Software Engineering Principles

💎 Accessibility For Designers Checklist (PDF: https://lnkd.in/e9Z2G2kF), a practical set of cards on WCAG accessibility guidelines, from accessible color, typography, animations, media, layout and development — to kick-off accessibility conversations early on. Kindly put together by Geri Reid. WCAG for Designers Checklist, by Geri Reid Article: https://lnkd.in/ef8-Yy9E PDF: https://lnkd.in/e9Z2G2kF WCAG 2.2 Guidelines: https://lnkd.in/eYmzrNh7 Accessibility isn’t about compliance. It’s not about ticking off checkboxes. And it’s not about plugging in accessibility overlays or AI engines either. It’s about *designing* with a wide range of people in mind — from the very start, independent of their skills and preferences. In my experience, the most impactful way to embed accessibility in your work is to bring a handful of people with different needs early into design process and usability testing. It’s making these test sessions accessible to the entire team, and showing real impact of design and code on real people using a real product. Teams usually don’t get time to work on features which don’t have a clear business case. But no manager really wants to be seen publicly ignoring their prospect customers. Visualize accessibility to everyone on the team and try to make an argument about potential reach and potential income. Don’t ask for big commitments: embed accessibility in your work by default. Account for accessibility needs in your estimates. Create accessibility tickets and flag accessibility issues. Don’t mistake smiling and nodding for support — establish timelines, roles, specifics, objectives. And most importantly: measure the impact of your work by repeatedly conducting accessibility testing with real people. Build a strong before/after case to show the change that the team has enabled and contributed to, and celebrate small and big accessibility wins. It might not sound like much, but it can start changing the culture faster than you think. Useful resources: Giving A Damn About Accessibility, by Sheri Byrne-Haber (disabled) https://lnkd.in/eCeFutuJ Accessibility For Designers: Where Do I Start?, by Stéphanie Walter https://lnkd.in/ecG5qASY Web Accessibility In Plain Language (Free Book), by Charlie Triplett https://lnkd.in/e2AMAwyt Building Accessibility Research Practices, by Maya Alvarado https://lnkd.in/eq_3zSPJ How To Build A Strong Case For Accessibility, ↳ https://lnkd.in/ehGivAdY, by 🦞 Todd Libby ↳ https://lnkd.in/eC4jehMX, by Yichan Wang #ux #accessibility

What are the SOLID principles? 🤔 As a .NET developer, I'm always looking for ways to improve the quality and maintainability of my code. I want to share my experiences using the SOLID principles and how they've helped me create better code. First, let's define what we mean by SOLID. The acronym 𝗦𝗢𝗟𝗜𝗗 stands for: - Single responsibility principle - Open/closed principle - Liskov substitution principle - Interface segregation principle - Dependency inversion principle Let's break them down. 🔬 1. 𝗦𝗶𝗻𝗴𝗹𝗲 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗦𝗥𝗣) A class should have only one reason to change. In other words, a class should have a single, well-defined responsibility. That class should entirely encapsulate responsibility. 2. 𝗢𝗽𝗲𝗻/𝗖𝗹𝗼𝘀𝗲𝗱 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗢𝗖𝗣) Software entities (classes, functions, etc.) should be open for extension but closed for modification. You should be able to add new functionality to a class without changing its existing code, but you should not need to modify the class itself to do so. 3. 𝗟𝗶𝘀𝗸𝗼𝘃 𝗦𝘂𝗯𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗟𝗦𝗣) Subtypes must be substitutable for their base types. In other words, if a class is derived from another class, you should be able to use the derived class in the same way as the base class without any issues. 4. 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗦𝗲𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗜𝗦𝗣) Clients should not depend on interfaces they do not use. This means you should design your interfaces as specific and focused as possible. 5. 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗜𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗗𝗜𝗣) High-level modules should not depend on low-level modules. Both should depend on abstractions. Design your software so high-level modules depend on abstractions rather than concrete implementations. I struggled with creating code that was easy to understand and maintain, but the SOLID principles provided a clear set of guidelines to follow when designing software. One of the first principles I focused on was SRP. It encourages developers to design classes with a single, well-defined responsibility and to keep the code for that responsibility entirely encapsulated within the class. This makes it easier to understand a class's purpose and change its behavior without affecting other parts of the system. Imagine a class doing a few things, like performing calculations and interacting with the database. We could refactor the class to have a single responsibility of calculating the result and create separate classes for interacting with the database. This would make the code easier to understand and maintain. Hope that was helpful. :) P.S. If you enjoyed this post, repost it ♻️ to share it with your audience.

Abstraction is a fundamental principle in software engineering, but premature abstraction does more harm than good ⚡ Premature abstraction occurs when we create generalized solutions before fully understanding the specific problems we're solving. For example, starting to write the first implementation in a completely abstract way without even needing to write the second one, ever. It's a trap to fall into, especially for those who take pride in writing "clean" and "elegant" code. They see patterns emerging in our early development stages and think, "Ahhh, I can abstract this into a reusable component!". But in most cases, you would never need to have the second implementation of the functionality. The problem isn't that abstraction is bad. The issue arises when we abstract too early. Such implementation would lead to leaky abstraction that fails to encapsulate the complexity it was meant to hide. Four key problems with premature abstraction 1. makes code harder to understand 2. adds layers of indirection that make the code harder to navigate 3. makes you not trust your own subsequent changes to the codebase 4. adding a generic handler requires accounting for all possible cases, leading to unnecessary checks and operations. It is important to resist the urge to abstract too early and here's what I do; I follow the principle of "Rule of Three" - the core idea is to wait until there are at least three concrete implementations before attempting to abstract. This ensures that I have abstracted the implementation only after having a deeper understanding of the commonalities and differences between various use cases. Here are the four things I stick to while building large applications, 1. implement solutions for specific use cases first 2. don't try to solve problems you don't have yet 3. look for patterns that emerge naturally 4. refactor your existing code incrementally Again, not saying to not use abstractions. They are great at hiding out complexities and making code extensible, but just nudging to create systems that can evolve and adapt over time rather than being overly complicated on day 0. Remember, the goal of abstraction is to manage complexity, not to showcase our ability to write clever code. #AsliEngineering #SoftwareEngineering #OOP

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?

Check out this simple breakdown of the four core LLM architectures shaping most of today’s advanced systems. Not all LLMs reason the same way. Building reliable AI systems, or choosing the right model for a workflow requires a clear mental model of the architectures that drive them. Let’s review: 1.🔸Decoder Only (GPT, LLaMA, Grok) Generates text by predicting one token at a time using previous context. Best for generative workloads, conversational systems, and multi step reasoning. 2.🔸Encoder Only (BERT, RoBERTa) Processes text with full bidirectional context to learn dense semantic representations. Best for classification tasks, retrieval, and embedding generation. 3.🔸Encoder Decoder (T5, FLAN T5, BART) Separates understanding from generation. The encoder builds meaning, the decoder transforms it. Best for translation, summarization, and any structured text to text transformation. 4.🔸Mixture of Experts (Mixtral, Gemini 1.5) Routes each input to a subset of specialized expert networks. Best for scaling model capacity while controlling compute cost. These architectures go beyond academic details to determine latency profiles, context handling, fine tuning strategies, and how models behave under real workloads. A clear grasp of them helps you design better pipelines, choose the right foundation models, and understand why two LLMs can produce very different outcomes on the same task. Let me know your thoughts! #LLM

The hardest step in Goldratt's Theory of Constraints isn't finding the bottleneck. It's step three: subordinate everything else to the constraint. Translation for software teams: if your code review process can absorb 1.2x the current volume, don't generate 2x. If you can't measure outcomes on twice the features, don't commit twice the features to production. This means deliberately throttling AI output. I'll say that again because it sounds heretical. It means looking at a tool that can generate code faster than ever, and choosing to slow it down. Not because the tool is bad. Because the system downstream can't absorb what the tool produces. Most organizations do the opposite. They celebrate the increased commit volume. They trumpet the PR throughput numbers. They showcase the individual productivity gains. Meanwhile, review queues grow. CI recovery time gets worse. Deployment problems increase. 96% of the most frequent AI users end up working evenings and weekends. They optimized the part of the system that wasn't the bottleneck. That's not progress. That's inventory accumulation. The question isn't "how much code can we generate?" It's "how much code can our system absorb, validate, and deliver?" Generate up to that limit. Not beyond it. If that feels wasteful, you've identified the real investment priority: increasing the capacity of the constraint. But exploit and subordinate come first. Throwing money at capacity before you've maximized what you already have is just expensive inventory management. #TheoryOfConstraints #EngineeringLeadership #AIReadiness

The recent news on AWS center in the Middle East going down because of the war made me relive my experience decades ago! I once helped build what we proudly called a best-in-class disaster recovery architecture. We did everything right—on paper. ✔️ Business Impact Analysis done ✔️ RTO & RPO agreed with stakeholders ✔️ Sophisticated tools deployed ✔️ DR site fully provisioned We were confident. Almost too confident and then came the day that tested everything ! A dual power supply failure hit our primary data center. Within minutes, 300+ servers went down abruptly. What followed was worse than downtime: Critical application databases got corrupted AND THEN The DR site also got corrupted ! Real-time transactions came to a complete standstill. With every passing hour, we lost millions of dollars in revenue. In that moment, all our architecture diagrams, tools, and planning meant one thing: NOTHING —because the system didn’t recover !!! What this experience taught me: 1) Testing isn’t real until it’s brutal Table-top simulations give comfort. Full-scale failover drills expose truth. Test like it’s already failing: -Simulate real load -Introduce chaos scenarios -Assume components will fail unexpectedly 2) DR is not a technology problem—it’s a systems problem We focused heavily on tools. We underestimated dependencies. Ensure: -End-to-end recovery (infra + app + data integrity) -Isolation between primary and DR (to avoid cascade failures) -Backup validation, not just backup completion 3) Communication is your real recovery engine In crisis, confusion spreads faster than outages. Build: -Clear SOPs for business continuity -Pre-defined escalation paths -Regular cross-team drills (not just IT—include business teams) 4) Leadership presence changes outcomes War rooms are intense. Fatigue, panic, and noise creep in. As a tech leader: -Your presence brings calm -Your clarity drives prioritization -Your energy keeps teams going Sometimes, leadership is less about answers… and more about Stability 5) Assume your DR will fail—and design for that This was the hardest lesson. Build layers: - Immutable backups - Offline recovery options -“Last resort” recovery playbooks Because resilience is not about one backup plan. It’s about what happens when that backup plan fails... Have you ever seen a #DR plan fail in real life? How often do you run full-scale disaster recovery drills? What’s the one thing most organizations still get wrong about resilience? Curious to hear real experiences—those are always more valuable than frameworks. #DR #disasterrecovery #drill #test #BCP #leadership #technology #resilience

On Protecting the Data Privacy of Large Language Models (LLMs): A Survey From the research paper: In this paper, we extensively investigate data privacy concerns within Large LLMs, specifically examining potential privacy threats from two folds: Privacy leakage and privacy attacks, and the pivotal technologies for privacy protection during various stages of LLM privacy inference, including federated learning, differential privacy, knowledge unlearning, and hardware-assisted privacy protection. Some key aspects from the paper: 1)Challenges: Given the intricate complexity involved in training LLMs, privacy protection research tends to dissect various phases of LLM development and deployment, including pre-training, prompt tuning, and inference 2) Future Directions: Protecting the privacy of LLMs throughout their creation process is paramount and requires a multifaceted approach. (i) Firstly, during data collection, minimizing the collection of sensitive information and obtaining informed consent from users are critical steps. Data should be anonymized or pseudonymized to mitigate re-identification risks. (ii) Secondly, in data preprocessing and model training, techniques such as federated learning, secure multiparty computation, and differential privacy can be employed to train LLMs on decentralized data sources while preserving individual privacy. (iii) Additionally, conducting privacy impact assessments and adversarial testing during model evaluation ensures potential privacy risks are identified and addressed before deployment. (iv)In the deployment phase, privacy-preserving APIs and access controls can limit access to LLMs, while transparency and accountability measures foster trust with users by providing insight into data handling practices. (v)Ongoing monitoring and maintenance, including continuous monitoring for privacy breaches and regular privacy audits, are essential to ensure compliance with privacy regulations and the effectiveness of privacy safeguards. By implementing these measures comprehensively throughout the LLM creation process, developers can mitigate privacy risks and build trust with users, thereby leveraging the capabilities of LLMs while safeguarding individual privacy. #privacy #llm #llmprivacy #mitigationstrategies #riskmanagement #artificialintelligence #ai #languagelearningmodels #security #risks

In today’s always-on world, downtime isn’t just an inconvenience — it’s a liability. One missed alert, one overlooked spike, and suddenly your users are staring at error pages and your credibility is on the line. System reliability is the foundation of trust and business continuity and it starts with proactive monitoring and smart alerting. 📊 𝐊𝐞𝐲 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: 💻 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: 📌CPU, memory, disk usage: Think of these as your system’s vital signs. If they’re maxing out, trouble is likely around the corner. 📌Network traffic and errors: Sudden spikes or drops could mean a misbehaving service or something more malicious. 🌐 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: 📌Request/response counts: Gauge system load and user engagement. 📌Latency (P50, P95, P99):  These help you understand not just the average experience, but the worst ones too. 📌Error rates: Your first hint that something in the code, config, or connection just broke. 📌Queue length and lag: Delayed processing? Might be a jam in the pipeline. 📦 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 (𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐨𝐫 𝐀𝐏𝐈𝐬): 📌Inter-service call latency: Detect bottlenecks between services. 📌Retry/failure counts: Spot instability in downstream service interactions. 📌Circuit breaker state: Watch for degraded service states due to repeated failures. 📂 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: 📌Query latency: Identify slow queries that impact performance. 📌Connection pool usage: Monitor database connection limits and contention. 📌Cache hit/miss ratio: Ensure caching is reducing DB load effectively. 📌Slow queries: Flag expensive operations for optimization. 🔄 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 𝐉𝐨𝐛/𝐐𝐮𝐞𝐮𝐞: 📌Job success/failure rates: Failed jobs are often silent killers of user experience. 📌Processing latency: Measure how long jobs take to complete. 📌Queue length: Watch for backlogs that could impact system performance. 🔒 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: 📌Unauthorized access attempts: Don’t wait until a breach to care about this. 📌Unusual login activity: Catch compromised credentials early. 📌TLS cert expiry: Avoid outages and insecure connections due to expired certificates. ✅𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐀𝐥𝐞𝐫𝐭𝐬: 📌Alert on symptoms, not causes. 📌Trigger alerts on significant deviations or trends, not only fixed metric limits. 📌Avoid alert flapping with buffers and stability checks to reduce noise. 📌Classify alerts by severity levels – Not everything is a page. Reserve those for critical issues. Slack or email can handle the rest. 📌Alerts should tell a story : what’s broken, where, and what to check next. Include links to dashboards, logs, and deploy history. 🛠 𝐓𝐨𝐨𝐥𝐬 𝐔𝐬𝐞𝐝: 📌 Metrics collection: Prometheus, Datadog, CloudWatch etc. 📌Alerting: PagerDuty, Opsgenie etc. 📌Visualization: Grafana, Kibana etc. 📌Log monitoring: Splunk, Loki etc. #tech #blog #devops #observability #monitoring #alerts

Is your software built like a set of Lego bricks, or a single block of concrete? It’s a simple question, but the answer has huge implications for the future of your business. If your system is one monolithic block, making a small change can feel like drilling into concrete – slow, risky, and likely to throw a spanner in the works elsewhere. In my experience, ‘future-proofing’ isn't about gazing into a crystal ball; that’s a fool's game. It's about applying solid principles from the outset. Here at Full Metal, we focus on a few core pillars. One of the most important is building a flexible architecture. We design systems as a collection of smaller, independent services that talk to each other through clear contracts (APIs). This Lego-brick approach means we can update, improve, or even replace one piece without knocking the whole thing down. Another pillar is ensuring knowledge is shared. The biggest risk to any long-term project is critical information walking out the door when a key person leaves. We make documentation a core part of the process, not an afterthought. If only one person knows how a part of your system works, you don't have an asset; you have a ticking time bomb. If your lead developer won the lottery tomorrow, how much undocumented knowledge would walk out of the door with them? Read more on our blog here: https://lnkd.in/eq9wF3Xh #SoftwareArchitecture #DevOps #FutureProofing