Improving Predictive Accuracy

𝗜𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗺𝗽𝗼𝗻 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂𝗿 𝗱𝗼𝗰𝘁𝗼𝗿? Menstrual blood might be the most overlooked goldmine of women’s health research. And almost nobody is talking about it. Meanwhile, everyone gets excited when "longevity start-ups" launch their latest shiny Quest and LabCorp wrappers (and raise millions for it, of course). Same tests. Different branding. Yawn. So, I did some research to see what ACTUAL innovation exists in the space. Here is what I found: Researchers at ETH Zürich have developed 𝗠𝗲𝗻𝘀𝘁𝗿𝘂𝗔𝗜, the first technology to read biomarkers directly from menstrual blood. Currently, they use sanitary towels and integrate paper-based biosensors in them. No needles. No labs. No technicians to get your blood. They currently use a smart pad that doubles as a test strip. Until today, menstrual blood has long been seen as “waste”. Now, research is beginning to recognise it as a good source for health information. In clinical trials, MenstruAI's pads successfully measured:   • 𝗖-𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝘁𝗲𝗶𝗻 (𝗖𝗥𝗣): which is one of the most common and general markers of inflammation.   • 𝗖𝗮𝗿𝗰𝗶𝗻𝗼𝗲𝗺𝗯𝗿𝘆𝗼𝗻𝗶𝗰 𝗮𝗻𝘁𝗶𝗴𝗲𝗻 (𝗖𝗘𝗔): a tumour marker often elevated in cancers.  • 𝗖𝗔-𝟭𝟮𝟱: A specific protein that is associated with endometriosis and ovarian cancer. What's next? They are planning bigger studies to explore everyday use as well. I love that this is an affordable, needle-free screening that could reach women everywhere. Coming from a public health perspective, it also means that it could include underserved regions with limited healthcare access as well! This is TRUE innovation that has the potential to change health outcomes for so many women. And it's certainly not a luxury biohack.  Imagine getting monthly bloodwork effortlessly, not just for disease markers, but for vitamins, nutrition, and overall health. Prevention, made accessible. That’s the kind of longevity tool I’m here for. 

Smart Menstrual Pads: The Next Frontier in Women’s Health Diagnostics 🩸💡 Menstrual blood is a rich, underutilized source of health information, containing cells, proteins, and biomarkers that reflect systemic and reproductive health. A smart menstrual pad—capable of analyzing menstrual blood in real-time—could transform women’s health by enabling early detection of diseases like: Cervical and ovarian cancers. Endometriosis and PCOS. Even non-reproductive conditions like thyroid disorders or metabolic syndromes. 🌟 The benefits: Non-invasive, at-home testing during a natural physiological process. Early detection = better outcomes. Empowerment through self-monitoring. 💬 Challenges: Data privacy, interpretation of complex biomarkers, regulatory hurdles. But the potential is enormous: Transforming a routine monthly occurrence into a powerful diagnostic tool. Would you embrace such technology as part of your regular health monitoring? #WomensHealth #FemTech #EarlyDetection #Biomarkers #HealthcareInnovation #FutureOfMedicine

Post 3/8 Making LLMs Smarter with Retrieval-Augmented Generation (RAG) LLMs are powerful, but they have limitations: ⚠️ Outdated knowledge (fixed at training time) ⚠️ Hallucinations (generating incorrect info) ⚠️ Limited domain-specific knowledge (can’t access private/company data) Enter Retrieval-Augmented Generation (RAG)—a technique that fetches real-time information before generating responses, making AI more accurate and reliable. 🔹 How RAG Works 1️⃣ User asks a question (e.g., “What are the latest AI laws?”) 2️⃣ AI retrieves relevant data from external sources 3️⃣ It combines this info with LLM-generated text 4️⃣ User gets an accurate, fact-based response ✅ Keeps AI up-to-date (fetches fresh data) ✅ Reduces hallucinations (answers grounded in facts) ✅ Customizes AI for businesses (integrates internal knowledge bases) 🔹 Example: A legal AI without RAG might guess a law. With RAG, it retrieves actual legal texts before responding. 🔹 Practical Guide – Build a Simple RAG System Want to experiment with RAG? Try this: 📌 Install FAISS for document retrieval pip install faiss-cpu openai tiktoken 📌 Retrieve and use real-time data in AI responses import faiss, openai, numpy as np from tiktoken import encoding_for_model openai.api_key = "your-api-key" docs = ["RAG improves accuracy by retrieving external docs.", "Transformers use self-attention for NLP.", "The EU AI Act regulates AI safety."] encoder = encoding_for_model("text-embedding-ada-002") doc_embeddings = np.array([encoder.encode(doc) for doc in docs]) index = faiss.IndexFlatL2(doc_embeddings.shape[1]) index.add(doc_embeddings) def retrieve_info(query): query_emb = np.array([encoder.encode(query)]) _, idxs = index.search(query_emb, k=1) return docs[idxs[0][0]] query = "How does RAG improve AI?" retrieved_info = retrieve_info(query) response = openai.ChatCompletion.create( model="gpt-4", messages=[ {"role": "system", "content": "Use retrieved knowledge to improve accuracy."}, {"role": "user", "content": f"Context: {retrieved_info}\nAnswer: {query}"} ] ) print(response["choices"][0]["message"]["content"]) 🔹 What’s Next? Next, we’ll explore embeddings—the backbone of AI search and knowledge retrieval. 💡 Have you used RAG-based AI? What challenges did you face? Let’s discuss! #AI #MachineLearning #LLMs #RAG #ArtificialIntelligence #NLP #TechInnovation #engineeringtidbits

Most UX designs fail and here’s the hidden reason. The problem isn’t just poor visuals or clunky flows. It’s short-sighted thinking. Second order thinking forces designers to look beyond quick fixes and consider the long-term ripple effects of their choices. It leads to designs that work today and stay relevant as users and systems evolve. Why It Matters in UX See ripple effects → Think beyond what happens now to what happens next. Build lasting trust → Short wins shouldn’t erode long term value. Design for change → Systems, tech, and users won’t stay the same. Key Applications - Anticipate behavior → Will a new feature help or frustrate? - Think system-wide → A smoother sign-up may invite spam. - Design for growth → Today’s flow must scale tomorrow. - Stay ethical → Engaging ≠ addictive. Protect users. - Align with the bigger picture → Don’t just chase KPIs design for trust and loyalty. Practical Tools Journey mapping → Trace long-term impact. Prototyping → Test what if scenarios early. Stakeholder discussions → Show how small tweaks ripple system-wide. Future proofing → Create solutions that adapt as needs evolve. Questions to Ask What might break later? Will this still work in 2 years? Are there ethical blind spots? Are we solving or creating a problem? By thinking deeper and planning ahead, UX designers move beyond short-term fixes and craft products that stay valuable over time. P.S. How do you keep your designs future-ready as user needs shift? ♻️ Repost to spark better UX thinking in your network. Hi, I’m Sivaprasad I help organizations uncover product value through user research & UX design. Follow Sivaprasad Paliyath for more daily insights.

When people hear the term "software architect," they often imagine someone sketching flowcharts and models to describe a system. However, true software architecture goes beyond merely drawing diagrams. It's about strategy, anticipation, and vision. Effective software design isn’t just about creating something that works today; it’s about crafting a system that will remain functional tomorrow, next year, or even a decade from now. At the core of software design is considering multiple perspectives and blending them into a cohesive and sustainable vision. Every architectural decision must balance business goals, the technology landscape, and user experience while solving both current and potential challenges. Architects need to look beyond immediate requirements to foresee what may arise as the system evolves. If business needs change, will the software remain valuable? If new technologies emerge, can the design adapt? Most importantly, can it scale to accommodate growing user demand, evolving features, and shifting usage patterns? An essential aspect of architectural foresight is planning for problems that have not yet materialized. While it’s straightforward to design around today’s known requirements, experienced architects also anticipate future challenges. Whether preparing for significant user growth, addressing emerging security threats, or planning for maintenance needs years down the line, designing with the future in mind adds complexity but ensures a more resilient product. Rather than addressing challenges as they arise, architects consider them from the outset, incorporating scalability, flexibility, and reliability solutions. The best software designs don’t merely satisfy immediate requirements; they are intentionally adaptable. Effective architecture isn’t rigid—it’s designed to withstand and respond to inevitable change. From integrating loosely coupled components that facilitate future updates to designing modules that make adding new features seamless, architects make deliberate choices to prepare for long-term evolution. Ultimately, software architecture is as much about vision as it is about structure. It’s about creating systems that adapt to growth, technology changes, and user evolution. While diagrams may be a part of the process, the real work of software design lies in addressing future problems, meeting long-term needs, and ensuring that the product remains robust as both technology and its users continue to evolve. #SoftwareArchitecture

One big challenge for designers looking to shape the future is navigating a sea of unknowns. How can designers help prep our businesses for the future, when the world keeps changing? One way I like to de-risk the future is a STEEP analysis. STEEP represents the Social, Technological, Economic, Environmental, and Political forces shaping our world. It helps by + Spotting long-term shifts that shape industries over the next five years + Find opportunities and adapt strategies for what’s to come. + Bring teams together to have conversations about what's around the corner + Create strategies grounded in a broader context + Helps businesses stay relevant and stay ahead How to Get Started 1. Gather a diverse group from across business units. Brainstorm: How might trends in each category impact your business?  2. Share and discuss. Tease apart ideas, cluster similar themes, and challenge each other’s perspectives. 3. Summarize key themes. What deserves attention? What’s most critical? 4. Plan next steps—where to dive deeper, what areas to research, and where to begin planning. How I’ve Used STEEP to Tackle the Unknown STEEP Analysis helps prepare for challenges even when the details are unclear: - Regulatory Change: Anticipating shifts in EU laws to stay ahead of compliance. - Next-Gen Values: Adapting to Gen Z’s evolving buying behaviors to meet their expectations. - Tech Disruption: Evaluating emerging technologies to weigh risks and opportunities. STEEP isn’t just a tool for planning—it’s a way to build alignment, curiosity, and readiness for the future. Curious about STEEP? Have use cases how you’ve used it? Other frameworks to recommend? I wanna hear it. ____ 👋 I’m Rachel. Leading Zalando's innovation and strategy design team. I help teams de-risk big questions of how do we prepare for what we don't know is coming next. ➕ Follow for insights on design strategy, futurism, leading through ambiguity. ____

𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: 𝗧𝗵𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗹𝗶𝗻𝗸 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝘀𝗲𝗮𝗿𝗰𝗵 Large language models are powerful, but without relevant context they often produce inaccurate results. The real breakthrough comes when we combine LLMs with vector databases, which are specialized systems designed to store, index, and search vector embeddings. These embeddings capture the semantic meaning of unstructured content such as documents, images, and audio, allowing AI to retrieve information based on meaning rather than keywords. Traditional databases are designed for structured data and exact matches. Vector databases enable similarity-based search, helping AI systems understand context and return results that are relevant even when wording differs. 𝗛𝗼𝘄 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗪𝗼𝗿𝗸 Unstructured data is converted into vector embeddings using models like OpenAI, Hugging Face, or Instructor models. These vectors are stored in specialized databases and indexed using advanced algorithms such as:   • 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗡𝗮𝘃𝗶𝗴𝗮𝗯𝗹𝗲 𝗦𝗺𝗮𝗹𝗹 𝗪𝗼𝗿𝗹𝗱 (𝗛𝗡𝗦𝗪): Builds multi-layer graphs for highly efficient navigation   • 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Compresses embeddings for faster retrieval while conserving memory   • 𝗜𝗻𝘃𝗲𝗿𝘁𝗲𝗱 𝗙𝗶𝗹𝗲 𝗜𝗻𝗱𝗲𝘅 (𝗜𝗩𝗙): Clusters similar vectors to accelerate searches When a query arrives, the database locates the closest embeddings using similarity metrics like cosine similarity, Euclidean distance, or dot product and returns the most relevant results. 𝗞𝗲𝘆 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀   • Retrieval Augmented Generation to improve LLM accuracy and reduce hallucinations   • Semantic search to retrieve documents or products based on meaning instead of keywords   • Recommendations for products, videos, or personalized content   • Multimodal search for finding similar images, videos, or audio files   • Fraud detection by identifying patterns that match suspicious behaviors 𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀   • 𝗖𝗹𝗼𝘂𝗱 𝗵𝗼𝘀𝘁𝗲𝗱 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Pinecone, Weaviate, Qdrant, Milvus, Redis Vector   • 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗮𝗻𝗱 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝗹𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀: FAISS, ScaNN, Annoy   • 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: LangChain, LlamaIndex   • 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻𝘀: PostgreSQL pgvector, Elasticsearch, MongoDB 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Vector databases allow AI systems to reason with knowledge they were never trained on. They enable conversational agents to provide contextually accurate responses, enhance recommendation engines, and power intelligent multimodal search capabilities. LLMs provide reasoning. Vector databases connect knowledge. Together, they unlock the next generation of enterprise AI systems. Follow Umair Ahmad for more insights. #AI #VectorDatabases #SemanticSearch #RAG #MachineLearning #LLMOps 

Menstrual blood isn’t a waste — it's more than that! According to the #WHO.... over 500 million women around the world lack access to basic healthcare. Johns Hopkins Medicine reports that #endometriosis takes an average of 7 to 10 years to get diagnosed. While....Cancer Research UK states that early detection of #ovarian cancer can increase survival rates by over 90 percent. Now imagine if the period in females could help detect health issues early — without going to a lab or clinic. Researchers at ETH Zurich have developed #MenstruAI, a smart and affordable diagnostic platform that uses menstrual blood to check for serious health conditions. A sanitary pad is designed with a paper-based sensor (similar to a covid test strip) that detects important health markers: 1. CRP (c-reactive protein): shows inflammation 2. CEA (carcinoembryonic antigen): linked to some cancers 3. CA-125: often found in ovarian cancer and endometriosis Once the pad is used, you take a photo of it using the #MenstruAI app....which uses image analysis and ML to read color changes in the sensor and give personalized health insights. No needles.....No lab visits..... Just accurate, non-invasive monitoring — anytime, anywhere. This could be a game-changer for women, especially in areas where regular health screenings are hard to access. It’s a discreet, private, and powerful alternative to conventional tests. What's your thoughts on this? #menshealth #menstrualhealth #digitalhealth #healthtech #womenshealth #periodpower #ethzurich #medtech #innovation #healthcareaccess #noninvasivetesting

The AI-Powered Design Workflow is Slowly Taking Shape. We’re inching closer to a paradigm shift where design isn’t just a visual or collaborative process — it’s a dynamic, code-driven, test-ready system deeply integrated with AI. Here’s how the future is shaping up: 1. Design Systems now live on code repositories, becoming direct inputs for intelligent design generation. 2. Designers and PMs collaborate on AI-native workspaces (like Notebook LM or Gemini), feeding structured PRDs that allow AI agents to generate functional flows and screens using system components on tools like Cursor. Designers will now spend more time inside the code — tuning, refining, and evolving these prototypes — and far less time on a visual canvas for 80% of BAU flows. A separate Visual Design team will work in parallel to develop the visual assets, illustrations, and motifs that plug into the system to complete the final VD. 3. Design becomes an interactive loop, where flows evolve through iterative, instruction-based dialogue with the agent. 4. Custom, complex, and transformative design exercises will still need human intuition. When the brief demands something beyond the system — a new interaction model, an unconventional layout, or a radically different experience — designers jump into a Canvas (Figma or similar), explore deeply, and then feed those bespoke patterns back into the system to regenerate flows contextually. 5. Internal presentations and design reviews? Automatically generated from the working prototype — flows, screens, and interaction maps, ready on demand. 6. User testing? Plug and play. Prototypes can be directly tested through platforms that support Universal Design Protocols — imagine running tests on UserTesting without exporting anything. 7. Engineering handoff? Either use the prototype as a precise reference, or — in ideal setups — push directly to production through a review pipeline. 8. UAT, QA and governance layers plug in seamlessly. And the moonshot? 9. Interface-as-a-Service (IAAS): Where AB testing logic, success metrics, and business rules are fed in — and the system auto-generates variants, runs tests, and reports outcomes. The interfaces becomes a living, learning, optimizing system. This isn’t the future in theory — it’s the direction things are moving toward. Curious to hear: What’s your take on this AI-powered workflow? Which parts are you already experimenting with? #AIUX #ProductDesign #DesignSystems #FutureOfDesign #IaaS #UXLeadership #AIWorkflow #DesignOps

Revolutionizing Women’s Health Imagine a world where a simple menstrual pad could help detect early signs of cancer or other life-threatening conditions. That future is already taking shape. Engineers and healthcare innovators are developing smart menstrual pads embedded with biosensors capable of analyzing menstrual blood for disease markers. From early cancer detection to reproductive health insights, this technology taps into an underutilized yet information-rich biological sample. 📱 Some prototypes even sync with mobile apps, giving users immediate, actionable feedback. This innovation isn’t just about diagnostics—it’s about empowerment. It's about bridging the gap between tech and everyday healthcare for women worldwide. 🔬 As we continue advancing femtech, it’s time to rethink how we approach routine health monitoring—and menstrual blood might just be the key.