Conceptual Design Sketching

Skin-Like fragrances redefining the business? GenZ & Alpha are pushing a powerful transformation. While previous generations often sought iconic, recognizable perfumes, fragrances that announced themselves before the wearer entered the room, today’s younger consumers are gravitating toward something subtler, personal, and more aligned with their lifestyles: lets see what is skin-like fragrances? +83% of Gen Z report using fragrance regularly +71% demand personalization +33.6% mix unique scents +20% layering up >From statement to SUBTLETY? In the past, fragrance signaled status and identity, with powerhouse perfumes of the 1980s and designer scents of the 1990s projecting outward. Today, Gen Z and Gen Alpha prioritize connection, favoring intimate, skin-like fragrances that blend with their skin and evoke comfort and authenticity. >What are SKIN-LIKE fragrances? They are perfumes designed to enhance the wearer’s natural scent rather than cover it up. Built with airy musks, transparent woods, and notes like rice, paper, or milk, these perfumes whisper rather than shout. They evoke the feeling of freshly washed skin, sun-warmed sheets, or the gentle memory of someone close. →Authenticity over artifice: +71% beauty consumers want scents that feel personal. Younger consumers value experiences and products that feel real and true to them. →Quiet luxury: In fashion and beauty, understatement is trending. Just as minimalist aesthetics dominate Instagram feeds, fragrances are following suit. →Wellness culture: +80% users say fragrance is essential for mood enhancement and self-expression. Soft, comforting scents align with the broader self-care movement. →Digital influence: +83 % Gen Z buy perfume based on TikTok. Social networks have amplified this narrative, turning niche concepts like “your-skin-but-better” perfumes into viral sensations. The FUTURE of fragrance buying? The rise of skin-like fragrances is reshaping perfume marketing and sales. Brands now focus on personalization, minimalism, and emotional storytelling over celebrity prestige. Digitally savvy consumers discover scents through social media, online sampling, and recommendations, relying on authenticity, reviews, and relatability instead of traditional counters. A redefined RELATIONSHIP with scent? In this new landscape, fragrance is less about performance and more about intimacy. It’s about how a scent connects with memory, identity, and mood. The younger generation is telling the industry that perfume doesn’t have to be loud to be powerful, it just has to feel like you. Find my curated search of examples and get inspired for your next success. Featured Brands: Aesop Byredo Clean Reserve DedCool Diptyque Ellis Brooklyn Escentric Molecules Glossier Juliette Has a Gun Le Labo Maison Louis Marie Nomenclature Phlur Skylar The Nue Co #beautybusiness #beautyprofessionals #fragrances #perfumes #genz

How to structure a Figma project from start to finish. It’s the question I get asked most. For me: Full freedom to start. Strong systems after is the key. Here’s my full process, using a small (!) solo design project as an example. 👉 Phase 0: Setup & Alignment Before opening Figma, I talk to development. We align on: • Tools and stack • Communication setup • Constraints, timing, and handoff process This saves countless hours later. 👉 Phase 1: Creative Exploration Nothing structured yet. It’s messy... very messy. I even (hold your breath) use groups (I know!!!! But they are so messy fast!), no auto layout (maybe for a button), no components, randomly pick colours. My goal is speed and flow. I move, drag, test, and iterate until it feels right, it works for me, but might not be he right path for everyone. I also enjoy combining hand sketches and Figma files. I usually work with a mix of wireframes and first ideas. But it highly depends on the project, team size and how many hats I wear Once it looks right, I share it with the team and development. No specs yet, just look and feel. I usually work on 2-3 master pages. 👉 Phase 2: Basic structure & cleanup • Typography styles and clear hierarchy • Colour variables and clear system (base, text, highlights) • Components with Auto Layout I also like to build a small prototype for early feedback. 👉 Phase 3: Structure & Systemisation (loops!) Now it gets real. The first draft might look finished, but it’s not. • Review every component with variants, responsiveness, and states • Refine typography styles and add variables and modes • Align naming and documentation with dev This takes time, a lot of time...and tight feedback loops with development. Build out the rest of the screens with this system 👉 Phase 4: Maintenance & Evolution (loops!) Once the design is structured, it becomes a living system. New features start in a creative playground, then merge back in. Old components get cleaned up. It’s important to agree on a clear system and responsibilities: Who adds, updates, and maintains components, and when. Doing this as a team keeps everything aligned. The result: a consistent, scalable design (and a whole separate stor) 👉 In a nutshell: I prefer full creative freedom at the start and strong systems after, especially for smaller teams or solo projects. Larger teams might merge or parallelise some of these steps. ⚠️ Quick note: This process reflects how I work on small, often solo design projects. In larger teams or product orgs, some steps might run in parallel, follow stricter systems, or use branching/version control instead. The goal here isn’t a “rulebook” just a transparent look at what works for me and pick what works for you. Feel free to agree, disagree or simply share your findings in the comments. How do you structure your Figma files and workflow for smaller (or larger) projects? #Figma #moonlearning #workflow

When we develop ontologies, we’re carefully crafting taxonomies, relationships and hierarchies. This is knowledge engineering. But at a deeper level, as we start to blend ontologies into AI, we’re also doing something mathematically elegant: we’re projecting high-dimensional data into a lower-dimensional conceptual space, much like dimensionality-reduction techniques in linear algebra. We’re factorising data. 🔵 What Do I Mean by Factorisation? In linear algebra or machine learning, factorisation is the process of breaking down a complex system into a set of simpler, lower-dimensional components. It’s how we go from messy, high-dimensional data to something more structured and usable - for instance, latent features in a matrix factorisation model. Ontology achieves a similar compression, but through abstraction and discretisation rather than algebraic multiplication. The first step is deciding what matters. What are the meaningful concepts we care about? What should we be paying attention to? This act of naming - of defining ontological classes - is not just descriptive. It’s selective. It’s a cognitive filter. Once you’ve made those choices, you’re effectively projecting the chaotic surface of your data onto a smaller, more meaningful subspace - a conceptual lens. This is your factorised view of the world. 🔵 Ontological Classes as Features Let’s say you’re working in tax law, healthcare, or finance. The raw data is sprawling - case notes, transaction logs, guidance manuals, APIs, spreadsheets. But once you define your ontological classes - Travel Expense, Employee, Business Purpose, or Diagnosis - you begin to compress that data into a smaller set of dimensions. These aren’t just labels. They’re axes of interpretation. Your AI models now have something to hook into. Your data pipelines know what to extract, link, store and serve. You’ve constrained the entropy of your system, not by discarding information, but by organising it around meaning. 🔵 Why This Matters for AI LLMs are famously good at handling unstructured data. But their real potential shines when they’re coupled with structure, especially when that structure reflects your domain’s core distinctions. A well-designed ontology acts as a kind of “feature engineering” for knowledge-centric AI. You’ve defined priors for your latent variables. You’ve chosen which concepts should anchor your interpretation, and you’ve factorised your data accordingly. The result? Faster iteration, more explainable results, and a far more coherent internal representation of your domain. 🔵 The Takeaway Ontology isn’t just a documentation exercise or a knowledge management tool. It’s a strategic, high-leverage move in the data pipeline. When done well, it’s a way of compressing meaning, factorising chaos, and bringing clarity to your AI efforts. If you’re serious about data-driven systems - especially those that aim to be intelligent - then ontology is not optional. It’s your starting point.

🔬 UX Concept Testing. How to test your UX design without spending too much time and effort polishing mock-ups and prototypes ↓ ✅ Concept testing is an early real-world check of design ideas. ✅ It happens before a new product/feature is designed and built. ✅ It helps you find an idea that will meet user and business needs. ✅ Always low-fidelity, always pre-launch, always involves real users. 🚫 Testing, not validation: ideas are not confirmed, but evaluated. ✅ What people think, do, say and feel are often very different things. ✅ You’ll need 5 users per feature or a group of features. ✅ You will discover 85% of usability problems with 5 users. ✅ You will discover 100% of UX problems with 20–40 users. 🚫 Poor surveys are a dangerous, unreliable tool to assess design. 🚫 Never ask users if they prefer one design over the other. ✅ Ask what adjectives or qualities they connect with a design. ✅ Tree testing: ask users to find content in your navigation tree. ✅ Kano model survey: get user’s sentiment about new features. ✅ First impression test: ask to rate a concept against your keywords. ✅ Preference test: ask to pick a concept that better conveys keywords. ✅ Competitive testing: like preference test, but with competitor’s design. ✅ 5-sec test: show for 5 secs, then ask questions to answer from memory. ✅ Monadic testing: segment users, test concepts in-depth per segment. ✅ Concept testing isn’t one-off, but a continuous part of the UX process. In design process, we often speak about “validation” of the new design. Yet as Kara Pernice rightfully noted, the word is confusing and introduces bias. It suggests that we know it works, and are looking for data to prove that. Instead, test, study, watch how people use it, see where the design succeeds and fails. We don’t need polished mock-ups or advanced prototypes to test UX concepts. The earlier you bring your work to actual users, the less time you’ll spend on designing and building a solution that doesn’t meet user needs and doesn’t have a market fit. And that’s where concept testing can be extremely valuable. Useful resources: Concept Testing 101, by Jenny L. https://lnkd.in/egAiKreK A Guide To Concept Testing in UX, by Maze https://lnkd.in/eawUR-AM Concept Testing In Product Design, by Victor Yocco, PhD https://lnkd.in/egs-cyap How To Test A Design Concept For Effectiveness, by Paul Boag https://lnkd.in/e7wre6E4 The Perfect UX Research Midway Method, by Gabriella Campagna Lanning https://lnkd.in/e-iA3Wkn Don’t “Validate” Designs; Test Them, by Kara Pernice https://lnkd.in/eeHhG77j UX Research Methods Cheat Sheet, by Allison Grayce Marshall https://lnkd.in/eyKW8nSu #ux #testing

SOME IDEAS ARE BAD AI will give you glossy, attractive images every time. Polished surfaces. Perfect lighting. Instant wow-factor. But that shine can be deceptive, it CAN seduce before you’ve even asked if the idea is worth pursuing. Sketching works the other way around. It doesn’t seduce, it reveals. A sketch strips an idea to its bones, showing its strengths and exposing its flaws. How long would I keep sketching an idea if I did not like the direction? Some sketches prove the concept. Others prove it should be abandoned. Both are valuable. Because the purpose of sketching isn’t to impress (even when some sketches are lovely), it’s to represent, to explain, to think out loud on paper. Good ideas survive the sketchbook. Bad ones die there. And that’s how design moves forward.

If you’re an AI engineer, product builder, or researcher- understanding how to specialize LLMs for domain-specific tasks is no longer optional. As foundation models grow more capable, the real differentiator will be: how well can you tailor them to your domain, use case, or user? Here’s a comprehensive breakdown of the 3-tiered landscape of Domain Specialization of LLMs. 1️⃣ External Augmentation (Black Box) No changes to the model weights, just enhancing what the model sees or does. → Domain Knowledge Augmentation Explicit: Feeding domain-rich documents (e.g. PDFs, policies, manuals) through RAG pipelines. Implicit: Allowing the LLM to infer domain norms from previous corpora without direct supervision. → Domain Tool Augmentation LLMs call tools: Use function calling or MCP to let LLMs fetch real-time domain data (e.g. stock prices, medical info). LLMs embodied in tools: Think of copilots embedded within design, coding, or analytics tools. Here, LLMs become a domain-native interface. 2️⃣ Prompt Crafting (Grey Box) We don’t change the model, but we engineer how we interact with it. → Discrete Prompting Zero-shot: The model generates without seeing examples. Few-shot: Handpicked examples are given inline. → Continuous Prompting Task-dependent: Prompts optimized per task (e.g. summarization vs. classification). Instance-dependent: Prompts tuned per input using techniques like Prefix-tuning or in-context gradient descent. 3️⃣ Model Fine-tuning (White Box) This is where the real domain injection happens, modifying weights. → Adapter-based Fine-tuning Neutral Adapters: Plug-in layers trained separately to inject new knowledge. Low-Rank Adapters (LoRA): Efficient parameter updates with minimal compute cost. Integrated Frameworks: Architectures that support multiple adapters across tasks and domains. → Task-oriented Fine-tuning Instruction-based: Datasets like FLAN or Self-Instruct used to tune the model for task following. Partial Knowledge Update: Selective weight updates focused on new domain knowledge without catastrophic forgetting. My two cents as someone building AI tools and advising enterprises: 🫰 Choosing the right specialization method isn’t just about performance, it’s about control, cost, and context. 🫰 If you’re in high-risk or regulated industries, white-box fine-tuning gives you interpretability and auditability. 🫰 If you’re shipping fast or dealing with changing data, black-box RAG and tool-augmentation might be more agile. 🫰 And if you’re stuck in between? Prompt engineering can give you 80% of the result with 20% of the effort. Save this for later if you’re designing domain-aware AI systems. Follow me (Aishwarya Srinivasan) for more AI insights!

Your architecture diagrams are lying to you. And even worse, your team is making decisions based on them. Outdated diagrams. Inconsistent notation. Different people drawing different versions of the “truth.” It’s no surprise teams struggle to align on architecture when everyone is looking at a different picture. This is exactly why I created a free eBook on the C4 model. The C4 model gives you a structured, layered way to communicate architecture that doesn’t fall apart the moment the system changes. Instead of boxes-and-lines chaos, you get clear, consistent views, from high-level context to concrete containers and components, all designed for the audience who needs them. In the eBook, I walk through a real e-commerce modernization and show how the C4 model helps teams reason about design, boundaries, and responsibilities. You’ll also find interactive IcePanel diagrams you can explore directly from the live project behind the guide. 👉 I’m sharing a short extract below. Get the full PDF + the interactive model here: https://lnkd.in/eb_Cmecd If your #architecture feels unclear, inconsistent, or impossible to communicate, this will change how you document systems going forward. #c4model #diagrams #architecture

CAD ≠ Design One of the most common misconceptions in the industry is that “doing CAD” means you’ve done the design work. But here's the truth: CAD is a tool — not a design process. Too often, teams jump straight into modeling without truly understanding the problem, defining the requirements, or exploring multiple concepts. The result? Beautiful models of poorly thought-out ideas. Great design starts with thinking, not modeling. Sketching, systems thinking, trade-offs, iterations — all of this should happen before the first sketch in SolidWorks or CATIA. CAD brings precision and manufacturability — not creativity by default. Let’s reshape the way we think about product development: Start with the problem. Design the solution. Then CAD it. #ProductDesign #IndustrialDesign #CADDesign #DesignThinking #MechanicalEngineering #EngineeringDesign #InnovationInDesign #SolidWorks #CATIA #AutoCAD #3DModeling #DesignProcess #PrototypeToProduct #ProductDevelopment #ManufacturingDesign #MechanicalDesign #EngineeringLife #ConceptToCreation #TechDesign #DFM (Design for Manufacturing) #HumanCenteredDesign #EngineeringStudents #DesignEngineer #CreativeEngineering #DigitalProductDesign #DesignCommunity #EngineeringInnovation #EngineeringTools #DesignEducation

💡 CAD ≠ Design One of the biggest misconceptions in engineering and product development is this: “If I’m doing CAD, I’m designing.” Not quite. 🛠 CAD is a tool — not the design process itself. Too often, teams jump straight into 3D modeling without: • Fully understanding the problem • Defining clear requirements • Exploring multiple concepts and trade-offs The result? Stunning models… of weak ideas. Great design doesn’t start in SolidWorks or CATIA. It starts with systems thinking, sketching, iterations, and asking the right questions. 👉 CAD brings precision and manufacturability, not creativity by default. Let’s reshape how we approach product development: 🔁 Think first. Design the solution. Then CAD it. #Engineering #DesignThinking #ProductDevelopment #CAD #MechanicalEngineering #SolidWorks #CATIA #DesignProcess #SystemsThinking

A conceptual design process for a public space, specifically focusing on the evolution of a staircase into a more integrated and user-friendly environment. The image is divided into four sections (A, B, C, D) showing different stages of development. Interpretation of Each Section: A (Stairs): This section shows a basic, unadorned staircase. It serves as the starting point for the design concept. B (Movement): This section introduces the concept of movement and user interaction. The dotted lines with arrows represent people walking up and down the staircase, suggesting potential flow and circulation patterns. C (Directionality): This section focuses on the directionality and flow within the space. The arrows indicate the movement of people in different directions, suggesting the creation of designated pathways or zones. The green areas likely represent planting or other landscape elements. D (Integration): This section shows the most developed stage, integrating greenery and human interaction. The stylized trees create a more natural and inviting environment, while the stick figures illustrate people using the space for various activities. Overall Purpose and Message: The image aims to visually communicate the conceptual design process for transforming a simple staircase into a more dynamic and engaging public space. It highlights the importance of considering movement, flow, and integration of greenery in creating user-friendly environments. #urbandesign #architecture #publicdesign #construction #remodeling