Innovation Competitions And Prizes

Many engineers can build an AI agent. But designing an AI agent that is scalable, reliable, and truly autonomous? That’s a whole different challenge.  AI agents are more than just fancy chatbots—they are the backbone of automated workflows, intelligent decision-making, and next-gen AI systems. However, many projects fail because they overlook critical components of agent design.  So, what separates an experimental AI from a production-ready one?  This Cheat Sheet for Designing AI Agents breaks it down into 10 key pillars:  🔹 AI Failure Recovery & Debugging – Your AI will fail. The question is, can it recover? Implement self-healing mechanisms and stress testing to ensure resilience.  🔹 Scalability & Deployment – What works in a sandbox often breaks at scale. Using containerized workloads and serverless architectures ensures high availability.  🔹 Authentication & Access Control – AI agents need proper security layers. OAuth, MFA, and role-based access aren’t just best practices—they’re essential.  🔹 Data Ingestion & Processing – Real-time AI requires efficient ETL pipelines and vector storage for retrieval—structured and unstructured data must work together.  🔹 Knowledge & Context Management – AI must remember and reason across interactions. RAG (Retrieval-Augmented Generation) and structured knowledge graphs help with long-term memory.  🔹 Model Selection & Reasoning – Picking the right model isn't just about LLM size. Hybrid AI approaches (symbolic + LLM) can dramatically improve reasoning.  🔹 Action Execution & Automation – AI isn't useful if it just predicts—it must act. Multi-agent orchestration and real-world automation (Zapier, LangChain) are key.  🔹 Monitoring & Performance Optimization – AI drift and hallucinations are inevitable. Continuous tracking and retraining keeps your AI reliable.  🔹 Personalization & Adaptive Learning – AI must learn dynamically from user behavior. Reinforcement learning from human feedback (RHLF) improves responses over time.  🔹 Compliance & Ethical AI – AI must be explainable, auditable, and regulation-compliant (GDPR, HIPAA, CCPA). Otherwise, your AI can’t be trusted.  An AI agent isn’t just a model—it’s an ecosystem. Designing it well means balancing performance, reliability, security, and compliance.  The gap between an experimental AI and a production-ready AI is strategy and execution.  Which of these areas do you think is the hardest to get right?

Ever heard of the "death by pilot" trap? It's a common pitfall with early stage construction tech startups. Drawing from my experience, I've been sketching out a concept I've labelled 'Crossing The Chasm Twice', exploring the unique go-to-market hurdles faced by companies in our industry. The construction industry, with its distinct project delivery systems and fragmentation, requires a specialized approach to crossing the user chasm not once, but twice: internally within a company and then externally to the broader market. At its core, the idea is that the short duration and isolated nature of construction projects complicates the ability to establish a strong, company-wide adoption. This scenario often leads startups into a "death by pilot" trap where initial successes don't translate into broader acceptance. The article explains Geoffrey Moore's concept of "The Chasm" and its relevance to technology adoption, emphasizing the need for startups to transition from early adopters to the mainstream market. For startups in construction, this journey is a two-step process. Often, it begins with securing pilots with initial construction companies. However, navigating the intricacies of the project delivery system and user turnover presents its own 'Chasm' for those initial companies. After being successful crossing that first 'Chasm', the young company then needs to cross the final 'Chasm' into the early majority of the industry at large. This requires adapting to diverse client needs and their bespoke project requirements, often ensnaring the young company in additional pilots and a continuation of the "death by pilot" cycle. Check out the full article here: https://lnkd.in/g4ynKvhR The article provides 12 detailed pieces of actionable advice for startups and construction companies alike, including strategies for navigating protracted sales cycles, focusing on scalable solutions, and harnessing key roles within construction organizations to propel adoption. Construction firms are advised to understand 'internal market fit', incorporate multiple projects in pilots, provide financial sustainability for startups, and leverage key roles on project teams to help scale adoption. By understanding the nuances of construction project delivery and crafting tailored go-to-market strategies, innovative construction firms can better partner and startups can better defy the odds in our industry's transformation. #construction #buildingconstruction #gotomarket #digitaltransformation #constructiontechnology #constructiontech #technologyexcellence #builtenvironment

AI models like ChatGPT and Claude are powerful, but they aren’t perfect. They can sometimes produce inaccurate, biased, or misleading answers due to issues related to data quality, training methods, prompt handling, context management, and system deployment. These problems arise from the complex interaction between model design, user input, and infrastructure. Here are the main factors that explain why incorrect outputs occur: 1. Model Training Limitations AI relies on the data it is trained on. Gaps, outdated information, or insufficient coverage of niche topics lead to shallow reasoning, overfitting to common patterns, and poor handling of rare scenarios. 2. Bias & Hallucination Issues Models can reflect social biases or create “hallucinations,” which are confident but false details. This leads to made-up facts, skewed statistics, or misleading narratives. 3. External Integration & Tooling Issues When AI connects to APIs, tools, or data pipelines, miscommunication, outdated integrations, or parsing errors can result in incorrect outputs or failed workflows. 4. Prompt Engineering Mistakes Ambiguous, vague, or overloaded prompts confuse the model. Without clear, refined instructions, outputs may drift off-task or omit key details. 5. Context Window Constraints AI has a limited memory span. Long inputs can cause it to forget earlier details, compress context poorly, or misinterpret references, resulting in incomplete responses. 6. Lack of Domain Adaptation General-purpose models struggle in specialized fields. Without fine-tuning, they provide generic insights, misuse terminology, or overlook expert-level knowledge. 7. Infrastructure & Deployment Challenges Performance relies on reliable infrastructure. Problems with GPU allocation, latency, scaling, or compliance can lower accuracy and system stability. Wrong outputs don’t mean AI is "broken." They show the challenge of balancing data quality, engineering, context management, and infrastructure. Tackling these issues makes AI systems stronger, more dependable, and ready for businesses. #LLM

How could I build a career if I couldn't even handle a "simple" networking event? Twenty years later, I'm CHRO. And I still hate networking events. But I cracked the code. Traditional networking assumes collecting 50 business cards equals success. For introverts? One deep conversation beats 50 shallow hellos. Quality over quantity isn't just our preference. It's our superpower. So I built my own system. ——————————————— → The 100-Point Energy Budget Every event, you start with 100 energy points: • Random small talk: -15 • Meaningful conversation: -5 • Pretending to laugh at bad jokes: -20 • Finding a fellow introvert: +10 • Strategic "email break": +5 Hit 20 points? Leave. That's not quitting. It's resource management. ——————————————— → The 3-Deep Rule While extroverts collect 50 cards, I build 3 real connections. They get names. I get allies. They get LinkedIn adds. I get coffee meetings. They get forgotten. I get remembered. One meaningful conversation > 50 forgettable handshakes. Tell people you're "gathering insights for research." Now it's an interview, not small talk. Arrive 15 minutes early. Quieter room, better conversations. ——————————————— → The Opener That Works "I'm testing a theory that admitting you're an introvert at networking events creates better connections. You're participant seven." People lean in. They want in on your experiment. Ask what matters: "What problem are you tackling right now?" "If you weren't here, what would you rather be doing?" ——————————————— → The Lighthouse Strategy Don't circulate. Plant yourself somewhere visible. Let people come to you. Or volunteer at check-in for 30 minutes. Meet everyone, defined role, then disappear. Set 45-minute alarms. Energy check. Below 5? Bathroom break. ——————————————— → Permission Granted You can officially: • Leave after 52 minutes • Eat lunch alone at conferences • Say "I need to recharge" • Build your network through LinkedIn • Skip events that don't serve you My biggest deals came from 1-on-1 coffees, not cocktail parties. My best hires came from deep conversations, not speed networking. ——————————————— → The Truth Successful introverted executives didn't learn to act like extroverts. They learned to network like strategists. My record? 12-minute holiday party appearance. Two conversations. Both mattered. Still got promoted. Once had my assistant call with an "urgent client matter" 45 minutes into a dinner. The client was my cat. Zero regrets. Your quiet nature isn't a bug — it's an executive feature. Your energy management isn't high maintenance — it's self-leadership. The revolution isn't about becoming louder. It's about quiet leaders writing the rules. From a comfortable distance. Through screens or deep connection. Like the evolved professionals we are. ♻️ Share to save an introvert from networking hell 📩 Get my Networking Energy Toolkit → https://lnkd.in/dfhfHWe5

How to Win a Hackathon in 2025 | Complete Beginner’s Roadmap Here’s the Ultimate Hackathon Roadmap — from beginner to winner. Hackathons = Opportunity + Growth + Fun Hackathons are not just coding events—they are career-changing opportunities.   Whether you're a beginner or experienced, hackathons can boost your skills, network, and resume. Let’s break the myth:   “Only experts can win hackathons.”   Not true. With the right strategy, anyone can win. Why Hackathons Matter - Real-world projects = Real skills. - Network with industry mentors, professionals, and peers. - Winning or participating = Big resume boost. - Many companies scout talent through hackathons. Beginner? No Worries. Here's How to Start 👇 1. Basics First     - Learn HTML, CSS, JavaScript for web dev basics.     - Pick a language: Python or JavaScript (easy for beginners).     - Know Git & GitHub for collaboration. 2. Pick Your Stack     - Frontend: React, Vue, or plain HTML/CSS/JS     - Backend: Node.js, Flask, or Firebase     - Database: MongoDB or Firebase 3. Build Mini Projects    - Portfolio site     - To-do app     - Weather app     Start small, build confidence. How to Find Hackathons - Platforms: Devpost, HackerEarth, Dare2Compete, Unstop, MLH.io   - Join college hackathons or online global hackathons. Before Hackathon: Be Ready   - Form a team: 3–4 members with diverse skills.   - Learn APIs, basic UI/UX, and version control (Git).   - Explore tools like Figma, Canva, Notion. During Hackathon: Game Plan 🎮 1. Understand the Problem Statement     - Don’t rush. Read twice, brainstorm ideas. 2. Divide & Conquer    - Assign roles: frontend, backend, design, presentation. 3. Focus on MVP (Minimum Viable Product)     - Build something that works.     - Fancy UI can wait—functionality first. 4. Use Pre-built Tools     - Use libraries, APIs—don’t reinvent the wheel.     - AI tools can speed up your dev process. 5. Prepare a Clear Demo     - Record a video demo or present live.     - Explain problem, solution, tech used, and future scope. How to Win Hackathons 🏆 - Solve real problems, not generic ones.   - Clear UI, smooth UX = Big plus.   - Focus on impact, innovation, and execution.   - Pitch with confidence. Storytelling matters.  Bonus Tips for Beginners - Hackathons are learning marathons. Even if you don’t win, you gain skills, friends, and experience. - Don’t fear competition. Everyone starts somewhere. - Participate, learn, grow. Next time, you’ll win. My Hackathon Strategy (That Works) 1. Understand → Plan → Build → Present.   2. Work smart, not hard. Use templates, tools, open-source resources.   3. Stay calm under pressure. Take breaks, hydrate, and keep coding. It’s not about how much you know—it's about how well you apply what you know. Remember:   Every hackathon makes you better.   Every project makes you stronger.  Start today—the journey is worth it. Follow Vikram Gaur #Hackathon #LearnByDoing #HackathonRoadmap #GTC2025 #AI

Too Much. Too Little. Too Dirty. The new State of Global Water Resources Report (WMO, 2024) is a wake-up call for investors. Its conclusion is blunt: “Too much, too little, or too polluted water is undermining lives, ecosystems, and economies.” And the UK is right in the middle of it. - Too Much: 2024 was among the wettest years, with rainfall up to 30% above average. Floods paralysed parts of northern and western Europe, and the “1-in-100-year” events now arrive every few years. - Too Little: Even after record rain, summer droughts hit again. The WMO calls it “hydrological whiplash”, violent swings between flood and drought that drain productivity, hammer infrastructure, and distort valuations. - Too Dirty: Water quality across Europe continues to deteriorate. Globally, only 50% of monitoring stations report “good” water quality — proof that pollution is now a systemic drag on growth and productivity. So What? - Government: Half the world still lacks early-warning systems for floods and droughts. The UK isn’t immune. Water must now be treated as critical national infrastructure, not just a utility. - Investors: Water is now a systemic financial risk. In 2024 alone, water-related disasters caused $200 billion in global losses and displaced tens of millions. -Companies: WMO data shows 60% of global river basins experienced abnormal flows last year. That’s code for volatility in supply chains, operations, and insurance exposure. If your suppliers rely on stable rainfall or clean water, they’re already in the red zone. The Call to Action A growing number of UK asset owners have found water to be their most significant nature-related risk. As CDP noted last year, water remains invisible and unpriced — most portfolios still ignore it. For asset owners, consultants, and trustees — it’s time to run your TNFD analysis: • Integrate water resilience into climate and nature frameworks. • Stress-test portfolios for floods, droughts, and water-quality shocks. • Engage portfolio companies — equities and bonds — on exposure and resilience plans. • Consider allocating to natural infrastructure — wetlands, river restoration, peatland protection — where returns come from avoided losses and stable yield. 📘 Read the full State of Global Water Resources 2024 report here: World Meteorological Organization #WaterRisk #ClimateResilience #AssetOwners #LGPS #InvestmentConsultants #Infrastructure #NaturalCapital #ImpactInvesting

On fundraising and pitching (for early stage enterprise AI startups) Something that keeps coming up in my conversations with founders is how much pitching and fundraising has changed in the AI era. Building is cheap now. You can come up with an idea, build it, and test it at very little cost (unless you're doing something capital-intensive like robotics). It's great time to be building companies but the bar has moved for fundraising. Even at the earliest stages, investors expect more traction because getting to a working product is no longer the hard part. So how do you pitch? 1. Cover the basics You'll be surprised to know how many people don't cover the basics in a pitch meeting. What are you building? Who is building it? Why and why now? What's the vision for what this could become? How much are you raising and what will you do with it? Do you have customers? Do they love the product? How do you know? Will you eventually make money? How? You can add more on pilots, sales cycles, etc., but get these out of the way first. 2. Then mention your spikes I think that most investors invest in spikes, and your ability to <waves hands> figure it all out. Spikes can vary - maybe you've been obsessed with this space since childhood, maybe you have an incredible team, maybe you're just really good at one specific thing. Internalize what you or your company are uniquely good at, and lean into that. Trying to check every box often dilutes the thing that makes you interesting. And you’d be surprised how far that gets you instead of a long list of advisors or flashy names. That’s ok too, but you’re selling yourself short. 3. A few tensions worth addressing (for enterprise AI) And now for the questions that annoy every (enterprise) AI startup founder (I’m sorry but you will get asked these at some point anyway, so you might as well prep for it) a. ⁠“What if OpenAI builds this?" aka what is your wedge?  Could be community, trust, physical goods or hardware. figure out why you're differentiated and defensible b. “Your sales cycles are long" Focus on stickiness, ACV, upsell and cross-sell potential. c. “You have a few LOIs, some pilots, some monthly contracts. What counts as traction?" Walk through what meaningful traction is to you and be clear on what it is rather than what you want it to be. A 3-month discounted pilot with clear graduation terms? That's traction to me. 4. Specificity over polish Some of the good pitches I see are usually not the most polished (I'd be surprised if they were at this stage). But they are able to convey the specifics clearly (on customer, problem, team, outcomes) and signal that this founder is going to figure it all out. Note that I haven’t really talked about TAM or competition. Hot take - at the earliest stages, that stuff matters less than you think. If you're preparing for a raise and want to pressure-test your narrative, I'd love hear from you. Send me a note at aarthi@schemavc.com.

✈️ PINNs in Aerospace Engineering: Applications, Challenges, and Outlook Physics-Informed Neural Networks (PINNs) offer a promising approach for solving PDEs in aerospace problems using a mesh-free framework, integrating data with explicit physical knowledge during training. This document presents a technical overview focused on: 🔹 Comparison between PINNs, traditional numerical methods (CFD/FEM), and purely data-driven models, highlighting: - Efficiency with sparse data - Guaranteed physical consistency - Generalization and extrapolation capabilities 🔹 Applications in aerospace engineering, including: - Aerodynamic and structural optimization - Advanced materials modeling - SHM and predictive maintenance via Digital Twins - Parameter inference and governing equation discovery 🔹 Current challenges and research directions, such as: - Scalability (XPINNs, cPINNs, DPINNs) - Functional interpolation (TFC, Deep-TFC, X-TFC) - Generalization to arbitrary geometries (PIPN) - Certification of models in regulated environments 🔹 Future outlook: - Integration with Digital Twins and hybrid Physics-AI architectures - Methodological standardization - Improved robustness, efficiency, and interpretability This content is intended for researchers, engineers, and professionals applying AI to complex physical systems. #PINNs #PhysicsInformedNeuralNetworks #AerospaceEngineering #DigitalTwins #InverseProblems #DeepLearning #CFD #TFC #AI4Science #ModelBasedAI #SHM #StructuralOptimization #ScientificMachineLearning

Networking for Introverts Lessons from my Singapore Management University workshop Networking advice often sounds the same: "Speak up, hand out business cards, follow up within 24 hours." Useful, yes—but let's take it a step further. Here’s the advice that’s helped even the shyest professionals stand out: 1️⃣ Do Recon on Attendees (Without Being Creepy) Before events, research key attendees or speakers on LinkedIn. Note shared interests or recent achievements to weave into conversations. And if Wi-Fi is spotty at events? Save profiles offline for reference. Being prepared makes even the most introverted among us feel in control. 2️⃣ Ask Thoughtful, Unexpected Questions After building some quick rapport, try asking: "What’s the most exciting thing happening in your industry right now?" "If you weren’t in [current role], what would you be doing?" It shows genuine curiosity and sparks meaningful conversations. 3️⃣ It’s not about you—it’s about them. Practice active listening to uncover their hidden professional needs. Ask questions like, “What’s been your biggest challenge this year?” and offer insights or solutions. Giving value leaves a lasting impression. Networking doesn’t have to feel forced or superficial. Introverts can thrive by leveraging their strengths—preparation, thoughtfulness, and a genuine desire to connect. What’s one unorthodox networking tip that’s worked for you? Share it in the comments! 👇

Holi, Colors & AI Conversations: The Real Challenges in Model Customization 🎨🤖 After an amazing Holi celebration in our society, filled with vibrant colors and laughter, I caught up with a few friends from the industry in the afternoon. What started as a casual discussion quickly turned into an insightful conversation about AI/ML model customization and the biggest challenge—Data Integrity. One of the key questions that came up was: "Mihir, what do you think is the biggest roadblock in AI model customization?" As I unpacked the topic, we identified seven major challenges that organizations face when customizing AI models: 1️⃣ Data Privacy & Security 🔐 AI thrives on data, but how do we ensure privacy, security, and compliance with regulations (GDPR, CCPA) while still leveraging data effectively? Striking this balance remains a tough challenge. 2️⃣ Data Quality & Preparation 📊 AI models are only as good as the data they learn from. Inconsistent, biased, or poor-quality data can lead to unreliable results, making data cleansing and preprocessing non-negotiable. 3️⃣ Measuring Real Impact ("As-Is" vs. AI-Driven) 📈 How do we objectively measure AI’s success? Comparing AI-powered decisions with existing processes helps assess whether the model is truly adding value or just making things more complex. 4️⃣ Developer Talent & Skills in Generative AI 🧑💻 AI is evolving rapidly, but do we have enough skilled engineers who can bridge the gap between technical AI models and business impact? The talent shortage in this space is real. 5️⃣ Access to Real-Time Data ⏳ While historical data is important, real-time insights drive better decisions. The challenge is integrating and processing real-time data efficiently for AI models to generate accurate, dynamic outputs. 6️⃣ Handling Diverse Data Structures 🔄 AI models don’t just work with clean, structured databases. They need to interpret text, images, videos, voice, sensor data, and more. Managing this complexity without losing context is a constant challenge. 7️⃣ Keeping Up with Rapid Model Changes ⚡ AI models are not static—they evolve. Continuous learning, retraining, and adapting to new data patterns require robust pipelines, automation, and governance, which many companies struggle to implement effectively. By the end of the discussion, one thing was clear: AI/ML customization is not just about building models—it’s about integrating them into a trusted, scalable, and high-impact ecosystem. Would love to hear from my network—which of these challenges resonate with you the most? How are you addressing them? Let’s keep the conversation going! 🚀 #AI #MachineLearning #DataIntegrity #GenAI #ModelCustomization #HoliVibes #TechTalks #DataQuality #AIChallenges