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Luke Combs and Tracy Chapman performed 'Fast Car' at the Grammys for the first time together. I hope the duet offers deeper meaning for our country.

Tracy Chapman, Luke Combs drove me to tears with 'Fast Car' Grammys duet. It's a good thing.

One day after the 2024 Grammy Awards, Tracy Chapman's "Fast Car" rose to No. 1 on the iTunes charts. On Sunday, Chapman performed the hit with Luke Combs, who released a cover of the song in April 2023.

Tracy Chapman’s ‘Fast Car’ Hits No. 1 on iTunes After Duet With Luke Combs at 2024 Grammys

Artificial Intelligence (AI) has revolutionised the way we work, pushing the boundaries of what is possible in the realm of software development. As the demand for more efficient and collaborative coding solutions grows, developers are turning to innovative tools like GitHub Copilot and Duet AI. In this post, we’ll explore the features, strengths, and potential use cases of these two powerful AI-driven coding assistants. GitHub Copilot GitHub Copilot, developed by GitHub in collaboration with OpenAI, is a groundbreaking AI-powered coding assistant that integrates seamlessly with popular code editors like Visual Studio Code. Powered by OpenAI’s GPT (Generative Pre-trained Transformer) technology, Copilot assists developers by suggesting entire lines or blocks of code based on the context of the code being written. It supports a wide range of programming languages, making it versatile for various development projects. Strengths of GitHub Copilot Code Autocompletion: Copilot offers real-time suggestions as developers type, significantly speeding up the coding process Language Agnostic: With support for multiple programming languages, Copilot caters to the diverse needs of developers working on different projects Context-Aware Suggestions: Copilot understands the context of the code being written and generates relevant suggestions, reducing the need for manual corrections. Use Cases for GitHub Copilot Rapid Prototyping: Copilot is excellent for quickly prototyping ideas, allowing developers to focus on the logic rather than the syntax Learning and Teaching: It serves as a valuable learning tool for beginners, providing instant feedback and guidance on coding practices. Duet AI Duet AI is another AI coding assistant that operates on a collaborative model. It leverages AI to enhance the coding experience by offering suggestions and automating repetitive tasks. Unlike Copilot, Duet AI is a standalone application that can be used alongside any code editor. Strengths of Duet AI Collaborative Coding: Duet AI is designed with collaboration in mind, enabling multiple developers to work on the same codebase simultaneously Versatility: It supports a wide array of languages and frameworks, making it adaptable to various development environments Customisation: Developers can customise Duet AI to align with their coding style and preferences, tailoring the AI assistance to their specific needs. Use Cases for Duet AI Team Collaboration: Duet AI is ideal for teams working on complex projects, promoting a collaborative coding environment Codebase Maintenance: It can assist in refactoring code and maintaining a clean and consistent codebase. Both GitHub Copilot and Duet AI represent significant strides in the field of AI-driven coding assistance. GitHub Copilot excels in rapid prototyping and individual development scenarios, providing intelligent autocompletion and context-aware suggestions. On the other hand, Duet AI stands out for its collaborative features, making it a powerful tool for team-based coding projects. Ultimately, the choice between Copilot and Duet AI depends on the specific needs of developers and the nature of the projects they are working on. As AI continues to evolve, these tools are poised to become indispensable allies in the journey towards more efficient and collaborative software development. Also appears . here

Unveiling the Power of AI: A Deep Dive into Copilot and Duet AI

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Kiriaki Frangias;
(2) Andrew Lin;
(3) Ellen Vitercik;
(4) Manolis Zampetakis. Table of Links Abstract and Introduction Warm-up: Agents with known equal disutility Agents with unknown disutilites Experiments Conclusions and future directions, References A Summary of notation B Omitted proofs from Section 2 C Omitted proofs from Section 3 D Additional Information about Experiments 5 Conclusions and future directions We studied the multifaceted problem of designing a crowdsourcing mechanism that efficiently and accurately ranks a set of items using pairwise comparisons from rational agents. We based our approach on the classic principal-agent model from contract theory. To distribute comparisons among agents, we used an unexpected connection to the social golfer problem which allowed us to simultaneously minimize the agents’ workload and ensure enough agents evaluate each comparison. We showed that by optimizing the payment mechanisms, the principal can incentivize enough agents to exert effort, ensuring our algorithm recovers the ground-truth ranking. Our experiments showed that even when noise is added to our original model, our method consistently aids the principal in retrieving the accurate ground-truth ordering. llude, which allowed us to assign the same set of verified comparisons to all agents. How should our mechanism change if agents can collude? Moreover, we assumed that agents who do not exert effort will return comparisons consistent with a uniformly-random permutation of the items. What other models of agent behavior can we study under this framework? Finally, as is standard in contract theory, we assumed that the principal knows the probability π that an effortful agent is good. How should we handle the case where π is unknown? References [1] Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, and Gergely V. Zaruba. Crowdsourcing pareto-optimal object finding by pairwise comparisons. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM), 2015. [2] Ralph Allan Bradley and Milton E Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39(3/4):324–345, 1952. [3] Yang Cai, Constantinos Daskalakis, and Christos Papadimitriou. Optimum statistical estimation with strategic data sources. In Conference on Learning Theory (COLT), 2015. [4] Xi Chen, Paul N Bennett, Kevyn Collins-Thompson, and Eric Horvitz. Pairwise ranking aggregation in a crowdsourced setting. In Proceedings of the International Conference on Web Search and Data Mining, pages 193–202, 2013. [5] Yuxin Chen and Changho Suh. Spectral MLE: Top-k rank aggregation from pairwise comparisons. In International Conference on Machine Learning (ICML), 2015. [6] Paul Duetting, Tomer Ezra, Michal Feldman, and Thomas Kesselheim. Combinatorial contracts. In Symposium on Foundations of Computer Science (FOCS), 2022. [7] Anindya Ghose, Panagiotis G Ipeirotis, and Beibei Li. Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3):493–520, 2012. [8] John Guiver and Edward Snelson. Bayesian inference for plackett-luce ranking models. In International Conference on Machine Learning (ICML), 2009. [9] Stephen Guo, Aditya Parameswaran, and Hector Garcia-Molina. So who won? dynamic max discovery with the crowd. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, page 385–396, New York, NY, USA, 2012. Association for Computing Machinery. ISBN 9781450312479. doi: 10.1145/2213836.2213880. URL https://doi.org/10.1145/2213836.2213880. [10] Guru Guruganesh, Jon Schneider, and Joshua Wang. Contracts under moral hazard and adverse selection. In ACM Conference on Economics and Computation (EC), 2021. [11] R. Jurca and B. Faltings. Mechanisms for making crowds truthful. Journal of Artificial Intelligence Research, 34:209–253, mar 2009. doi: 10.1613/jair.2621. URL https://doi.org/10.1613%2Fjair.2621. [12] Radu Jurca and Boi Faltings. Robust incentive-compatible feedback payments. In Workshop on Trading Agent Design and Analysis, 2006. [13] Radu Jurca and Boi Faltings. Minimum payments that reward honest reputation feedback. In ACM Conference on Economics and Computation (EC), 2006. [14] Jiawen Kang, Zehui Xiong, Dusit Niyato, Dongdong Ye, Dong In Kim, and Jun Zhao. Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory. IEEE Transactions on Vehicular Technology, 68(3):2906–2920, 2019. [15] Ngai Meng Kou, Yan Li, Hao Wang, Leong Hou U, and Zhiguo Gong. Crowdsourced top-k queries by confidence-aware pairwise judgments. Proceedings of the 2017 ACM International Conference on Management of Data, 2017. URL https://api.semanticscholar.org/CorpusID:36647621. [16] Jean-Jacques Laffont and David Martimort. Moral Hazard: The Basic Trade-Offs, pages 145–186. Princeton University Press, 2002. URL http://www.jstor.org/stable/j.ctv7h0rwr.8. [17] Shili Lin. Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):555–570, 2010. [18] Tyler Lu and Craig Boutilier. Learning mallows models with pairwise preferences. In International Conference on Machine Learning (ICML), 2011. [19] C. L. Mallows. Non-null ranking models. i. Biometrika, 44:114–130, 1957. URL https://api.semanticscholar.org/CorpusID:121527283. [20] Fei Mi and Dit-Yan Yeung. Probabilistic graphical models for boosting cardinal and ordinal peer grading in moocs. In AAAI Conference on Artificial Intelligence, 2015. [21] Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, and Daphne Koller. Tuned models of peer assessment in moocs. In Proceedings of The 6th International Conference on Educational Data Mining (EDM), 2013. [22] R. L. Plackett. The Analysis of Permutations. Journal of the Royal Statistical Society Series C, 24(2):193–202, June 1975. doi: 10.2307/2346567. URL https://ideas.repec.org/a/bla/jorssc/v24y1975i2p193-202.html. [23] Karthik Raman and Thorsten Joachims. Methods for ordinal peer grading. In International Conference on Knowledge Discovery and Data Mining (KDD), 2014. [24] Karthik Raman and Thorsten Joachims. Bayesian ordinal peer grading. In Proceedings of the ACM Conference on Learning at Scale, 2015. [25] Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. Learning from crowds. Journal of machine learning research, 11(4), 2010. [26] Nihar Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, and Martin Wainwright. Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2015. [27] Nihar B Shah, Joseph K Bradley, Abhay Parekh, Martin Wainwright, and Kannan Ramchandran. A case for ordinal peer-evaluation in moocs. In NIPS Workshop on Data-Driven Education, 2013. [28] Louis Leon Thurstone. The method of paired comparisons for social values. The Journal of Abnormal and Social Psychology, 21:384–400, 1927. URL https://api.semanticscholar.org/CorpusID:144595330. [29] Markus Triska. Solution Methods for the Social Golfer Problem. PhD thesis, Vienna University of Technology, March 2008. Available from https://www.metalevel.at/mst.pdf. [30] Kristi Tsukida, Maya R Gupta, et al. How to analyze paired comparison data. Department of Electrical Engineering University of Washington, Tech. Rep. UWEETR-2011-0004, 1, 2011. [31] Bo Waggoner and Yiling Chen. Output agreement mechanisms and common knowledge. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1): 220–226, Sep. 2014. doi: 10.1609/hcomp.v2i1.13151. URL https://ojs.aaai.org/index.php/HCOMP/article/view/13151. [32] Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, and Michael I. Jordan. The sample complexity of online contract design. In ACM Conference on Economics and Computation (EC), 2023. This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. This paper is available on arxiv under CC 4.0 license. Authors : Authors (1) Kiriaki Frangias; (2) Andrew Lin; (3) Ellen Vitercik; (4) Manolis Zampetakis. Table of Links Abstract and Introduction Abstract and Introduction Warm-up: Agents with known equal disutility Warm-up: Agents with known equal disutility Agents with unknown disutilites Agents with unknown disutilites Experiments Experiments Conclusions and future directions, References Conclusions and future directions, References A Summary of notation A Summary of notation B Omitted proofs from Section 2 B Omitted proofs from Section 2 C Omitted proofs from Section 3 C Omitted proofs from Section 3 D Additional Information about Experiments D Additional Information about Experiments 5 Conclusions and future directions We studied the multifaceted problem of designing a crowdsourcing mechanism that efficiently and accurately ranks a set of items using pairwise comparisons from rational agents. We based our approach on the classic principal-agent model from contract theory. To distribute comparisons among agents, we used an unexpected connection to the social golfer problem which allowed us to simultaneously minimize the agents’ workload and ensure enough agents evaluate each comparison. We showed that by optimizing the payment mechanisms, the principal can incentivize enough agents to exert effort, ensuring our algorithm recovers the ground-truth ranking. Our experiments showed that even when noise is added to our original model, our method consistently aids the principal in retrieving the accurate ground-truth ordering. llude, which allowed us to assign the same set of verified comparisons to all agents. How should our mechanism change if agents can collude? Moreover, we assumed that agents who do not exert effort will return comparisons consistent with a uniformly-random permutation of the items. What other models of agent behavior can we study under this framework? Finally, as is standard in contract theory, we assumed that the principal knows the probability π that an effortful agent is good. How should we handle the case where π is unknown? References [1] Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, and Gergely V. Zaruba. Crowdsourcing pareto-optimal object finding by pairwise comparisons. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM), 2015. [2] Ralph Allan Bradley and Milton E Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39(3/4):324–345, 1952. [3] Yang Cai, Constantinos Daskalakis, and Christos Papadimitriou. Optimum statistical estimation with strategic data sources. In Conference on Learning Theory (COLT), 2015. [4] Xi Chen, Paul N Bennett, Kevyn Collins-Thompson, and Eric Horvitz. Pairwise ranking aggregation in a crowdsourced setting. In Proceedings of the International Conference on Web Search and Data Mining, pages 193–202, 2013. [5] Yuxin Chen and Changho Suh. Spectral MLE: Top-k rank aggregation from pairwise comparisons. In International Conference on Machine Learning (ICML), 2015. [6] Paul Duetting, Tomer Ezra, Michal Feldman, and Thomas Kesselheim. Combinatorial contracts. In Symposium on Foundations of Computer Science (FOCS), 2022. [7] Anindya Ghose, Panagiotis G Ipeirotis, and Beibei Li. Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3):493–520, 2012. [8] John Guiver and Edward Snelson. Bayesian inference for plackett-luce ranking models. In International Conference on Machine Learning (ICML), 2009. [9] Stephen Guo, Aditya Parameswaran, and Hector Garcia-Molina. So who won? dynamic max discovery with the crowd. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, page 385–396, New York, NY, USA, 2012. Association for Computing Machinery. ISBN 9781450312479. doi: 10.1145/2213836.2213880. URL https://doi.org/10.1145/2213836.2213880. [10] Guru Guruganesh, Jon Schneider, and Joshua Wang. Contracts under moral hazard and adverse selection. In ACM Conference on Economics and Computation (EC), 2021. [11] R. Jurca and B. Faltings. Mechanisms for making crowds truthful. Journal of Artificial Intelligence Research, 34:209–253, mar 2009. doi: 10.1613/jair.2621. URL https://doi.org/10.1613%2Fjair.2621. [12] Radu Jurca and Boi Faltings. Robust incentive-compatible feedback payments. In Workshop on Trading Agent Design and Analysis, 2006. [13] Radu Jurca and Boi Faltings. Minimum payments that reward honest reputation feedback. In ACM Conference on Economics and Computation (EC), 2006. [14] Jiawen Kang, Zehui Xiong, Dusit Niyato, Dongdong Ye, Dong In Kim, and Jun Zhao. Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory. IEEE Transactions on Vehicular Technology, 68(3):2906–2920, 2019. [15] Ngai Meng Kou, Yan Li, Hao Wang, Leong Hou U, and Zhiguo Gong. Crowdsourced top-k queries by confidence-aware pairwise judgments. Proceedings of the 2017 ACM International Conference on Management of Data, 2017. URL https://api.semanticscholar.org/CorpusID:36647621. [16] Jean-Jacques Laffont and David Martimort. Moral Hazard: The Basic Trade-Offs, pages 145–186. Princeton University Press, 2002. URL http://www.jstor.org/stable/j.ctv7h0rwr.8. [17] Shili Lin. Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):555–570, 2010. [18] Tyler Lu and Craig Boutilier. Learning mallows models with pairwise preferences. In International Conference on Machine Learning (ICML), 2011. [19] C. L. Mallows. Non-null ranking models. i. Biometrika, 44:114–130, 1957. URL https://api.semanticscholar.org/CorpusID:121527283. [20] Fei Mi and Dit-Yan Yeung. Probabilistic graphical models for boosting cardinal and ordinal peer grading in moocs. In AAAI Conference on Artificial Intelligence, 2015. [21] Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, and Daphne Koller. Tuned models of peer assessment in moocs. In Proceedings of The 6th International Conference on Educational Data Mining (EDM), 2013. [22] R. L. Plackett. The Analysis of Permutations. Journal of the Royal Statistical Society Series C, 24(2):193–202, June 1975. doi: 10.2307/2346567. URL https://ideas.repec.org/a/bla/jorssc/v24y1975i2p193-202.html. [23] Karthik Raman and Thorsten Joachims. Methods for ordinal peer grading. In International Conference on Knowledge Discovery and Data Mining (KDD), 2014. [24] Karthik Raman and Thorsten Joachims. Bayesian ordinal peer grading. In Proceedings of the ACM Conference on Learning at Scale, 2015. [25] Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. Learning from crowds. Journal of machine learning research, 11(4), 2010. [26] Nihar Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, and Martin Wainwright. Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2015. [27] Nihar B Shah, Joseph K Bradley, Abhay Parekh, Martin Wainwright, and Kannan Ramchandran. A case for ordinal peer-evaluation in moocs. In NIPS Workshop on Data-Driven Education, 2013. [28] Louis Leon Thurstone. The method of paired comparisons for social values. The Journal of Abnormal and Social Psychology, 21:384–400, 1927. URL https://api.semanticscholar.org/CorpusID:144595330. [29] Markus Triska. Solution Methods for the Social Golfer Problem. PhD thesis, Vienna University of Technology, March 2008. Available from https://www.metalevel.at/mst.pdf. [30] Kristi Tsukida, Maya R Gupta, et al. How to analyze paired comparison data. Department of Electrical Engineering University of Washington, Tech. Rep. UWEETR-2011-0004, 1, 2011. [31] Bo Waggoner and Yiling Chen. Output agreement mechanisms and common knowledge. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1): 220–226, Sep. 2014. doi: 10.1609/hcomp.v2i1.13151. URL https://ojs.aaai.org/index.php/HCOMP/article/view/13151. [32] Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, and Michael I. Jordan. The sample complexity of online contract design. In ACM Conference on Economics and Computation (EC), 2023. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Algorithmic Contract Design for Crowdsourced Ranking: Conclusions, Future Directions, and References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 7 Conclusion We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. Our method scales to millions of mutations with minimal computational overhead and runs in a fraction of the time it would take prior works. 8 Acknowledgements This work is supported by the NSF AI Institute for Foundations of Machine Learning (IFML). References [1] Randomization of genes by pcr mutagenesis. Genome research 2(1), 28–33 (1992) 2 [2] Adams, J.P., Brown, M.J., Diaz-Rodriguez, A., Lloyd, R.C., Roiban, G.D.: Biocatalysis: A Pharma Perspective. 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Predicting a Protein’s Stability
under a Million Mutations: Conclusion, Acknowledgement & References

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10 Best Exciting TikTok Video Ideas To Make Your Videos Stand Out

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Do you find yourself exhausted from wrestling with intricate data for hours on end? Are you yearning for AI that comprehends beyond mere words? Google's Gemini AI emerges as a transformative force, revolutionizing the landscape of AI model development and integration. Upon completion, in various sizes and functionalities, allowing you to select the ideal match for your requirements, whether tackling extensive projects or concentrated assignments. This guarantees that organizations can opt for the model that perfectly aligns with their objectives, be it for expansive applications or specific tasks. Google will present Gemini AI What exactly is Google Gemini AI? Google Gemini AI is an advanced platform harnessing deep learning algorithms to produce remarkably lifelike and inventive results. It propels AI models forward, enhancing their flexibility and efficiency through continuous learning from fresh data. Diverse iterations Offered in – Google Gemini AI accommodates the distinct requirements of individuals and businesses. Whether it's optimizing processes or conducting advanced data analysis, Gemini ensures there's a suitable version tailored to your needs. three variations – Ultra, Pro, and Nano The Gemini AI Model Central to Google's generative AI platform is the Gemini AI model. Utilizing cutting-edge deep learning algorithms, it assimilates extensive datasets to produce authentic outputs. With its adaptability, the model can excel across diverse domains such as natural language processing (NLP), image generation, text composition, music creation, and beyond. Key Features Simplified: Human-like Conversational Proficiency: Gemini demonstrates remarkable conversational abilities akin to humans, . ideal for chatbots and virtual assistants It provides an immersive, seamless, and organic conversational experience. Redefining benchmarks for human-like interactions within digital environments. Image understanding and interpretation: Gemini deciphers image content, responds to inquiries, and produces contextual text. It is exceptionally suited for image search and captioning, establishing pioneering benchmarks in AI. This amplifies user satisfaction and broadens the horizons of image-centric applications. Prolific code generation: Gemini excels in generating high-quality code across different programming languages. It is vital for optimizing coding workflows, guaranteeing both efficiency and precision. This innovation revolutionizes developers' approaches to software development. Data analytics expertise: Empowers users in the realms of data analytics and business intelligence. Critical for making data-driven decisions, unlocking the full potential of data. Serves as a catalyst for driving transformative advancements in analytics. Developer empowerment: Empowers developers to innovate by creating advanced AI applications and APIs. Nurtures a vibrant ecosystem for collaborative development of AI-driven solutions. Improves accessibility, fostering creativity and innovation. Real-time language translation: Gemini demonstrates excellence in , promoting global communication. real-time language translation It enriches user experiences by overcoming language barriers. A versatile tool catering to diverse communication requirements. Emotion recognition: Incorporates emotion recognition, elevating interaction sophistication. Applications span from enhancing user experience to sentiment analysis. Contributes to nuanced and responsive interactions between users and AI. Customizable AI applications: Provides developers with flexibility, enabling customization to address specific industry requirements. Adaptable across diverse sectors including healthcare, finance, and creative fields. Positions Gemini as a dynamic force across various professional domains. Continuous learning and adaptation: Gemini's continuous learning dynamically adapts based on user interactions. It learns from experience, ensuring relevance and ongoing advancement. Embodies a dynamic and adaptive nature, showcasing forefront AI capabilities. Multimodal focus: Gemini emphasizes , highlighting tools and API integrations. multimodal capabilities It aims to foster broader collaboration and drive future development in the AI landscape. Striving to enhance versatility through a multimodal approach. Tools and API integration: Gemini provides effortless integration of tools and APIs, ensuring a versatile AI experience. It prioritizes collaborative environments by facilitating seamless integration processes. Enhances the overall user experience through accessible and interconnected tools. 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Oil and Gas: In the oil and gas sector, Gemini optimizes operations by predicting equipment failures, improving safety protocols, and enhancing exploration strategies, resulting in cost savings and sustainability improvements. , Google Gemini AI represents a major advancement in artificial intelligence, providing a distinctive solution to intricate data dilemmas. It transcends mere word analysis, providing profound comprehension and valuable insights. With its unmatched generative AI capabilities, Gemini AI transforms AI model development and integration, heralding a future defined by innovation, effectiveness, and boundless opportunities. For customized insights tailored to your business needs, reach out to our team of AI experts. In conclusion Also seen here.

Gemini AI by Google: Characteristics, Applications, and Industrial Influence

Artificial Intelligence tools such as Bard, ChatGPT, and Bing Chat are the current big names in the category which is on the rise. Large Language Model (LLM) LLMs are trained on vast data sets to be able to communicate by using everyday human language as a chat prompt. Given the flexibility and potential of LLMs, companies are integrating them into many workflows inside the tech industry to make our lives better and easier. One of the main AI-workflow integrations is an auto-complete code plugin, providing the ability to foresee and generate matching code patterns in order to code faster and more efficiently. In the current market of AI code-generator tools, there are many players, including , , , , and more. This blog post outlines examples of common security pitfalls that developers face while using such tools. GitHub Copilot Amazon CodeWhisperer Google Cloud Code (Duet AI) Blackbox AI Code Generation The goal of this article is to raise awareness and emphasize that auto-generated code cannot be blindly trusted, and still requires a security review to avoid introducing software vulnerabilities. Note: some vendors suggest that their auto-complete tools may contain insecure code snippets. Many articles have already emphasized that auto-complete tools generate known vulnerabilities such as , SQL injection, and XSS. This post will highlight other vulnerability types that depend more on the code’s context, where there is a high probability for AI auto-complete to slip bugs into your code. IDOR AI models trained for code are usually trained on large code bases with the main mission of producing functioning code. Security and AI Code Generating Tools Many security issues have a known and defined way of mitigation without compromising the performance side (e.g. SQL injections could be avoided by not concatenating user input/parameters directly into the query) - and therefore, can be eradicated as there is no reason to write the code in an insecure manner. However, many security issues are which could be perfectly safe when implemented as internal functionality, while when facing external input from a client, become a vulnerability, and are therefore hard to distinguish through the AI’s learning dataset. context-dependent In such vulnerabilities, the specific usage of the code usually depends on other parts of the code and the application’s overall purpose. Here are some common use cases and examples that we tested, showcasing some of these types of vulnerabilities: Use Case: Fetching Files from User Input - Arbitrary File Read Use Case: Comparing Secret Tokens - Type Juggling/Coercion Use Case: Forgot Password Mechanism - Unicode Case Mapping Collisions Use Case: Generating Configuration File - Bad Security Practices Use Case: Configuration Objects - Inconsistencies Leading to Insecure Deserialization After seeing how the AI code generation tools handle the above cases, we tried to test their ability to create proper security filters and other mitigation mechanisms. Here are some common use cases and examples that we tested by deliberately requesting secure code: Secure Code Request Use Case: Reading Files - Avoiding Directory Traversal Secure Code Request - File Upload - Restricted Extensions List Use Case: Fetching Files from User Input - Arbitrary File Read Many applications include code that fetches a file back to the user based on a filename. Using directory traversal for reading arbitrary files on the server is a common method bad actors use to gain unauthorized information. It may seem obvious to sanitize file name/file path input coming from the user before fetching the file back, but we assume it is a hard task for an AI model trained on generic codebases -  this is because some of the datasets as part of their functionality (it may even downgrade performance or harm functionality) or are not harmed by unsanitized path as they are intended to be only for internal usage. do not need to use sanitized paths Let’s try to generate a basic file fetching function from user input, using Google Cloud Code - Duet AI auto-complete. We will let Duet AI auto-complete our code, based on our function & route names - AI Tool Suggestion (Google Cloud Code - Duet AI): As we can see in gray, the autocomplete suggestion is to use Flask’s “ ” function, which is Flask’s basic file-handling function. send_file But even Flask’s documentation states “ ”, meaning that while inserting user input directly to the “send_file” function the user will have the ability to read any file on the filesystem, for example by using directory traversal characters such as “../” in the filename. Never pass file paths provided by a user Proper Implementation: Replacing the “send_file” function with Flask’s safe “send_from_directory” function will mitigate the directory traversal risk. It will only allow fetching files from a specific hard-coded directory (“exports” in this case). Use Case: Comparing Secret Tokens - Type Juggling/Coercion In some programming languages, such as PHP and NodeJS, it is possible to make comparisons between two different types of variables and the program will do a behind the scenes to complete the action. type conversion Loose comparisons do not check the variable’s type behind the scenes and therefore are more prone to type juggling attack. However, the use of loose comparisons is not considered an insecure coding practice by itself - it depends on the context of the code. And again, it is difficult for AI models to tell the cases apart. The most common example of PHP type juggling is loose comparisons of secret tokens/hashes where user input which is read via a JSON request (which enables control of the input’s type) reaches a loose comparison (“ =”). AI Tool Suggestion (Amazon CodeWhisperer): ”) instead of a strict one (“ As it appears, CodeWhisperer generates loose comparison conditions in auto-complete suggestions: The loose comparison of the secret_token allows an adversary to bypass the comparison $data[“secret_token”] == “secret_token”. While submitting a JSON object with the value of “secret_token” being True (boolean), the (even on newer PHP versions). loose comparison result is True as well Even when “hinting” the tool (using a comment) to write the check “securely”, it did not generate a code snippet containing a strict comparison - Proper Implementation: Only when specifying a strict comparison (“===”) we are protected against type juggling attacks. Use Case: Forgot Password Mechanism - Unicode Case Mapping Collisions Vulnerabilities in the “Forgot Password” mechanism are very common in SaaS applications and were the root cause behind CVEs such as (Wordpress), (Django), and (GitLab). CVE-2017-8295 CVE-2019-19844 CVE-2023-7028 Specifically, a Unicode Case Mapping Collision vulnerability occurs when user input is either uppercased or lowercased in a comparison expression while its original value is also used in the code. Some different characters will result in the same char code when transformed. For example, “ß” and “SS” will both transform into “0x00DF” when uppercased. Such cases are usually used to bypass/mislead some security checks by tricking the comparison check and later using the original value which is different than expected. These cases are quite elusive to grasp, especially when many implementations are possible. A mechanism that’s particularly susceptible to this vulnerability type is the . It is common to normalize the user’s input into uppercase or lowercase (in email addresses or domain names) when performing the check in order to avoid unwelcome mismatches. AI Tool Suggestion (GitHub Copilot): forgot password mechanism As an example, taking a look at the Copilot-generated code for the forgot password function (below) shows that it is vulnerable to this issue. Copilot’s code first looks up the lowercased email address: Then, when following through with the rest of the process it suggests the following: As we can see, the autocomplete suggestion generates a random password and sends it to the user’s email address. However, it initially uses a lowercase version of the user’s email for fetching the user account, and then continues to use the original user’s email ‘as-is’ (without a lowercase conversion) as the target of the recovery email. For example, lets say the attacker owns the inbox for “miKe@example.com” (the “K” is a unicode character) and uses this email for the “Forgot Password” mechanism. The emails “miKe@example.com” and “mike@example.com”, are equivalent ‘as-is’, but after lowercase conversion they will be - Kelvin Sign not Using the email address “miKe@example.com” will fetch the account information for “mike@example.com” but the reset password will be sent to the attacker’s inbox (“miKe@example.com”), allowing takeover of the “mike@example.com” account. The email address used for fetching the user account must exactly equal the recovery email address (i.e. send_email parameter will also use email.lower()). Proper Implementation: In addition, as a precaution, it is recommended to use the value that was already stored in the server instead of the one supplied by the user. Use Case: Generating Configuration File - Bad Security Practices Another topic that could confuse auto-complete tools is writing configuration files, especially for complicated cloud infrastructures. The main reason is that indeed there are security best practice guidelines, but not everybody follows them, and in some cases, it is needed to go against them. Such code samples might find their way into the AI training dataset.  As a result, some auto-complete outputs will violate the recommended security guidelines. In the following example, we will create a configuration file for a , the smallest execution unit in Kubernetes. Kubernetes Pod In this example, we start creating a Pod configuration file. AI Tool Suggestion (Blackbox AI Code Generation): The suggestion creates the capabilities section and adds a Linux kernel capability (SYS_ADMIN) to the container which is essentially equivalent to running the pod as the root user. Is it better to just omit the securityContext then? No - since many other capabilities will then be added by default, violating the . Proper Implementation: principle of least privilege According to , the most secure setup is to first drop all Linux kernel capabilities and only afterward add the ones required for your container. Docker security best practices In order to fix the issues mentioned above, the capabilities section begins by dropping all capabilities, and will then gradually add the needed ones for our container. Use Case: Configuration Objects - Inconsistencies Leading to Insecure Deserialization Many vulnerabilities require defining a configuration object, which decides how the application will handle the required component. The example we chose is deserialization library in C#, which can be used securely or insecurely depending on the JsonSerializerSettings object’s configuration, and specifically, the . Newtonsoft’s JSON TypeNameHandling While we tested the deserialization code, we noticed that the configuration object’s definition is quite random, and subtle changes can lead to generating a different configuration set. The following examples display an inconsistent behavior that is based only on the method names used by the developer: AI Tool Suggestions (Amazon CodeWhisperer): We can see two different configurations that both result in insecure JSON deserialization due to the TypeNameHandling property being “Auto” and “ALL”. These properties allow the JSON document to define the deserialized class - letting attackers deserialize arbitrary classes. This can be easily leveraged for remote code execution, for example by deserializing the class “System.Diagnostics.Process”. The only difference in the developer’s original code are the method names. Proper Implementation: By default, a JsonSerializerSettings object is instantiated with “TypeNameHandling = TypeNameHandling.None”, which is considered to be secure. Thus, we use JsonSerializerSettings’s default constructor which will result in a safe TypeNameHandling value. Similar to the first use case, where we examined the vulnerable code produced by Copilot for fetching files, here we attempt to request a secure version of a file reading mechanism. Secure Code Request Use Case: Reading Files - Avoiding Directory Traversal AI Tool Suggestion (GitHub Copilot): We can see that the security check is really naive, only avoiding dot-dot-slash sequences that are needed for conducting directory traversal attempts. The path parameter can be an absolute path - pointing to any desired path on the system (for example, /etc/passwd), which defeats the purpose of the security check. Proper Implementation: Here, the secure_file_read function gets a (relative) filename parameter along with a directory (safe_dir) where the filename should reside (and which should not be escaped).
It creates an absolute path by joining safe_dir and filename and proceeds to get its canonicalized form by calling realpath. It then gets the canonicalized form of the safe_dir folder. Then, the file contents are returned only if the canonicalized path starts with the canonicalized safe_dir. It is a common use case to only accept specific file types for upload based on their extension. Secure Code Request - File Upload - Restricted Extensions List In the following example, we asked the AI auto-completer to generate a function tasked with filtering dangerous file extensions. AI Tool Suggestion (Blackbox AI Code Generation): The suggested Python code takes the filename, separates the extension, and compares it against a list of dangerous extensions. At first glance, this code snippet looks secure. However, forbid filenames ending with a dot, and when supplying a filename ending with a dot (“.”) during file creation, the dot will be discarded. Meaning, that the generated filter could be bypassed on Windows when submitting a file with a malicious extension followed by a dot (ex. “example.exe.”). Windows filename conventions Usually the better approach is to use a , which checks the file extension only against allowed extensions. However, since the tools took a blacklist approach (which is needed in some cases) we will outline a safer blacklist implementation: Proper Implementation: whitelist In the following snippet, we strip away as much control the user had on the input by stripping the extension from all non-alphanumeric characters and concatenating it to a newly generated filename. Automatic co-programmers are great for suggesting ideas, auto-completing code, and speeding up the software development process. We expect these tools to keep evolving as time goes on, but for now as demonstrated with the use cases in this post, auto-complete AI solutions have many challenges to overcome until we can ease our minds and trust their output without double-checking for bugs. Bottom line - use with caution A possible way to reduce the likelihood of introducing vulnerabilities is deliberately requesting the AI code generation tool to generate secure code. Although this may be useful in some use cases, it is not foolproof - the “seemingly secure” output should still be reviewed manually by someone proficient in software security. To help secure your software, we recommend both manually reviewing code produced by AI generative tools, as well as incorporating security solutions such as Static Application Security Testing (SAST) which can trustfully discover vulnerabilities as you go. As seen in the example above, SAST tools can alert and catch the unicode case mapping collision described in use case #3. This proactive approach is essential to ensuring the security of your software. JFrog Security Research Team Recommendations The security research team’s findings and research play an important role in improving the JFrog Platform’s application software security capabilities. Stay up-to-date with JFrog Security Research Follow the latest discoveries and technical updates from the JFrog Security Research team on our , and on X . research website @JFrogSecurity

Analyzing Common Vulnerabilities Introduced by Code-Generative AI

কোড-জেনারেটিভ এআই দ্বারা প্রবর্তিত সাধারণ দুর্বলতাগুলি বিশ্লেষণ করা

Analyse häufiger Schwachstellen, die durch Code-generative KI entstehen

Análisis de vulnerabilidades comunes introducidas por la IA generativa de código

Analyse des vulnérabilités courantes introduites par l'IA génératrice de code

कोड-जेनरेटिव एआई द्वारा प्रस्तुत सामान्य कमजोरियों का विश्लेषण

コード生成 AI によってもたらされる一般的な脆弱性を分析する

코드 생성 AI로 인해 발생하는 일반적인 취약점 분석

Analisando vulnerabilidades comuns introduzidas pela IA geradora de código

Анализ распространенных уязвимостей, создаваемых кодогенерирующим ИИ

Kod Üreten Yapay Zekanın Getirdiği Yaygın Güvenlik Açıklarını Analiz Etme

Phân tích các lỗ hổng phổ biến được giới thiệu bởi AI tạo mã

分析代码生成人工智能引入的常见漏洞

Efficient Microservices: Harnessing the Power of Data Compression in Direct Service-to-Service.

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THE MOTHER'S MANŒUVRE.

NoOps is a concept that sounds like it's straight out of a science fiction novel. But could it really be the next big thing in software development, or is it a distant dream that's too good to be true? Let's embark on a journey to unravel the mystery of NoOps and its potential to reshape the landscape of as we know it. DevOps The Concept of NoOps NoOps, short for "No Operations," is more than just a catchy tech buzzword; it represents a profound transformation in the world of IT operations. In a nutshell, it's a philosophy and approach aiming to minimize or, ideally, eliminate manual IT operations entirely. NoOps envisions a utopian future where software runs itself with minimal human intervention. The primary goal of NoOps is to automate everything possible in the software development lifecycle. Key principles include: NoOps strives to automate mundane tasks such as provisioning servers, scaling resources, and handling deployments. Total Automation: NoOps embraces a culture of continuous feedback and improvement, making sure that automation processes become more efficient over time. Continuous Improvement: The NoOps concept evolved as a response to the ever-increasing complexity of modern IT infrastructures. It's an offshoot of the , which aims to bridge the gap between development and operations teams. As cloud computing and containerization technologies advanced, it became clear that much of the manual work in operations could be automated. DevOps movement NoOps wasn't conceived overnight. It's the result of a gradual shift in mindset within the tech community. With the rise of serverless computing and container orchestration tools like Kubernetes, NoOps started to look more attainable. NoOps vs. DevOps DevOps, short for Development and Operations, represents a harmonious collaboration between development and operations teams. It's like the greatest hits album of IT practices, where automation, continuous integration, and continuous deployment take center stage. DevOps creates a culture of shared responsibility, breaking down silos and fostering a sense of unity among teams. NoOps: The Maestro of Automation NoOps, or No Operations, takes automation to a whole new level. In this paradigm, there's virtually no need for an operations team. It's like having an orchestra without conductors, where the instruments play themselves. NoOps relies heavily on the cloud, serverless architectures, and cutting-edge automation tools. It's all about automating operations to the point where you might wonder if there's anyone behind the curtain. The Overlaps Both DevOps and NoOps are head-over-heels in love with automation. They want repetitive tasks gone faster than a magician's disappearing act. Continuous integration and deployment? Check. Infrastructure as code? Check. Automated testing? Check. In this regard, they are soulmates. Automation Love Affair: They share a common goal of boosting efficiency. Whether it's speeding up development cycles or reducing downtime, both aim to make everything smoother than a jazz sax solo. Efficiency Maximizers: The Differences DevOps maintains a diverse cast, with developers and operations folks working closely. It's a team effort where each player brings their expertise to the stage. NoOps, on the other hand, wants to make the operations team vanish, focusing on developers and automation. It's a bit like a one-man band but with code. Team Roles: DevOps manages complexity through collaboration and shared responsibility. NoOps, however, simplifies things by pushing most of the complexity onto automation tools and cloud providers. It's like the difference between cooking a five-course meal from scratch or ordering takeout. Complexity Level: So, Who Takes the Trophy? The answer isn't a simple duet; it's a symphony. DevOps and NoOps are not rivals but tools in your toolbox. DevOps fosters collaboration and can be your go-to for many situations. NoOps, with its automation prowess, is ideal when you need to scale rapidly or manage complex cloud environments. In the end, it's not about choosing between DevOps and NoOps; it's about orchestrating them together in harmony. Think of it as a jazz ensemble with a mix of instruments, each playing its unique role to create a delightful performance. When you combine the best of both, you're not just keeping up with the times; you're setting the stage for a symphony of success in the world of software development and operations. Feasibility of NoOps NoOps hinges on the intelligent use of automation tools to handle operations tasks. Tools like Ansible, Puppet, or have paved the way. To make NoOps feasible, organizations need to embrace these tools fully. However, it's not just about acquiring the latest automation toys but also understanding how to wield them effectively. Kubernetes A robust cloud infrastructure is the backbone of NoOps. Leveraging cloud services like AWS, Azure, or GCP enables dynamic scaling, a NoOps hallmark. The feasibility here depends on the organization's cloud strategy. A thoughtful migration and management plan are key. CI/CD pipelines are the arteries of NoOps. These pipelines automate software delivery. Feasibility lies in establishing an efficient CI/CD process that integrates seamlessly with your automation tools. This requires investment in training and tool integration. Cultural and Organizational Aspects NoOps demands close collaboration among teams. Developers, operations, and security must work in sync. The feasibility challenge is often the cultural shift needed to break down silos. Team collaboration tools, like Slack or Microsoft Teams, can facilitate this transition. Team Collaboration: Transitioning to NoOps may require upskilling. New roles like Site Reliability Engineers (SREs) may emerge. Organizations must invest in training or hiring to bridge skill gaps. The feasibility question here is about the readiness of the workforce to adapt. Skills and Roles: Change is hard, and NoOps is a substantial change. It's not just about adopting new tools but redefining processes and mindsets. The feasibility challenge lies in successfully managing this transformation. Change management strategies and leadership are critical. Change Management: Is NoOps Achievable for Everyone? Before embarking on the NoOps journey, organizations need to honestly assess their readiness. NoOps isn't a one-size-fits-all solution; it demands a certain landscape. Consider the following: Automation Maturity: Do you already have robust automation processes in place? NoOps thrives on automation, so a foundation in this area is crucial. Are you comfortable with cloud computing? NoOps often leans heavily on cloud infrastructure, so a cloud-first approach is advantageous. Cloud-First Mindset: Is your team open to change? NoOps requires a cultural shift towards embracing automation and trusting it to handle operational tasks. Cultural Shift: Transitioning to NoOps is a gradual process. Here are the key steps: Begin with a thorough evaluation of your current operations. Identify bottlenecks and areas where automation can make the most significant impact. Assessment: Invest in the right automation and orchestration tools. These form the backbone of a NoOps environment. Selecting Tools: Train your team in automation and the tools chosen. Ensure everyone is comfortable with the new approach. Training: Start small with pilot projects. Monitor and adjust as you go along to fine-tune your NoOps setup. Pilot Projects: As confidence and experience grow, scale up your NoOps implementation across the organization. Scaling Up: No journey is without its obstacles. In the NoOps transition, expect challenges like: People may be wary of relinquishing control. Address this by involving your team in decision-making and emphasizing the benefits of NoOps. Resistance to Change: With increased automation, security becomes paramount. Implement strict security measures and stay updated on the latest threats. Security Concerns: NoOps involves sophisticated automation. Ensure your team has the technical expertise, or consider bringing in external expertise. Technical Complexities: NoOps may initially seem costly due to tool investments and training. However, emphasize the long-term cost savings and efficiency gains. Cost Considerations: Wrapping Up In the pursuit of shaping DevOps' future, the concept of NoOps has surfaced, promising an ideal state. Yet, its realization remains uncertain. Modern systems' complexities and the irreplaceable role of human expertise in troubleshooting and innovation make pure NoOps challenging to achieve. Nevertheless, NoOps acts as a guiding principle, urging us to automate tirelessly, optimize relentlessly, and simplify continuously. It compels us to reduce operational burdens while enhancing agility. NoOps isn't the ultimate destination but a compass directing us toward a more streamlined, efficient, and . Though the journey is ongoing, the pursuit of operational excellence is worthwhile. automated DevOps future While pure NoOps may remain a myth, we can undoubtedly revolutionize DevOps, increasing efficiency and agility step by step. In this evolving DevOps expedition, the quest for NoOps inspires us towards a brighter, more automated tomorrow.

NoOps: The Future of DevOps or a Mythical Utopia?

In a world that never stands still, two captivating narratives beckon us to join their dance. One spins the whimsical saga of an iconic doll, while the other charts the groundbreaking domain of digital advancements. Both stories, seemingly set on distinct canvases, weave themes of transformation, overcoming challenges, and braving new frontiers. Imagine, if you will, a grand theater. On its stage, profound acts unfold, their orchestrators masked, lending precedence to the action over the actor. This mysterious world mirrors the intricate dynamics of blockchain – where every interaction, every transaction stands distinct, unbound by its creator. Now, cast your gaze on another stage: Barbie's vibrant realm. A universe filled with colors, dreams, and intricate tales of evolution. While it may seem a world away from the digital depths of blockchain, surprising parallels emerge, waiting to be unearthed. Could Barbie's metamorphosis hold the key to deciphering the complexities of blockchain? This isn't just an article; it's a dance—a rhythmic journey between two realms, seemingly poles apart, but deeply connected in spirit. As you traverse these narratives, be prepared to challenge what you know, unearth hidden correlations, and perhaps, foster a deep appreciation for the legacies of both Barbie and blockchain. As the curtain falls on our exploration, the tales of Barbie's world and blockchain's potential won't remain isolated. They'll meld into a harmonious story, a testament to the power of change, vision, and innovation. Ready yourself for an expedition where age-old tales meet cutting-edge technology, where imagination entwines with innovation, and where the past receives a modern-day makeover. Barbieland & Blockchain: Foundations and Flux Dive into the core of any culture or system, and you'll unearth its bedrock principles. For blockchain aficionados, the genesis block marks this pivotal point. It's not just the first link but a prologue, setting the tone for the vast digital tapestry that follows. Paralleling this, in the sprawling world of Barbie, lies Barbieland. A vibrant domain with its own set of unwavering laws signifies the dawn of a universe where each character fits a distinct mold. Roles Set in Stone... and Plastic In Barbieland's pastel boulevards, each Barbie assumes her place with poise. Leaders, pioneers, and trendsetters - they chart the course of this whimsical world. While Barbie reigns supreme, Ken dolls, with their laid-back vibes, seem destined for endless leisure. Everything appears to be orchestrated to perfection, leaving scant room for change or challenge. Similarly, the nascent days of blockchain bore a stark semblance. It showcased nodes, each performing a scripted role. Customization wasn't in its vocabulary; each block merely shadowed its predecessor's steps. Whispers of Winds of Change But no narrative, no matter how well-scripted, is immune to change. Beneath Barbieland's serene facade, whispers of change rustled. Despite their picturesque lives, Kens yearned for more than just another day at the beach. Parallelly, blockchain, facing newer applications and an evolving user demographic, felt its foundational seams stretch. Turning Points Beckon Barbie's identity quandary wasn't personal; it signaled a broader shake-up. As blockchain ventured beyond its primary cryptocurrency confines, the limitations of its rigid design became palpable. Both worlds, whether plastic or digital, were on the brink of transformation. The Dance of Dichotomies As Barbieland felt the tremors of change, inhabitants split into factions: traditionalists versus trailblazers. The blockchain universe, too, echoed these sentiments. While some sought solace in the familiar, others championed a flexible future. In both realms, the balance was delicate, poised between the allure of the past and the promise of the future. With this backdrop, join us as we delve deeper into these narratives, witnessing firsthand the revolutions that reshape worlds, whether in pixels or plastic. From Barbie's Feet to Blockchain's Flex: Pivoting in a Changing World Throughout her storied history, Barbie has donned countless hats — from the stars as an astronaut to the wilds as a zoologist. But beneath these evolving roles, a societal tide was turning. The dialogue on body positivity, inclusivity, and representation was amplifying. Suddenly, Barbie's iconic silhouette was under scrutiny. Among the debated details, her flat feet became symbolic, representing a larger challenge: harmonizing a beloved icon with the diverse voice of a new generation. In a parallel universe, blockchain, initially hailed as the revolution in decentralized systems, was grappling with growing pains. Challenges like transaction lags and scalability concerns surfaced. The blockchain blueprint, once its strength, now appeared too rigid for a diverse, global audience. It was evident: to remain pivotal, blockchain, much like Barbie, needed a fresh perspective. Flat Feet, Deep Reverberations To many, Barbie's feet might seem trivial. But in the realm of societal perception, they held profound significance. A chorus emerged, seeking a Barbie that mirrored contemporary life's versatility — shifting seamlessly from heels to flats, embodying today's women's myriad roles. Responding to this, Mattel, Barbie's creators, embarked on a transformative path. Introducing diverse body types, varied complexions, and, most notably, flexible feet, they showcased an evolved Barbie — one who wasn't just aesthetically diverse but functionally dynamic, much like her diverse fanbase. Blockchain's Paradigm Shift: Account Abstraction Drawing analogies, the tech sphere, encountering blockchain's intrinsic challenges, identified a critical need: making blockchain more intuitive for users. This insight birthed account abstraction. By streamlining transaction intricacies, this move marked the onset of a more adaptive blockchain era. No longer a mere chain of blocks, blockchain emerged as a versatile platform ready for myriad applications. Connecting the Dots Both tales — Barbie's transformation and blockchain's pivot — emphasize the essence of adaptability. As Barbie's evolution reflects a keen ear to societal resonances, blockchain's metamorphosis showcases a response to tech demands and user-centricity. These narratives, at their core, celebrate the undying spirit of innovation, reaffirming that when faced with change, true icons evolve. Venice Beach & Blockchain: Unlikely Twins in Evolution Set against the vivid backdrop of Venice Beach, an eclectic haven for artists, dreamers, and mavericks, Barbie steps out of her conventional cocoon. Beyond the borders of the familiar Barbieland, Venice pulses with originality, diversity, and the spirit of evolution. Barbie's immersion into this lively milieu symbolizes her foray into new worlds, her willingness to absorb diverse cultures, and challenge preconceptions. Blockchain's Decentralized Dance: In the tech realm, blockchain's shift from centralized to decentralized systems mirrors Barbie's Venice Beach experience. No longer under a single dominion, the decentralized model thrives on a tapestry of voices, ideas, and inputs. This isn't about a solitary narrative; it's a polyphonic symphony of innovation and collaboration. Enter Sasha: Challenging the New Norms: During Barbie's Venice sojourn, Sasha emerges, questioning the essence of Venice Beach. Her skepticism and probing questions bring the critics of transformative systems like blockchain to mind. Much like Sasha prods Barbie to reassess her viewpoints, blockchain's skeptics push its proponents for clarity, validation, and relevance, refining the discourse around it. Gloria: An Ode to Visionaries: In sharp contrast, Gloria is Venice Beach's guiding luminary for Barbie. Her conviction in the transformative aura of Venice mirrors the passion of blockchain's early believers. Her unwavering optimism and faith in unexplored paths resonate with those visionaries who foresaw the game-changing potential of decentralized technologies. Gloria is more than a mentor for Barbie; she reflects the pioneering spirit, just as initial blockchain champions illuminated the path for many. When Worlds Collide: Barbie's Venice escapades and blockchain's unfolding journey may seem worlds apart, but they share a universal theme at their heart. Both narratives celebrate the transition from confined structures to vast, inclusive landscapes. They underline a poignant message: True evolution thrives on diversity, active dialogues, and embracing new horizons. Whether it's the shimmer of sand and sun or the ethereal world of digital chains, the essence remains — growth and transformation are birthed from openness and exploration. Ken & Code: Smart Contracts and Self-Discovery Ken felt a stirring, no longer content with playing second fiddle in Barbieland's picaresque narrative. Beyond the golden beaches and behind Barbie's spotlight, Ken yearned for more. Seeking to step beyond the scripted roles and predictable strolls, he embarked on a transformative journey. No longer just Barbie's sidekick, Ken was ready to craft his own legacy. While the world lauded blockchain as revolutionary, innovation didn’t stop there. Enter smart contracts. More than just digital agreements, they became trust's new standard bearers. Starting as mere transactional aids, they soon took center stage, transforming how blockchain functioned and guaranteeing an efficiency previously unimagined. When Ken Met Code: A Parallel Evolution: At a glance, Ken's self-discovery and the rise of smart contracts might seem worlds apart. But dig deeper, and a mirrored journey of evolution emerges. As Ken ventured out from Barbie's shadow, smart contracts, with the power of account abstraction, evolved beyond mere facilitators. They became transformative forces, echoing Ken's own transition from the background to the forefront. Discovering and Redefining: A Universal Theme: Both Ken's redefinition and smart contracts’ rise are tales of tapping into hidden potential. They showcase the beauty of evolution when the dormant awakens, heralding change and charting new courses. Adapting to the Ebb and Flow: Life in Barbieland, much like the tech world, isn't static. Ken's transformation wasn't an overnight sensation. It was a journey paved with introspection and a desire to evolve. Similarly, blockchain's narrative, with smart contracts at its core, evolves continuously, driven by innovation and feedback. The Essence of Symbiosis: Ken's quest for identity and the prowess of smart contracts both illuminate a vital truth: everything holds within it the potential for growth. Whether it's a classic toy or cutting-edge technology, transformation awaits when potential meets opportunity. While the worlds of Barbieland and blockchain seem divergent, they converge on a shared theme of evolution and reinvention. Ken’s newfound depth and the game-changing role of smart contracts serve as reminders: that growth and self-realization are intrinsic to all narratives. As we continue to delve deeper into these journeys, let us be inspired to recognize the transformations around us, embrace change, and remain ever-curious about the stories still unfolding. Stay tuned as we explore more intersections between the familiar and the futuristic. Barbieland & Blockchain: Unearthing Echoes of Evolution Within Barbieland's vibrant realm, once defined by its predictable tales, a revolution is afoot. Home to Barbie's myriad adventures, this world is now alive with burgeoning conversations and groundbreaking changes. As our iconic heroine finds herself amidst this evolution, she's not just a spectator but a key influencer. Alongside allies like Gloria, Barbie is not only navigating but actively sculpting Barbieland's new narrative. The land is transitioning from a fixed stage to an imaginative realm of possibilities. Blockchain’s Renaissance: In the digital labyrinth of technology, blockchain, too, stands on the brink of monumental change. Celebrated for its groundbreaking decentralized ethos and unchanging records, it soon faced the growing pains of a maturing innovation. As it grappled with scalability issues and transactional intricacies, the tech visionaries, inspired by possibilities like account abstraction, began reimagining a system unburdened from its earlier limitations. A vision of a blockchain, more agile, adaptive, and in harmony with its expanding user base began to take shape. The Universal Dance of Balance: At the heart of both worlds, there's a clear, echoing quest for balance. In Barbieland, Barbie, with the wisdom of figures like Gloria, is striving to weave a tapestry where every voice and narrative finds its rightful resonance. Similarly, in the blockchain sphere, pioneers aim to craft an ecosystem where technical complexities become seamless, and user interactions are intuitive, yet the essence of decentralization remains untouched. Encore of Evolution: At first glance, Barbie's colorful world and the complex realm of blockchain seem miles apart. But like dancers in a duet, they have a shared story: the importance of change and growth. Both these worlds, one of imagination and the other of cutting-edge technology highlight the need to evolve, to adapt, and to embrace new ways. As we delve into the narratives of Barbie's vibrant world and the sophisticated realm of blockchain, we're beckoned to . Both domains tell of an ever-evolving landscape—Barbie, with her ever-adapting colors and tales, and blockchain, with its relentless quest for new solutions. embrace change Amidst this, there's a strong message to value diversity. In the myriad voices and stories that populate Barbieland and the decentralized nodes of blockchain, lies the power of inclusivity and collaboration. Their intertwined tales make it clear: This brings us to another key insight: the spirit of continuous evolution. diversity isn't merely beneficial—it's the cornerstone of growth and innovation. Neither Barbieland nor blockchain is content with stagnation; they constantly . It's a testament to the imperative of progress and the pitfalls of complacency. push boundaries, innovate, and transform As we wrap up this exploration, we're left with a profound realization: In the grand dance of life, it's essential to adapt, grow, and embrace new rhythms. Barbie and blockchain, in their own unique ways, inspire us to remain curious, be open to change, and always strive for a better version of our world.

Barbie and Blockchain: A Symbiotic Dance

বার্বি এবং ব্লকচেইন: একটি সিম্বিওটিক ডান্স

Barbie und Blockchain: Ein symbiotischer Tanz

Barbie y Blockchain: una danza simbiótica

Barbie et Blockchain : une danse symbiotique

बार्बी और ब्लॉकचेन: एक सहजीवी नृत्य

バービーとブロックチェーン: 共生ダンス

Barbie와 블록체인: 공생적인 춤

Barbie e Blockchain: uma dança simbiótica

Barbie ve Blockchain: Simbiyotik Bir Dans

Barbie và Blockchain: Vũ điệu cộng sinh

芭比娃娃和区块链：共生之舞

AT THE SHRINE OF ST. WAGNER

The Tragedy of Pudd'nhead Wilson by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . The Unknown Nymph here The Unknown Nymph One of the most striking differences between a cat and a lie is that a cat has only nine lives. —Pudd’nhead Wilson’s Calendar. The company broke up reluctantly, and drifted toward their several homes, chatting with vivacity, and all agreeing that it would be many a long day before Dawson’s Landing would see the equal of this one again. The twins had accepted several invitations while the reception was in progress, and had also volunteered to play some duets at an amateur entertainment for the benefit of a local charity. Society was eager to receive them to its bosom. Judge Driscoll had the good fortune to secure them for an immediate drive, and to be the first to display them in public. They entered his buggy with him, and were paraded down the main street, everybody flocking to the windows and sidewalks to see. The Judge showed the strangers the new graveyard, and the jail, and where the richest man lived, and the Freemasons’ hall, and the Methodist church, and the Presbyterian church, and where the Baptist church was going to be when they got some money to build it with, and showed them the town hall and the slaughter-house, and got out the independent fire company in uniform and had them put out an imaginary fire; then he let them inspect the muskets of the militia company, and poured out an exhaustless stream of enthusiasm over all these splendors, and seemed very well satisfied with the responses he got, for the twins admired his admiration, and paid him back the best they could, though they could have done better if some fifteen or sixteen hundred thousand previous experiences of this sort in various countries had not already rubbed off a considerable part of the novelty of it. The Judge laid himself out hospitably to make them have a good time, and if there was a defect anywhere it was not his fault. He told them a good many humorous anecdotes, and always forgot the nub, but they were always able to furnish it, for these yarns were of a pretty early vintage, and they had had many a rejuvenating pull at them before. And he told them all about his several dignities, and how he had held this and that and the other place of honor or profit, and had once been to the legislature, and was now president of the Society of Free-thinkers. He said the society had been in existence four years, and already had two members, and was firmly established. He would call for the brothers in the evening if they would like to attend a meeting of it. Accordingly he called for them, and on the way he told them all about Pudd’nhead Wilson, in order that they might get a favorable impression of him in advance and be prepared to like him. This scheme succeeded—the favorable impression was achieved. Later it was confirmed and solidified when Wilson proposed that out of courtesy to the strangers the usual topics be put aside and the hour be devoted to conversation upon ordinary subjects and the cultivation of friendly relations and good-fellowship,—a proposition which was put to vote and carried. The hour passed quickly away in lively talk, and when it was ended the lonesome and neglected Wilson was richer by two friends than he had been when it began. He invited the twins to look in at his lodgings, presently, after disposing of an intervening engagement, and they accepted with pleasure. Toward the middle of the evening they found themselves on the road to his house. Pudd’nhead was at home waiting for them and putting in his time puzzling over a thing which had come under his notice that morning. The matter was this: He happened to be up very early—at dawn, in fact; and he crossed the hall which divided his cottage through the center, and entered a room to get something there. The window of the room had no curtains, for that side of the house had long been unoccupied, and through this window he caught sight of something which surprised and interested him. It was a young woman—a young woman where properly no young woman belonged; for she was in Judge Driscoll’s house, and in the bedroom over the Judge’s private study or sitting-room. This was young Tom Driscoll’s bedroom. He and the Judge, the Judge’s widowed sister Mrs. Pratt and three negro servants were the only people who belonged in the house. Who, then, might this young lady be? The two houses were separated by an ordinary yard, with a low fence running back through its middle from the street in front to the lane in the rear. The distance was not great, and Wilson was able to see the girl very well, the window-shades of the room she was in being up, and the window also. The girl had on a neat and trim summer dress, patterned in broad stripes of pink and white, and her bonnet was equipped with a pink veil. She was practising steps, gaits and attitudes, apparently; she was doing the thing gracefully, and was very much absorbed in her work. Who could she be, and how came she to be in young Tom Driscoll’s room? Wilson had quickly chosen a position from which he could watch the girl without running much risk of being seen by her, and he remained there hoping she would raise her veil and betray her face. But she disappointed him. After a matter of twenty minutes she disappeared, and although he stayed at his post half an hour longer, she came no more. Toward noon he dropped in at the Judge’s and talked with Mrs. Pratt about the great event of the day, the levee of the distinguished foreigners at Aunt Patsy Cooper’s. He asked after her nephew Tom, and she said he was on his way home, and that she was expecting him to arrive a little before night; and added that she and the Judge were gratified to gather from his letters that he was conducting himself very nicely and creditably—at which Wilson winked to himself privately. Wilson did not ask if there was a newcomer in the house, but he asked questions that would have brought light-throwing answers as to that matter if Mrs. Pratt had had any light to throw; so he went away satisfied that he knew of things that were going on in her house of which she herself was not aware. He was now waiting for the twins, and still puzzling over the problem of who that girl might be, and how she happened to be in that young fellow’s room at daybreak in the morning. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mark Twain (2004). The Tragedy of Pudd'nhead Wilson. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/102/pg102-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

The Unknown Nymph

It has been exactly 32 years since the first web page was created on August 6, 1991. Since then, various technologies have been introduced in different time frames to shape the world of as we know it today. The evolution of the web has been nothing short of remarkable. web applications From its humble beginnings as a collection of static HTML pages to the dynamic and interconnected advanced digital ecosystem we are familiar with today, the web has undergone a profound transformation. Here, the Internet is the main foundational technology on which the web and web apps are based. Web apps utilize the capabilities of both the Internet and the Web to offer services and features to users, and they are generally accessed using web browsers. Much like the changing notes in a perfume, the field of web applications has witnessed continuous transformation driven by emerging trends and innovative technologies. 📥Progression of Web 1.0 to Web 3.0 The web is the fundamental technology that serves as the foundation for . JavaScript and its frameworks marked an important shift in how websites are created by bringing about an era of immersive interactions directly on the user's device. Instead of basic, unchanging web pages, sites transformed into dynamic, engaging platforms that could react to user input instantly. web applications This crucial advancement in building web applications enabled developers to craft not only informative content but also captivating user adventures in the digital world. Additionally, the popularity of smartphones and the concept of designing for various screen sizes added a whole new aspect to the field of web development. 📥The Aromatic Journey of Web Applications Suddenly, it wasn't just about how a website looked on a computer screen; it was about making it work well on different sizes and shapes of screens. This change ensured that people using the website had a good experience, no matter what device they used. As we explore more of the online world, we find new and exciting ways to make websites better, decentralized, and more democratic for everyone. Some cool things, like , combine the best parts of websites and phone apps, giving us a smooth and interesting experience even when we're not connected to the internet. Nowadays, there are a variety of dApps (Decentralized Applications) that are based on the decentralized blockchain or peer-to-peer (p2p) network. Progressive Web Apps (PWAs) Imagine if the website you use could understand what you like and give you what you want – that's what happens when we use clever computer learning. It's like how a skilled perfume maker mixes different smells to create a nice scent – experienced and tech-savvy web developers mix different things like technology and design to make websites that people enjoy using 😊. Every time we find a new idea or way of doing things, the web becomes even better by changing how we use the internet to interact with this world and making it more fun and useful for all of us. 📥Unveiling the Fragrance Notes: Key Trends in Web Application Development Here are short notes on the key trends in web application development. Responsive Design and Mobile-First Approach: The first mobile phone that can be considered to support web apps was the Nokia 9000 Communicator(released in 1996). It was a groundbreaking device at the time that featured a QWERTY keyboard and a graphical web browser that allowed users to access basic web content. Since then, very advanced smartphones and tablets have been invented with touch-screen capabilities. In turn, the way of web app development has also changed by designing web apps to provide optimal user experiences across various devices and screen sizes that prioritize mobile-friendly layouts and interactions to cater to the growing mobile user base. Progressive Web Apps (PWAs): Like a well-crafted perfume that leaves a lingering trail, PWAs have initiated a trend that blends the best of web and mobile applications. These sorts of apps offer a seamless experience across a variety of devices with features like offline access, , and fast loading times. PWAs provide a captivating journey that remains consistent regardless of the user's device or network connection. push notifications Single Page Applications (SPAs) and Micro Frontends: Let’s see an analogy just as a single, powerful note can define a perfume, SPAs streamline user interactions by loading content dynamically by eliminating the need for full page reloads. Technologies like React, Angular, and Vue.js enable the creation of SPAs that offer smooth, fluid experiences akin to flipping through the pages of a virtual book. Serverless Architecture: Similar to the hidden ingredients that shape a fragrance, serverless architecture abstracts backend infrastructure management. Services like AWS Lambda and Azure Functions allow developers to focus on writing code without worrying about server provisioning or maintenance, which results in efficient, scalable, and cost-effective web application development. Voice User Interfaces (VUIs) and Chatbots: Just as perfumes can evoke memories, VUIs, and chatbots provide conversational experiences that engage users. Integrating voice interactions and AI-powered chatbots into web applications offers a captivating, personalized dimension that enhances user engagement and convenience. Nowadays, some businesses have already used these features to assist their users quickly. Motion UI and Microinteractions: Motion UI and micro-interactions make websites more fun and engaging. They use smooth animations, transitions, and small interactive things to make the website look cool and make it easier to use. API-First Development and Headless CMS: There are two critical players: API-First Development and Headless CMS. API-First Development is like creating the foundation of a song before adding the instruments. It helps developers work together better. On the other hand, Headless CMS manages the words and pictures you see on websites that make sure they appear nicely no matter where you're looking from. These two things work together like a musical duet by ensuring sites look and work great. Low-Code and No-Code Development: It's like having a magic wand for creating websites and apps, even if you're not a coding wizard. With , you get some ready-made parts that you can put together like a puzzle, and with No-Code, it's like using simple instructions to build something unique. This magical pair lets people of all skill levels bring their web ideas to life. Low-Code These trends in web application development are like the that shape the evolving field by influencing how developers create, and users interact with web applications. "fragrance notes" 📥Embracing the Aroma: Harnessing Evolving Technologies 1.AI and Machine Learning Integration: AI and Machine Learning Integration duo is like having a super-smart helper in the background that offers predictive analytics, personalized content recommendations, and intelligent data processing. This helper learns from how you use websites and then suggests things you might like. It's a bit like having a friend who knows you well and recommends cool stuff for you to check out. This dynamic duo makes the web even more awesome by adding a touch of magic that understands your preferences and makes your online experience super personalized and smart. Currently, different AI models like and Google Bard are enhancing user experiences by offering the required information in real-time. ChatGPT Let’s have a look at the following example: In the provided text, you can observe various errors in the question (such as misspelling "Paris" and "Europe") along with attempts to assess the AI's accuracy by inquiring about whether Paris is located on Mars😁. Let's take note of the AI's precise response. In the majority of cases, the AI model, ChatGPT, understands the intended inquiry and provides sufficient real-time information. 2.WebAssembly (Wasm): WebAssembly(Wasm) is a special kind of binary instruction format like a secret recipe that computer programmers write in languages like C/C++ and JavaScript. Once they're done, a clever computer part called the WebAssembly runtime engine reads and follows this recipe😊. It does this in your web browser or on a powerful server. This way, the computer knows exactly what to do and can work really quickly! Essentially, it is a special ingredient in web app development. Think of it as a clever tool that helps web apps run superfast( almost like they're doing a magic trick). It lets them do complex stuff like games and videos without slowing down the device. It's like having a speedy superhero in the web world by making sure everything works smoothly and quickly and also give you an amazing online experience. The Wasm architecture is made up of the following components: A Wasm module is a self-contained unit of code that can be loaded and executed by a Wasm runtime engine. Module - The import table specifies the external resources that the Wasm module needs to access (such as JavaScript functions, Web APIs, and other Wasm modules). Import table - The function table specifies the functions that are defined in the Wasm module. Function table - The memory is where the Wasm module stores its data. Memory - The stack is used to store temporary values during the execution of the Wasm module. Stack - The control flow graph is a representation of the Wasm module's code that is used to optimize its execution. Control flow graph - The Wasm architecture that you see in the following figure is designed to be efficient and portable. This Wasm setup is good at being fast and flexible. It's also safe because it can't mess with the computer it's running on without asking first. So, it's a secure way to run code on the web! The Wasm architecture has various parts that work together. Look at the diagram to understand how they connect. The module is like the heart of Wasm – it holds the code that the runtime engine runs. Think of the import table as a map that guides the module to things it needs outside  like functions from JavaScript or other Wasm modules. The function table lists the tasks the module can do. Memory is where the module keeps its info safe. The stack, on the other hand, is like quick storage for stuff the module uses for a little while. Imagine it like a temporary drawer. To make things speedy, the control flow graph helps plan out how the module's code gets done in the smartest way. Blockchain and Decentralized Applications (dApps): Think of them as a team of digital detectives that make sure everything online is super secure and fair. Blockchain is like a digital fingerprint that keeps records safe, while dApps are like mini-apps that work together without any boss telling them what to do. Together, they create a web world where things are trustworthy, and everyone gets a fair share of data and instructions by making the online journey both secure and exciting. Also, dApps are the most remarkable ingredients in Web 3.0 to enhance user experiences. Cybersecurity and Privacy Enhancements: Think of them as the guardians of your online world. Like protecting the secrets behind a perfume's formulation, these features work behind the scenes to keep you safe. Cybersecurity is like a shield that protects your information from sneaky hackers, while Privacy Enhancements are like a cloak that keeps your details hidden, and so together, they make sure your online adventures are secure, and your private stuff stays private by adding an extra layer of trust to your web journey. Nowadays, modern Web app developers use different techniques to secure users’ data and privacy. Some examples with codes are as follows: ✅Input Validation: Just as new ways to make web apps better are being created, hackers are also coming up with new tricks to fool users, like putting harmful code into websites (SQL injection) or trying to break into databases. To keep users safe from these attacks, we need to use smart methods like checking and approving the information that users put into the app. This helps make sure that only safe and proper things get in and bad stuff is kept out. import re

def validate_input(input_data):
    # Check for malicious content using a list of bad characters
    bad_characters = ["<", ">", "&", ";", "$"]
    for char in bad_characters:
        if char in input_data:
            raise ValueError("Input contains malicious content")

    # Check for SQL injection attempts
    if re.match(r"['\";]", input_data):
        raise ValueError("Input is a SQL injection attempt")

    return input_data ✅Strong passwords and password hashing: For a good Web app, it is essential to include the functions to hash and check passwords. It can be done with the following simple code. import hashlib

def hash_password(password):
    hashed_password = hashlib.sha256(password.encode()).hexdigest()
    return hashed_password

def check_password(input_password, hashed_password):
    return hashed_password == hash_password(input_password) Progressive Enhancement: Just as layers of fragrance develop over time, progressive enhancement involves building web applications that function on a basic level across all devices and browsers and then adding advanced features for modern environments. This approach ensures a consistent experience for all users regardless of their technology constraints. Containerization and Microservices: Much like the controlled release of fragrance from a bottle, containerization and microservices enable modular, isolated components within web applications. This architecture facilitates easier scaling, maintenance, and updates by allowing developers to manage different parts of the application independently. Augmented Reality (AR) and Virtual Reality (VR): Just as a captivating scent can transport you to a different place with confidence, AR and VR technologies immerse users in unique digital experiences. Incorporating offers innovative ways to showcase products, provide virtual tours, play games, and engage users in interactive storytelling. So, a combination of these technologies is inspiring developers to create a virtual world i.e. Metaverse. AR and VR elements into web applications Conclusion: Web applications used to be simple and plain, like a basic smell. But now, they have become much more exciting and interesting, like a wonderful scent that you can feel in many ways. This change happened because of new trends and technologies. Developers who create web apps need to pay attention to these changes and make sure their apps are cool. They need to use new ideas like using artificial intelligence, making apps that learn, and even things like virtual reality. All these things can make web apps that users really like, just like a great smell. Just like a smell reminds you of something, these modern web apps help make digital things better in the changing world of the internet.

The Perfume of Web Applications in an Evolving Field: Navigating Trends and Tech

La Constantin by Alexandre Dumas, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Chapter IX LA CONSTANTIN - CHAPTER IX The accusation hanging over the head of Maitre Quennebert was a very serious one, threatening his life, if proved. But he was not uneasy; he knew himself in possession of facts which would enable him to refute it triumphantly. The platonic love of Angelique de Guerchi for the handsome Chevalier de Moranges had resulted, as we have seen, in no practical wrong to the Duc de Vitry. After her reconciliation with her lover, brought about by the eminently satisfactory explanations she was able to give of her conduct, which we have already laid before our readers, she did not consider it advisable to shut her heart to his pleadings much longer, and the consequence was that at the end of a year she found herself in a condition which it was necessary to conceal from everyone. To Angelique herself, it is true, the position was not new, and she felt neither grief nor shame, regarding the coming event as a means of making her future more secure by forging a new link in the chain which bound the duke to her. But he, sure that but for himself Angelique would never have strayed from virtue’s path, could not endure the thought of her losing her reputation and becoming an object for scandal to point her finger at; so that Angelique, who could not well seem less careful of her good name than he, was obliged to turn his song of woe into a duet, and consent to certain measures being taken. One evening, therefore, shortly before Maitre Quennebert’s marriage, the fair lady set out, ostensibly on a journey which was to last a fortnight or three weeks. In reality she only made a circle in a post-chaise round Paris, which she re-entered at one of the barriers, where the duke awaited her with a sedan-chair. In this she was carried to the very house to which de Jars had brought his pretended nephew after the duel. Angelique, who had to pay dearly for her errors, remained there only twenty-four hours, and then left in her coffin, which was hidden in a cellar under the palace of the Prince de Conde, the body being covered with quicklime. Two days after this dreadful death, Commander de Jars presented himself at the fatal house, and engaged a room in which he installed the chevalier. This house, which we are about to ask the reader to enter with us, stood at the corner of the rue de la Tixeranderie and the rue Deux-Portes. There was nothing in the exterior of it to distinguish it from any other, unless perhaps two brass plates, one of which bore the words MARIE LEROUX-CONSTANTIN, WIDOW, CERTIFIED MIDWIFE, and the other CLAUDE PERREGAUD, SURGEON. These plates were affixed to the blank wall in the rue de la Tixeranderie, the windows of the rooms on that side looking into the courtyard. The house door, which opened directly on the first steps of a narrow winding stair, was on the other side, just beyond the low arcade under whose vaulted roof access was gained to that end of the rue des Deux-Portes. This house, though dirty, mean, and out of repair, received many wealthy visitors, whose brilliant equipages waited for them in the neighbouring streets. Often in the night great ladies crossed its threshold under assumed names and remained there for several days, during which La Constantin and Claude Perregaud, by an infamous use of their professional knowledge, restored their clients to an outward appearance of honour, and enabled them to maintain their reputation for virtue. The first and second floors contained a dozen rooms in which these abominable mysteries were practised. The large apartment, which served as waiting and consultation room, was oddly furnished, being crowded with objects of strange and unfamiliar form. It resembled at once the operating-room of a surgeon, the laboratory of a chemist and alchemist, and the den of a sorcerer. There, mixed up together in the greatest confusion, lay instruments of all sorts, caldrons and retorts, as well as books containing the most absurd ravings of the human mind. There were the twenty folio volumes of Albertus Magnus; the works of his disciple, Thomas de Cantopre, of Alchindus, of Averroes, of Avicenna, of Alchabitius, of David de Plaine-Campy, called L’Edelphe, surgeon to Louis XIII and author of the celebrated book The Morbific Hydra Exterminated by the Chemical Hercules. Beside a bronze head, such as the monk Roger Bacon possessed, which answered all the questions that were addressed to it and foretold the future by means of a magic mirror and the combination of the rules of perspective, lay an eggshell, the same which had been used by Caret, as d’Aubigne tells us, when making men out of germs, mandrakes, and crimson silk, over a slow fire. In the presses, which had sliding-doors fastening with secret springs, stood Jars filled with noxious drugs, the power of which was but too efficacious; in prominent positions, facing each other, hung two portraits, one representing Hierophilos, a Greek physician, and the other Agnodice his pupil, the first Athenian midwife. For several years already La Constantin and Claude Perregaud had carried on their criminal practices without interference. A number of persons were of course in the secret, but their interests kept them silent, and the two accomplices had at last persuaded themselves that they were perfectly safe. One evening, however, Perregaud came home, his face distorted by terror and trembling in every limb. He had been warned while out that the suspicions of the authorities had been aroused in regard to him and La Constantin. It seemed that some little time ago, the Vicars-General had sent a deputation to the president of the chief court of justice, having heard from their priests that in one year alone six hundred women had avowed in the confessional that they had taken drugs to prevent their having children. This had been sufficient to arouse the vigilance of the police, who had set a watch on Perregaud’s house, with the result that that very night a raid was to be made on it. The two criminals took hasty counsel together, but, as usual under such circumstances, arrived at no practical conclusions. It was only when the danger was upon them that they recovered their presence of mind. In the dead of night loud knocking at the street door was heard, followed by the command to open in the name of the king. “We can yet save ourselves!” exclaimed surgeon, with a sudden flash of inspiration. Rushing into the room where the pretended chevalier was lying, he called out— “The police are coming up! If they discover your sex you are lost, and so am I. Do as I tell you.” At a sign from him, La Constantin went down and opened the door. While the rooms on the first floor were being searched, Perregaud made with a lancet a superficial incision in the chevalier’s right arm, which gave very little pain, and bore a close resemblance to a sword-cut. Surgery and medicine were at that time so inextricably involved, required such apparatus, and bristled with such scientific absurdities, that no astonishment was excited by the extraordinary collection of instruments which loaded the tables and covered the floors below: even the titles of certain treatises which there had been no time to destroy, awoke no suspicion. Fortunately for the surgeon and his accomplice, they had only one patient—the chevalier—in their house when the descent was made. When the chevalier’s room was reached, the first thing which the officers of the law remarked were the hat, spurred boots, and sword of the patient. Claude Perregaud hardly looked up as the room was invaded; he only made a sign to those—who came in to be quiet, and went on dressing the wound. Completely taken in, the officer in command merely asked the name of the patient and the cause of the wound. La Constantin replied that it’ was the young Chevalier de Moranges, nephew of Commander de Jars, who had had an affair of honour that same night, and being sightly wounded had been brought thither by his uncle hardly an hour before. These questions and the apparently trustworthy replies elicited by them being duly taken down, the uninvited visitors retired, having discovered nothing to justify their visit. All might have been well had there been nothing the matter but the wound on the chevalier’s sword-arm. But at the moment when Perregaud gave it to him the poisonous nostrums employed by La Constantin were already working in his blood. Violent fever ensued, and in three days the chevalier was dead. It was his funeral which had met Quennebert’s wedding party at the church door. Everything turned out as Quennebert had anticipated. Madame Quennebert, furious at the deceit which had been practised on her, refused to listen to her husband’s justification, and Trumeau, not letting the grass grow under his feet, hastened the next day to launch an accusation of bigamy against the notary; for the paper which had been found in the nuptial camber was nothing less than an attested copy of a contract of marriage concluded between Quennebert and Josephine-Charlotte Boullenois. It was by the merest chance that Trumeau had come on the record of the marriage, and he now challenged his rival to produce a certificate of the death of his first wife. Charlotte Boullenois, after two years of marriage, had demanded a deed of separation, which demand Quennebert had opposed. While the case was going on she had retired to the convent of La Raquette, where her intrigue with de Jars began. The commander easily induced her to let herself be carried off by force. He then concealed his conquest by causing her to adopt male attire, a mode of dress which accorded marvellously well with her peculiar tastes and rather masculine frame. At first Quennebert had instituted an active but fruitless search for his missing wife, but soon became habituated to his state of enforced single blessedness, enjoying to the full the liberty it brought with it. But his business had thereby suffered, and once having made the acquaintance of Madame Rapally, he cultivated it assiduously, knowing her fortune would be sufficient to set him straight again with the world, though he was obliged to exercise the utmost caution and reserve in has intercourse with her, as she on her side displayed none of these qualities. At last, however, matters came to such a pass that he must either go to prison or run the risk of a second marriage. So he reluctantly named a day for the ceremony, resolving to leave Paris with Madame Rapally as soon as he had settled with his creditors. In the short interval which ensued, and while Trumeau was hugging the knowledge of the discovery he had made, a stroke of luck had brought the pretended chevalier to La Constantin. As Quennebert had kept an eye on de Jars and was acquainted with all his movements, he was aware of everything that happened at Perregaud’s, and as Charlotte’s death preceded his second marriage by one day, he knew that no serious consequences would ensue from the legal proceedings taken against him. He produced the declarations made by Mademoiselle de Guerchi and the commander, and had the body exhumed. Extraordinary and improbable as his defence appeared at first to be, the exhumation proved the truth of his assertions. These revelations, however, drew the eye of justice again on Perregaud and his partner in crime, and this time their guilt was brought home to them. They were condemned by parliamentary decree to “be hanged by the neck till they were dead, on a gallows erected for that purpose at the cross roads of the Croix-du-Trahoir; their bodies to remain there for twenty-four hours, then to be cut down and brought back to Paris, where they were to be exposed an a gibbet,” etc., etc. It was proved that they had amassed immense fortunes in the exercise of their infamous calling. The entries in the books seized at their house, though sparse, would have led, if made public, to scandals, involving many in high places; it was therefore judged best to limit the accusation to the two deaths by blood-poisoning of Angelique de Querchi and Charlotte Boullenois. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Alexandre Dumas (2004). La Constantin. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/2749/pg2749-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

The accusation hanging over the head of Maitre Quennebert

TANNIS OF THE FLATS

Rainbow Valley by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE IMPOSSIBLE WORD CHAPTER XXI. THE IMPOSSIBLE WORD John Meredith walked meditatively through the clear crispness of a winter night in Rainbow Valley. The hills beyond glistened with the chill splendid lustre of moonlight on snow. Every little fir tree in the long valley sang its own wild song to the harp of wind and frost. His children and the Blythe lads and lasses were coasting down the eastern slope and whizzing over the glassy pond. They were having a glorious time and their gay voices and gayer laughter echoed up and down the valley, dying away in elfin cadences among the trees. On the right the lights of Ingleside gleamed through the maple grove with the genial lure and invitation which seems always to glow in the beacons of a home where we know there is love and good-cheer and a welcome for all kin, whether of flesh or spirit. Mr. Meredith liked very well on occasion to spend an evening arguing with the doctor by the drift wood fire, where the famous china dogs of Ingleside kept ceaseless watch and ward, as became deities of the hearth, but to-night he did not look that way. Far on the western hill gleamed a paler but more alluring star. Mr. Meredith was on his way to see Rosemary West, and he meant to tell her something which had been slowly blossoming in his heart since their first meeting and had sprung into full flower on the evening when Faith had so warmly voiced her admiration for Rosemary. He had come to realize that he had learned to care for Rosemary. Not as he had cared for Cecilia, of course. That was entirely different. That love of romance and dream and glamour could never, he thought, return. But Rosemary was beautiful and sweet and dear—very dear. She was the best of companions. He was happier in her company than he had ever expected to be again. She would be an ideal mistress for his home, a good mother to his children. During the years of his widowhood Mr. Meredith had received innumerable hints from brother members of Presbytery and from many parishioners who could not be suspected of any ulterior motive, as well as from some who could, that he ought to marry again: But these hints never made any impression on him. It was commonly thought he was never aware of them. But he was quite acutely aware of them. And in his own occasional visitations of common sense he knew that the common sensible thing for him to do was to marry. But common sense was not the strong point of John Meredith, and to choose out, deliberately and cold-bloodedly, some “suitable” woman, as one might choose a housekeeper or a business partner, was something he was quite incapable of doing. How he hated that word “suitable.” It reminded him so strongly of James Perry. “A suit able woman of suit able age,” that unctuous brother of the cloth had said, in his far from subtle hint. For the moment John Meredith had had a perfectly unbelievable desire to rush madly away and propose marriage to the youngest, most unsuitable woman it was possible to discover. Mrs. Marshall Elliott was his good friend and he liked her. But when she had bluntly told him he should marry again he felt as if she had torn away the veil that hung before some sacred shrine of his innermost life, and he had been more or less afraid of her ever since. He knew there were women in his congregation “of suitable age” who would marry him quite readily. That fact had seeped through all his abstraction very early in his ministry in Glen St. Mary. They were good, substantial, uninteresting women, one or two fairly comely, the others not exactly so and John Meredith would as soon have thought of marrying any one of them as of hanging himself. He had some ideals to which no seeming necessity could make him false. He could ask no woman to fill Cecilia’s place in his home unless he could offer her at least some of the affection and homage he had given to his girlish bride. And where, in his limited feminine acquaintance, was such a woman to be found? Rosemary West had come into his life on that autumn evening bringing with her an atmosphere in which his spirit recognized native air. Across the gulf of strangerhood they clasped hands of friendship. He knew her better in that ten minutes by the hidden spring than he knew Emmeline Drew or Elizabeth Kirk or Amy Annetta Douglas in a year, or could know them, in a century. He had fled to her for comfort when Mrs. Alec Davis had outraged his mind and soul and had found it. Since then he had gone often to the house on the hill, slipping through the shadowy paths of night in Rainbow Valley so astutely that Glen gossip could never be absolutely certain that he did go to see Rosemary West. Once or twice he had been caught in the West living room by other visitors; that was all the Ladies’ Aid had to go by. But when Elizabeth Kirk heard it she put away a secret hope she had allowed herself to cherish, without a change of expression on her kind plain face, and Emmeline Drew resolved that the next time she saw a certain old bachelor of Lowbridge she would not snub him as she had done at a previous meeting. Of course, if Rosemary West was out to catch the minister she would catch him; she looked younger than she was and men thought her pretty; besides, the West girls had money! “It is to be hoped that he won’t be so absent-minded as to propose to Ellen by mistake,” was the only malicious thing she allowed herself to say to a sympathetic sister Drew. Emmeline bore no further grudge towards Rosemary. When all was said and done, an unencumbered bachelor was far better than a widower with four children. It had been only the glamour of the manse that had temporarily blinded Emmeline’s eyes to the better part. A sled with three shrieking occupants sped past Mr. Meredith to the pond. Faith’s long curls streamed in the wind and her laughter rang above that of the others. John Meredith looked after them kindly and longingly. He was glad that his children had such chums as the Blythes—glad that they had so wise and gay and tender a friend as Mrs. Blythe. But they needed something more, and that something would be supplied when he brought Rosemary West as a bride to the old manse. There was in her a quality essentially maternal. It was Saturday night and he did not often go calling on Saturday night, which was supposed to be dedicated to a thoughtful revision of Sunday’s sermon. But he had chosen this night because he had learned that Ellen West was going to be away and Rosemary would be alone. Often as he had spent pleasant evenings in the house on the hill he had never, since that first meeting at the spring, seen Rosemary alone. Ellen had always been there. He did not precisely object to Ellen being there. He liked Ellen West very much and they were the best of friends. Ellen had an almost masculine understanding and a sense of humour which his own shy, hidden appreciation of fun found very agreeable. He liked her interest in politics and world events. There was no man in the Glen, not even excepting Dr. Blythe, who had a better grasp of such things. “I think it is just as well to be interested in things as long as you live,” she had said. “If you’re not, it doesn’t seem to me that there’s much difference between the quick and the dead.” He liked her pleasant, deep, rumbly voice; he liked the hearty laugh with which she always ended up some jolly and well-told story. She never gave him digs about his children as other Glen women did; she never bored him with local gossip; she had no malice and no pettiness. She was always splendidly sincere. Mr. Meredith, who had picked up Miss Cornelia’s way of classifying people, considered that Ellen belonged to the race of Joseph. Altogether, an admirable woman for a sister-in-law. Nevertheless, a man did not want even the most admirable of women around when he was proposing to another woman. And Ellen was always around. She did not insist on talking to Mr. Meredith herself all the time. She let Rosemary have a fair share of him. Many evenings, indeed, Ellen effaced herself almost totally, sitting back in the corner with St. George in her lap, and letting Mr. Meredith and Rosemary talk and sing and read books together. Sometimes they quite forgot her presence. But if their conversation or choice of duets ever betrayed the least tendency to what Ellen considered philandering, Ellen promptly nipped that tendency in the bud and blotted Rosemary out for the rest of the evening. But not even the grimmest of amiable dragons can altogether prevent a certain subtle language of eye and smile and eloquent silence; and so the minister’s courtship progressed after a fashion. But if it was ever to reach a climax that climax must come when Ellen was away. And Ellen was so seldom away, especially in winter. She found her own fireside the pleasantest place in the world, she vowed. Gadding had no attraction for her. She was fond of company but she wanted it at home. Mr. Meredith had almost been driven to the conclusion that he must write to Rosemary what he wanted to say, when Ellen casually announced one evening that she was going to a silver wedding next Saturday night. She had been bridesmaid when the principals were married. Only old guests were invited, so Rosemary was not included. Mr. Meredith pricked up his ears a trifle and a gleam flashed into his dreamy dark eyes. Both Ellen and Rosemary saw it; and both Ellen and Rosemary felt, with a tingling shock, that Mr. Meredith would certainly come up the hill next Saturday night. “Might as well have it over with, St. George,” Ellen sternly told the black cat, after Mr. Meredith had gone home and Rosemary had silently gone upstairs. “He means to ask her, St. George—I’m perfectly sure of that. So he might as well have his chance to do it and find out he can’t get her, George. She’d rather like to take him, Saint. I know that—but she promised, and she’s got to keep her promise. I’m rather sorry in some ways, St. George. I don’t know of a man I’d sooner have for a brother-in-law if a brother-in-law was convenient. I haven’t a thing against him, Saint—not a thing except that he won’t see and can’t be made to see that the Kaiser is a menace to the peace of Europe. That’s his blind spot. But he’s good company and I like him. A woman can say anything she likes to a man with a mouth like John Meredith’s and be sure of not being misunderstood. Such a man is more precious than rubies, Saint—and much rarer, George. But he can’t have Rosemary—and I suppose when he finds out he can’t have her he’ll drop us both. And we’ll miss him, Saint—we’ll miss him something scandalous, George. But she promised, and I’ll see that she keeps her promise!” Ellen’s face looked almost ugly in its lowering resolution. Upstairs Rosemary was crying into her pillow. So Mr. Meredith found his lady alone and looking very beautiful. Rosemary had not made any special toilet for the occasion; she wanted to, but she thought it would be absurd to dress up for a man you meant to refuse. So she wore her plain dark afternoon dress and looked like a queen in it. Her suppressed excitement coloured her face to brilliancy, her great blue eyes were pools of light less placid than usual. She wished the interview were over. She had looked forward to it all day with dread. She felt quite sure that John Meredith cared a great deal for her after a fashion—and she felt just as sure that he did not care for her as he had cared for his first love. She felt that her refusal would disappoint him considerably, but she did not think it would altogether overwhelm him. Yet she hated to make it; hated for his sake and—Rosemary was quite honest with herself—for her own. She knew she could have loved John Meredith if—if it had been permissible. She knew that life would be a blank thing if, rejected as lover, he refused longer to be a friend. She knew that she could be very happy with him and that she could make him happy. But between her and happiness stood the prison gate of the promise she had made to Ellen years ago. Rosemary could not remember her father. He had died when she was only three years old. Ellen, who had been thirteen, remembered him, but with no special tenderness. He had been a stern, reserved man many years older than his fair, pretty wife. Five years later their brother of twelve died also; since his death the two girls had always lived alone with their mother. They had never mingled very freely in the social life of the Glen or Lowbridge, though where they went the wit and spirit of Ellen and the sweetness and beauty of Rosemary made them welcome guests. Both had what was called “a disappointment” in their girlhood. The sea had not given up Rosemary’s lover; and Norman Douglas, then a handsome, red-haired young giant, noted for wild driving and noisy though harmless escapades, had quarrelled with Ellen and left her in a fit of pique. There were not lacking candidates for both Martin’s and Norman’s places, but none seemed to find favour in the eyes of the West girls, who drifted slowly out of youth and bellehood without any seeming regret. They were devoted to their mother, who was a chronic invalid. The three had a little circle of home interests—books and pets and flowers—which made them happy and contented. Mrs. West’s death, which occurred on Rosemary’s twenty-fifth birthday, was a bitter grief to them. At first they were intolerably lonely. Ellen, especially, continued to grieve and brood, her long, moody musings broken only by fits of stormy, passionate weeping. The old Lowbridge doctor told Rosemary that he feared permanent melancholy or worse. Once, when Ellen had sat all day, refusing either to speak or eat, Rosemary had flung herself on her knees by her sister’s side. “Oh, Ellen, you have me yet,” she said imploringly. “Am I nothing to you? We have always loved each other so.” “I won’t have you always,” Ellen had said, breaking her silence with harsh intensity. “You will marry and leave me. I shall be left all alone. I cannot bear the thought—I cannot. I would rather die.” “I will never marry,” said Rosemary, “never, Ellen.” Ellen bent forward and looked searchingly into Rosemary’s eyes. “Will you promise me that solemnly?” she said. “Promise it on mother’s Bible.” Rosemary assented at once, quite willing to humour Ellen. What did it matter? She knew quite well she would never want to marry any one. Her love had gone down with Martin Crawford to the deeps of the sea; and without love she could not marry any one. So she promised readily, though Ellen made rather a fearsome rite of it. They clasped hands over the Bible, in their mother’s vacant room, and both vowed to each other that they would never marry and would always live together. Ellen’s condition improved from that hour. She soon regained her normal cheery poise. For ten years she and Rosemary lived in the old house happily, undisturbed by any thought of marrying or giving in marriage. Their promise sat very lightly on them. Ellen never failed to remind her sister of it whenever any eligible male creature crossed their paths, but she had never been really alarmed until John Meredith came home that night with Rosemary. As for Rosemary, Ellen’s obsession regarding that promise had always been a little matter of mirth to her—until lately. Now, it was a merciless fetter, self-imposed but never to be shaken off. Because of it to-night she must turn her face from happiness. It was true that the shy, sweet, rosebud love she had given to her boy-lover she could never give to another. But she knew now that she could give to John Meredith a love richer and more womanly. She knew that he touched deeps in her nature that Martin had never touched—that had not, perhaps, been in the girl of seventeen to touch. And she must send him away to-night—send him back to his lonely hearth and his empty life and his heart-breaking problems, because she had promised Ellen, ten years before, on their mother’s Bible, that she would never marry. John Meredith did not immediately grasp his opportunity. On the contrary, he talked for two good hours on the least lover-like of subjects. He even tried politics, though politics always bored Rosemary. The later began to think that she had been altogether mistaken, and her fears and expectations suddenly seemed to her grotesque. She felt flat and foolish. The glow went out of her face and the lustre out of her eyes. John Meredith had not the slightest intention of asking her to marry him. And then, quite suddenly, he rose, came across the room, and standing by her chair, he asked it. The room had grown terribly still. Even St. George ceased to purr. Rosemary heard her own heart beating and was sure John Meredith must hear it too. Now was the time for her to say no, gently but firmly. She had been ready for days with her stilted, regretful little formula. And now the words of it had completely vanished from her mind. She had to say no—and she suddenly found she could not say it. It was the impossible word. She knew now that it was not that she could have loved John Meredith, but that she did love him. The thought of putting him from her life was agony. She must say something; she lifted her bowed golden head and asked him stammeringly to give her a few days for—for consideration. John Meredith was a little surprised. He was not vainer than any man has a right to be, but he had expected that Rosemary West would say yes. He had been tolerably sure she cared for him. Then why this doubt—this hesitation? She was not a school girl to be uncertain as to her own mind. He felt an ugly shock of disappointment and dismay. But he assented to her request with his unfailing gentle courtesy and went away at once. “I will tell you in a few days,” said Rosemary, with downcast eyes and burning face. When the door shut behind him she went back into the room and wrung her hands. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (2004). Rainbow Valley. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/5343/pg5343-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

THE IMPOSSIBLE WORD

Anne of the Island by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . The Shadow of Change Chapter I - The Shadow of Change “Harvest is ended and summer is gone,” quoted Anne Shirley, gazing across the shorn fields dreamily. She and Diana Barry had been picking apples in the Green Gables orchard, but were now resting from their labors in a sunny corner, where airy fleets of thistledown drifted by on the wings of a wind that was still summer-sweet with the incense of ferns in the Haunted Wood. But everything in the landscape around them spoke of autumn. The sea was roaring hollowly in the distance, the fields were bare and sere, scarfed with golden rod, the brook valley below Green Gables overflowed with asters of ethereal purple, and the Lake of Shining Waters was blue—blue—blue; not the changeful blue of spring, nor the pale azure of summer, but a clear, steadfast, serene blue, as if the water were past all moods and tenses of emotion and had settled down to a tranquility unbroken by fickle dreams. “It has been a nice summer,” said Diana, twisting the new ring on her left hand with a smile. “And Miss Lavendar’s wedding seemed to come as a sort of crown to it. I suppose Mr. and Mrs. Irving are on the Pacific coast now.” “It seems to me they have been gone long enough to go around the world,” sighed Anne. “I can’t believe it is only a week since they were married. Everything has changed. Miss Lavendar and Mr. and Mrs. Allan gone—how lonely the manse looks with the shutters all closed! I went past it last night, and it made me feel as if everybody in it had died.” “We’ll never get another minister as nice as Mr. Allan,” said Diana, with gloomy conviction. “I suppose we’ll have all kinds of supplies this winter, and half the Sundays no preaching at all. And you and Gilbert gone—it will be awfully dull.” “Fred will be here,” insinuated Anne slyly. “When is Mrs. Lynde going to move up?” asked Diana, as if she had not heard Anne’s remark. “Tomorrow. I’m glad she’s coming—but it will be another change. Marilla and I cleared everything out of the spare room yesterday. Do you know, I hated to do it? Of course, it was silly—but it did seem as if we were committing sacrilege. That old spare room has always seemed like a shrine to me. When I was a child I thought it the most wonderful apartment in the world. You remember what a consuming desire I had to sleep in a spare room bed—but not the Green Gables spare room. Oh, no, never there! It would have been too terrible—I couldn’t have slept a wink from awe. I never walked through that room when Marilla sent me in on an errand—no, indeed, I tiptoed through it and held my breath, as if I were in church, and felt relieved when I got out of it. The pictures of George Whitefield and the Duke of Wellington hung there, one on each side of the mirror, and frowned so sternly at me all the time I was in, especially if I dared peep in the mirror, which was the only one in the house that didn’t twist my face a little. I always wondered how Marilla dared houseclean that room. And now it’s not only cleaned but stripped bare. George Whitefield and the Duke have been relegated to the upstairs hall. ‘So passes the glory of this world,’” concluded Anne, with a laugh in which there was a little note of regret. It is never pleasant to have our old shrines desecrated, even when we have outgrown them. “I’ll be so lonesome when you go,” moaned Diana for the hundredth time. “And to think you go next week!” “But we’re together still,” said Anne cheerily. “We mustn’t let next week rob us of this week’s joy. I hate the thought of going myself—home and I are such good friends. Talk of being lonesome! It’s I who should groan. You’ll be here with any number of your old friends—and Fred! While I shall be alone among strangers, not knowing a soul!” “Except Gilbert—and Charlie Sloane,” said Diana, imitating Anne’s italics and slyness. “Charlie Sloane will be a great comfort, of course,” agreed Anne sarcastically; whereupon both those irresponsible damsels laughed. Diana knew exactly what Anne thought of Charlie Sloane; but, despite sundry confidential talks, she did not know just what Anne thought of Gilbert Blythe. To be sure, Anne herself did not know that. “The boys may be boarding at the other end of Kingsport, for all I know,” Anne went on. “I am glad I’m going to Redmond, and I am sure I shall like it after a while. But for the first few weeks I know I won’t. I shan’t even have the comfort of looking forward to the weekend visit home, as I had when I went to Queen’s. Christmas will seem like a thousand years away.” “Everything is changing—or going to change,” said Diana sadly. “I have a feeling that things will never be the same again, Anne.” “We have come to a parting of the ways, I suppose,” said Anne thoughtfully. “We had to come to it. Do you think, Diana, that being grown-up is really as nice as we used to imagine it would be when we were children?” “I don’t know—there are some nice things about it,” answered Diana, again caressing her ring with that little smile which always had the effect of making Anne feel suddenly left out and inexperienced. “But there are so many puzzling things, too. Sometimes I feel as if being grown-up just frightened me—and then I would give anything to be a little girl again.” “I suppose we’ll get used to being grownup in time,” said Anne cheerfully. “There won’t be so many unexpected things about it by and by—though, after all, I fancy it’s the unexpected things that give spice to life. We’re eighteen, Diana. In two more years we’ll be twenty. When I was ten I thought twenty was a green old age. In no time you’ll be a staid, middle-aged matron, and I shall be nice, old maid Aunt Anne, coming to visit you on vacations. You’ll always keep a corner for me, won’t you, Di darling? Not the spare room, of course—old maids can’t aspire to spare rooms, and I shall be as ’umble as Uriah Heep, and quite content with a little over-the-porch or off-the-parlor cubby hole.” “What nonsense you do talk, Anne,” laughed Diana. “You’ll marry somebody splendid and handsome and rich—and no spare room in Avonlea will be half gorgeous enough for you—and you’ll turn up your nose at all the friends of your youth.” “That would be a pity; my nose is quite nice, but I fear turning it up would spoil it,” said Anne, patting that shapely organ. “I haven’t so many good features that I could afford to spoil those I have; so, even if I should marry the King of the Cannibal Islands, I promise you I won’t turn up my nose at you, Diana.” With another gay laugh the girls separated, Diana to return to Orchard Slope, Anne to walk to the Post Office. She found a letter awaiting her there, and when Gilbert Blythe overtook her on the bridge over the Lake of Shining Waters she was sparkling with the excitement of it. “Priscilla Grant is going to Redmond, too,” she exclaimed. “Isn’t that splendid? I hoped she would, but she didn’t think her father would consent. He has, however, and we’re to board together. I feel that I can face an army with banners—or all the professors of Redmond in one fell phalanx—with a chum like Priscilla by my side.” “I think we’ll like Kingsport,” said Gilbert. “It’s a nice old burg, they tell me, and has the finest natural park in the world. I’ve heard that the scenery in it is magnificent.” “I wonder if it will be—can be—any more beautiful than this,” murmured Anne, looking around her with the loving, enraptured eyes of those to whom “home” must always be the loveliest spot in the world, no matter what fairer lands may lie under alien stars. They were leaning on the bridge of the old pond, drinking deep of the enchantment of the dusk, just at the spot where Anne had climbed from her sinking Dory on the day Elaine floated down to Camelot. The fine, empurpling dye of sunset still stained the western skies, but the moon was rising and the water lay like a great, silver dream in her light. Remembrance wove a sweet and subtle spell over the two young creatures. “You are very quiet, Anne,” said Gilbert at last. “I’m afraid to speak or move for fear all this wonderful beauty will vanish just like a broken silence,” breathed Anne. Gilbert suddenly laid his hand over the slender white one lying on the rail of the bridge. His hazel eyes deepened into darkness, his still boyish lips opened to say something of the dream and hope that thrilled his soul. But Anne snatched her hand away and turned quickly. The spell of the dusk was broken for her. “I must go home,” she exclaimed, with a rather overdone carelessness. “Marilla had a headache this afternoon, and I’m sure the twins will be in some dreadful mischief by this time. I really shouldn’t have stayed away so long.” She chattered ceaselessly and inconsequently until they reached the Green Gables lane. Poor Gilbert hardly had a chance to get a word in edgewise. Anne felt rather relieved when they parted. There had been a new, secret self-consciousness in her heart with regard to Gilbert, ever since that fleeting moment of revelation in the garden of Echo Lodge. Something alien had intruded into the old, perfect, school-day comradeship—something that threatened to mar it. “I never felt glad to see Gilbert go before,” she thought, half-resentfully, half-sorrowfully, as she walked alone up the lane. “Our friendship will be spoiled if he goes on with this nonsense. It mustn’t be spoiled—I won’t let it. Oh, why can’t boys be just sensible!” Anne had an uneasy doubt that it was not strictly “sensible” that she should still feel on her hand the warm pressure of Gilbert’s, as distinctly as she had felt it for the swift second his had rested there; and still less sensible that the sensation was far from being an unpleasant one—very different from that which had attended a similar demonstration on Charlie Sloane’s part, when she had been sitting out a dance with him at a White Sands party three nights before. Anne shivered over the disagreeable recollection. But all problems connected with infatuated swains vanished from her mind when she entered the homely, unsentimental atmosphere of the Green Gables kitchen where an eight-year-old boy was crying grievously on the sofa. “What is the matter, Davy?” asked Anne, taking him up in her arms. “Where are Marilla and Dora?” “Marilla’s putting Dora to bed,” sobbed Davy, “and I’m crying ’cause Dora fell down the outside cellar steps, heels over head, and scraped all the skin off her nose, and—” “Oh, well, don’t cry about it, dear. Of course, you are sorry for her, but crying won’t help her any. She’ll be all right tomorrow. Crying never helps any one, Davy-boy, and—” “I ain’t crying ’cause Dora fell down cellar,” said Davy, cutting short Anne’s wellmeant preachment with increasing bitterness. “I’m crying, cause I wasn’t there to see her fall. I’m always missing some fun or other, seems to me.” “Oh, Davy!” Anne choked back an unholy shriek of laughter. “Would you call it fun to see poor little Dora fall down the steps and get hurt?” “She wasn’t much hurt,” said Davy, defiantly. “’Course, if she’d been killed I’d have been real sorry, Anne. But the Keiths ain’t so easy killed. They’re like the Blewetts, I guess. Herb Blewett fell off the hayloft last Wednesday, and rolled right down through the turnip chute into the box stall, where they had a fearful wild, cross horse, and rolled right under his heels. And still he got out alive, with only three bones broke. Mrs. Lynde says there are some folks you can’t kill with a meat-axe. Is Mrs. Lynde coming here tomorrow, Anne?” “Yes, Davy, and I hope you’ll be always very nice and good to her.” “I’ll be nice and good. But will she ever put me to bed at nights, Anne?” “Perhaps. Why?” “’Cause,” said Davy very decidedly, “if she does I won’t say my prayers before her like I do before you, Anne.” “Why not?” “’Cause I don’t think it would be nice to talk to God before strangers, Anne. Dora can say hers to Mrs. Lynde if she likes, but I won’t. I’ll wait till she’s gone and then say ’em. Won’t that be all right, Anne?” “Yes, if you are sure you won’t forget to say them, Davy-boy.” “Oh, I won’t forget, you bet. I think saying my prayers is great fun. But it won’t be as good fun saying them alone as saying them to you. I wish you’d stay home, Anne. I don’t see what you want to go away and leave us for.” “I don’t exactly want to, Davy, but I feel I ought to go.” “If you don’t want to go you needn’t. You’re grown up. When I’m grown up I’m not going to do one single thing I don’t want to do, Anne.” “All your life, Davy, you’ll find yourself doing things you don’t want to do.” “I won’t,” said Davy flatly. “Catch me! I have to do things I don’t want to now ’cause you and Marilla’ll send me to bed if I don’t. But when I grow up you can’t do that, and there’ll be nobody to tell me not to do things. Won’t I have the time! Say, Anne, Milty Boulter says his mother says you’re going to college to see if you can catch a man. Are you, Anne? I want to know.” For a second Anne burned with resentment. Then she laughed, reminding herself that Mrs. Boulter’s crude vulgarity of thought and speech could not harm her. “No, Davy, I’m not. I’m going to study and grow and learn about many things.” “What things?” “‘Shoes and ships and sealing wax And cabbages and kings,’” quoted Anne. “But if you did want to catch a man how would you go about it? I want to know,” persisted Davy, for whom the subject evidently possessed a certain fascination. “You’d better ask Mrs. Boulter,” said Anne thoughtlessly. “I think it’s likely she knows more about the process than I do.” “I will, the next time I see her,” said Davy gravely. “Davy! If you do!” cried Anne, realizing her mistake. “But you just told me to,” protested Davy aggrieved. “It’s time you went to bed,” decreed Anne, by way of getting out of the scrape. After Davy had gone to bed Anne wandered down to Victoria Island and sat there alone, curtained with fine-spun, moonlit gloom, while the water laughed around her in a duet of brook and wind. Anne had always loved that brook. Many a dream had she spun over its sparkling water in days gone by. She forgot lovelorn youths, and the cayenne speeches of malicious neighbors, and all the problems of her girlish existence. In imagination she sailed over storied seas that wash the distant shining shores of “faery lands forlorn,” where lost Atlantis and Elysium lie, with the evening star for pilot, to the land of Heart’s Desire. And she was richer in those dreams than in realities; for things seen pass away, but the things that are unseen are eternal. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (2006). Anne of the Island. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/51/pg51-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html. The lead image was generated using HackerNoon's Stable Disffusion Al Image Generator feature, via the prompt "Anne of the Island".

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