Importance of Coding Principles

When you start working on a new (for you) but existing codebase, you will notice patterns you strongly disagree with. Naming conventions that seem stupid. Abstractions that seem over-engineered. Error handling that you would have done very differently. Match them anyway, in the code that you write. A codebase is a shared 'language'. When every file follows the same conventions, you and your peers can move through the code quickly, make changes confidently, and reason about what things do without stopping to decode style differences. The moment you introduce your own patterns, you add a 'second dialect'. Now the next person reading your code has to context-switch mid-file. The cost of diverging is almost always higher than the cost of going along with something imperfect. The original author is not necessarily wrong either - they had context you do not. Try to understand that. There is a right time to change conventions; wait until then. You can always initiate a team discussion or raise a smaller pull request that updates a small fraction of the codebase and demonstrates the benefits. Hope this helps.

SOLID Principles: The Bedrock of Clean, Maintainable Code As software engineers, we strive for code that's robust, flexible, and easy to maintain. Let's revisit SOLID principles - a set of guidelines that, when followed, lead to better software design. Let's break them down: 𝗦 - 𝗦𝗶𝗻𝗴𝗹𝗲 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 • Each class should have one, and only one, reason to change • Keep your code simple, focused, and easier to understand • Think: "Does this class do too much?" 𝗢 - 𝗢𝗽𝗲𝗻-𝗖𝗹𝗼𝘀𝗲𝗱 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 • Software entities should be open for extension, but closed for modification • Add new features without altering existing code • Use abstractions and polymorphism to achieve this 𝗟 - 𝗟𝗶𝘀𝗸𝗼𝘃 𝗦𝘂𝗯𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 • Derived classes must be substitutable for their base classes • Subclasses should extend, not replace, the behavior of the base class • Ensures different parts of your code can work together seamlessly 𝗜 - 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗦𝗲𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 • Many client-specific interfaces are better than one general-purpose interface • Keep interfaces focused and lean • Prevents classes from implementing methods they don't need 𝗗 - 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗜𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 • Depend on abstractions, not concretions • High-level modules shouldn't depend on low-level modules; both should depend on abstractions • Promotes flexibility and easier testing through decoupling Implementing SOLID principles might seem challenging at first, but the long-term benefits are substantial: • Increased code maintainability • Easier testing and debugging • Enhanced scalability and flexibility How have you applied SOLID principles in your projects? What challenges did you face, and how did you overcome them?

What if every time you asked AI to 'improve' your code, you were actually making it less secure? Our research, presented as 'Security Degradation in Iterative AI Code Generation: A Systematic Analysis of the Paradox' at IEEE ISTAS25-IEEE International Symposium on Technology and Society 2025, revealed a counterintuitive finding:- iterative AI-based code 'improvement' can introduce more security vulnerabilities, not fewer. Analyzing 400 code samples across 40 rounds of iterations, we discovered a 37.6% increase in critical vulnerabilities after just five iterations. Key findings every developer should know include:- 1. Efficiency-focused prompts showed the most severe security issues. 2. Even security-focused prompts introduced new vulnerabilities while fixing obvious ones. 3. Code complexity strongly correlates with vulnerability introduction. 4. Later iterations consistently produced more vulnerabilities than early ones. As builders working with agentic AI solutions, this research challenges the assumption that iterative refinement always improves code quality. The reality is that AI autonomy in code iteration can create a dangerous illusion of improvement while systematically degrading security. The bottom line is that human expertise isn't just helpful in AI-assisted development, it's absolutely essential. The future of secure coding lies in human-AI collaboration, not AI autonomy. My co-authors Profs Shivani Shukla and Romilla Syed and I are grateful for the engaging discussions at IEEE ISTAS and excited to see how this research shapes safer AI-assisted development practices. Ready to discuss the security implications of your AI development workflow? Drop a comment or DM, let's explore how to build more secure AI-assisted systems together. Check out the paper - https://lnkd.in/d7EYwnaR #AISecurity #CodeGeneration #CyberSecurity #ISTAS2025 #SecurityResearch #HumanAICollaboration #ResponsibleAI #SoftwareSecurity

𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗕𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 🅻🅴🆂🆂🅾🅽 9/10 𝗪𝗿𝗶𝘁𝗲 𝗦𝗲𝗰𝘂𝗿𝗲 𝗖𝗼𝗱𝗲 𝗮𝗻𝗱 𝗣𝗿𝗲𝘃𝗲𝗻𝘁 𝗩𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗪𝗵𝘆 𝗦𝗲𝗰𝘂𝗿𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Insecure code can lead to vulnerabilities that attackers exploit, resulting in data breaches, financial losses, or damage to a company's reputation. By learning secure coding practices, you ensure the software you write is reliable and safe from malicious exploits. 𝗞𝗲𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗼𝗳 𝗦𝗲𝗰𝘂𝗿𝗲 𝗖𝗼𝗱𝗶𝗻𝗴: • 𝗜𝗻𝗽𝘂𝘁 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Never trust user inputs. Validate and sanitize all inputs to prevent injection attacks (e.g., SQL injection). • 𝗔𝘃𝗼𝗶𝗱 𝗛𝗮𝗿𝗱𝗰𝗼𝗱𝗶𝗻𝗴 𝗦𝗲𝗰𝗿𝗲𝘁𝘀: Store sensitive data (e.g., passwords, API keys) securely using environment variables or secure vaults. • 𝗟𝗲𝗮𝘀𝘁 𝗣𝗿𝗶𝘃𝗶𝗹𝗲𝗴𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: Grant only the necessary permissions. Avoid giving your application or users more privileges than needed. • 𝗘𝗿𝗿𝗼𝗿 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴: Do not expose sensitive information in error messages. • 𝗦𝗲𝗰𝘂𝗿𝗲 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀: Regularly update libraries and frameworks to fix known vulnerabilities. Use tools like 𝘯𝘱𝘮 𝘢𝘶𝘥𝘪𝘵  or  𝘱𝘪𝘱-𝘢𝘶𝘥𝘪𝘵. • 𝗨𝘀𝗲 𝗦𝗲𝗰𝘂𝗿𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Always encrypt data in transit using HTTPS or similar protocols. 𝗜 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 𝗯𝗲𝗹𝗼𝘄 𝗯𝗼𝗼𝗸𝘀:  1. "𝘛𝘩𝘦 𝘚𝘦𝘤𝘶𝘳𝘦 𝘊𝘰𝘥𝘪𝘯𝘨 𝘏𝘢𝘯𝘥𝘣𝘰𝘰𝘬" by Julia H. Allen and Sean Barnum: A beginner-friendly guide that introduces secure coding practices. 2. "𝘚𝘦𝘤𝘶𝘳𝘦 𝘊𝘰𝘥𝘪𝘯𝘨 𝘪𝘯 𝘊 𝘢𝘯𝘥 𝘊++" by Robert C. Seacord: A classic book focusing on secure programming techniques in C and C++. 3. "𝘚𝘦𝘤𝘶𝘳𝘦 𝘊𝘰𝘥𝘪𝘯𝘨 𝘉𝘦𝘴𝘵 𝘗𝘳𝘢𝘤𝘵𝘪𝘤𝘦𝘴 𝘈 𝘊𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘎𝘶𝘪𝘥𝘦" by Gerardus Blokdyk. By following these practices, students will not only improve their programming skills but also build a strong foundation in creating robust, secure applications. 𝘐𝘧 𝘺𝘰𝘶 𝘧𝘰𝘶𝘯𝘥 𝘵𝘩𝘪𝘴 𝘱𝘰𝘴𝘵 𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵, 𝘪𝘯𝘵𝘦𝘳𝘦𝘴𝘵𝘪𝘯𝘨, 𝘰𝘳 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘷𝘦, 𝘧𝘦𝘦𝘭 𝘧𝘳𝘦𝘦 𝘵𝘰 𝘴𝘩𝘢𝘳𝘦 𝘰𝘳 𝘳𝘦𝘱𝘰𝘴𝘵 𝘪𝘵 𝘸𝘪𝘵𝘩 𝘺𝘰𝘶𝘳 𝘧𝘰𝘭𝘭𝘰𝘸𝘦𝘳𝘴 𝘵𝘰 𝘩𝘦𝘭𝘱 𝘪𝘵 𝘳𝘦𝘢𝘤𝘩 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘢𝘶𝘥𝘪𝘦𝘯𝘤𝘦. #Students #CS #ComputerScience #Programming #SelfImprovement #Learning #Knowledge #SecureCoding

Use these essential techniques for crafting high-quality code in your data job. Here’s how you can ensure your code meets the highest standards: 1. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: Stick to the conventions and standards in your language of choice, whether it's Python, R, or SQL. This includes using meaningful variable names, intending your code into blocks, and organizing code in logical modules.     2. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Use tools like Git to manage changes and collaborate more effectively. Version control isn’t just for software developers but for every professional writing code.     3. 𝗖𝗼𝗱𝗲 𝗥𝗲𝘃𝗶𝗲𝘄𝘀: Engage with your team to review each other's code. This not only helps catch errors before they become problems but also promotes a culture of learning and knowledge sharing within your team.     4. 𝗪𝗿𝗶𝘁𝗲 𝗧𝗲𝘀𝘁𝘀: Although it might seem over the top for some data projects, writing tests for your code can catch bugs early and save hours of debugging later. It ensures that your code functions as expected and makes modifications safer and more reliable. You can include different tests like unit tests for functions and schema checks for your inputs.     5. 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗹𝘆: Revisit and revise your code regularly. Refactoring helps to improve your code efficiency and readability. As you learn and grow, you'll find ways to make your existing code better.     6. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴: Good documentation is not just about commenting within the code. Document your data sources, your algorithms, and your methodologies in an accessible way. This is crucial for when others (or you in the future) need to understand and possibly build on your work. A good place for this additional documentation is architectural decision records placed in your repository.     7. 𝗙𝗼𝗰𝘂𝘀 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗼𝗻 𝗢𝗻𝗲 𝗧𝗵𝗶𝗻𝗴: Make sure your function is doing exactly one thing. Having to use "and" in the function name or the comment describing the function then split it! It will make it much easier to understand and modify later on.     8. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗟𝗶𝗻𝘁𝗲𝗿𝘀 𝗮𝗻𝗱 𝗙𝗼𝗿𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Tools that automatically check your code for style issues, errors, or syntactic discrepancies can drastically improve your code quality. They enforce consistency and help you focus on the logic rather than the format. You level up your code from good to great by including these techniques in your workflow. High-quality code means fewer errors, easier maintenance, and more reliable results. What strategies do you use to maintain high code quality? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #cleancode #python #codingstandards

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐒𝐎𝐋𝐈𝐃 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐥-𝐋𝐢𝐟𝐞 𝐒𝐭𝐨𝐫𝐢𝐞𝐬 🚀 Imagine you're building a house. You need strong foundations, right? In software development, SOLID principles are like those foundations, ensuring your code is robust and easy to maintain. 🔶 Single Responsibility Principle (SRP): Think of a librarian. Their job is to manage books, not cook food. Similarly, a class should have only one responsibility. 🔶 Open/Closed Principle (OCP): Consider a smartphone. You can add apps without changing the core system. Your code should allow new features without altering existing ones. 🔶 Liskov Substitution Principle (LSP): A cat is an animal, so you can treat it like one. But, you wouldn't expect it to bark! Objects of a subclass should replace objects of a parent class without unexpected behavior. 🔶 Interface Segregation Principle (ISP): Imagine a TV remote. It has buttons for all functions, but you only need the power button. Classes should not be forced to implement interfaces they don’t use. 🔶 Dependency Inversion Principle (DIP): Think of a car. It relies on an engine, but you can swap it with another without redesigning the car. High-level modules shouldn’t depend on low-level modules; both should depend on abstractions. 𝘞𝘢𝘯𝘵 𝘵𝘰 𝘥𝘪𝘷𝘦 𝘥𝘦𝘦𝘱𝘦𝘳? 🎥 𝘊𝘩𝘦𝘤𝘬 𝘰𝘶𝘵 𝘵𝘩𝘪𝘴 𝘷𝘪𝘥𝘦𝘰 𝘰𝘯 𝘚𝘖𝘓𝘐𝘋 𝘱𝘳𝘪𝘯𝘤𝘪𝘱𝘭𝘦𝘴: https://lnkd.in/gUpwVY2G #SOLIDPrinciples #Coding #SoftwareEngineering #ProgrammingTips #LearnCoding

Anthropic's latest guide is goldmine for AI engineers: (it's about context engineering) A poor LLM can possibly work with an appropriate context, but a SOTA LLM can never make up for an incomplete context. That is why production-grade LLM apps don’t just need instructions but rather structure, which is the full ecosystem of context that defines their reasoning, memory, and decision loops. And all advanced agent architectures now treat context as a multi-dimensional design layer, not a line in a prompt. Here’s the mental model to use when you think about the types of contexts for Agents: 1) Instructions → This defines the who, why, and how: → Who’s the agent? (PM, researcher, coding assistant) → Why is it acting? (goal, motivation, outcome) → How should it behave? (steps, tone, format, constraints) 2) Examples: This shows what good and bad look like: → This includes behavioral demos, structured examples, or even anti-patterns. → Models learn patterns much better than plain rules 3) Knowledge: This is where you feed it domain knowledge. → From business processes and APIs to data models and workflows → This bridges the gap between text prediction and decision-making 4) Memory: You want your Agent to remember what it did in the past. This layer gives it continuity across sessions. → Short-term: current reasoning steps, chat history → Long-term: facts, company knowledge, user preferences 5) Tools: This layer extends the Agent's power beyond language and takes real-world action. → Each tool has parameters, inputs, and examples. → The design here decides how well your agent uses external APIs. 6) Tool Results → This layer feeds the tool's results back to the model to enable self-correction, adaptation, and dynamic decision-making. These are the exact six layers that help you build fully context-aware Agents. 👉 Over to you: Have I missed any context layer in this? ____ Find me → Avi Chawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

After 2,000+ hours using Claude Code across real production codebases, I can tell you the thing that separates reliable from unreliable isn't the model, the prompt, or even the task complexity. It's context management. About 80% of the coding agent failures I see trace back to poor context - either too much noise, the wrong information loaded at the wrong time, or context that's drifted from the actual state of the codebase. Even with a 1M token window, Chroma's research shows that performance degrades as context grows. More tokens is not always better. I built the WISC framework (inspired by Anthropic's research) to handle this systematically. Four strategy areas: W - Write (externalize your agent's memory) - Git log as long-term memory with standardized commit messages - Plan in one session, implement in a fresh one - Progress files and handoffs for cross-session state I - Isolate (keep your main context clean) - Subagents for research (90.2% improvement per Anthropic's data) - Scout pattern to preview docs before committing them to main context S - Select (just in time, not just in case) - Global rules (always loaded) - On-demand context for specific code areas - Skills with progressive disclosure - Prime commands for live codebase exploration C - Compress (only when you have to) - Handoffs for custom session summaries - /compact with targeted summarization instructions These work on any codebase. Not just greenfield side projects! I've applied this on enterprise codebases spanning multiple repositories, and the reliability improvement is consistent. I also just published a YouTube video going over the WISC framework in a lot more detail. Very value packed! Check it out here: https://lnkd.in/ggxxepik

Best Practices for Writing Clean and Maintainable Code One of the worst headaches is trying to understand and work with poorly written code, especially when the logic isn’t clear. Writing clean, maintainable, and testable code—and adhering to design patterns and principles—is a must in today’s fast-paced development environment. Here are a few strategies to help you achieve this: 1. Choose Meaningful Names: Opt for descriptive names for your variables, functions, and classes to make your code more intuitive and accessible. 2. Maintain Consistent Naming Conventions: Stick to a uniform naming style (camelCase, snake_case, etc.) across your project for consistency and clarity. 3. Embrace Modularity: Break down complex tasks into smaller, reusable modules or functions. This makes both debugging and testing more manageable. 4. Comment and Document Wisely: Even if your code is clear, thoughtful comments and documentation can provide helpful context, especially for new team members. 5. Simplicity Over Complexity: Keep your code straightforward to enhance readability and reduce the likelihood of bugs. 6. Leverage Version Control: Utilize tools like Git to manage changes, collaborate seamlessly, and maintain a history of your code. 7. Refactor Regularly: Continuously review and refine your code to remove redundancies and improve structure without altering functionality. 8. Follow SOLID Principles & Design Patterns: Applying SOLID principles and well-established design patterns ensures your code is scalable, adaptable, and easy to extend over time. 9. Test Your Code: Write unit and integration tests to ensure reliability and make future maintenance easier. Incorporating these tips into your development routine will lead to code that’s easier to understand, collaborate on, and improve. #CleanCode #SoftwareEngineering #CodingBestPractices #CodeQuality #DevTips

🔐 Secure Coding Training Isn’t About Checklists—It’s About Survival In 2023, a LastPass engineer’s use of an outdated SDK led to a breach exposing 33M passwords. Cost: $25M+ in damages. The root cause? A developer trained on “general best practices” but not on company-specific attack vectors. Training isn’t a compliance 🏋️♂️ checkbox. It’s your last line of defense. 💥 Why Most Training Fails (And How to Fix It) 🔥 Generic Content ≠ Real Threats Example: A module on “SQLi basics” won’t stop a dev from misconfiguring your GraphQL API. ✅ Fix: Build custom labs replicating your tech stack’s weaknesses (e.g., AWS Lambda permissions, Kubernetes secrets). 🔥 Annual Workshops Are Obsolete The average app’s attack surface changes every 8 weeks (OWASP 2024). ✅ Fix: Monthly micro-trainings > yearly marathons. 🔥 Boring = Ignored Shopify’s “Hack the Stack” program lets devs exploit vulnerabilities in a sandboxed clone of their production environment. Retention: 89%. 🚀 Actionable Steps for Leaders 1️⃣ Ditch Theoretical Lectures Use platforms like Secure Code Warrior for hands-on, language-specific drills (Python, JS, Go). 2️⃣ Gamify Consequences 🎯 Reward devs who find flaws with bonuses or public recognition. ⚠️ Penalize repeat offenders (e.g., hardcoding secrets) with “Security Shadowing” sessions. 3️⃣ Track Impact, Not Completion Rates Adobe measures success by the drop in vulnerabilities per 1k lines of code post-training. ⚖️ The Controversy Is Right… and Wrong Critics aren’t wrong that most programs suck. But the fix isn’t less training—it’s better training. 📈 Example: Google’s “SSRF for Go Developers” module reduced misconfigurations by 72% in 6 months. Does your org still use training modules from the pre-API economy era? #SecureCoding #DevSecOps #Cybersecurity #TechTraining #SoftwareDevelopment