#Structures

Curvature whispers the hidden slope of multivariable change. The Hessian matrix captures the local shape of a surface by organizing second-order partial derivatives into a structured landscape. From gradients to critical points, it reveals where functions curve upward, downward, or saddle away. Geometry speaks through the matrix of curvature.

𝐖𝐇𝐀𝐓 𝐈𝐒 𝐌𝐎𝐍𝐎𝐋𝐈𝐓𝐇𝐈𝐂 𝐀𝐑𝐂𝐇𝐈𝐓𝐄𝐂𝐓𝐔𝐑𝐄 𝐀𝐍𝐃 𝐖𝐇𝐄𝐍 𝐓𝐎 𝐔𝐒𝐄 𝐈𝐓? A Monolithic Architecture is a software architecture where an entire application is built, deployed, and managed as one unified unit. Here's what you need to know: HOW IT WORKS -> UI, business logic, and database access live within one application. -> Components work together inside a single codebase. -> The entire application is deployed as one unit. KEY COMPONENTS -> Presentation Layer -> Business Logic Layer -> Data Access Layer -> Database ADVANTAGES -> Simple to develop and deploy -> Easier initial testing -> Straightforward debugging -> Lower infrastructure complexity -> Great for small teams DISADVANTAGES -> Difficult to scale individual components -> Changes can affect the entire application -> Large codebases become harder to maintain -> Entire application often needs redeployment -> Technology flexibility is limited WHEN TO USE IT -> Startups and MVPs -> Small to medium-sized applications -> Projects with limited development teams -> Applications with predictable workloads -> Projects requiring fast initial development REAL-WORLD EXAMPLES -> Blogging platforms -> Inventory management systems -> Small e-commerce applications -> School management systems -> Internal business applications MONOLITHIC VS MICROSERVICES Monolithic: -> One application -> One deployment unit -> Simpler operations Microservices: -> Multiple independent services -> Independent deployments -> Greater operational complexity KEY TAKEAWAY Don't choose microservices just because they are popular. Start simple. Build a well-structured monolith. Split into services when real business and technical requirements justify it. Learn Software Architecture: Grab Software Architectures Ebook: Follow @e_opore for more software engineering lessons.

The aesthetics of reflections and refractions in water-oil emulsion at the macro level #light #structures #design #artdirection #sidefx #houdini

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The latest biomedical robotic mechanism engineered at MIT navigates the gastrointestinal tract in a serpentine fashion. During preclinical animal evaluations, the flexible apparatus successfully traversed approximately 1.8 meters of the small intestine within an eleven-minute window, demonstrating real-time diagnostic imaging, precise biopsy collection, and localized medication delivery. Designated as BIOSENTER, the device was created to assist healthcare professionals in maneuvering through a constricted, lubricated organ that extends beyond 6 meters within the human anatomy. The internal architecture of the small intestine is highly convoluted and remains notoriously challenging to access through standard medical methods. Clinicians currently utilize specialized procedures like double-balloon enteroscopy, which mandates the sequential inflation and deflation of anchoring balloons to push an endoscope forward, though this methodology remains technically demanding and heavily time-consuming. Biomedical engineers derived the structural blueprint for this device directly from the locomotory mechanics of snakes. The adaptable frame of BIOSENTER integrates internal pneumatic compartments that expand in specific sequences, allowing operators to steer through sharp anatomical bends by dynamically modifying internal pressure. At the anterior and posterior extremities, expanding segments sequentially grasp and detach from the mucosal lining, enabling the device to propel itself forward through the intestinal lumen. These specific anchoring segments are enveloped in engineered materials modeled after kirigami, the traditional artistic method of cutting and folding paper substrates. When these segments inflate, microscopic structural elements elevate to elevate friction against the internal intestinal wall, securing necessary traction while avoiding harmful stretching of the fragile biological tissue. An internal conduit traversing the center of the mechanism accommodates an optical camera, a fluid nozzle, and specialized instruments required for harvesting tissue specimens. The design permits endoscopic insertion through either the oral cavity or the rectum depending on the target clinical site. The mechanism currently remains connected to external operational hardware via an umbilical tether and is directed via manual control inputs, with independent computerized tracking representing a primary objective for subsequent iterations. Investigating teams documented zero macroscopic tissue trauma during the most recent preclinical trials, though the diagnostic system still requires extensive safety evaluations, expanded distance testing, and structured comparisons with current standards of care prior to commencing human trials. The research findings, appearing in Science Robotics, provide an initial proof of concept for an automated platform that could ultimately optimize the diagnosis and management of obscured intestinal pathologies.

𝗔 𝘃𝗶𝘀𝘂𝗮𝗹 𝗴𝘂𝗶𝗱𝗲 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 With so many options around, choosing the right datastore can be confusing. Even with AI today. In this diagram, we can see a selection choice for a datastore based on a use case. Data can be: - 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 (𝗦𝗤𝗟 𝘁𝗮𝗯𝗹𝗲 𝘀𝗰𝗵𝗲𝗺𝗮) - 𝗦𝗲𝗺𝗶-𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 (𝗝𝗦𝗢𝗡, 𝗫𝗠𝗟, 𝗲𝘁𝗰.) - 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 (𝗕𝗹𝗼𝗯) In the case of structured they can be relational or columnar, while in the case of semi-structured there is a wide range of possibilities, from key-value to graph. Which database have you used for which workload?

Active Directory Pentest Mindmap: Complete Attack Path 🧠 🔥 Telegram: ✴ Twitter: The AD Pentest Mindmap is a visual roadmap that helps attackers and defenders understand the full attack lifecycle—from enumeration to domain dominance—in a structured way. ⚡ Key Features of AD Pentest Mindmap 🔍 Visual breakdown of attack methodology 🧩 Covers tools, techniques & attack paths ⚙️ Organized in hierarchical tree structure 🛡️ Easy navigation for learners & professionals 📡 Simplifies complex AD attack chains 🎯 Covered Attack Areas 💥 Enumeration (Users, Groups, Shares) 🧪 Credential Attacks & Lateral Movement 🧬 Privilege Escalation Techniques 🌐 Persistence & Post Exploitation ⚡ Domain Dominance strategies 📖 Resource: #ActiveDirectory #RedTeam #Pentesting #CyberSecurity #EthicalHacking #OSCP #InfoSec

𝗟𝗔𝗬𝗘𝗥𝗦 𝗢𝗙 𝗔𝗜 — 𝗙𝗥𝗢𝗠 𝗖𝗟𝗔𝗦𝗦𝗜𝗖𝗔𝗟 𝗔𝗜 𝗧𝗢 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗔𝗜 AI didn't become powerful overnight. Today's AI systems are built on decades of research, with each layer adding new capabilities on top of the previous one. Here’s the evolution: 01 — 𝗖𝗟𝗔𝗦𝗦𝗜𝗖𝗔𝗟 𝗔𝗜 The foundation of early AI. → Symbolic AI → Expert systems → Knowledge representation → Logic & reasoning These systems primarily relied on rules and structured knowledge. 02 — 𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 AI started learning patterns from data instead of relying only on manually written rules. → Supervised learning → Unsupervised learning → Classification → Regression → Reinforcement learning 03 — 𝗡𝗘𝗨𝗥𝗔𝗟 𝗡𝗘𝗧𝗪𝗢𝗥𝗞𝗦 Neural networks made it possible to learn more complex patterns. → Perceptrons → Hidden layers → Activation functions → Cost functions → Backpropagation 04 — 𝗗𝗘𝗘𝗣 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 Deep neural networks pushed AI capabilities much further. → Transformers → CNNs → RNNs → LSTMs → Autoencoders This became the foundation for major advances in language, vision, speech, and other AI applications. 05 — 𝗚𝗘𝗡𝗘𝗥𝗔𝗧𝗜𝗩𝗘 𝗔𝗜 AI moved from simply recognizing patterns to creating new content. → LLMs → Diffusion models → Multimodal models → Image & audio generation → VAEs Now AI can generate text, images, audio, video, and code. 06 — 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗔𝗜 The newest layer focuses on AI systems that can work toward goals. → Memory → Planning → Tool use → Decision-making → Autonomous execution Instead of simply answering a question, agents can plan actions, use tools, observe results, and continue working toward an objective. 𝗧𝗛𝗘 𝗘𝗩𝗢𝗟𝗨𝗧𝗜𝗢𝗡: 𝗖𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗔𝗜 ↓ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 ↓ 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 ↓ 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 ↓ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 ↓ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗧𝗛𝗘 𝗕𝗜𝗚 𝗦𝗛𝗜𝗙𝗧: From AI that follows rules... To AI that learns patterns... To AI that generates content... To AI that can reason, use tools, make decisions, and take action. The future isn't just about bigger AI models. It’s about building smarter systems around them. Save this roadmap to understand how AI has evolved. Follow @AamirAnsar94694 for more AI, tools, automation and tech insights. #AI #ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI #AgenticAI #AIAgents #AITools

🔥📃Check out our new preprint. Together with @petrovic_iv, we provide insights on a key GPCR phosphomotif and arrestin isoform selectivity based on several #structures, #NMR, #biochemistry, mass spectrometry, and cellular assays.

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25 Blogs to Learn 25 DevOps Concepts 👇 1) Terraform Project Structure: 2) Terraform State: 3) Terraform Graphs: 4) Terraform Dynamic Blocks: 5) Crossplane with ArgoCD: 6) GitOps Push vs. Pull Architecture: 7) Kyverno with ArgoCD: 8) Multi Cloud GitOps Workflow: 9) Dockerfile vs. Docker Compose: 10) Docker Image: 11) Docker Cache: 12) Docker Stats: 13) Docker Scout: 14) GitHub Actions Workflow: 15) GitHub Actions Optimization: 16) GitHub Branching Strategy: 17) Ansible Architecture: 18) Ansible Roles: 19) Ansible Dynamic Inventory: 20) Kubernetes Architecture: 21) POD Lifecycle: 22) pod.yaml Breakdown: 23) VPC Network Segmentation: 24) Lambda Integrations: 25) Serverless Architecture: 50K+ read my DevOps and Cloud newsletter: What do we cover: DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps 🔁 Consider a repost if this is helpful.

𝗛𝗼𝘄 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗥𝗲𝗮𝗱𝘆 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗶𝗻 𝟳 𝗦𝘁𝗮𝗴𝗲𝘀 A demo proves an AI agent can work. Production proves it can work reliably, securely, and at scale. Here are the 7 essential engineering stages. 𝟭. 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 | 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 & 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 Define the use case, target users, measurable KPIs, and risks (security, privacy, bias). 𝟮. 𝗗𝗲𝘀𝗶𝗴𝗻 | 𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Choose ReAct or Plan-Execute, define decision flows, guardrails, and human approval points. 𝟯. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 | 𝗧𝗼𝗼𝗹𝘀 & 𝗠𝗲𝗺𝗼𝗿𝘆 Integrate APIs, manage context and memory, and enforce least-privilege tool access. 𝟰. 𝗣𝗿𝗼𝗺𝗽𝘁 | 𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 Build structured prompts, function schemas, few-shot examples, and output constraints. 𝟱. 𝗚𝗿𝗼𝘂𝗻𝗱 | 𝗥𝗔𝗚 & 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 Prepare and chunk data, implement keyword/vector retrieval, and enforce source permissions. 𝟲. 𝗧𝗲𝘀𝘁 | 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Test accuracy, hallucinations, prompt injection, tool failures, latency, and cost. 𝟳. 𝗗𝗲𝗽𝗹𝗼𝘆 | 𝗟𝗟𝗠𝗢𝗽𝘀 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Use CI/CD, tracing, monitoring, alerts, versioning, and rollback mechanisms. 𝗪𝗵𝗮𝘁 𝗠𝗼𝘀𝘁 𝗧𝗲𝗮𝗺𝘀 𝗠𝗶𝘀𝘀 • No clear KPIs → No way to measure value. • Poor grounding → Unreliable responses. • Weak guardrails → Unauthorized actions. • Missing evaluations → Failures reach users. • No observability → Difficult incident recovery. 𝗧𝗵𝗲 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 𝗟𝗟𝗠 + 𝗣𝗿𝗼𝗺𝗽𝘁 + 𝗧𝗼𝗼𝗹𝘀 = 𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝗔𝗴𝗲𝗻𝘁 + 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 + 𝗘𝘃𝗮𝗹𝘀 + 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 = 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 Building an agent is easy. Engineering one you can trust is the real challenge. Which stage do you see teams skipping most often?

LANDS CONFIRMED CONNECTING @wolfdotgame #landreveal #structures #barns

I Explored ONE OF Star Citizen's Fortified Mega Structures...Here's What I Found #starcitizen #youtube

AI agents can do more than send one prompt to one model. In this course, Mumshad teaches you the basics of LLMs, workflows, tools, and agent loops. You'll build agent personalities, use structured JSON outputs, add guardrails and human approvals, and more.

Throwing up a few of the other #Zerg #structures together for brevity: the Hatchery, central hub for the Zerg base #starcraft #blizzard #blizzardart #conceptart #illustration #rts #digitalart

One day I might write a whole thesis on block play and structures in early years… #justsocool #stories #structures

With a total load of approx 1MN, we believe that we have put the largest load ever on a #tidalenergy #turbine blade in our large #structures test #laboratory in the @MaREIcentre @nuigalway. This is equivalent loading to the curb weight of approximately 10 double decker buses (1/4

#Structures - #Strutture - #Strukturen - #Structuren - #Estructuras

Success in uncertain environments depends less on prediction and more on structured experimentation.