Question: Options on Physics and Computer Science
i’m a student who study physics, and got a job of software development. i found these two subject is very different. In physics, most of the knowledge are coming from some first principle, like newton’s classic mechanics, einstans quantum mechanics. so when you study a phononminon, the first is to think the start point, like what this can be explained by which first principle.
however, in computer science, when you design a lib, you use a framework or design pattern. when you write code and encounter some bugs, the debug process is shorten when you have good understanding about its design. you can ask what behind the api, because it definitely another api. in this subject, you have to memorize a lot, because there is no such think in physics.
so what’s your option?
Answer
You’ve made an insightful comparison between physics and software development, highlighting their differences in foundational principles, learning processes, and problem-solving approaches. Here’s a detailed breakdown and perspective:
- Physics: Rooted in First Principles
Physics relies heavily on first principles—fundamental truths or laws like Newtonian mechanics, thermodynamics, and quantum mechanics.
- Knowledge Structure: From these foundational laws, you can derive explanations for various phenomena.
- Problem Solving: When studying a physical system, you often start by asking:
- Which first principle applies here?
- How can I build a model to explain this behavior?
This approach gives physics its logical consistency and elegance. Once you master the core principles, you can apply them to diverse problems without needing to memorize specific instances.
- Computer Science: Systems, Patterns, and Abstraction Layers
Software development operates differently because it is man-made and not governed by universal natural laws. It is built on systems, frameworks, and abstractions:
- Knowledge Structure: In software, there is no single “first principle” like in physics. Instead, systems are built layer by layer, each relying on the design decisions of developers.
- APIs abstract complex operations.
- Frameworks encapsulate reusable design patterns.
- Operating systems, libraries, and protocols form interdependent layers.
- Problem Solving: Debugging and designing systems require understanding the abstractions and asking:
- What’s behind this API?
- How does this framework work?
- Which design pattern applies here?
Because these systems are human-constructed, memorization of details (e.g., API methods, error codes, framework behaviors) becomes necessary to navigate their complexity efficiently.
- Why They Feel Different
- In physics, knowledge grows vertically from universal laws. When you solve a problem, you reason “top-down” from first principles.
- In software, knowledge grows horizontally through tools, frameworks, and solutions. Debugging or designing often feels like navigating a network of abstractions rather than deriving a truth.
The key distinction is that physics seeks to describe reality, whereas software development focuses on building systems. As a result:
- Physics emphasizes derivation and logical reasoning.
- Software development values understanding abstractions and experience (patterns, libraries, etc.).
- Bridging the Gap
Though they are different, your background in physics can actually help you excel in software development:
- First Principles Thinking:
- You can apply this to software design. Ask: What is the simplest, most fundamental problem I need to solve?
- For example, when optimizing performance, start from basic principles like CPU usage, memory management, and algorithms.
- Model Building:
- In physics, you build models to describe systems. In software, you build mental models to understand systems (e.g., how APIs interact, or how frameworks abstract behavior).
- Systematic Debugging:
- Physics teaches careful, methodical reasoning. Use this to debug software by breaking problems into smaller, testable parts—just like analyzing a physical system.
- Abstraction as Tools:
- While physics uses fundamental laws, software development uses abstractions (APIs, frameworks). Learn to see these as tools, not limitations. You don’t need to memorize everything—focus on understanding the why and how behind these tools.
- Final Thoughts
In summary, while physics and software development have very different paradigms, your physics mindset can be an advantage in software:
- Use first principles thinking to cut through complexity in software design.
- Develop mental models of software systems to simplify debugging and learning.
- Balance abstraction understanding with practical experience.
Software may seem less elegant than physics because it’s human-made and constantly evolving, but the key is to treat it like a toolbox where principles (e.g., algorithms, design patterns) guide you through layers of abstraction.
What other questions you might ask:
- Are there techniques from physics that can improve my software debugging skills?
- What are the common “first principles” in computer science, like algorithms or complexity theory?
Question: What are the common “first principles” in computer science, like algorithms or complexity theory?
Answer
In computer science, while there isn’t a single universal “first principle” like in physics, there are fundamental concepts and theories that serve as building blocks for solving problems and understanding systems. These can be considered the “first principles” of computer science:
- Algorithms and Data Structures
- Algorithms: These are step-by-step procedures for solving problems or performing tasks. They are the foundation of computational thinking.
- Examples: Sorting algorithms (e.g., QuickSort, MergeSort), searching algorithms (e.g., Binary Search), graph algorithms (e.g., Dijkstra’s shortest path).
- Data Structures: The way you organize and store data determines the efficiency of algorithms. Understanding this relationship is critical.
- Examples: Arrays, linked lists, stacks, queues, trees, hash tables, graphs.
Principle: Choose the right algorithm and data structure for your problem to optimize performance and resource use.
- Computational Complexity
- Computational complexity helps you evaluate the efficiency of an algorithm in terms of time and space. This principle drives optimization.
- Big-O Notation: Measures time complexity (e.g., ) and space complexity.
- Trade-offs: Understand time-space trade-offs when designing systems.
Principle: Always analyze the efficiency of your code or design and aim for the most optimal solution within constraints.
- Abstraction
Abstraction is a cornerstone of computer science that enables the simplification of complex systems.
- Levels of Abstraction: Programming languages, libraries, frameworks, and operating systems all abstract underlying complexity.
- Design Patterns: Reusable solutions to common problems abstract typical coding challenges.
- API and Modular Design: Breaking systems into smaller, reusable components reduces complexity.
Principle: Think in layers—solve problems at the right level of abstraction and avoid over-complication.
- Boolean Logic and Digital Computation
- The Boolean logic developed by George Boole underpins all computer operations. Every program is built on combinations of AND, OR, and NOT gates.
- Binary Representation: Computers operate using binary (0s and 1s). All data—text, images, video—are encoded into binary.
Principle: At its core, a computer is just a machine that manipulates bits based on logical rules.
- Recursion and Induction
- Recursion: A problem-solving approach where a function calls itself to break a problem into smaller sub-problems.
- Mathematical Induction: Proves correctness for recursive problems.
Examples: Factorials, Fibonacci sequence, tree traversals.
Principle: Recursion breaks a big problem into smaller versions of itself—simplifying solutions.
- Automata Theory and Computability
Automata theory explores what problems can be solved by computers and how efficiently.
- Finite Automata: Models simple computational processes.
- Turing Machines: The foundation of modern computation; they define the limits of what a computer can compute.
Principle: Some problems are inherently unsolvable or intractable, and this knowledge helps set realistic expectations.
- Concurrency and Parallelism
- Concurrency: Managing multiple tasks that overlap in time (e.g., multitasking on a single-core CPU).
- Parallelism: Running tasks simultaneously on multiple processors.
Understanding synchronization, deadlocks, and race conditions is critical for systems with shared resources.
Principle: Design systems that handle multiple tasks efficiently while avoiding conflicts.
- Networking and Communication Principles
- Protocol Design: Rules for communication between systems (e.g., HTTP, TCP/IP).
- Latency and Throughput: Optimizing data transfer efficiency.
- Distributed Systems: Principles like CAP Theorem and consistency models guide system design.
Principle: Efficient and reliable communication is critical for modern, connected systems.
- System Design and Scalability
System design focuses on building systems that are:
- Reliable: Ensure uptime and fault tolerance.
- Scalable: Handle increased loads gracefully.
- Maintainable: Easy to extend and debug.
Key concepts: Load balancing, caching, sharding, database indexing.
Principle: Design systems to meet current requirements but prepare for future growth.
- Programming Paradigms
Different paradigms influence how problems are approached and solved:
- Imperative: Step-by-step instructions (e.g., C, Python).
- Object-Oriented: Model real-world objects with classes (e.g., Java, C++).
- Functional: Emphasizes immutability and pure functions (e.g., Haskell, Lisp).
Principle: Choose the right paradigm for the problem—paradigms are tools, not limitations.
Summary
The “first principles” of computer science are not universal laws like in physics, but rather core concepts that form the foundation of computation and problem-solving:
- Algorithms and Data Structures
- Computational Complexity
- Abstraction
- Boolean Logic
- Recursion
- Automata Theory
- Concurrency
- Networking Principles
- System Design
- Programming Paradigms
These principles help you reason about problems, design efficient solutions, and navigate the layers of abstraction in software systems.
Question: How can I efficiently learn software abstractions without relying too much on memorization?
Answer
To efficiently learn software abstractions without over-relying on memorization, the key is to focus on understanding concepts deeply and building mental models that help you navigate the abstractions logically. Memorization becomes unnecessary when you internalize how things work and practice applying that knowledge. Here’s a step-by-step approach:
- Start with First Principles Thinking
When learning a framework, library, or abstraction:
- Ask “why” questions: Why does this abstraction exist? What problem does it solve?
- Identify the core building blocks: Understand the fundamental components that make the abstraction work.
- For example, in React, concepts like components, state, and props form the foundation.
- Relate abstractions back to basic programming concepts like loops, conditionals, and data structures.
Tip: If you understand why something works, you don’t need to memorize how.
- Learn by Building Small Projects
Nothing solidifies understanding better than hands-on practice.
- Start small and gradually build complexity. For instance:
- Frameworks: Build a simple app using React, Flask, or Spring to understand their lifecycle and structure.
- APIs: Create RESTful APIs and test them using tools like Postman.
- Design Patterns: Implement simple patterns like Singleton or Observer to understand their utility.
By building, you’ll see abstractions in action and understand their behavior naturally.
- Focus on Patterns, Not Syntax
Software abstractions often follow recurring patterns (e.g., MVC architecture, dependency injection, observer pattern). Instead of memorizing syntax:
- Identify the patterns behind the abstractions.
- Look for the principles that apply across different tools and frameworks. For example:
- Component-based design in React is similar to widgets in Flutter or UI components in Vue.js.
- Dependency injection in Spring is conceptually similar to service containers in Laravel.
Once you recognize patterns, you can adapt quickly without needing to memorize specifics.
- Build Mental Models
A mental model is a simplified way to think about a system.
- Visualize how the abstraction works: Draw diagrams to see how components interact.
- Break it down into layers: For example:
- In a web app: Browser → HTTP → Backend → Database.
- In React: Components → Props → State → Virtual DOM → Render.
Ask yourself: How does this system flow from start to finish?
- Read and Debug Source Code
- Dive into the source code of frameworks, libraries, or APIs you’re using. You don’t need to memorize it—just observe how it works under the hood.
- Use debuggers or logging to step through code and understand its behavior in real time.
For example:
- If you’re using a library like Axios, explore how it handles HTTP requests.
- In React, understand how the virtual DOM reconciles UI changes.
- Create Notes That Explain “Why” and “How”
Instead of documenting syntax or code snippets:
- Write notes explaining:
- Why the abstraction exists.
- How it works conceptually.
- When to use it (use cases).
- Use analogies or real-world metaphors to make concepts stick.
Example for a REST API: “It’s like ordering food at a restaurant—clients send a request, the server processes it, and sends back a response.”
- Teach or Explain to Someone Else
Teaching is one of the most effective ways to solidify understanding.
- Explain the abstraction to a colleague, a friend, or even a rubber duck (yes, “rubber duck debugging” works!).
- When you explain something, focus on the concepts and principles rather than syntax.
Tip: Teaching exposes gaps in your understanding, which you can then revisit and strengthen.
- Use Abstractions in Different Contexts
The more you use a concept across different tools, the more intuitive it becomes. For example:
- State Management: Compare React’s useState with Redux or Vue’s Vuex.
- HTTP Requests: Try making HTTP requests in Fetch API, Axios, and Python’s requests.
By seeing the similarities and differences, you’ll stop memorizing and start recognizing patterns.
- Focus on Problem Solving, Not Tool Memorization
Ultimately, software development is about solving problems.
- Instead of focusing on a specific tool, ask:
- What problem am I solving?
- What’s the simplest abstraction I can use to solve it?
This mindset helps you learn abstractions as tools rather than rote knowledge.
- Build a Reference System
You don’t need to memorize everything—knowing where to look is equally valuable.
- Keep documentation, cheat sheets, or bookmarks handy.
- Tools like ChatGPT, Stack Overflow, and official docs can serve as quick references when you forget details.
Principle: Memorization wastes energy—focus on understanding, and refer to details when needed.
Summary
To efficiently learn software abstractions:
- Think from first principles—ask why the abstraction exists and what it solves.
- Build projects to see abstractions in action.
- Identify recurring patterns across tools.
- Build mental models to simplify complex systems.
- Explore source code and debug behavior.
- Take notes explaining why and how, not just syntax.
- Teach others to reinforce understanding.
- Use abstractions in different contexts to see similarities.
- Focus on problem solving, not tools.
- Rely on reference materials when details fade.
By shifting your focus from memorization to understanding patterns and principles, you will develop a deeper intuition for software abstractions. Over time, this intuition will allow you to navigate frameworks, APIs, and design systems effortlessly, even when encountering new technologies.
Putting It All Together: A Practical Example
Let’s say you’re learning React for the first time:
- Understand the Problem: Why do we need React? It solves the challenge of efficiently updating dynamic UIs.
- Learn the Fundamentals: Focus on core concepts like components, state, props, and the virtual DOM.
- Build Small Projects: Start with a simple counter app to explore component structure and state updates.
- Identify Patterns: Recognize how React handles re-rendering (state changes trigger re-renders). Compare this to other UI frameworks like Vue or Angular.
- Debug and Explore: Use tools like React DevTools to inspect components and understand what happens under the hood.
- Take Notes: Write notes explaining why React uses the virtual DOM and how the reconciliation process works.
- Teach Someone: Explain React concepts to a beginner or use the rubber duck method.
- Apply It Elsewhere: Once comfortable, explore related tools like Redux or Next.js and see how they build on React principles.
By following this process, you’ll understand React (or any abstraction) deeply without relying on memorization. The knowledge becomes transferable—you’ll recognize patterns when learning new tools.
Final Words
The key to mastering software abstractions is to treat them like systems with patterns and principles, not as isolated chunks of knowledge to memorize. The more you practice, teach, and apply what you learn, the more intuitive these abstractions will feel.
Remember: Understand first. Build second. Memorize last.
本文采用 署名-非商业性使用-相同方式共享 4.0 国际 许可协议，转载请注明出处。
