Back4App VS Tensor-Puzzles

• Scalability
  Back4App provides a scalable backend solution that can grow with your application's needs, effortlessly handling an increasing number of users and data.
• Ease of Use
  The platform offers an intuitive interface and comprehensive documentation, making it easy for developers to set up and manage backend operations without deep programming knowledge.
• Real-time Database
  Back4App supports real-time data synchronization, ensuring that data is consistently updated across all clients in real time.
• Multiplatform Support
  It provides SDKs for multiple platforms including iOS, Android, and web, which allows developers to implement backend services across different devices seamlessly.
• Cost-effective
  Back4App offers a range of pricing plans suitable for different project sizes, including a free tier that is beneficial for small projects and startups.
• Open-source Core
  Built on top of the open-source Parse framework, it allows for greater customization and the benefit of community-driven development.

Possible disadvantages

• Learning Curve
  Despite its user-friendly interface, developers new to BaaS (Backend as a Service) platforms might face an initial learning curve.
• Vendor Lock-in
  While Back4App offers flexibility, there is a dependency on the platform for backend management, which could pose challenges if migrating to another service in the future.
• Limited Customization
  For highly specific or complex backend requirements, Back4App's predefined services might be limiting compared to building a custom backend from scratch.
• Performance Overhead
  Using a BaaS can introduce performance overhead compared to a highly optimized custom backend solution tailored to the application's unique requirements.
• Pricing at Scale
  Although the service is cost-effective for smaller projects, costs can escalate for larger applications with significant data and user management needs.

• Interactive Learning
  Tensor-Puzzles provides a hands-on, puzzle-based approach to learning tensor operations, which is far more engaging and effective than passively reading documentation. Each puzzle challenges you to implement a common operation using only a limited set of primitives, reinforcing deep understanding.
• Builds Strong Foundations
  By constraining users to basic operations like arange, where, and indexing, the puzzles force learners to truly understand how tensor broadcasting, reshaping, and manipulation work under the hood, rather than relying on high-level API calls they don't fully comprehend.
• Progressive Difficulty
  The puzzles are ordered from simple operations (like ones, sum, outer product) to more complex ones (like convolution and matrix multiplication), providing a well-structured learning path that gradually builds skills and confidence.
• Immediate Feedback with Test Suite
  Each puzzle comes with built-in tests that automatically verify your solution, giving immediate feedback on correctness. This allows self-paced learning without needing an instructor or external validation.
• Concise and Focused
  The repository is lightweight and focused purely on tensor manipulation skills. It doesn't require complex setup or dependencies beyond basic PyTorch/NumPy, making it very accessible and easy to get started with quickly.

Possible disadvantages

• Limited Explanations
  The puzzles provide minimal instructional content or explanations. Learners who are completely new to tensors or broadcasting may struggle without supplementary resources, as the repository assumes some baseline familiarity with the concepts.
• Narrow Scope
  The puzzles focus exclusively on tensor manipulation using a restricted set of operations. They don't cover broader deep learning topics like autograd, neural network architectures, training loops, or real-world data preprocessing.
• Artificial Constraints
  The restriction to only a few primitive operations, while pedagogically useful, can feel artificially limiting. In real-world code, you would use the full API, so the skills learned don't always directly translate to practical coding patterns.
• Lack of Guided Solutions
  There are no official step-by-step solutions or detailed walkthroughs provided. If a learner gets stuck on a puzzle, they may have difficulty progressing without seeking external help from community discussions or forums.
• Limited Community and Maintenance
  As a relatively niche educational project, the repository has a smaller community compared to major learning platforms. Issues, discussions, and updates may be infrequent, and learners may find fewer resources for troubleshooting or extending the puzzles.

Serverless GraphQL #01 - Introduction to GraphQL on Parse using Back4App

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• Where to host a node/postgres/Redis app?

  I'm using back4app.com which is a cloud service for parse server, you can fire cloud code using node. Recently they introduce containers, but I didn't use it. Source: over 3 years ago

Tracking Tensor-Puzzles since Jul 2022.