: A collection of elements that follows the Last In First Out (LIFO) principle.Use Cases: Undo mechanisms in text editors, expression evaluation.Example: Backtracking algorithms.Queues:Definition: A collection of elements that follows the First In First Out (FIFO) principle.Use Cases: Order processing systems, breadth-first search in graphs.Example: Print job management.Hash Tables:Definition: A structure that maps keys to values for efficient data retrieval.Use Cases: Implementing associative arrays, database indexing.Example: Storing user data with unique IDs.Trees:Definition: A hierarchical structure with nodes connected by edges.Types: Binary Trees, Binary Search Trees, AVL Trees, B-Trees.Use Cases: Hierarchical data representation, database indexing, file systems.Example: Directory structure in a file system.Graphs:Definition: A collection of nodes connected by edges.Types: Directed, Undirected, Weighted, Unweighted.Use Cases: Network routing, social network analysis.Example: Representing cities and roads in a map.Heaps:Definition: A specialized tree-based structure that satisfies the heap property.Types: Min-Heap, Max-Heap.Use Cases: Implementing priority queues, heap sort algorithm.Example: Scheduling tasks by priority.Understanding data structures equips you with the tools to solve complex problems. From sorting and searching to managing dynamic data, the right data structure can make all the difference in finding an elegant solution.Many job interviews and coding assessments focus on data structures and algorithms. A strong grasp of these concepts not only prepares you for technical interviews but also demonstrates your problem-solving skills to potential employers.#SoftwareDevelopment #DataStructures #Coding #Programming #TechTips #Efficiency #Scalability #PerformanceOptimization

18

Building an Inventory Management System: An ExperienceDescription General :IntroductionThe purpose of this project was to design a comprehensive inventory management system using Flask. This initiative was aimed at helping small businesses manage their stock efficiently by providing a user-friendly interface for adding, modifying, and deleting items in their inventory.Team Members:Ibrahim Hassani (myself)Ibrahim Hassani: Lead developer, focused on backend development and database integration.This application is primarily aimed at small business owners who need a simple tool to manage their stock without the difficulties and costs associated with larger systems. My personal focus was on the robustness and security of the backend, particularly the authentication mechanisms using check_password_hash and generate_password_hash for user login and registration.Personal StoryWhen I was a child, my family owned a small shop in our neighborhood. I often saw my parents struggling to manage the inventory using paper and pen. This initiative is a tribute to those times, aiming to provide a simple-to-use solution for people like my parents who may not have technological expertise but need an easy solution.Project AccomplishmentsWe successfully developed an inventory management system with the following features:Implemented secure login and registration systems.Users can create, read, update, and delete inventory items.Data Export: Ability to generate and download inventory data in Excel format.Architecture OverviewThe application follows a traditional MVC model:Frontend: HTML, CSS, and Bootstrap for design and responsiveness.Backend: Flask framework to handle requests and business logic.Database: SQLite was chosen for its simplicity and ease of use in a small-scale application.Technologies UsedFlask: Chosen for its lightweight and ease of use to facilitate rapid development.SQLite: Chosen for its simplicity and integration with Flask.Technical Challenge: Implementing a Secure Authentication SystemA robust and secure authentication system was necessary to ensure that only authorized users could access and manage inventory data.🚀 About MeMy name is Ibrahim Hassani , a passionate full-stack software engineering student with a focus on developing practical and efficient solutions. I am dedicated to continuous learning and excited about contributing to impactful projects.GitHub:https://lnkd.in/eYJbbVu6LinkedIn:https://lnkd.in/eXW2DpUi

6

1 Comment

Hi guys,I love doing pet projects. Mostly, because it allows me to create and solve problems, already solved by someone else, myself.Recently I stumbled across such simple and easily predictable problem, that I was amused, how I missed this small detail in my small project.Today I suggest you to take a guess, what problem I stumbled upon and maybe suggest your solution to this simple problem.I will briefly describe the project internal architecture, using abstracts. I don’t think it is important, what data is actually flowing between modules, yet I may share details down the road, if this post gain traction.Here is the simplified view of conceptExternal Sources: A cloud symbol represents the external sources providing information to Module S.Module S: Gathers information from external sources.Module R: Receives data from Module S, uses an external API to process the data.External API: Interacts with Module R for processing data.Module P: Enriches data received from Module R without altering it.Module TP: Provides the enriched data to the end user.End User: Represents the recipient of the final data output from Module TP.Arch overviewHere is a bit more details regarding external components.As I described before, there is no microservices, in their traditional form. Modules will suit this description the best. Those modules use the same database.As you can see, all modules uses the same db. That’s the reason I would prefer to call it modules. This architecture still allows for some scalability. It is possible to run multiple instances of different modules. Let’s say, there is more external sources. Given the nature of the project there is no time constraints on the end user part. Tho, it would be great to be able to gather info from external sources as fast as possible. This architecture provides opportunity to start multiple instances of Module S.however, once I have started three module S instances, I was met with mentioned before problem. At the start of the project my solution was viewed as time saving MVP solution, which worked fine. The issue is linked directly to the multiple instanses and the problem lies solely in the design, not in the external systemsSo, I invite you to try to guess the problem, and suggest the solution you see fit best.I came up with three, yet each one comes with its own downsidesExited to hear your thoughtsValid

18

COMCAT: Enhancing Software Maintenance through Automated Code Documentation and Improved Developer Comprehension Using Advanced Language ModelsThe field of software engineering continually evolves, with a significant focus on improving software maintenance and code comprehension. Automated code documentation is a critical area within this domain, aiming to enhance software readability and maintainability through advanced tools and techniques.A major challenge in software maintenance is the high cost and effort associated with code comprehension. Developers spend considerable time understanding existing code, which can be inefficient and error-prone. This issue is particularly pronounced in large codebases where documentation may be sparse or outdated, leading to increased maintenance costs and reduced productivity. Estimates indicate that software maintenance accounts for 66% to 90% of total software lifetime costs, with approximately half attributed to code comprehension. Given these statistics, enhancing software readability and understanding is essential for cost-effectiveness and efficiency in software development and maintenance.Existing methods for automated code documentation include template-based, information retrieval, and learning-based approaches. Template-based tools use predefined structures to generate comments, providing a consistent format. Information retrieval techniques extract and reuse existing documentation, leveraging databases or online sources to fill documentation gaps. Learning-based methods, particularly deep learning models, have shown promise in generating accurate and context-aware comments. These models train on large code and corresponding documentation datasets, improving their ability to produce relevant comments that enhance comprehension.Researchers from Vanderbilt University and Universidad Nacional Autónoma de México introduced a novel tool called COMCAT. This tool leverages Large Language Models (LLMs) to generate comments that improve code comprehension. COMCAT uses a three-step pipeline: identifying suitable locations for comments, predicting the most helpful type of comment, and generating comments based on context and developer expertise. The tool’s design integrates human judgment to guide LLMs, enhancing their ability to produce comments that align with developers’ needs.The COMCAT pipeline automates the documentation process by splitting source code into snippets, classifying these snippets, and using an LLM to generate relevant comments. The Code Parser component splits the code into segments that capture commonly used structures, such as loops and variable declarations. The Code Classifier then predicts the most helpful type of comment for each snippet, and the Prompter uses an LLM to generate a comment based on the selected location and comment type. This approach aims to provide comprehensive and accurate documentation that aligns with human developers’ needs, improving code’s o...

Clean Architecture is a polarizing concept. Either u love it of hate it. I have successfully implemented this along with other architectural concepts like CQRS /DDD etc..It is language agnostic and I have introduced this to different teams to implement it in c# and node(nestjs) micro-services. There is some learning curve involved and initially it is confusing and can be frustrating. The initial pain is worth it as it leads to better code organization and other best practices, which makes it easier to maintain in the long run.

2

Our project, the Love Calculator, is designed to provide users with a fun and interactive way to determine compatibility with their partners. By inputting their names, users can receive a compatibility score and gain insight into their relationship dynamics.Team Members, Roles, and Timeline:Len Ocran: Sole developer, designer, and project manager.Timeline: Development spanned two months, from January to February 2024.Project Audience:The Love Calculator is created for individuals interested in exploring the compatibility of their relationships. It caters to a wide range of users, from couples curious about their compatibility to singles interested in potential matches.Project Focus:As the sole team member, my focus was on every aspect of the project, including development, design, and project management. I aimed to create an intuitive and engaging user experience while ensuring the accuracy and reliability of the compatibility algorithm.Our Love Calculator project was born out of a personal fascination with the intricacies of human relationships. For me, delving into the dynamics of love and compatibility has always been more than just a passing interest—it's been a journey of self-discovery and understanding.Description of Architecture:The architecture of the Love Calculator application follows a client-server model. The client-side consists of the user interface where users input their names and receive compatibility scores. The server-side handles the computation of compatibility scores based on user input and returns the results to the client. Data flows from the client to the server for processing, and the results are sent back to the client for display.Technologies Used:Frontend: HTML, CSS, JavaScriptReasoning: We chose to use pure HTML, CSS, and JavaScript for the frontend to have full control over the user interface and to solidify our understanding of these core web technologies.Backend: Python, FlaskReasoning: We opted for Flask as our backend framework due to its simplicity and flexibility, allowing us to quickly set up endpoints for handling user requests and computations.Database: SQLiteReasoning: SQLite was chosen for its lightweight nature and simplicity, making it suitable for storing user data and compatibility results without the need for a complex database management systemName: Len Ocran, student at ALX SWEGitHub link:https://lnkd.in/di3nxAqKLinkedIn profile:https://lnkd.in/dpbpYrbq

3

🚗 Just Wrapped Up an Exciting Project: APS Fault Prediction for Vehicle Safety with ML Ops! 🛠️🔍🛠️ Thrilled to share my latest accomplishment – successfully building a robust APS (Active Air Pressure System) fault prediction model using Python, XG Boost, and ML Ops tools like Apache Spark! 📊 The APS is a critical component for optimal braking and vehicle safety.🔍 Our Approach: Through meticulous data collection, EDA, and preprocessing, we harnessed the power of the XG Boost algorithm, achieving an impressive 97% accuracy in forecasting APS faults. 📈 This proactive approach can save valuable time, resources, and enhance vehicle maintenance.💡 Innovative Integration: Leveraging the capabilities of GitHub Actions and Docker, we streamlined the deployment process, ensuring the model is readily available for analysis and decision-making. 🚀 Additionally, we embraced Apache Spark for efficient data processing, and Apache Airflow for batch data handling and automated report generation, boosting efficiency by 80%.🌟 Key Benefits: This project isn't just about code – it's about real-world impact. By detecting APS anomalies early, we empower vehicle manufacturers and maintenance teams with insights for proactive maintenance, swift repairs, and heightened safety measures. 🚦⚙️🛠️ Tools Utilized: Python, pandas, numpy, imblearn, scikit-learn, xgboost, Apache Spark, GitHub Actions, Docker, and Apache Airflow. A big shoutout to these tools for making our project journey seamless!🔗 Dive into the details and explore the code on my GitHub repository: [APS Fault Prediction Project](https://lnkd.in/dit5ZiFV)🙏 Grateful to my mentors Alakh Pandey, Krish Naik, Sunny Savita, Priya Bhatia, Pragya Rustagi, Vishwa Mohan, PW Skills, PW (PhysicsWallah) for their guidance. Let's connect on LinkedIn for more updates and projects. #DataScienceCommunity #Mentors🚀 Excited to hear your thoughts and connect with fellow tech enthusiasts, data scientists, and automotive aficionados! Let's drive innovation together. 🚗💡🔧Linkedin: https://lnkd.in/d4ZNN_abGitHub: https://lnkd.in/dit5ZiFV#DataScience #MachineLearning #MLops #VehicleSafety #APS #PredictiveModeling #Innovation #Python #XGBoost #ApacheSpark #GitHub #ApacheAirflow #TechCommunity #CodeJourney #DataDrivenInsights #AI #DataAnalytics #BigData #DataEngineering #DataScienceCommunity #TechInnovation #AutomotiveTech #SafetyFirst #DataScientists #TechEnthusiasts #MLCommunity #GitHubProject #CodingLife #AIinTransportation #SmartVehicles

19

At My Project Ideas, we explore the world of technology, focusing on everything from Artificial Intelligence (AI) and SAP solutions to comprehensive tutorials on programming languages like Python, Java, C++, JavaScript, HTML CSS and more. Check out the channel here: https://lnkd.in/gP5Jsx2AWhether you're a beginner eager to write your first line of code or an experienced developer looking to expand your skill set, our channel is designed to fuel your curiosity and propel your skills to new heights.📚 What We Offer:1. AI Hands-On Experience: Explore machine learning, deep learning, neural networks, and AI applications with hands-on projects and case studies.2. SAP Solutions: Get an in-depth understanding of SAP modules, integration techniques, and real-world business solutions that will set you apart in the tech industry.3. Coding Projects: Step-by-step guides on creating exciting projects using various programming languages.4. Language Tutorials: From Python for beginners to advanced C++, our tutorials are tailored to help you learn at your own pace.5. Tech Insights: Stay updated with the latest trends, tools, and technologies in the tech world.🎯 Why Subscribe?1. Regular Updates: New weekly videos to keep you updated with the latest tech trends.2. Interactive Learning: Engage with a community of like-minded tech enthusiasts.3. Clear, Concise Tutorials: Our tutorials are designed to be easy to follow, ensuring you understand the 'how' and 'why' behind the code.🔔 Subscribe to My Project Ideas and hit the bell icon to never miss an update from us. You can explore our comprehensive playlists to find tutorials and projects specific to your needs and interests.📢 Engage with Us:Like: If you find our videos helpful, give us a thumbs up!Comment: Have questions or feedback? Let us know in the comments section below.Share: Love our content? Share our videos with your friends and colleagues who might find them helpful.🔗Follow Us:- Github: https://lnkd.in/gKpBVM-K🌐 Visit Our Website: https://lnkd.in/deEp3Yic for more resources and learning materials.📩 For Business Inquiries: info@rudelabs.inMy Project Ideas is more than just a tech channel; it’s a thriving community eager to build and share knowledge. Whether you’re looking to enhance your programming skills, understand AI algorithms, or master SAP systems, we're here to assist every step of the way. Join us on this journey to tech excellence!#ai #tech #SAP #programming #coding #codingtutorials #techchannel #learncoding #machinelearning #softwaredevelopment

9