Technical Skill Development

🔐 Periodic Table of Cybersecurity — A Visual Framework for Cyber Warriors! 🔷 What is this? The Periodic Table of Cybersecurity is a visual representation of: Threats Vulnerabilities Tools Frameworks Protocols Attack Techniques Roles Defense Practices Each item is represented with a symbol, a category color, and a simplified label — just like the classic chemical periodic table. 🧩 Categories & Sample Elements 🟣 Threats (TH) Cyber dangers that target confidentiality, integrity, or availability. PH – Phishing: Social engineering attack to steal credentials. MA – Malware: Malicious software like viruses, worms, etc. RB – Ransomware: Encrypts data, demands payment for decryption. DD – DDoS: Distributed Denial of Service; overloads systems. 🔵 Vulnerabilities (VL) Flaws in software, hardware, or configuration that attackers exploit. BP – Buffer Overflow: Exceeding memory buffer limits. ZD – Zero-Day: Exploited before the vendor releases a patch. JK – Insecure JWT Key: Weak token signing practices. Vo – Volatility: Data loss risk from RAM-based artifacts. 🟠 Tools (TO) Essential hacking and defense tools in a cybersecurity professional’s arsenal. ME – Metasploit: Exploitation framework. JB – John the Ripper: Password cracker. BP – Burp Suite: Web vulnerability scanner. PSP – Frida: Dynamic instrumentation toolkit. WA – Hydra: Password brute-forcing tool. 🟢 Frameworks (FR) Structured guidelines, standards, and models for managing security. CE – CEH (Certified Ethical Hacker) CIS – CIS Controls: Best practices for securing IT systems. ISO – ISO 27001: Information security management standard. NIST – NIST Cybersecurity Framework IDP – Identity Provider (e.g., SSO via Azure AD) 🟧 Protocols (PK) Communication or encryption protocols essential for secure data transfer. TLS – Transport Layer Security IPSec – Internet Protocol Security RDP – Remote Desktop Protocol SFTP – Secure File Transfer Protocol ⚫ Attack Techniques (AT) Tactics used by threat actors to infiltrate or disrupt systems. SQL – SQL Injection: Injecting SQL code in forms. XSS – Cross-Site Scripting: Injecting scripts in web apps. MITM – Man-in-the-Middle: Intercepting communication between two parties. BOF – Buffer Overflow: Overwriting memory segments. 🔴 Roles (RO) Common career roles in cybersecurity. PT – Penetration Tester: Simulates attacks to find weaknesses. SA – SOC Analyst: Monitors and analyzes threats in Security Operations Center. IR – Incident Responder: Handles breaches and security events. CM – CISO: Chief Information Security Officer. 🟫 Defense Practices (DP) Controls and techniques to secure assets and mitigate risks. FW – Firewall: Monitors and filters network traffic. IDS – Intrusion Detection System: Identifies unauthorized access. AV – Antivirus: Scans for known malware. PKI – Public Key Infrastructure: Secure key management and encryption. #Cybersecurity #Infosec #EthicalHacking #SOC #ThreatHunting #CyberAwareness #LearningNeverStops #PeriodicTable

SEO plan 2025 A – Audit Your Website: Begin with a comprehensive SEO audit. Use tools like Screaming Frog or Ahrefs to identify broken links, duplicate content, and technical errors. B – Build Backlinks: Quality backlinks remain crucial. Focus on guest posting, digital PR, and creating link-worthy content. C – Core Web Vitals: Optimize for Google’s Core Web Vitals (LCP, FID, CLS) to enhance user experience and improve rankings. D – Data-Driven Decisions: Use Google Analytics and Search Console to track performance and guide your SEO strategies. E – E-A-T Compliance: Establish Expertise, Authoritativeness, and Trustworthiness in your niche, especially for YMYL (Your Money Your Life) websites. F – Fresh Content: Regularly update or add new content. Google rewards websites that stay current and relevant. G – Google Business Profile: For local SEO, optimize and maintain an accurate Google Business Profile listing. H – Headings Optimization: Use H1, H2, H3 tags properly to structure content for both users and search engines. I – Internal Linking: Build a logical internal link structure to guide users and distribute link equity. J – JavaScript SEO: Ensure content rendered via JavaScript is crawlable and indexable by search engines. K – Keyword Research: Use modern tools like Semrush or Ubersuggest to identify long-tail and intent-driven keywords. L – Link Structure: Maintain clean and SEO-friendly URLs with proper slugs and no unnecessary parameters. M – Mobile Optimization: Ensure your website is mobile-responsive, as mobile-first indexing is now the standard. N – Niche Authority: Focus on creating depth in your content to become an authority in your niche. O – On-Page SEO: Optimize titles, meta descriptions, images (alt tags), and content around target keywords. P – Page Speed: Use tools like Google PageSpeed Insights to identify and fix slow-loading pages. Q – Quality Content: Always prioritize content that provides real value to users over keyword-stuffed articles. R – Responsive Design: Adapt your site design for all screen sizes and devices. S – Schema Markup: Implement structured data to enhance search listings with rich snippets. T – Technical SEO: Fix crawl errors, sitemaps, robots.txt, canonical tags, and other backend elements. U – User Experience (UX): A seamless UX improves dwell time, reduces bounce rate, and supports SEO. V – Voice Search Optimization: Target conversational queries and FAQs for better visibility in voice results. W – Web Security (HTTPS): Secure your site with SSL – it's a ranking factor and builds trust. X – XML Sitemap: Keep your XML sitemap updated and submit it to Google Search Console. Y – YouTube SEO: If you use videos, optimize titles, descriptions, and tags for better visibility on YouTube and Google. Z – Zero-Click Searches: Optimize for featured snippets, People Also Ask, and knowledge panels. #seoexpert #seo #topratedseoexpert #seotips #expartagency

Cryptocurrencies have emerged from their "nuclear winter," with Bitcoin smashing the $100K barrier. President Trump has signed an executive order promoting digital assets and sparking discussions about a strategic crypto stockpile. A popular question I get is: How will quantum computing impact Bitcoin? Can quantum break the cryptography that protects cryptocurrencies? Is your digital wallet at risk? Could blockchain integrity be compromised? Most importantly, what steps should we take now to make crypto quantum-safe? My article below explores these critical questions. At DigiCert, we’re already helping organizations transition to Post-Quantum Cryptography (PQC), ensuring their security remains resilient with the emerging quantum revolution.

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗜𝟮𝗖, 𝗦𝗣𝗜 & 𝗨𝗔𝗥𝗧: 𝗧𝗵𝗲 𝗦𝗶𝗹𝗲𝗻𝘁 𝗛𝗲𝗿𝗼𝗲𝘀 𝗼𝗳 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 If you've ever written embedded C/C++ code for a microcontroller, you've probably used one of these protocols to make hardware components talk to each other. 💬 Need to send sensor data? Display something on an LCD? Store to external memory? That’s where I2C, SPI, and UART come in — your essential communication tools in embedded development. Here’s a quick breakdown: 👉 𝗨𝗔𝗥𝗧 (Universal Asynchronous Receiver/Transmitter) 🔌 • Point-to-point, asynchronous communication • No clock signal needed • Common for serial communication between PCs and microcontrollers • Simple, low-cost — but not ideal for multiple devices 👉 𝗜𝟮𝗖 (Inter-Integrated Circuit) 🔄 • Synchronous, 2-wire protocol (SDA & SCL) • Supports multiple masters and slaves • Ideal for sensors and peripherals with unique addresses • Slower than SPI, but scalable and efficient 👉 𝗦𝗣𝗜 (Serial Peripheral Interface) ⚡ • Synchronous, full-duplex communication • 4 lines: MISO, MOSI, SCLK, SS • Extremely fast — great for displays, SD cards, memory chips • Needs extra wiring for multiple devices 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? Choosing the right protocol impacts 𝘀𝗽𝗲𝗲𝗱, 𝗽𝗼𝘄𝗲𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆, 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, and 𝗰𝗼𝘀𝘁. Mastering these “invisible” connections makes your embedded systems more reliable and optimized — whether it’s a smartwatch or an industrial controller. #EmbeddedSystems #IoT #Electronics #I2C #SPI #UART #Microcontrollers #EmbeddedC #Cpp #C

If you're in tech, Python is a skill that can take you far. But where do you start, and how do you progress? Having mentored developers and switched careers into tech myself, I've put together a roadmap that's helped many navigate their Python journey. Here's a breakdown of key areas to focus on as you level up your Python skills: 1. Core Python    Start with the basics - syntax, variables, and data types. Then move on to control structures and functions. This foundation is crucial. 2. Advanced Python    Once you're comfortable with the basics, dive into decorators, generators, and asynchronous programming. These concepts will set you apart. 3. Data Structures    Get really good with lists, dictionaries, and sets. Then explore more advanced structures. You'll use these constantly. 4. Automation and Scripting    Learn to manipulate files, scrape websites, and automate repetitive tasks. This is where Python really shines in day-to-day work. 5. Testing and Debugging    Writing tests and debugging efficiently will save you countless hours. Start with unittest and get familiar with pdb. 6. Package Management    Understanding pip and virtual environments is crucial for managing projects. Don't skip this. 7. Frameworks and Libraries    Depending on your interests, explore web frameworks like Django, data science libraries like Pandas, or machine learning tools like TensorFlow. 8. Best Practices    Familiarize yourself with PEP standards and stay updated on Python enhancements. Clean, readable code is invaluable. Remember, the key isn't just learning syntax - it's applying what you learn to real projects. Start small, but start building. What area of Python are you currently focusing on?

Tech writers don’t write. → Not in the way most people think. We don’t sit down with a blank page and “make it up.” We’re not wordsmiths polishing clever sentences. We’re not decorators. We’re architects. And in the age of AI, our role has quietly evolved into something far more powerful—and far more essential. Here’s what the new tech writer actually does: 1.⁠ ⁠We curate. We filter the noise. From dev notes, internal wikis, messy Notion pages, AI-generated drafts—we gather what matters and discard what doesn’t. 2.⁠ ⁠We verify. We don’t just copy and paste. We check, clarify, recheck. Because what’s written in the spec doc isn’t always what’s true in production. 3.⁠ ⁠We restructure. We’re not just editing for grammar. We’re rearchitecting information to match how real users actually read and retain it. Good docs don’t just inform. They guide. 4.⁠ ⁠We translate. We bridge the gap between engineering and end user. Between product complexity and business clarity. Between AI output and human understanding. 5.⁠ ⁠We strategize. We don’t “just write the docs.” We shape documentation ecosystems—mapping user journeys, designing content models, identifying gaps before they become support tickets. If you’re hiring a writer to “clean up” your AI-generated documentation, you’re looking for the wrong skillset. You don’t need a cleaner. You need an operator. One who understands: • How your product works • What your users need • What your GTM team is saying • What your AI tools are missing • And how to bring it all together—seamlessly Because in 2025, tech writers aren’t just writers. We’re content strategists with dev-level instincts. And the companies that understand this? They’re the ones whose products get adopted faster, retained longer, and supported less.

Are you planning to start your journey to become a proficient Python developer? Here's a roadmap that can help you build a strong foundation and master advanced concepts to excel in the world of Python programming. 🔹 Basics: Start from scratch and learn the fundamental concepts of Python, including basic syntax, variables, data types, and conditional statements. Lay the groundwork for your Python knowledge and coding skills. 🔹 Advanced Topics: Delve into the more intricate aspects of Python, such as iterators, decorators, lambdas, and recursion. Explore how these concepts can enhance your code's efficiency and maintainability. 🔹 Package Managers: Familiarize yourself with essential package managers like PyPI and Pip. Learn how to leverage external libraries and modules to enhance your Python projects and streamline development. 🔹 Testing Your Apps: Discover the importance of testing in Python development. Learn about unittest, pyunit, pytest, doctest, and nose frameworks to ensure your applications are robust and reliable. 🔹 Datastructures and Algorithms: Gain proficiency in essential data structures like arrays, linked lists, sets, dictionaries, and hash tables. Additionally, explore sorting algorithms, binary search trees, heaps, stacks, and queries to optimize data manipulation. 🔹 Version Control Systems: Understand the significance of version control in collaborative coding. Master Git, GitHub, GitLab, and BitBucket to efficiently manage your projects and collaborate with other developers. 🔹 Type Casting and Expectations: Learn how to handle data types effectively and set expectations for your functions. Ensure seamless data manipulation and code reliability through proper type handling. 🔹 Object-Oriented Programming (OOP): Dive into the world of OOP and comprehend classes, methods, inheritance, and dunder methods. Harness the power of OOP to build modular, maintainable, and scalable Python applications. 🔹 Asynchronous Python: Explore the asynchronous world with libraries like gevent, Tornado, aiohttp, and Sanic. Understand the concepts behind asynchronous programming and boost the performance of your web applications. 🔹 Web Frameworks: Learn popular web frameworks like Django, Flask, and Pyramid. Create web applications and RESTful APIs with ease, catering to diverse project needs. What would you like to add? Happy to learn more from the experiences!

📌 MS Fabric Breakdown # 1: Architecture (How to Build a BI Solution with Microsoft Fabric) Since Fabric’s release in 2023, a lot of Power BI-centric organizations are moving their entire data stack into Fabric. And honestly it makes perfect sense. If you're already spending thousands on Power BI licensing, why not unify your entire data architecture under one platform? You get a single environment for: → Ingestion → Storage (Lakehouse & Warehouse) → Modeling → Dashboarding All in one place and you solve most of your data silos problems. In this first post of the Fabric series, I’m sharing a high-level BI architecture you can use as a framework for your implementation: 1️⃣ 𝐊𝐧𝐨𝐰 𝐘𝐨𝐮𝐫 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐞𝐬 Before diving into technical planning, align your BI goals with real business needs. Ask yourself: → Which departments need reporting the most right now? → What KPIs are critical to track in the short term? → Who are the key stakeholders and decision-makers? This helps you focus your resources and deliver impact from day one. 2️⃣ 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞𝐬 Now it’s time to map where your data lives. Some typical examples: ⤷ SQL Server & on-prem databases ⤷ ERPs (SAP, Oracle, etc.) ⤷ SaaS platforms (Salesforce, HubSpot, Stripe, etc.) ⤷ Excel files & manual spreadsheets And remember to prioritize what’s valuable to the business. Don’t waste time and resources ingesting data no one uses. 3️⃣ 𝐌𝐚𝐩 𝐎𝐮𝐭 𝐘𝐨𝐮𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 Once you know your sources, you need to design the data platform. There are many schools of thought on how to build modern BI architecture. But one of the most practical and scalable is the Medallion Architecture, built across three layers: 1) Bronze Layer: Raw data in its original format 2) Silver Layer: Cleaned, structured, and business-ready tables 3) Gold Layer: Modeled datasets optimized for reporting In Fabric, you can easily orchestrate everything using Data Factory (equivalent of ADF if you're familiar with Azure Ecosystem) and store in OneLake, using Lakehouses and Warehouses depending on your use case. 4️⃣ 𝐂𝐫𝐞𝐚𝐭𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 𝐟𝐨𝐫 𝐒𝐞𝐥𝐟-𝐒𝐞𝐫𝐯𝐢𝐜𝐞 Your final goal is not just storage. It's decision-making. You can build reusable semantic models with KPIs and business logic defined upstream (vs PBI Desktop). This enables business users to explore, visualize, and analyze data without engineering support. Power BI then becomes a front-end for exploration not just a reporting tool. Next in the series: We’ll break down each layer (Bronze → Silver → Gold) with practical examples and tips and how Power BI fits within this architecture. 📥 Save this post if you’re planning to implement Fabric. #MicrosoftFabric #PowerBI #BusinessIntelligence

Boosting API Performance: Best Practices and Techniques Improving API performance often involves a combination of strategies and techniques. Here are some methods to enhance API performance, focusing on pagination, asynchronous logging, connection pooling, caching, load balancing, and payload compression: 1. Pagination:   Implement server-side pagination to limit the amount of data transferred in a single request/response. Allow clients to request a specific page or range of data.   Use query parameters like `page` and `pageSize` to control the pagination, and ensure your API documentation is clear on how to use it. 2. Asynchronous Logging:   Log asynchronously to avoid introducing latency to API responses. Use a message queue or a dedicated logging service to process logs in the background.   This decouples the logging process from the request/response cycle, improving API responsiveness. 3. Connection Pooling:   Use connection pooling for database and other resource intensive operations. Connection pooling helps efficiently manage and reuse database connections, reducing overhead. 4. Caching:   Implement caching mechanisms to store frequently requested data. Consider using in memory caching systems like Redis or Memcached to speed up data retrieval.   Utilise HTTP caching headers (e.g., `CacheControl`, `ETag`) to instruct clients and intermediaries to cache responses, reducing the load on your API. 5. Load Balancing:   Set up load balancers to distribute incoming traffic across multiple API servers or instances. This ensures even load distribution and redundancy.   Consider using dynamic load balancing algorithms to adapt to changing server loads. 6. Payload Compression:   Compress API responses before sending them to clients. Use popular compression algorithms like GZIP, Brotli, or Zstandard to reduce data transfer times.   Ensure that clients support decompression of compressed payloads. Remember that the effectiveness of these methods depends on the specific requirements of your API and the technologies you are using. Monitoring and performance testing are crucial to fine tune and optimise your API further. Additionally, consider using content delivery networks (CDNs) to distribute static content, and use API gateways to manage and secure your API endpoints effectively.