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

🏗️ The construction industry is the largest in the world by dollars spent, but one of the slowest in productivity gains and tech adoption. Construction-related spending is about 13% of global GDP. Over the past few decades, the construction industry's productivity has only improved by about 1% -- lagging behind other sectors like manufacturing and information technology. Construction is hard and productivity lags behind other industries because projects are mostly one-off projects that requires different materials, skills, and tools. Because each build is a unique challenge, the industry doesn't enjoy the economies of scale seen other industries. Construction is also heavily reliant on manual labor and processes, and there's a historic shortage of that labor: the workforce is short north of 500,000 jobs. Because the construction industry is fragmented and manual, tech's high cost, safety concerns, and the need to reskill an already tight labor market, construction has been slow to adopt new technologies. However, new construction tech is needed to: - Increase the sector's productivity - Improve the accuracy of jobs and avoid costly reworks - Increase worker safety Some solutions include: 🦾 Construction automation (robotics) 👷♀️ Labor up-skilling 💻 Gen AI tools to boost efficiency More to come on this topic—something Fifth Wall is very interested in. #construction #proptech #realestate

Excited to announce the humanitarian innovations that will run through the joint WFP Innovation Accelerator and Google for Startups Humanitarian Ventures Accelerator. The program aims to develop and scale 🔟 groundbreaking World Food Programme solutions anchored in technological innovation, bringing us closer to a hunger-free future. Here the 10 teams (full description in link) - which innovation do you find most exciting? 1) SHAPES SHAPES enables WFP Country Offices to customize scenarios involving various shocks (like extreme weather or economic inflation) and assistance strategies (such as cash transfers) 2)GeoTar GeoTar is a user-friendly geospatial vulnerability profiling and targeting tool for decision-makers. This results in a 30 percent increase in targeting accuracy and savings of US$300,000 for each country using it 3)ETC Chatbot The ETC Chatbot serves as a tool for people impacted by disasters. It helps them conveniently access up-to-date information about essential services and offers ongoing assistance when crises strike. It also helps collect critical data and coordinate emergency responses across various UN agencies. So far, the ETC Chatbot has supported over 100,000 vulnerable people, giving them access to life-saving information. 4)R2C2 The Rapid Response Connectivity Carrier (R2C2) is a 90-metre communications tower and tethered drone that improves coordination of emergency response by enhancing real-time communication. A cable runs between the tower and a LTE (4G) transmitter drone that flies 24 hours a day, 5)Machine learning for drought seasonal forecast and anticipatory action This solution uses AI and machine learning to improve predictions of weather risks for vulnerable people, allowing for quick warnings and timely help. 6) DARTS Data Assurance and Reconciliation Tool Simplified is a user-friendly web app backed up by sophisticated machine learning modules. This system enables WFP to implement controls on large cash transfer data and eases the process of comparing transactions and activity, which is vital in cash assistance programmes. 7)PRISMA Prisma is an supply chain “control tower”. It gives WFP teams in different countries powerful tools for understanding the current context (descriptive analytics), predicting future scenarios (predictive analytics), and suggesting the best actions to take (prescriptive analytics). 8)Route The Meals Route The Meals applies mathematical models to simplify and improve the planning of delivery paths and warehouse placements. 9)Global Upstream Planning It is a system that models WFP’s global supply chain network. 10) UN AI Mobility . The UN Booking Hub, managed by WFP, is a worldwide single platform resource for many UN agencies. It streamlines critical travel and lodging services, including guesthouses, UN aircraft, and UN vehicles. This tool ensures the safe movement of international humanitarian groups when on emergency missions. #innovation #Technology #TechforGood

🏗️ 𝙏𝙝𝙚 𝘾𝙝𝙖𝙡𝙡𝙚𝙣𝙜𝙚 𝙤𝙛 𝘾𝙤𝙣𝙨𝙩𝙧𝙪𝙘𝙩𝙞𝙣𝙜 𝘽𝙪𝙞𝙡𝙙𝙞𝙣𝙜𝙨 𝙬𝙞𝙩𝙝 𝙐𝙣𝙤𝙧𝙩𝙝𝙤𝙙𝙤𝙭 𝘿𝙚𝙨𝙞𝙜𝙣𝙨 🏗️ In today’s world of architectural innovation, unorthodox building designs are becoming more common, pushing the boundaries of what’s possible in construction. While these designs are visually stunning and unique, they come with their own set of challenges that require advanced planning, skill, and technology. Here are a few key challenges faced when constructing non-conventional buildings: 1️⃣ Structural Integrity: Designing and ensuring the structural stability of unusual shapes and curves requires advanced engineering techniques, innovative materials, and precise calculations. 2️⃣ Complex Formwork & Shuttering: For unconventional designs, the formwork required is often highly customized, making it more complex and costly to assemble and dismantle. 3️⃣ Material Sourcing: Non-standard designs often require specialized materials or custom-built elements that may not be readily available, increasing lead times and procurement complexity. 4️⃣ Precision in Execution: Achieving the exact specifications of intricate designs requires extreme precision in measurements, coordination between teams, and the use of advanced construction technologies like BIM or laser scanning. 5️⃣ Skilled Workforce: Unorthodox designs demand specialized skills from the workforce, including experience with unique construction techniques and familiarity with cutting-edge equipment. 6️⃣ Cost & Time Overruns: These complex designs can lead to budget and schedule overruns if not meticulously planned and managed. The need for trial-and-error in some cases adds further uncertainty. Despite these challenges, the satisfaction of delivering an iconic structure makes the effort worthwhile. It's where creativity meets engineering, and when done right, these buildings stand as a testament to innovation in the construction industry. For more insights on construction challenges, Follow Civil Engineer DK #UnorthodoxDesign #InnovativeConstruction #CivilEngineering #ArchitecturalChallenges #StructuralEngineering #ConstructionManagement #ComplexStructures #BuildingInnovation

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

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

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

Stop waiting for Microsoft or Google’s career page. They might just find you at a hackathon. 𝐄𝐩𝐢𝐬𝐨𝐝𝐞 𝟑/𝟏𝟎 – 𝐇𝐚𝐜𝐤𝐚𝐭𝐡𝐨𝐧𝐬 & 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧𝐬 You may have been surprised by the hook line and wondering how? In college, I used to believe hackathons and coding competitions were important, But only for adding certificates to my resume, gaining experience and adding achievement. But I was wrong, When one of my friends got selected at Amazon through a competition called Amazon Wow. That is when I realized hackathons and coding contests are not just about experience, They have become direct hiring funnels. Today, many companies prefer hiring through hackathons instead of waiting for job applications. Here are some well-known competitions and the companies behind them: 1. Big Tech like Google (Code Jam, Kick Start), Microsoft (Imagine Cup), Amazon (HackOn, Alexa Prize). 2. Large MNCs like Flipkart (GRiD), Amazon India (HackOn), Infosys (HackWithInfy), TCS (CodeVita). 3. Finance giants like JPMorgan (Code for Good), Goldman Sachs (GS Hackathon), Morgan Stanley (Code to Give). 4. Startups like Razorpay, Zomato, and Unstop-run challenges now hire directly from leaderboards. That is why are hackathons the new hiring trend? Because they reveal what no resume ever can. In just a few hours or days, companies can see: ➡️ How you think under pressure ➡️ How you collaborate in a team ➡️ How you turn an idea into something real Opportunities do not always knock at your door, Sometimes, they come disguised as a weekend hackathon you almost skipped. So if you are preparing for a tech role, do not just practice alone in silence, Show up at hackathons and compete in coding contests. Because your next offer letter may not come from the placement cell, It might just come from the hackathon you decide to join this month. #Hackathons #CodingCompetitions #Google #Microsoft #FlipkartGRID #CodeVita #HackWithInfy #CareerGrowth #SatyamSeries

The real challenge for organizations building with AI is learning to develop software with a non-deterministic approach. Like quantum computing, it requires thinking under a radically different paradigm. Key implications: - Embracing uncertainty in AI outputs - Focusing on outcomes over processes - Managing bias and ethical considerations - Designing for continuous learning and adaptation - Fostering human-AI collaboration This shift demands new skills, methodologies, and frameworks. Organizations that adapt will be better positioned to harness the full potential of generative AI. As we integrate AI into our development processes, we must: - Design systems that handle variability - Allow AI to find solutions within defined parameters - Continuously evaluate and mitigate biases - Create adaptive applications that evolve over time - Redefine the role of developers as AI collaborators How is your organization tackling this paradigm shift? What challenges and opportunities do you see in this new approach to software development? #ArtificialIntelligence #SoftwareDevelopment #TechInnovation