Referred Link - https://www.linkedin.com/posts/shraddha-sahu-5b99b8142_top-agile-tools-for-efficient-project-management-activity-7338146179228061698-u-tA

Here’s a quick guide to some of the best Agile tools out there:

IceScrum
▸ Agile management with Scrum and Kanban boards

Taiga
▸ Open-source support for Scrum and Kanban

Scrumpy
▸ Collaborative Planning Poker for sprint estimation

Hansoft
▸ Scalable Agile and Lean project management

YouTrack
▸ Issue tracking with Agile workflow support

Kanbanchi
▸ Kanban for Google Workspace users

ScrumDesk
▸ Scrum and Kanban with backlog management

Yodiz
▸ Agile issue tracking and project management

ScrumDo
▸ Customizable Agile management with analytics

Leankit
▸ Visual Lean workflow management tool

Pipefy
▸ Workflow automation and process optimization

VivifyScrum
▸ Simple Scrum and Kanban platform

KanbanTool
▸ Visual Kanban projects with analytics

Targetprocess
▸ Enterprise Agile planning and visualization

VersionOne Lifecycle
▸ Scalable enterprise Agile project management

Asana
▸ Task and project management software

Jira
▸ Widely used Agile software development tool

Businessmap
▸ Workflow analytics tool for process improvement

Agilefant
▸ Lightweight Agile and traditional project tool

Zoho Projects
▸ Flexible project management with Agile support

Wrike
▸ Customizable Agile project collaboration

ClickUp
▸ All-in-one productivity and management platform

Pivotal Tracker
▸ Story-based incremental software planning

Monday. com
▸ Customizable work management platform

Axosoft
▸ Scrum and bug-tracking for developers 

Tags:

#ProjectManagement, #ProductManagement, #Agile, #Scrum, #Tools, 

 Referred Link - https://www.linkedin.com/feed/update/urn:li:activity:7305904274570362881/

The Open-Source AI Stack (Full Video Explanation Available at the End)

You don’t need to spend a fortune to build an AI application. The best AI developer tools are open-source, and an excellent ecosystem is evolving that can make AI accessible to everyone.

The key components of this open-source AI stack are as follows:

1 - Frontend
To build beautiful AI UIs, frameworks like NextJS and Streamlit are extremely useful. Also, Vercel can help with deployment.

2 - Embeddings and RAG libraries
Embedding models and RAG libraries like Nomic, JinaAI, Cognito, and LLMAware help developers build accurate search and RAG features.

3 - Backend and Model Access
For backend development, developers can rely on frameworks like FastAPI, Langchain, and Netflix Metaflow. Options like Ollama and Huggingface are available for model access.

4 - Data and Retrieval
For data storage and retrieval, several options like Postgres, Milvus, Weaviate, PGVector, and FAISS are available.

5 - Large-Language Models
Based on performance benchmarks, open-source models like Llama, Mistral, Qwen, Phi, and Gemma are great alternatives to proprietary LLMs like GPT and Claude.

Watch the full video here: https://lnkd.in/e-zqnuuF

Tags:

#ArtificialIntelligence, #AIDevelopment, #OpenSource

Referred Link -  https://www.linkedin.com/posts/habib-shaikh-b8331653_python-learning-roadmap-foundations-activity-7271014564001902592-xAA3

Python learning roadmap:

Foundations:
- Set up Python & IDE (e.g., PyCharm, VSCode).
- Learn basics: syntax, data types, operators, control flow, functions.

Data Structures & Algorithms:
- Master lists, tuples, sets, dictionaries.
- Learn advanced structures (stacks, queues, trees) and algorithms (searching, sorting).

OOP:
- Understand classes, objects, inheritance, polymorphism, encapsulation, decorators, and magic methods.

Advanced Concepts:
- Explore functional programming, error handling, file I/O, modules, and packages.

Standard Library:
- Get familiar with modules like os, sys, datetime, collections, itertools, re.

Web Development:
- Learn HTML/CSS basics, frameworks (Flask, Django, FastAPI), and build RESTful APIs.

Data Science & ML:
- Learn NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow/PyTorch.

Testing & Debugging:
- Master testing with unittest/pytest and debugging tools.

DevOps & Deployment:
- Learn Git, Docker, CI/CD with GitHub Actions.

Best Practices:
- Follow PEP 8, optimize performance, and learn security basics.

Specializations:
- Web scraping, GUI, game dev, networking.

Continuous Learning:
- Build projects, contribute to open-source, attend conferences.

Tags:

#PythonLearningRoadmap, #LearningRoadmap

 Referred Link - https://www.linkedin.com/posts/dileeppandiya_dissecting-the-architecture-of-netflix-a-activity-7263040985083924480-wZYU/

Dissecting the Architecture of Netflix: A Look at the Tech Stack Behind Scalable Streaming

This diagram outlines the architecture behind Netflix’s seamless streaming experience, breaking down the tech stack that powers everything from frontend interfaces to backend services, streaming infrastructure, and big data management. It’s a solid example of how a well-designed architecture supports millions of concurrent users and delivers high-quality video on demand.

Key Layers:
Frontend 💻 – The frontend stack includes APIs, mobile and web technologies like GraphQL, Swift, Kotlin, JavaScript, and React. These tools deliver a consistent experience across devices, handling requests with efficiency and speed.

Backend 🔗 – Backend services are built using Spring Boot, with Netflix’s own Zuul and Eureka handling service discovery and communication in a microservices architecture. Databases like EVCache, CockroachDB, MySQL, and Cassandra manage vast amounts of user data and support high-availability.

Streaming Infrastructure 🎥 – Netflix relies on Open Connect and integrates with Amazon Cloudfront and S3 for content delivery, ensuring fast and reliable video streaming worldwide.

Big Data 📊 – To deliver personalized recommendations and insights, Netflix uses a powerful data stack with Amazon Redshift, Apache Iceberg, Apache Spark, and Druid, optimizing both storage and real-time processing.

CI/CD 🔄 – A streamlined CI/CD pipeline with Jenkins, Spinnaker, PagerDuty, and Confluence supports rapid development cycles, testing, and deployment, enabling continuous improvement and scalability.

Why This Matters:
This architecture demonstrates the importance of modularity, scalability, and resilience in high-demand applications. Netflix’s use of in-house and open-source solutions, combined with cloud integrations, exemplifies a robust setup for handling massive concurrent traffic while maintaining performance.

For those building similar architectures – what tools or strategies have worked best for you? Drop your insights below—let’s exchange ideas on scalable, resilient systems.

Tags:

#Architecture, #NetflixArchitecture