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Building a Better Blockchain: Lessons in Innovation and Adoption

Once at Last.fm we had an integer overflow in an identify field. I can’t recall where exactly. But I do remember that the inconvenience of having a bunch of Hadoop jobs disrupted while we rushed to update the relevant type couldn’t spoil the collective pride for having more than 2 billion of whatever needed so many ids. Being frugal with identifiers is seldom a good idea, but for me the worst identifier-related headaches came from IDs that had semantic value. At Tuenti (once the largest social network in Spain) there was a concept similar to Facebook pages. Pages had types and subtypes. A page type might have been “group”, which had subtypes “business” or “community”. Another type could be “place” with subtypes like “store” or “landmark”. Page identifiers were strings composed by concatenating numeric identifiers of the type, subtype, and then an increment field in a DB. If you visited https://tuenti.com/p/3_2_6691 you’d instantly know the meaning. It was a “place” (3) of type “store” (2), and the store ID was 6691. Page IDs decomposed in this way would be useful for multiple purposes. Choosing a type-specific implementation of controllers to compose the relevant page, routing to a database shard, that kind of thing. At some point Product wanted to change the classification of pages. To their frustration, this became problematic because the entire taxonomy was encrusted across the code all the way from URLs to databases. Another example are New Relic entities. Entities are an abstraction that broadly represents anything that can send telemetry to New Relic. A host, a Kubernetes cluster, an application, a JVM or a network router can be entities. Of course those are all things you want to identify, so entities have Global Unique IDentifier, or GUID. Every single telemetry datapoint is stamped with the GUID of the entity that produced it, so they act as the keystone of the New Relic platform. Features like Service maps, distributed tracing, entity relationships, and many others are built upon them. Entity GUIDs have meaning. An example of a GUID is 1|APM|APPLICATION|23 where 1 is the account, APM is the domain, APPLICATION is the unique type within that domain, and 23 is a unique identifier within the domain and type. That application might be running in a host 1|INFRA|HOST|12. If we wanted to store that relation, we’d have an entry in some database saying: "1|INFRA|HOST|12" RUNS "1|APM|APPLICATION|23". Like at Tuenti, these semantics come handy. If you’re processing millions of telemetry datapoints per second, it’s useful to tell the type of reporting entity on the fly by decomposing the GUID, rather than perform an expensive lookup to an external service. Account IDs can be used to route data to cells (this talk from Andrew Bloomgarden explains how NR used this pattern to scale). Domains like INFRA or APM corresponded to the original product verticals at New Relic. Years later Product decided (with good criteria) that they created unnecessary fragmentation on the user experience. Types change, get renamed or merged with others. Sometimes (more often than it seems) entities had to migrate from one account to another. In all these cases you would be altering the identifier of many entities. It was painful but possible to work around many of these problems. But replacing GUIDs with semantic-free identifiers was straight impossible. By virtue of being present in thousands of URLs, NRQL queries, etc. GUIDs had become a public API that thousands of customers relied upon. A technical solution to replace identifiers would have been a major project, but doable. What wasn’t possible was to run a find/replace across the private documentation and workflows of your entire customer base. If you look closely, the world is full of semantic identifiers. Sometimes they are hard to avoid, but almost always a pain in the neck. Because they embed a specific model of the world. But models become obsolete faster than we’d like. Addresses make notable examples. The “complex and idiosyncratic” Japanese address system reflects the organic growth of its urban areas. In British postal codes the final part can designate anything from a street to a flat depending on the amount of mail received by the premises. When I was a kid, license plates would give up the province where the owner lived causing an array of nuisances. They were alleviated with the adoption of European standards. But partly. The root problem being that, like the Domain Name System identifiers (“Galo’s website”) remain tied to administrative authorities (“.net”), which can change regulations, or even disappear. Nowadays I find most semantic identifiers in resource management. For some reason, when infrastructure teams define access rules to the resources of a particular service, they prefer to create a group named after the team that owns that service, something like “owner-team-access-list”. Identifiers tied to the org chart don’t like it when the service moves to another team, or owner-team is reorged away. Once at Last.fm we had an integer overflow in an identify field. I can’t recall where exactly. But I do remember that the inconvenience of having a bunch of Hadoop jobs disrupted while we rushed to update the relevant type couldn’t spoil the collective pride for having more than 2 billion of whatever needed so many ids. Last.fm Being frugal with identifiers is seldom a good idea, but for me the worst identifier-related headaches came from IDs that had semantic value. At Tuenti (once the largest social network in Spain) there was a concept similar to Facebook pages. Pages had types and subtypes. A page type might have been “group”, which had subtypes “business” or “community”. Another type could be “place” with subtypes like “store” or “landmark”. Tuenti Page identifiers were strings composed by concatenating numeric identifiers of the type, subtype, and then an increment field in a DB. If you visited https://tuenti.com/p/3_2_6691 you’d instantly know the meaning. It was a “place” (3) of type “store” (2), and the store ID was 6691. Page IDs decomposed in this way would be useful for multiple purposes. Choosing a type-specific implementation of controllers to compose the relevant page, routing to a database shard, that kind of thing. https://tuenti.com/p/3_2_6691 At some point Product wanted to change the classification of pages. To their frustration, this became problematic because the entire taxonomy was encrusted across the code all the way from URLs to databases. Another example are New Relic entities . Entities are an abstraction that broadly represents anything that can send telemetry to New Relic. A host, a Kubernetes cluster, an application, a JVM or a network router can be entities. Of course those are all things you want to identify, so entities have Global Unique IDentifier, or GUID. Every single telemetry datapoint is stamped with the GUID of the entity that produced it, so they act as the keystone of the New Relic platform. Features like Service maps, distributed tracing, entity relationships, and many others are built upon them. New Relic entities Entity GUIDs have meaning . An example of a GUID is 1|APM|APPLICATION|23 where 1 is the account, APM is the domain, APPLICATION is the unique type within that domain, and 23 is a unique identifier within the domain and type. That application might be running in a host 1|INFRA|HOST|12 . If we wanted to store that relation, we’d have an entry in some database saying: Entity GUIDs have meaning 1|APM|APPLICATION|23 APM APPLICATION 23 1|INFRA|HOST|12 "1|INFRA|HOST|12" RUNS "1|APM|APPLICATION|23" . "1|INFRA|HOST|12" RUNS "1|APM|APPLICATION|23" Like at Tuenti, these semantics come handy. If you’re processing millions of telemetry datapoints per second, it’s useful to tell the type of reporting entity on the fly by decomposing the GUID, rather than perform an expensive lookup to an external service. Account IDs can be used to route data to cells ( this talk from Andrew Bloomgarden explains how NR used this pattern to scale). this talk from Andrew Bloomgarden Domains like INFRA or APM corresponded to the original product verticals at New Relic. Years later Product decided (with good criteria) that they created unnecessary fragmentation on the user experience. Types change, get renamed or merged with others. Sometimes (more often than it seems) entities had to migrate from one account to another. In all these cases you would be altering the identifier of many entities. INFRA APM It was painful but possible to work around many of these problems. But replacing GUIDs with semantic-free identifiers was straight impossible. By virtue of being present in thousands of URLs, NRQL queries, etc. GUIDs had become a public API that thousands of customers relied upon. A technical solution to replace identifiers would have been a major project, but doable. What wasn’t possible was to run a find/replace across the private documentation and workflows of your entire customer base. If you look closely, the world is full of semantic identifiers. Sometimes they are hard to avoid, but almost always a pain in the neck. Because they embed a specific model of the world. But models become obsolete faster than we’d like. Addresses make notable examples. The “complex and idiosyncratic” Japanese address system reflects the organic growth of its urban areas. In British postal codes the final part can designate anything from a street to a flat depending on the amount of mail received by the premises. Japanese address system British postal codes When I was a kid, license plates would give up the province where the owner lived causing an array of nuisances . They were alleviated with the adoption of European standards . But partly. The root problem being that, like the Domain Name System identifiers (“Galo’s website”) remain tied to administrative authorities (“.net”), which can change regulations, or even disappear. array of nuisances European standards Domain Name System Nowadays I find most semantic identifiers in resource management. For some reason, when infrastructure teams define access rules to the resources of a particular service, they prefer to create a group named after the team that owns that service, something like “owner-team-access-list”. Identifiers tied to the org chart don’t like it when the service moves to another team, or owner-team is reorged away.

Identifiers are better off without meaning

Storing Passwords In A Database: A Better Way to Do So

The TechBeat: Can AI Audit Smart Contracts Better than Human Auditors? (4/15/2024)

The Noonification: UX Considerations for Better Multi-Factor Authentication (4/12/2024)

Keeping online identities and sensitive data safe is always critical. Cyberthreats are constantly growing more sophisticated, so defenses must grow with them. Part of keeping users’ information secure is through multifactor authentication (MFA). This tool adds layers of protection beyond the traditional password. Although MFA can enhance security, developers should also implement it to improve the user experience. Maintaining both assets empowers users, ensuring a wider adoption of MFA while bolstering their overall security. What Is Multifactor Authentication? MFA is a security system that requires users to provide more than one form of verification to prove their identity. It acts as a multilayered shield protecting accounts and information. Instead of simply asking for a password, MFA adds one or two more questions to the mix. It may request a code sent to someone’s phone or a fingerprint or facial recognition. Combining different pieces of evidence ensures it’s the actual user trying to access their account and not someone else. Is this extra work worth it? According to security experts, the answer is yes. Research suggests that MFA can stop about 80%-90% of cyberattacks, which rely on stealing or guessing simple passwords. A hacker can get a password, but they still need the other pieces of the puzzle to break into an account. MFAs deter cyberattackers from hacking users by adding more hurdles, making unauthorized access more challenging. They add more protections to ensure online accounts remain secure. The Importance of Good UX Security measures are crucial for protecting online activities. However, the success of those safety protocols depends on the user experience. UX is a concept that impacts how people interact with technology. Don Norman — the godfather of UX — introduced this term in 1993 and stressed the importance of designing functional and user-friendly technology. Good UX is about making tech as intuitive as engaging in everyday activities. When devices and platforms feel effortless, users are more likely to integrate them into their daily lives. Products designed with UX in mind should enable people to interact with them seamlessly without interruption. Simultaneously, UX should balance protective security measures well without imposing unnecessary barriers. Therefore, MFA should feel effortless and ensure users feel safe and secure in a digital environment. Enable Copy-Paste for Passwords Making account access convenient is crucial for the overall user experience. One feature that enhances this process is copying and pasting passwords. While turning off copy-paste functionality may seem intuitive to avoid cyberattacks, it keeps users from adopting secure login management practices. Around 30% of people worldwide rely on password managers to secure their login information. These digital vaults allow users to store passwords encrypted by a master password. These tools can generate and store complex identifiers for every account, significantly boosting security. Enabling copy and paste allows individuals to leverage their password manager without memorizing or manually entering them. Implement Adaptive Authentication Methods Adaptive or risk-based authentication can achieve a seamless yet secure user experience. This process evaluates the risk level or a login attempt in real-time. It considers various factors, such as someone’s location, device, network, time of access, and behavior patterns. Based on its assessment, it decides whether to allow or deny access or request additional verification. Using adaptive authentication is helpful to users because it offers swift and hassle-free access. However, when the system detects unusual behavior, it automatically introduces additional authentication steps. This security measure operates on the principle that security should be as stringent as necessary but as unobtrusive as possible. It respects people’s time and effort, making safety measures supportive rather than disruptive. Create Fallback Options A good digital security strategy recognizes that even the best plans can encounter unexpected challenges. This is especially true for MFA, where the primary verification method could be unavailable. Implementing fallback options is a critical UX consideration to ensure continuous access while maintaining safety. Fallback options are alternative methods for verifying a user’s identity when the primary function is unavailable. For example, if someone typically receives a verification code via SMS but doesn’t have cell service, a fallback option could be to send it via email. These other methods are essential for avoiding lockouts and ensuring people always have access to their accounts. When designing fallback options, each method should be secure enough to prevent unauthorized access but not so cumbersome that it frustrates the user. Fallback options can include backup codes, other communication channels, and security questions. Streamline the Authentication Flow How can developers seamlessly integrate MFA into the user’s journey? Keep the primary objective in mind, which is to minimize friction while maintaining high-end security. A smooth authentication process involves simplifying input. Reduce the amount of information people need to provide by leveraging data already available. This will keep MFA straightforward and encourage users to adopt this protection method. Another approach is to implement single sign-on (SSO). Using this in MFA reduces the need for multiple logins. Once users have verified their identity, they can access their applications without having to authenticate again. The fewer steps provided, the easier it is for someone to maintain strong safety. However, it is important to realize that while streamlining is desirable, maintaining security is crucial. Simplifying the authentication process without making it too easy for intruders can be challenging. That is why continuously refining the flow is vital to ensure it meets users’ needs and security protocols. Regularly Test and Update Cybersecurity threats are constantly evolving. Regular testing and updating are necessary to ensure MFA systems remain effective while providing a positive user experience. This helps identify potential vulnerabilities and improve functionality even when new security issues emerge. Testing involves simulating various scenarios to ensure MFA is working as intended. This includes checking for usability with actual users to ensure the system is accessible. It also helps identify areas where designers can enhance people’s experience by reducing time or streamlining actionable steps. Simplifying MFA Without Sacrificing Security Quality While multifactor authentication can enhance digital security, its success relies on a user-centric approach. Developers should follow the latest design principles and user feedback so more people can use the safest system. Utilizing both approaches will help individuals feel confident while encouraging them to keep their MFA settings on. Keeping online identities and sensitive data safe is always critical. Cyberthreats are constantly growing more sophisticated, so defenses must grow with them. Part of keeping users’ information secure is through multifactor authentication (MFA). This tool adds layers of protection beyond the traditional password. Although MFA can enhance security , developers should also implement it to improve the user experience. Maintaining both assets empowers users, ensuring a wider adoption of MFA while bolstering their overall security. security What Is Multifactor Authentication? MFA is a security system that requires users to provide more than one form of verification to prove their identity. It acts as a multilayered shield protecting accounts and information. users to provide more than one form of verification users to provide more than one form of verification Instead of simply asking for a password, MFA adds one or two more questions to the mix. It may request a code sent to someone’s phone or a fingerprint or facial recognition. Combining different pieces of evidence ensures it’s the actual user trying to access their account and not someone else. Is this extra work worth it? According to security experts, the answer is yes. Research suggests that MFA can stop about 80%-90% of cyberattacks , which rely on stealing or guessing simple passwords. A hacker can get a password, but they still need the other pieces of the puzzle to break into an account. about 80%-90% of cyberattacks about 80%-90% of cyberattacks MFAs deter cyberattackers from hacking users by adding more hurdles, making unauthorized access more challenging. They add more protections to ensure online accounts remain secure. The Importance of Good UX Security measures are crucial for protecting online activities. However, the success of those safety protocols depends on the user experience. UX is a concept that impacts how people interact with technology. Don Norman — the godfather of UX — introduced this term in 1993 and stressed the importance of designing functional and user-friendly technology. introduced this term in 1993 introduced this term in 1993 Good UX is about making tech as intuitive as engaging in everyday activities. When devices and platforms feel effortless, users are more likely to integrate them into their daily lives. Products designed with UX in mind should enable people to interact with them seamlessly without interruption. Simultaneously, UX should balance protective security measures well without imposing unnecessary barriers. Therefore, MFA should feel effortless and ensure users feel safe and secure in a digital environment. Enable Copy-Paste for Passwords Making account access convenient is crucial for the overall user experience. One feature that enhances this process is copying and pasting passwords. While turning off copy-paste functionality may seem intuitive to avoid cyberattacks, it keeps users from adopting secure login management practices. Around 30% of people worldwide rely on password managers to secure their login information. These digital vaults allow users to store passwords encrypted by a master password. These tools can generate and store complex identifiers for every account, significantly boosting security. Enabling copy and paste allows individuals to leverage their password manager without memorizing or manually entering them. 30% of people worldwide 30% of people worldwide Implement Adaptive Authentication Methods Adaptive or risk-based authentication can achieve a seamless yet secure user experience. This process evaluates the risk level or a login attempt in real-time. It considers various factors, such as someone’s location, device, network, time of access, and behavior patterns. Based on its assessment, it decides whether to allow or deny access or request additional verification. Using adaptive authentication is helpful to users because it offers swift and hassle-free access. However, when the system detects unusual behavior, it automatically introduces additional authentication steps. This security measure operates on the principle that security should be as stringent as necessary but as unobtrusive as possible. It respects people’s time and effort, making safety measures supportive rather than disruptive. Create Fallback Options A good digital security strategy recognizes that even the best plans can encounter unexpected challenges. This is especially true for MFA, where the primary verification method could be unavailable. Implementing fallback options is a critical UX consideration to ensure continuous access while maintaining safety. Fallback options are alternative methods for verifying a user’s identity when the primary function is unavailable. For example, if someone typically receives a verification code via SMS but doesn’t have cell service, a fallback option could be to send it via email. These other methods are essential for avoiding lockouts and ensuring people always have access to their accounts. When designing fallback options, each method should be secure enough to prevent unauthorized access but not so cumbersome that it frustrates the user. Fallback options can include backup codes, other communication channels, and security questions. Streamline the Authentication Flow How can developers seamlessly integrate MFA into the user’s journey? Keep the primary objective in mind, which is to minimize friction while maintaining high-end security. A smooth authentication process involves simplifying input. Reduce the amount of information people need to provide by leveraging data already available. This will keep MFA straightforward and encourage users to adopt this protection method. Another approach is to implement single sign-on (SSO). Using this in MFA reduces the need for multiple logins. Once users have verified their identity, they can access their applications without having to authenticate again. The fewer steps provided, the easier it is for someone to maintain strong safety. without having to authenticate without having to authenticate However, it is important to realize that while streamlining is desirable, maintaining security is crucial. Simplifying the authentication process without making it too easy for intruders can be challenging. That is why continuously refining the flow is vital to ensure it meets users’ needs and security protocols. Regularly Test and Update Cybersecurity threats are constantly evolving. Regular testing and updating are necessary to ensure MFA systems remain effective while providing a positive user experience. This helps identify potential vulnerabilities and improve functionality even when new security issues emerge. Testing involves simulating various scenarios to ensure MFA is working as intended. This includes checking for usability with actual users to ensure the system is accessible. It also helps identify areas where designers can enhance people’s experience by reducing time or streamlining actionable steps. checking for usability with actual users checking for usability with actual users Simplifying MFA Without Sacrificing Security Quality While multifactor authentication can enhance digital security, its success relies on a user-centric approach. Developers should follow the latest design principles and user feedback so more people can use the safest system. Utilizing both approaches will help individuals feel confident while encouraging them to keep their MFA settings on.

UX Considerations for Better Multi-Factor Authentication

উন্নত মাল্টি-ফ্যাক্টর প্রমাণীকরণের জন্য UX বিবেচনা

UX-Überlegungen für eine bessere Multi-Faktor-Authentifizierung

Consideraciones de UX para una mejor autenticación multifactor

Considérations UX pour une meilleure authentification multifacteur

बेहतर मल्टी-फैक्टर प्रमाणीकरण के लिए UX विचार

より良い多要素認証のためのUXの考慮事項

더 나은 다단계 인증을 위한 UX 고려 사항

Considerações de UX para melhor autenticação multifator

Daha İyi Çok Faktörlü Kimlik Doğrulama için Kullanıcı Deneyimi Konuları

Những cân nhắc về trải nghiệm người dùng để xác thực đa yếu tố tốt hơn

更好的多因素身份验证的用户体验注意事项

Radix Engine: A Better Model for “Enshrinement"

রেডিক্স ইঞ্জিন: "এনশ্রাইনমেন্ট" এর জন্য একটি ভাল মডেল

Radix Engine: Ein besseres Modell für „Enshrinement

Radix Engine: un mejor modelo para la “consagración”

Moteur Radix : un meilleur modèle pour « l’enchâssement »

रेडिक्स इंजन: “प्रतिष्ठा के लिए एक बेहतर मॉडल”

Radix Engine: 「殿堂入り」のより良いモデル

Radix 엔진: "Enshrinement"를 위한 더 나은 모델

Radix Engine: um modelo melhor para “consagração

Radix Engine: лучшая модель для «закрепления»

Radix Engine: “Kutsallaştırma” için Daha İyi Bir Model

Radix Engine: Một mô hình tốt hơn cho “Bảo quản

Radix Engine：一个更好的“供奉”模型

Smart contracts hold massive potential. But with great power comes great responsibility, and ensuring the security of these smart contracts is critical. This is where auditing comes in. With its superhuman processing power, can outperform human auditors in this critical role of keeping smart contracts safe? We’ll discuss the answer in this article. AI The Case for AI Speed and Scale: AI can analyze massive amounts of code swiftly, identifying patterns and potential vulnerabilities that might take humans weeks. This is crucial in the fast-paced world of blockchain development. Machine Learning Advantage: AI can learn from vast datasets of past exploits and successful audits, continuously improving its ability to detect novel threats. Reduced Bias: Human auditors are susceptible to biases, but AI can approach audits with cold objectivity. This is an AI-based on the audit of a protocol named Lockon, which allows for index investments in crypto. The report was generated in approximately 48 hours. The Lockon team was surprised to learn that this was an AI-based report because they found the points about vulnerabilities to be accurate. report While AI-based audits are not yet perfect, they offer significant benefits for projects looking to reduce audit costs. They are also increasingly used as a “Pre Audit” before engaging traditional audit firms, as identifying critical bugs in advance can reduce the costs paid to audit firms. Moreover, integrating AI-based audit services into the CI/CD process is beginning to be seen as a way to improve code quality. The Case for Human Auditors Understanding Context: Smart contracts don't exist in a vacuum. They interact with complex systems. Unlike humans, AI might need help to grasp the nuances of these interactions and the potential security implications. Explainability and Judgment Calls: When an AI flags an issue, explaining the cause and reason for the issue or vulnerability is crucial. Human auditors can interpret the code, assess the risk, and make informed decisions. Creativity and Unforeseen Threats: Cybercriminals are constantly innovating. AI might miss new attack vectors, while human auditors can leverage their experience and creativity to consider these possibilities. Many may argue that AI can do this given its machine-learning capabilities, but again, human intelligence has the upper hand. No doubt, AI is a game-changer for smart contract audits, but it's not a silver bullet. The future lies in collaboration. Imagine AI rapidly scanning code, highlighting potential risks, and freeing up human auditors to focus on complex scenarios and make final judgments. This powerful partnership can ensure the secure and efficient future of smart contracts. A Quick Comparison An example of an AI-based audit firm, between AI-based audits and human audits. Bunzz Audit has published a comparison The Bunzz team states, "Our research and development results have led us to conclude that a database plus AI approach is more suited for detecting vulnerability patterns than humans." The Future of AI Audits In February 2024, Vitalik Buterin highlighted the potential of AI in aiding formal verification of code and bug finding. “One application of AI that I am excited about is AI-assisted formal verification of code and bug finding,” he stated, adding: “Right now, Ethereum’s biggest technical risk probably is bugs in code, and anything that could significantly change the game would be amazing.” Blockchain projects rely on code, and just like any code, it can have errors. Formal Verification is a tool that helps identify these weaknesses in the logic of blockchain projects. By using Formal Verification, web3 builders can ensure their projects are more reliable and trustworthy. Imagine a future where Formal Verification is even more powerful. Some experts believe this advancement could be just as significant for blockchain technology as Zero-Knowledge proofs, a different technology that enhances privacy. However, there's a hurdle. Writing clear and perfect instructions for code, called "specifications," is currently expensive and time-consuming. Here's where things get interesting: some, like Vitalik Buterin, and front-runners like . I believe AI  could be the key to making this process cheaper and easier. Bunzz Audit

Can AI Audit Smart Contracts Better than Human Auditors?

এআই কি হিউম্যান অডিটরদের চেয়ে স্মার্ট চুক্তির অডিট করতে পারে?

Kann KI Smart Contracts besser prüfen als menschliche Prüfer?

¿Puede la IA auditar contratos inteligentes mejor que los auditores humanos?

L’IA peut-elle mieux auditer les contrats intelligents que les auditeurs humains ?

क्या AI स्मार्ट कॉन्ट्रैक्ट्स का ऑडिट मानव ऑडिटर्स से बेहतर कर सकता है?

AI は人間の監査人よりもスマート コントラクトを監査できますか?

AI가 인간 감사자보다 스마트 계약을 더 잘 감사할 수 있습니까?

A IA pode auditar contratos inteligentes melhor do que auditores humanos?

Может ли ИИ проверять смарт-контракты лучше, чем люди-аудиторы?

Yapay Zeka Akıllı Sözleşmeleri İnsan Denetçilerden Daha İyi Denetleyebilir mi?

AI có thể kiểm tra hợp đồng thông minh tốt hơn kiểm toán viên con người không?

人工智能能比人类审计员更好地审计智能合约吗？

I’ve started a new contract where I'm tasked with consolidating the company's infrastructure under AWS. They (the company) are starting to see some serious growth and have a team in place currently using PaaS and various other cloud providers. Infrastructure as code tools have been popular for a long time; I've mostly been a bit skeptical about using them given that with a small team size, the UI is often more than enough. But, when you need to start spinning up multiple environments (dev, staging, prod, demo, etc.), this starts to become a lot of overhead. An issue I've often seen is that it becomes the responsibility of one or two developers to maintain this as the rest of the team is not familiar with the tool and language syntax. Obviously, this is not ideal, so I wanted to research the space to see if there was a middle way where you can get the benefits of IaC, without the overhead of having to learn a new language and tool. I've previously used Terraform in other companies I've done work for but never from scratch and (full disclosure) always found the experience rather frustrating. So, I began to search for terraform alternatives and stumbled upon Pulumi which lets you provision resources using a number of programming languages (including Go), and Pulumi's AWS provider support is excellent. But, as in any other situation where you are considering marrying yourself to a new tool, it's very worthwhile to do some research and preferably build some small proof of concept before you commit. I've had some interesting discoveries through this process, why I choose pulumi over Terraform, and when I would consider Terraform over Pulumi. Short Intro to Terraform I think it's fair to say that Terraform has been the big dog in the IaC space for a long time. They've support for the majority of cloud providers, have been battle-tested at scale, and have a bunch of great tutorials. They also used to have an open source license, using Mozilla Public License v2, but decided recently to switch to what is known as the Business License, BSL, which can be considered as "source-available." I will not go into a longer debate about why this is not ideal or if it's fair or not, but it does pose a threat to the development of third-party tools and your options in terms of making tweaks if needed. For the unfamiliar, Terraform is written in its own "DSL" known as HCL. It's a declarative language, so you tell it the desired state you want your infrastructure to be in and Terraform figures out how to get your infra to that state. A short example of how you would provision a S3 bucket would look like this: 1resource "aws_s3_bucket" "my_bucket" {
2  bucket = "my-bucket"
3  acl    = "private"
4} Which would be written in a file with the extension. As we'll see later on, you have lots of options in terms of structuring your code into modules and different types of options for code reuse: blocks, , , etc. .tf data variables outputs Short Intro to Pulumi Pulumi is just a very strange name. Growing up, I had a friend we for some reason called Pummi (or maybe it was bummy, can't recall for sure), and all I can see when reading the name is his face. They're also rather new in the IaC space but have made some significant advances. Some years ago, I worked at a place that utilized AWS's CDK, and I imagine people would feel pulumi to be quite similar. You have a range of choices in terms of the language you want to use, JS/TS, Python, Go, etc, all have the same API, so switching be the biggest issue. I write all my stuff in Go these days, so the same example as above written using Go and Pulumi would look like this: shouldn't 1func main() {
 2	pulumi.Run(func(ctx *pulumi.Context) error {
 3		_, err := s3.NewBucket(ctx, "my-bucket", &s3.BucketArgs{
 4			Bucket: pulumi.String("my-bucket"),
 5			Acl:    pulumi.String("private"),
 6		})
 7		if err != nil {
 8			return err
 9		}
10		return nil
11	})
12} To anyone who has ever written Go, this should immediately make sense, even without knowing the Pulumi API or the AWS ecosystem. And this is one of the main benefits we will get back to, again and again, over the course of this post. Pulumi is also "true open source", in the sense that it operates with an Apache 2.0 license, which is a lot more permissive than the BSL license terraform uses. Which is great for the community and adoption rate, but, so was Terraform at one point. For now, it's a huge plus. However, it isn't all sunshine and rainbows. Pulumi is still young, so documentation is still not great when actually writing code. Their general-purpose docs do make up for this, but it can be frustrating having to jump in and out of the editor to look up what you're currently working on. Building the POC All of the applications are containerized being we can take full advantage of AWS's elastic container service, so the POC was a simple hello world API packed in a docker image, served using fargate and a load balancer in front. I've been aware of ECS and fargate (plus, vpc, subnets, security groups, etc.), but never used them, so there was also some learning involved. The first thing that became clear was that Terraform has a lot of great documentation and tutorials available. While Pulumi does also have documentation and available tutorials, they are not as "complete" as what you find with Terraform. However, I would find myself hitting a problem in the infrastructure setup with Terraform, and switching to pulumi, and even with less available information, I was able to reason my way out of the problem because I simply had to read Go code. The overhead of a new language and platform would be frustrating, but staying in something I already knew removed one leg of complexity, and the AWS concepts were now the only thing I had to figure out. I suspect that would be one of the main feelings of other engineers who don't do full-time DevOps as well. I managed to get the poc running using both tools, but a big help came from staying in an environment I was already familiar with. I'm a heavy (neo)vim user and while there are lots of good plugins for Terraform, the Go environment is something I'm in every day; nothing new was needed. If I dedicated the time, I don't suspect it would take super long to get familiar with HCL and Terraform to the point where the environment would feel close to as familiar as Go. But, given that I wouldn't have to work every day with Terraform after getting the initial setup done, that familiarity would soon start to fade. Meaning, that every time a new infrastructure part had to be added, I would need to re-familiarize myself. And, for the rest of the team, this would be the same but with the added overhead of getting into a part of the code they didn't help write. This could be mitigated by involving everyone from the get-go, but this is also a business, there are other priorities that need building too. Documentation might be the answer some of you are screaming at the screen right now, and I hear you, but documentation quickly goes stale. This is especially true in smaller teams where things move fast, if the majority can be documented by the code itself, you save yourself much trouble. Why I Choose Pulumi Over Terraform I ended up going with Pulumi. I know from experience that the "correctness" of these choices only shows its true self after you're well past the point of making the switch to the other candidate feasible. But, from my current point of view, this is what will best benefit the team I'm currently working with. Two questions kept coming up as I was trying to decide on which tool to use: What are our infrastructure requirements now and in the future? (i.e., are we ever going to need a full-time DevOps) What does the current team know? (i.e., programming languages, cloud providers, etc.) The first might seem arbitrary, but the likelihood of finding a full-time DevOps engineer who knows Terraform is properly much higher than knowing Go and pulumi. Given that the needed infrastructure now and in the foreseeable future most likely will not require a full-time DevOps role, terraform begins to feel a bit overkill. It's a new language, and everybody on the team would need to learn it as well as any best practices that come with it. All the while, getting into the nitty gritty of AWS which will also be a new learning experience for the team (myself included). The second, to me, was very important since I've, from multiple experiences, seen what happens when you have a big Terraform setup that maybe one engineer was in charge of creating. Most of the time, it ends up being that one person that has to maintain and fix it, effectively making them a devOps person (which might be against their will -> they switch job -> team is fucked). The rest of the team, despite the person writing documentation, making presentations, etc., are still very reluctant to touch it. It's something they are unfamiliar with; the infrastructure system can be quite difficult to "have in your head." You can potentially take down everything without knowing how to get back to a running state. It creates a lot of friction. You might argue: "Well, they will just have to learn it" and I agree; that would be a sensible path forward. But we're not trying to solve a technical problem right now, but rather, a human one. And without allocating the time for people to learn it, it probably will not happen. So, in lieu of a DevOps team, you need to optimize for what gets people to actually want to learn infrastructure. Over the two weeks I spent researching and building some POCs, I would often find very good tutorials and materials on setting up infrastructure with Terraform. But as soon as I hit my head against wall, not knowing a specific syntax/setup/approach used in the tutorial or what the specific AWS services were about, it became very hard to move on. Multiple times, I switched back to Pulumi where their docs had examples in Go code, which was much easier for me to wrap my head around since I already knew Go. I "only" had to figure out the AWS part. After, I would switch back to Terraform and fix the thing with my new understanding from pulumi/go. But it's important to note where that knowledge originated from, by reading code, which most of us are doing pretty much every day. Taking away the overhead of both having to learn a new language and a cloud provider, to only the cloud provider, helps a tremendous amount. You also get the added benefit of already knowing best practices, and when and why to break those practices. You get an API from pulumi, so you can start provisioning infrastructure directly in your application(s), which makes spinning up demo environments for sales processes trivial. When I Would Choose Terraform Up to a certain point, something becomes a standard because of its quality and appeal to a large part of the tool’s target group. After that point, better alternatives might actually exist, but since the standard requirement is to know said tool, everyone else just continues learning that (I'm looking at you, React. Switch those frontends back to HTML, and use htmx for interactivity!). If that's what’s going on here with Terraform and Pulumi is hard to say. I actually had a much better experience exploring terraform, compared to previous experiences, where I only had to tweak it a little bit. If your team includes members who already know Terraform or you know that you have some large-scale infrastructure needs in the near future, that will include a dedicated DevOps team, Terraform still seems like the better choice. I did pick Terraform and HCL up rather quickly, but I'm still quite confident that this would fade again after not using it daily, so if you don't have that limitation, terraform would win for me. The main argument from my side here is to remove friction from your team when they don't deal with infrastructure on a daily basis; learning a new language while your PM is blasting new tickets your way like his life depended on it is tough. And having to wait for the one team member who truly knows the infrastructure code coming back from holiday to fix things, is very sub-optimal. Was This a Good Decision? It's too early to tell; I will revisit this in a few months once we've had some more production experience with it and the team has started working more with it as well.

Pulumi vs Terrafrom: Which Is Better?

In 2024, there is no shortage of LLM-based applications. Major tech companies like Microsoft and Google are pushing forward ever-more-powerful versions of their flagship ChatGPT and Gemini models, and specialist players like Anthropic are pushing forward competing offerings with additional integrations. In the applied LLM world, companies and even governments are experimenting with chat applications for a variety of contexts. Despite the enormous entrepreneurial energy poured into LLMs, most high-profile applications are still limited by their focus on chat-like interfaces that take in and return human-readable text. This is understandable - after all, one of the most exciting developments of LLMs is the ability to comprehend and generate human language, allowing for a conversational user interface. However, chat interfaces overlook another very important use case for LLMs - text processing as an intermediate step in a larger data pipeline. Today, we will explore this use case and see how LLMs can be useful as part of a data pipeline, not just as a natural language generator. Function Calling LLMs In order to use LLMs as part of a data pipeline, we need to change the output format of the LLM - instead of generating paragraphs for humans to read, the model has to generate something that computers can read. Usually, this means structured output in a data format like JSON or instructions in  a programming language like Python or SQL. These output formats are a lot less forgiving than natural language, since a missing quotation mark or bracket can crash the entire pipeline. Therefore, we will need to rely on specialized methods and features that have been developed to support this use case. These features are collectively referred to as function calling, since the output tends to call functions or be consumed in function calls. Before we delve into these methods, though, let’s first explore a little more in depth why function calling features were developed in the first place. Use Cases Connecting to external services The original and most straightforward use case for function calling is connecting to external services. When OpenAI originally released GPT-4, it also updated the API with a feature that allowed the user to provide the model a set of python-style functions that the model could call to accomplish its goals. The model could, for example, be told that it can call an external function to calculate the price of a complex security. Using this information, the model would be able to write code that calculates the value of a portfolio made up of these securities, without having to possess specialized knowledge on securities pricing. Calling python-style functions is only the tip of the iceberg. Once the importance of function-calling was proven in the market, OpenAI and other LLM providers started to support other output formats, such as JSON or SQL. The important thing was that these models were outputting machine-readable outputs, which could be reliably read by other processes. Astute readers might recognize that we have done something similar in previous articles, using LLM to generate , , or . In these , we accomplished this using prompt engineering and output-checking, but now that function-calling is a widely-supported feature, we can do this more easily by relying on these model-level features. training data SQL JSON articles Chaining LLMs Function calling created new possibilities for multi-LLM applications, and soon developers were experimenting with chaining LLMs to create sophisticated systems. Some of these systems became known as , which could independently search the web, gather new data, and call another LLM with the new information. These pipelines are capable of surprising amounts of autonomy and are capable of solving sophisticated problems with very little input, though there are still limitations, such as API costs and behavioral safeguards, that prevent agents from gaining widespread adoption. agents Using LLM as input and intermediate processing Another use case for function-calling LLMs is input and intermediate data processing. LLMs can be used to parse unstructured inputs into structured data that can be used for downstream processing. While many of these tasks tend to be relegated to “traditional” NLP technologies, the flexible nature of transformer-based models means that a specifically trained model can perform much better on these tasks than other NLP technologies. As a result, many developers are trying to leverage these specialized models in their data pipelines. Now that we’re familiar with the theory behind function-calling, let’s look at the application we will be building today. Demo Application CB Insights Entity Relationship Analyzer In a , I created a simple RAG application to answer natural language questions about CB Insights popular newsletter. In today’s article, we will build a similar question-answering application, but instead of relying on embedding search and GPT3.5, we will be using entity recognition as the primary indexing method. Additionally, we will use DRAGON as the summarization engine so we can run the entire application on our laptop, no cloud services needed. previous article Why Use Entity Recognition? Before we delve into the implementation, let’s first explore the advantages of using NER as the retrieval technology instead of embedding search. While in production we will want to use each technique according to the demands of the specific situation, NER offers several advantages over a system that relies on embedding search. Debuggability: Because entity recognition is an easily verifiable task, the entire pipeline is much more debuggable. It is easy to check if the model correctly identifies all of the entities, and it is easy to build and improve a matching algorithm based on these entities. Comparatively, it is much more difficult to verify that the embedding algorithm is properly identifying similarities and differences in passages. Flexibility: By separating recognition and filtering, we make the pipeline much more flexible than an embedding-based search algorithm. We can add additional metadata, change search algorithms based on entity type, and even use embedding algorithms on top of the NER-based search results. This allows for much more powerful features to be developed based on combinations of technologies. For example, an NER-based pipeline can use entity-based search to narrow down a set of documents, then use an embedding algorithm to narrow down further within the search space. This tends to make the search much faster and more efficient. Identification Power: Due to the level of manual control that is possible, NER pipelines are much more suited for certain search and retrieval tasks. Embedding searches are based on the proximity of passages within the training data, which calculates the probability that two documents come from the same larger document. In some use cases, this will cause embedding search to miss important documents. For example, if we were trying to understand Microsoft’s AI strategy, we might not only want to retrieve information about Microsoft’s activities within the AI software space, but also information about investments in chipmaking, data centers, and robotics. A general purpose embedding algorithm will not be able to identify these connections, whereas an NER-based algorithm will be able to draw these connections. Now that we understand the benefits of using NER within a RAG pipeline, let’s get a deeper look at the specific technologies we’re using in our application. Technologies Used LLM Entity Recognition Entity Recognition is a “traditional” NLP technology for extracting structured data from unstructured language data. The extracted data can then be used in down-line processing or as metadata for the passage being analyzed. Historically, this was accomplished using small, purpose-built machine learning algorithms that first tags the parts of speech, followed by a second pass that determines if the proper nouns identified are named entities. With function-calling LLMs, we can accomplish the same task, with several added advantages. Because transformer architecture is able to understand the language structure better than smaller NLP models, the performance is likely to be more robust, for example in cases where the passage is improperly formatted. Because LLMs are being updated with the most recent training data, the entity recognition system is likely to stay more up-to-date than other open-source entity recognition models. Prompt engineering techniques can be used to embed additional data and give additional instructions, allowing for more flexible behavior even in off-the-shelf models. We have covered Dragon in a previous article where we highlighted its impressive performance in summarizing natural-language data. We will be using Dragon to perform the final step of analysis, where we will summarize all of the articles relevant to the selected entities. DRAGON SLIMs are a new family of miniaturized, locally-runnable models from - it’s no secret I am a fan of their miniaturization work - that specializes in function calling. These models have been fine-tuned specifically to generate outputs that can be interpreted by machines, allowing users to take advantage of the recent developments in function calling architecture without having to rely on external APIs. SLIMs LLMWare Today we will be using the SLIMs-NER model, which will perform the Named Entity Recognition on the newsletter articles. In addition to NER, there are also SLIMs models for sentiment analysis, SQL generation, and multi-step agents. Now we understand the technology, let’s implement the application! Implementation Data Downloading and Processing Let’s begin by downloading the CB Insights articles. Let’s import the dependencies: import requests
from bs4 import BeautifulSoup
import os
import pandas as pd
import json
import re And now the code to download the newsletter archive: res = requests.get('https://www.cbinsights.com/newsletter/')

soup = BeautifulSoup(res.text)
article_links = [[i.text, i['href']] for i in soup.find_all('a') if 'campaign-archive' in i['href'] ]
article_soups = [BeautifulSoup(requests.get(link).text) for title, link in article_links] Now that we have downloaded the newsletter archive, let’s process it into JSON format result_json = {}
for soup_meta, soup_art in zip(article_links, article_soups):
    article_tables = []
    cur_article = []
    for table in soup_art.find_all('table'):
        if table.attrs.get('mc:variant') == 'Section_Divider':
            article_tables.append(get_deduped_article_tables(cur_article))
            cur_article = []
        else:
            cur_article.append(table.text)
    article_tables.append(get_deduped_article_tables(cur_article))
    result_json[soup_meta[0]] = article_tables

articles_with_meta = []
for name, tables in result_json.items():
      print(name, pd.to_datetime(tables[0][1].strip()))
      articles_with_meta.append({
            'name': name, 'date':  pd.to_datetime(tables[0][1].strip()).strftime('%Y-%m-%d'), 'tables': tables
      })
df = pd.DataFrame(articles_with_meta) Now we are at the same place we were at in the previous article. Instead of going right to the RAG construction by creating embeddings, however, we will instead run an entity-recognition step. First, let’s import the dependencies for running SLIMs, a new set of models from LLMWare that allows us to perform entity recognition locally on our machine: from llmware.agents import LLMfx
from llmware.parsers import WikiParser
from collections import defaultdict

# define a function for identifying all the named entities
def run_ner(text):
    agent = LLMfx()
    agent.load_work(text)
    agent.load_tool("ner")
    named_entities = agent.ner()
    ner_dict= named_entities["llm_response"]
    return ner_dict Now we can run the named entity recognition on all of the articles: date_entities = defaultdict(dict)
for _, row in df.iterrows():
    for idx, t in enumerate(row['tables'][1:]):
        if 'Start Your Free Trial' in t:
            t = t[:t.index('Start Your Free Trial')]
        date_entities[row['date']][idx] = run_ner('\n'.join(t)) It might take a few minutes for the NER pipeline to complete, but this is all you need to recognize entities using a state-of-the-art miniaturized LLM. You can check the date_entities dictionary by printing some test items. For example, the code: date_entities[list(date_entities.keys())[0]] Should produce the output below: {0: {'people': ['Yahoo!'],
  'place': [],
  'company': ['Databricks', 'MosaicML'],
  'misc': []},
 1: {'people': [],
  'place': ['New York'],
  'company': ['CB Insights'],
  'misc': []}} Which shows all of the various entities that have been detected by the SLIMs model. Question-Answering with DRAGON Now that the entities have been detected, let’s build a question-answering workflow that demonstrates the power of this technique. For our example, we will use the test question: What role does OpenAI play in Microsoft's AI strategy? Let’s begin by importing the proper packages for running DRAGON: from llmware.prompts import Prompt
query = "What role does OpenAI play in Microsoft's AI strategy?"

model_name = "llmware/dragon-llama-7b-gguf"
prompter = Prompt().load_model(model_name) Now, we can build the function that answers the question using entity recognition. In order to leverage our NER data, we will want to implement the following workflow: Identify the entities in the user query. We can use the same SLIMs function we used in the previous step. Perform a lookup based on the results of the lookup, that is, we search for articles in the corpus that talk about the same entities. Once we find these articles, we can supply them to the question-answering model to extract the relevant information. Here is what the workflow looks like in code form: def answer_question_with_ner(query):
    ner_results = run_ner(query) # run NER on the user query
    search_entities = []
    for ent_type, ent_list in ner_results.items():
        search_entities.extend(ent_list)
    # create a list of entities to search for
    search_entities = list(set(search_entities))

    # now perform a lookup for articles that mention the identified entities, using set arithmetic. 
    articles = set(entity_to_place_map[search_entities[0]])
    for se in search_entities:
        articles &= set(entity_to_place_map[se])

    # now process the corpus into a prompt and feed it to the question-answering LLM.
    article_content = []
    for article in articles:
        article_content.extend(df[df['date'] == article[0]].iloc[0]['tables'][article[1]+1])
    response = prompter.prompt_main(query, 
        context='\n'.join(article_content),
        prompt_name="default_with_context", temperature=0.3)
    return response # return the response. Run the function, with the query from the first code block, and you should see the following result: Microsoft has poured billions of dollars into ChatGPT developer OpenAI. However, it's also placed a number of bets on other GenAI startups across computing, mapping, and gaming. You can also see in the “evidence” section of the response object that, indeed the two articles from the CB Insights archive that mention Microsoft and OpenAI have been retrieved, and that the LLM’s answer is based directly on the evidence. Note that because we are doing explicit entity recognition, the retrieval process is extremely transparent and debuggable. Should a question ever arise as to why the model retrieved these specific pieces of information from the corpus - all it takes is a simple print statement to see that the model chose the article because the query mentions “Microsoft” and “OpenAI”, and the two retrieved newsletter sections are the only sections that mention both entities. Additionally, when compared with embedding-based vector search, the NER lookup method provides much more precise answers. In my tests, the selected articles were in the top 10 most relevant articles ranked by OpenAI’s ada algorithm, but was not the one identified as having the closest proximity to the question at hand. Therefore, it is possible an application using embedding search may not have correctly answered the question at all, and the lack of debuggability would only contribute to the confusion. And with that, we have built a question-answering application using NER lookups, improving the quality of the pipeline in the process! Conclusion Today, we built an application using function calling, an exciting new LLM feature. Miniaturized, locally-runnable function-calling models is a revolutionary development that unlocks a new class of AI applications, and we are only seeing the first iteration of these technologies. It will be exciting to see what applications developers will create with these technologies in the coming months. If you have an idea in the AI space that you want to see implemented or simply want to chat about technology, please do not hesitate to reach out on or . Github LinkedIn If you want to learn more about LLMWare, the company behind SLIMs and DRAGON, you can find them on or . HugginFace Github

Function Calling LLMs: Combining Multiple Models for Better RAG Performance

ফাংশন কলিং এলএলএম: আরও ভাল RAG পারফরম্যান্সের জন্য স্লিম এবং ড্রাগনকে একত্রিত করা

Funktionsaufruf-LLMs: Kombination von SLIMs und DRAGON für eine bessere RAG-Leistung

LLM de llamadas a funciones: combinación de SLIM y DRAGON para un mejor rendimiento de RAG

Appel de fonctions LLM : combiner SLIM et DRAGON pour de meilleures performances RAG

फंक्शन कॉलिंग एलएलएम: बेहतर आरएजी प्रदर्शन के लिए एसएलआईएम और ड्रैगन का संयोजन

関数呼び出し LLM: SLIM と DRAGON を組み合わせて RAG パフォーマンスを向上させる

함수 호출 LLM: 더 나은 RAG 성능을 위해 SLIM과 DRAGON 결합

LLMs de chamada de função: combinando SLIMs e DRAGON para melhor desempenho de RAG

Вызов функций LLM: объединение SLIM и DRAGON для повышения производительности RAG

İşlev Çağırma Yüksek Lisansı: Daha İyi RAG Performansı için SLIM'leri ve DRAGON'u Birleştirme

Gọi chức năng LLM: Kết hợp SLIM và DRAGON để có hiệu suất RAG tốt hơn

函数调用 LLM：结合 SLIM 和 DRAGON 实现更好的 RAG 性能

Men and women have many differences, and this is even more prominent when inspecting our performance as drivers. Recent research provides a comprehensive look into how men and women fare on the roads, not just in terms of driving skills but also about safety, accident rates, and driving behaviors. A noteworthy point from the indicates that women are 13% better at parking than men, challenging the age-old stereotypes about women's driving abilities. Furthermore, women have been shown to be 12% better at multitasking behind the wheel. However, it's essential to note that multitasking while driving, regardless of gender, increases the risk of accidents and is not advisable for safe driving practices. research Statistically speaking, women emerge as safer drivers compared to men. In 2020, men were involved in 75% of fatal crashes, highlighting a significant gender disparity in driving safety. Despite this, female drivers exhibit a higher fatality rate in serious accidents than their male counterparts. Another recent shows that when in a fatal crash, a younger female occupant is approximately 20% more likely to suffer a fatal injury than a male occupant of the same age, regardless of seating position, airbag deployment, or seatbelt usage. study This is a startling statistic that warrants further investigation to understand the underlying causes. When it comes to accident rates per 100 million miles traveled, men have a crash rate of 2.1, making them 61% more likely to be involved in an accident than women, who have a crash rate of 1.3. This difference is underscored by the fact that men drive 62% more miles than women (16,000 vs. 10,000 miles annually) and account for 59% of vehicle accidents. In 2020, the division of drivers involved in all road traffic accidents was 59% male and 41% female. Moreover, male drivers were more than three times as likely to be involved in fatal car crashes compared to female drivers. Despite these figures, 2021 saw a slight majority of female licensed drivers in the U.S., with 117.6 million female licensees compared to 115.2 million male licensees. Men accounted for 73% of all traffic violations and 75% of speeding offenses based on data from 2017 to 2020. This propensity for risky behavior extends to driving under the influence, with 22% of male drivers in fatal crashes having a blood alcohol concentration of at least 0.8, compared to 16% of female motorists. Aggressive driving behaviors further differentiate male and female drivers. In 2020, 37.8% of male drivers admitted to tailgating to prevent vehicle merging, compared to 29.3% of female drivers. Similarly, a higher percentage of male drivers reported engaging in aggressive lane switching or following too closely behind another car. These statistics paint a nuanced picture of gender differences in driving, highlighting areas where women excel, such as parking and multitasking, and areas of concern, such as the higher fatality rate in serious accidents. The data also emphasize the need for continued efforts to promote safe driving practices for all, regardless of gender. Luckily, it’s 2024, and the whole point of technology is to make our lives easier – and safer. Enter artificial intelligence (AI). Can AI assist men and women in driving better? At least one shows that there is “no significant difference between male and female drivers in reaction to sudden traffic events alarmed by warning systems.” study According to the study, in terms of driving behavior, men behaved more dangerously than women. The findings also revealed that the number of crashes in critical situations was higher for female drivers. But when it comes to AI, women in the United States are more skeptical than men about some of its uses, particularly the possible widespread use of driverless passenger vehicles, according to a recent analysis of . Pew Research Center survey data Notwithstanding this skepticism, AI is expected to reduce accidents and is poised to dramatically transform driving by enhancing safety and honing driving skills. AI-powered Advanced Driver Assistance Systems (ADAS) provide real-time hazard detection and intervention capabilities, such as automatic braking and lane-keeping assistance, by analyzing data from vehicle sensors and cameras. Predictive analytics extend the safety net by foreseeing vehicle maintenance issues, thereby preventing accidents caused by vehicle malfunctions. To tackle accidents caused by human factors, AI systems scrutinize driver behavior to detect distractions or signs of fatigue, prompting drivers to take necessary breaks. Additionally, AI contributes to traffic management by analyzing and predicting traffic flows and conditions, guiding drivers away from congestion and potential accident hotspots. The advent of autonomous vehicles represents a significant leap forward. These vehicles, albeit still , are guided by
AI and aim to eliminate human error— a major factor in accidents—without the
distractions or impairments humans might experience. controversial AI could actually help us improve our driving by analyzing individual driving habits and offering customized feedback and improvement strategies. This approach not only enhances driver skills but also paves the way for insurance companies to tailor policies based on actual driving behavior, incentivizing safer driving practices. Through these comprehensive capabilities, AI is on course to significantly reduce accidents and refine our own driving practices, embedding safety and efficiency into the very fabric of road travel.

Can AI Help Us Drive Better?

If any of us in our businesses can be better than Apple at any specific thing, no matter how niche, I'm sure we would try to take advantage of this opportunity. It appears Steve Jobs had a preference for how meetings should be undertaken. And he believed this approach to meetings produced the best results, allowing people to engage more seriously and more carefully with the topics being discussed. However, there have been drawbacks to this approach of meetings, as we'll cover in the article. But the advent of new technologies has created an unexpected opening for the kind of meetings Steve Jobs envisioned while mitigating virtually all the downsides of this approach. AI note-taking This isn't really about how to take meetings now better than Apple. This is really about bringing Steve Jobs' vision of effective meetings to life. Let's dive in. How Steve Jobs Approached Meeting Notes A slight storm had been caused among tech Twitter and tech Threads the other week. The user, Darius, who claims to have worked directly with Steve Jobs, has made some bold statements about meetings, how we approach them, and what Steve Jobs thought. The offending tweet in question is detailed below. Darius positions himself as a sort of oracle of Jobs' knowledge, able to translate the great man's thoughts into business lessons for work and life in the modern age. The big claim here is that you shouldn't take notes in meetings. It's not just Steve Jobs who has peculiar approaches to meetings. Jeff Bezos recently appeared on where he talked about how he established a culture of meetings within Amazon. It was based around the memo format. In Jeff Bezos's ideal meeting, the memo for the meeting is written before the meeting. Lex Fridman’s podcast It is long, written in narrative form, not simply bullet points, and can take multiple pages. While attendees are free to read the memo before the meeting, there isn't an expectation to do so. Instead, the first part of the meeting is spent with everyone in a team silently reading the memo. According to Bezos, this can take up to half an hour of an hour-long meeting. He says doing it this way works best because it means that everyone has carved out time for the meeting and no one is pretending to know what the topic is. It means everyone can fully engage in the discussion the meeting was intended to stimulate. We can find Steve Jobs' approach in this context. If you have people together in a meeting, they are not being productive doing other things. So, you should be aiming to get maximum productivity out of them in that meeting. And what is the maximum productivity that can be brought out in a meeting? Is it communicating an idea? No, not really. You can do that with an email. It's about discussing ideas, engaging in discourse, analyzing each other's thoughts, with multiple people bringing things to the table, each interrogating and building upon the ideas presented. And in order to do that effectively, in order for everyone involved in the meeting to contribute to their fullest and engage intellectually to the maximum extent they can, they shouldn't be distracted by either a lack of knowledge or by having to take extensive meeting notes. Why We Shouldn’t Take Notes in Meetings So, why shouldn't we take notes in meetings? I'm very sympathetic to this view because taking notes in meetings is one of a litany of activities that can distract someone from what is being said, especially in the era of remote work. It's especially common for people to try to do other things while they're in the meeting, like completing other tasks, sending an email to somebody else, generally trying to be productive and get stuff done, feeling like it's not lost time. People think they're doing good things when they do this. They think they are setting a good example, but they're not. If they don't need to be in the meeting, then they shouldn't be in the meeting. If they should be in the meeting, then they should be present and engaged. And if everyone is taking notes, then it feels like everyone is slightly distracted. This produces a general distraction. But there's also value in noting that more junior employees are often tasked with the responsibility of keeping and updating valuable notes. While this can be a useful task to introduce someone to if they have no other context to the meeting, if they do have some engagement with the topics of the meeting, having to take notes stops them from being able to participate and present their ideas or make observations of their own. There is also a gendered side to this. I've been in meetings where the organized person in the meeting is a woman and because they are organized, they end up falling into the default role of minute taker, while one of the guys gets to take on the role of idea guy. As , a marketing operations leader and consultant, describes the gendered elements of this experience to me: Tracy Warner “There’s a known and well-documented gender bias with note taking. Many of the tasks that are getting captured by AI fall into these often mundane, typically female categories of work: coordinating the meetings, taking the notes, capturing action items, sharing those with the team.” Meeting notes are obstacles to engaging in the meeting. The ideal meeting would allow everyone to remain 100% focused on the discussion at hand with equal opportunity for all to participate and contribute as they see fit. This is what Jobs believed. And it's a very enticing view. How much more productive would we all be if we could maximize the benefit of the discussions we have in meetings so we don't need to have more meetings? We can get more done so that we don't have to do yet more work. What’s Wrong With This Policy? While the approach of not taking notes in meetings is intriguing, it isn't perfect. The very simple and obvious problem is that someone does need to take some notes. Otherwise, how are we going to remember what we thought, talked about, agreed on, or what action items were recorded? In any workplace, it's very common for people to find that they've attended a meeting where no one took notes, and as a result, things didn't get done and there's no shared concept of what occurred in the meeting. Beyond these obvious productivity considerations, there's the fact that everyone engages with and retains information differently. For some, like myself, having a long, free-form discussion is super valuable and a great way to learn and engage with ideas, still having a rough idea of things at the end to take action on. For others, when something is mentioned, it's very beneficial for them to quickly jot that down. It helps to reinforce that memory in their mind, not simply because it's written down, but because the process of writing it down helps them remember it better. People simply differ in how they absorb information. Add to that, there are more extreme versions of this need to write things down or to engage more carefully with things. This might be related to neurodivergence, slight memory issues, or simply being easily distracted, as many people with ADHD or similar can experience. The downsides to not recording any meeting notes seem painfully apparent. While many meetings do occur without the effective use of meeting notes, almost all important meetings will have someone taking those notes. It's not simply best practice; it's an obvious thing to do. In fact, in some circumstances, it can be legally required. How Can We Take Notes Better Than Apple? So, what's the synthesis here? Well, it's AI. You do not need to spend your time in the meeting taking notes if you know the AI has been taking notes for you. Either your AI has taken those notes, or the AI of the person who hosted the meeting has. The AI then automatically shares those notes at the end of the meeting with all the attendees. Then, everyone can have a copy of what was discussed, beyond simply a transcription of the meeting. Powerful AI executive assistant software like Xembly can give each attendee a summarized version of the meeting that doesn't include extraneous details like Gary's five-minute story at the beginning about his sister's wedding. It's also possible to collaboratively edit the meeting notes at the end of the meeting in case there's anything else someone wanted to add or specify. This creates a sense of shared truth within the organization, as everyone has copies of what was said and what was decided upon in the meeting, and each person was able to contribute without impacting their own productivity. Using an approach like this allows each person to remain 100% focused on the content of the meeting and to discuss each item freely without having to worry about their note-taking responsibilities. This can produce added efficiency gains for each and every meeting, resulting in a cumulative large gain in productivity. Companies can save eight hours a week per person by using Xembly, but it's not just about meeting notes. With modern tech, you can go beyond the notes themselves. Xembly's AI, Xena, can pull the action items from the meeting and then suggest attendees to whom those action items can be delegated, creating tasks for their task lists. This turns the into more of an AI project manager, again producing further efficiency gains for the team. AI executive assistant You can even integrate directly into Salesforce or HubSpot and have the AI update records for you. AI unlocks a whole new world of . meeting management If you combine all of this with other standard approaches that technology can enable now, like sharing a recording of the meeting for people to view (permission dependent), you can create a massive sense of transparency and shared knowledge in your team. You go from wanting to emulate Steve Jobs to actively emulating Ray Dalio and his principles of shared truth and radical transparency. By utilizing new technology you can make work better, organization more cohesive, and make the experience more inclusive - as we’ve seen across grounds of seniority, neurodivergence, and gender. It’s not the first technological step forward to automate elements of our lives, and it won’t be the last. With automation comes change and opportunity - as Tracy said to me: “I find administrative automation akin to the invention of the washing machine for women… as you take away the busy work of life, you free yourself for more important things. It’s no surprise that women began to enter the workforce as domestic work became increasingly automated.” is the only AI executive assistant designed from the ground up for , with a deep feature set and regulatory compliance built in, so give it a try and start taking your meeting notes better than Apple. Xembly enterprises

How to Take Meeting Notes Better Than Apple

অ্যাপলের চেয়ে মিটিং নোটগুলি কীভাবে নেওয়া যায়

So machen Sie Besprechungsnotizen besser als Apple

Cómo tomar notas de reuniones mejor que Apple

Comment prendre des notes de réunion mieux qu'Apple

ऐप्पल से बेहतर मीटिंग नोट्स कैसे लें

Apple よりも優れた会議メモの取り方

Apple보다 회의 메모를 더 잘 작성하는 방법

Como fazer anotações de reuniões melhor que a Apple

Как вести заметки совещаний лучше, чем Apple

Apple'dan Daha İyi Toplantı Notları Nasıl Alınır?

Cách ghi chú cuộc họp tốt hơn Apple

如何比苹果更好地做会议笔记

The ability to earn rather than buy your cryptocurrency has always been an important aspect of the crypto community. Unfortunately, for many who are new to the blockchain space, the days of being able to mine hundreds of with just your home computer are long gone. However, there are now more inclusive ways to earn cryptocurrency, and while the idea is still new, crypto-earning platforms could soon make central banks start to sweat. Bitcoin What is Crypto Earning? While most people are familiar with Bitcoin and Ethereum miners, for the high-tech, slightly mysterious subset of individuals working the blockchain for paydirt, using high-powered computers stuffed with GPUs, that's not the only way to earn your coins. Investors looking to earn coins or tokens without spending $20,000 on mining equipment can still participate in community initiatives by way of Proof of Stake. POS is an alternate consensus method used by certain blockchains to secure the network and validate transactions. While the first Proof of Stake coin, PeerCoin, was introduced as early as 2012, the rise of DeFi and Ethereum's announcement that it would be moving to Proof of Stake has revitalized this once controversial consensus method and sent it into the mainstream. Now, the only thing stopping you from earning hefty returns on your coins is knowing where exactly you should put your money. Your return will depend on the level of risk you're willing to stomach, the lock-up terms you choose for your coins or tokens, and whether you plan to go with a centralized or decentralized earning platform. Centralized vs. Decentralized platforms Like all things in cryptocurrency, or even just investing in general, there is always a risk. The world of cryptocurrency is still very much the wild west, mostly lacking regulation and consumer protection, and putting your trust in the wrong hands can often lead to disaster. Investors who previously lost their Bitcoin fortunes to shady platforms like Mt. Gox will be quick to warn you: "Not your keys, not your coins," a popular saying in the crypto community. This saying stems from the fact that when you are using a centralized platform, you do not control the private keys to your wallets. That means that, unlike your private wallet, which can't be taken from you, funds kept on platforms owned by someone else are always at risk to some degree of being lost to insolvency, have a higher risk of theft, or can even be seized should your country's flavor of cryptocurrency wane in some way. Part of the appeal of Decentralized Finance (DeFi) platforms is that you are in control of your private keys and recovery phrases, allowing you to participate without giving up wallet ownership. So why would you ever use a centralized platform, then? Well, in its current iteration, is not as user-friendly as it could be. There are a lot of ways to make a mistake, and many people simply are not interested in or prepared to be a custodian of their own finances. DeFi This is part of the reason why central banking and credit cards have become a consumer staple; they provide protection to the user. With a non-custodial wallet, this protection is gone. You're responsible for your own private keys and recovery phrase, and if you lose those, then you're certainly out of luck, but you won't be alone. It's estimated that between 2.78 million and 3.79 million Bitcoin have been lost in this manner. While if you use the custodial wallet, support can come to the rescue in most cases if you lose access to your account or if you were to perhaps send your coins to the wrong wallet, which is an easier mistake to make than you might think. Especially with cross-chain confusion now coming into play. Earning on custodial vs. Non-Custodial Platforms Custodial Platform Earning A custodial platform, such as Nexo or Celsius, simply pays out an advertised interest rate on held cryptocurrencies, just like your bank savings account. Earnings typically go up if you are willing to commit to a longer lock-up term, which is great if you are long on an investment. You will, of course, not earn as much as you would using DeFi, but your risk is mitigated as long as you trust the platform. This is because these platforms are taking on much of the risk of blockchain finance for you. In return, they take a cut of the profits for themselves. Interest is paid out daily or weekly, typically in the same currency that you are staking, making it a good way to grow your holdings while you wait. It's the simplest way to earn if you are not technologically inclined or are risk-averse. These platforms make money by doing their own staking or lending, at varying degrees of risk, and typically also provide other services that allow them to profit off of trade or transaction fees from the users of the platform. What are the Risks of Custodial Earning Platforms? Custodial platforms are not without risk. Besides the fact that you are typically not in control of your private keys, management of the exchange can also be an issue. While the platform's job is to absorb risk, if they get into trouble, things could go south quickly, and your investments could be at risk if the company becomes insolvent. Custodial platforms are not immune to bug exploits or the will of overbearing governments. Hackers exploiting smart contract vulnerabilities or damaging new regulations could put your holdings at risk. . This loss was not passed on to the consumer, but should a loss be large enough to put a platform out of business, it could be. Even FDIC-insured exchanges only offer protection to the fiat currencies in your account. Celsius is estimated to have lost $50 million in the Badger DAO exploit Non-Custodial Platform (DeFi) Earning While DeFi staking is much more complicated and more risks are involved, the potential for profit is also greater if you are willing to take those risks. There's also a greater variety of ways to earn going the DeFi route when compared to a centralized earning platform. You can participate in yield farming, which is essentially becoming a liquidity provider to a platform that provides lending services to traders. Or, you could stake your assets and become a validator for transactions, helping to secure the network, similar to what miners do in Proof of Work setups. You can also participate in liquidity mining, a process that rewards you with native tokens or governance rights rather than a standard APY yield like yield farming does. Each of these methods of earning has its own benefits and dangers, so it's imperative that you thoroughly research each method before deciding on one. There's a very real risk of losing your initial deposit if you aren't careful. What are the Risks of Non-Custodial Earning Platforms? In addition to worrying about potential vulnerabilities in the smart contracts you are utilizing and liquidation, higher-yield currencies are often highly volatile, and shady platform providers can quickly throw a wrench in your plan, like when Sushi Swap's developer decided to sell all of his tokens, , and leaving investors high and dry. plummeting the price of the currency by 50% It's important to realize that pretty much anybody can create a token or even an entire platform for something like yield farming overnight, often anonymously, and in a faraway country, outside the reach of your country's legal jurisdiction. This is why it's important to make sure to research not just the type of Defi activity you wish to pursue carefully but also to be sure to vet the platform you intend to do it on properly. Spreading your investments across multiple platforms can also help to avoid a total loss of assets. In short, both custodial and non-custodial platforms have their own unique benefits and dangers. It's important to know what you're getting into before putting your life savings into a risky situation in which there could be no way out if things go badly.

Better Than Your Bank: A Primer on the Benefits and Risks of Crypto Earning

In the ever-evolving landscape of software development, finding the right resources can be a daunting and time-consuming task. I've decided to help by curating a compilation of some of my favorite GitHub repositories, that will help you to improve your workflow and skills. This list includes valuable GitHub repositories for topics like learning, project ideas, career opportunities, data collections, database tools, and security. I've included direct links, descriptions, and visuals so that you can get an initial impression of each extension right away. 1. (Sponsored) esProc SPL ⭐ GitHub stars: 4K+ esProc SPL is a next-generation data processing language, that integrates with SQL databases and supports advanced analytics, and parallel processing. With esProc SPL, you can effortlessly transform and analyze massive datasets, uncover hidden patterns and trends, and derive actionable insights from your data. Some of the top features include: Experience fast processing speeds with esProc SPL's optimized algorithms and efficient memory management. ⚡ Top Performance: Access a comprehensive collection of pre-built functions, catering to a wide range of data manipulation tasks. 🚀 Rich Function Library: Enjoy a clear and concise syntax that fosters code readability and maintainability. ✨ Intuitive Syntax: Seamlessly integrate esProc SPL scripts into Java programs via JDBC, bridging the gap between data analysis and application development. 👨‍💻 Java Integration: Execute esProc SPL scripts independently, expanding your data processing capabilities beyond traditional limitations. 🧙‍♀️ Independent Execution: ⭐ Support their GitHub repo: https://github.com/SPLWare/esProc 🌱 Learning 2. A-to-Z Resources for Students ⭐ GitHub stars: 16K+ This repository is a comprehensive collection of resources beneficial for students, covering a wide range of topics. It includes study materials, programming resources, and tools that can aid in academic and personal development. 3. Learning Resources for Developers ⭐ GitHub stars: 5K+ This repository gathers an extensive list of learning resources for developers. From tutorials and courses to resources and tools, it serves as a valuable one-stop-shop for enhancing programming skills. 4. Every Programmer Should Know ⭐ GitHub stars: 75K+ A compilation of essential knowledge that every programmer should be aware of. Covering a variety of topics, it provides insights into algorithms, data structures, system design, and other fundamental concepts. 📀 Data 5. Public Datasets Collection ⭐ GitHub stars: 57K+ This repository curates a vast collection of public datasets from various domains. It's a valuable resource for data scientists and developers looking for high-quality datasets to analyze and derive insights from. 6. JSON Datasets Collection ⭐ GitHub stars: 3K+ This repository provides a curated list of datasets in JSON. It's a handy reference for developers working with JSON data in their projects. 7. Useful Database Tools ⭐ GitHub stars: 3K+ This repository compiles a list of tools and utilities for working with databases. From database design to management and optimization tools, it's a go-to resource for developers dealing with database-related tasks. 💡 Ideas 8. Everything for Indie Hackers ⭐ GitHub stars: 8K+ Focused on independent developers and creators, this repository gathers resources, tools, and advice for individuals pursuing indie projects. It's a supportive community-driven collection for those looking to thrive independently. 9. Machine Learning Project Ideas ⭐ GitHub stars: 7K+ A repository filled with project ideas for Machine Learning developers. It's a great source of inspiration for developers seeking new and interesting projects to work on. 10. Awesome Stacks for Apps ⭐ GitHub stars: 3K+ This repository showcases various technology stacks you can use. It's a valuable resource for developers exploring different tech stacks and architectures for their projects. 11. Frontend Dev Bookmarks ⭐ GitHub stars: 40K+ A curated collection of bookmarks for frontend developers, covering a wide range of topics from appearance and UI to compatibility and design workflow. It's a handy reference for staying updated on best practices and tools in the front-end development landscape. 💼 Career 12. CV Templates ⭐ GitHub stars: 21K+ Some useful templates for creating impressive curriculum vitae (CVs). It's a valuable tool for job seekers, helping them craft visually appealing resumes. 13. Resume Best Practices ⭐ GitHub stars: 6K+ This repository compiles various practical examples to use in resumes. It serves as a comprehensive guide for individuals aiming to enhance their resume presentation, improving their chances of getting a job. 14. Remote Work Resources ⭐ GitHub stars: 27K+ Focused on remote job opportunities, this repository provides a curated list of companies and job boards offering remote work. It's a go-to resource for those seeking remote employment options. 🔐 Security 15. Everything Security ⭐ GitHub stars: 11K+ A collection of awesome software, libraries, documents, books, resources, and cool stuff about security. 16. Web Security Practices ⭐ GitHub stars: 10K+ Focused specifically on web security, this repository gathers resources and tools to help developers secure their web applications. It covers best practices, tools, and frameworks for web security. 17. Privacy Focused Services ⭐ GitHub stars: 11K+ A curated list of resources and tools to enhance online privacy. It covers topics such as privacy-focused software, encryption tools, and practices to protect personal information online. 18. Privacy Tools Collection ⭐ GitHub stars: 5K+ A repository that curates privacy-related software and services. This collection includes tools, guides, and best practices to maintain digital privacy in various online activities. 19. Personal Security Checklist ⭐ GitHub stars: 13K+ A valuable checklist and resources to improve personal online security. It's a practical guide for individuals looking to protect their digital privacy. Writing has always been my passion and it gives me pleasure to help and inspire people. If you have any questions, feel free to reach out! Make sure to receive the best resources, tools, productivity tips, and career growth tips I discover by subscribing to ! my newsletter Also, connect with me on , , and ! Twitter LinkedIn GitHub Also published . here

The 19 Useful GitHub Repositories You Need to Become a Better Developer 🔥🚀

19 টি দরকারী GitHub সংগ্রহস্থল যা আপনাকে আরও ভাল বিকাশকারী হতে হবে 🔥🚀

Die 19 nützlichen GitHub-Repositories, die Sie benötigen, um ein besserer Entwickler zu werden 🔥🚀

Los 19 repositorios de GitHub útiles que necesitas para convertirte en un mejor desarrollador 🔥🚀

Les 19 référentiels GitHub utiles dont vous avez besoin pour devenir un meilleur développeur 🔥🚀

एक बेहतर डेवलपर बनने के लिए आपको 19 उपयोगी GitHub रिपॉजिटरी की आवश्यकता है 🔥🚀

より良い開発者になるために必要な 19 の便利な GitHub リポジトリ 🔥🚀

더 나은 개발자가 되기 위해 필요한 19가지 유용한 GitHub 리포지토리 🔥🚀

Os 19 repositórios GitHub úteis que você precisa para se tornar um desenvolvedor melhor 🔥🚀

19 полезных репозиториев GitHub, которые вам нужны, чтобы стать лучшим разработчиком 🔥🚀

Daha İyi Bir Geliştirici Olmak İçin İhtiyacınız Olan 19 Faydalı GitHub Deposu 🔥🚀

19 kho lưu trữ GitHub hữu ích mà bạn cần để trở thành nhà phát triển giỏi hơn 🔥🚀

成为更好的开发人员所需的 19 个有用的 GitHub 存储库 🔥🚀

You can jump to any section of the National Aeronautics Science & Technology Priorities here. Introduction & Purpose The Resilience Science and Technology Grand Pathways Framework aims to build resilient communities by identifying, aligning, and prioritizing science, technology, and innovation that equitably and justly strengthen multiple dimensions of American society. In recent years, America’s communities have faced unprecedented shocks and stresses due to natural hazards, accidents, and deliberate acts, some occurring in tandem or as cascading disasters. According to the National Oceanic and Atmospheric Administration, in 2021, the United States experienced 20 weather or climate disaster events with losses that exceeded $1 billion each; since 1980, 310 such events occurred.[1] Weather events, like the unusual cold snap in Texas in February 2021[2] , lead to pronounced secondary effects, such as prolonged power losses. Deliberate disruptions, like the Colonial Pipeline ransomware attack that disrupted oil supply chains[3], can have similar widespread impacts. Both of these events occurred while the Nation grappled with the COVID-19 pandemic, the opioid epidemic, and other health crises that have exacerbated pre-existing weaknesses in the built environment as well as social and institutional inequities. Helping individuals, families, and communities recover from these immense challenges while building their resilience to historical, ongoing, and future stressors is a challenge that requires a whole-of community effort, and that includes focusing and harnessing science, technology, and innovation. The Bipartisan Infrastructure Law highlights the need to address these challenges by investing over $50 billion to build the Nation’s resilience to the impacts of climate and extreme weather hazards, and directs investment in billions more in infrastructure that will need to be resilient to both natural and man-made hazards. [4] There are a number of other investments in the Bipartisan Infrastructure Law, as well as the Inflation Reduction Act[5] that, if built with all-hazards resilience and cybersecurity in mind, will significantly enhance the Nation’s resilience to climate change and a wide array of other threats and hazards. Further, the Biden Administration’s commitments to increasing social equity[6] and environmental justice[7] will help remedy persistent inequities that have diminished the resilience of communities. The National Science and Technology Council (NSTC)’s Subcommittee on Resilience Science and Technology (SRST) investigated methodologies and strategies to assist agencies focusing their resources as they make use of the generational investments afforded by the Bipartisan Infrastructure Law[8] and the Inflation Reduction Act.[9] SRST was created in 2019 to address cross-cutting resilience science and technology issues related to all threats and hazards, at all stages of risk management, considering both physical and social science dimensions. SRST is investigating the interdependencies among societal dimensions of resilience (including the key functions and cross-cutting enablers that support them), and identifying the critical gaps that impede resilience. The goal of these investigations is to identify mechanisms, or “grand pathways,” to align current and future resilience science and technology capabilities to address those gaps across multiple disruptions, resources and cross-cutting enablers, key functions, and societal dimensions to assist communities in advancing their own resilience. A sound, single framework will help prioritize investments and ensure that Federal dollars are spent to their best advantage. A resilient community anticipates, avoids, adapts to, withstands, and builds back better in the wake of disruptions. This report outlines the Resilience Science and Technology Grand Pathways Framework, which aims to identify science, technology, and innovation approaches that improve communities’ abilities to anticipate, avoid, adapt to, withstand, and build back better from acute shocks and chronic stresses with the potential to cause disruption or destruction. End users of the Grand Pathways Framework include Federal agencies and their partners, such as planners, policy makers, community leaders, innovators, and others. This effort builds on the Grand Challenges for Disaster Reduction (2005) and follow-on hazard specific implementation plans (2008) published by the NSTC’s Subcommittee on Disaster Reduction.[10] The Grand Challenges provided a 10-year strategy for Federal agencies and external partners and stakeholders to deliver science- and technology-based investments to increase disaster resilience, with efforts aimed at increasing understanding, reducing vulnerability, informing decisions and otherwise promoting risk-wise behaviors. [1] National Oceanic and Atmospheric Administration. 2021. “Billion-Dollar Weather and Climate Disasters.” https://www.ncdc.noaa.gov/billions/ [2] National Oceanic and Atmospheric Administration. 2021. “Valentine's Week Winter Outbreak 2021: Snow, Ice, & Record Cold.” https://www.weather.gov/hgx/2021ValentineStorm [3] Department of Energy. 2021. “Colonial Pipeline Cyber Incident.” https://www.energy.gov/ceser/colonial-pipeline-cyberincident [4] The Executive Office of the President. 2022. “President Biden's Bipartisan Infrastructure Law.” https://www.whitehouse.gov/bipartisan-infrastructure-law/#resilientinfrastucture [5] See, for example, The Executive Office of the President. 2022. “FACT SHEET: Inflation Redduction Act Advances Environmental Justice.” https://www.whitehouse.gov/briefing-room/statements-releases/2022/08/17/fact-sheetinflation-reduction-act-advances-environmental-justice/ [6] The Executive Office of the President. 2021. “Executive Order on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government.” https://www.whitehouse.gov/briefing-room/presidentialactions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-thefederal-government/ [7] The Executive Office of the President. 2021. “Executive Order on Tackling the Climate Crisis at Home and Abroad.” https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/27/executive-order-on-tackling-the-climatecrisis-at-home-and-abroad/ [8] The Bipartisan Infrastructure Law (P.L. 117-58) is also referred to as the Infrastructure Investment and Jobs Act (IIJA) of 2021, https://www.congress.gov/117/plaws/publ58/PLAW-117publ58.pdf [9] The Inflation Reduction Act is P.L. 117-169, https://www.congress.gov/117/plaws/publ169/PLAW-117publ169.pdf [10] National Science and Technology Council Subcommittee on Disaster Reduction. 2005. “Grand Challenges for Disaster Reduction.” https://www.sdr.gov/grandchallenges.html This was originally published in March 2023 on It has been broken-up into bite sized sections, each with unique headlines and AI-generated lead images, in line with the provisions of whitehouse.gov. Creative Commons 3.0.

Anticipate, Adapt, and Build Back Better: The Grand Pathways Framework for Resilient Communities

Some people have the nerve for taking risks. Give them a challenge and they’ll jump right into it. You’ll find these people leading some of the biggest initiatives, driving the most challenging projects, and making bold decisions at work. They’re admired and respected for their ability to step up when everyone else is trying to escape the risk. These people are the change drivers, thought leaders and visionaries who have a knack for solving tough problems, courage to step into the unknown and the skills to handle the uncertainty. But what makes these people such good risk takers? Is it their innate talent? Do they have some special abilities? Are they born to be risk-takers? Quite possible. But risk taking is also a skill that can be built with experience and practice. For most people, the idea of doing something unknown is terrifying. It brings out their biggest fears of failing, facing the embarrassment of falling short and not living up to other people’s expectations. Letting fear control their thinking makes them give up even before they have started. They become risk-averse and try to play safe. Playing safe not only limits their growth, it also reduces their appetite to take risks. They avoid risky projects, risky conversations, risky decisions or anything that appears remotely risky. What about you? Are you embracing risks at work or trying to play safe? When you keep playing safe, you fail to leverage the right opportunities to advance your career. Using risk strategically and managing it well can build credibility, increase your influence and open the doors to bigger and better opportunities. Risk taking involves courage and confidence. You should be willing to step out of your comfort zone and put a step into the unknown. So how can you do it well? Ask these 5 questions to make the decision, put it into action and increase your odds of success. Risk taking is intoxicating. Once you get started, you’ll be on an upward journey craving bigger and better risks. \Progress always involves risks. You can’t steal second base and keep your foot on first. — Frederick Wilcox Is the risk worth it? Taking risks without a proper evaluation criteria is foolish. It’s like jumping into a pool without knowing what to expect and hoping you would survive. Not all risks are worth taking. Some risks may be harmful for your career if you take them without being prepared. For example: signing up for responsibilities without proper skills, claiming to be a subject matter expert on areas you don’t know so well. While there’s an upside to taking the risk, there’s also a potential downside. There’s always a cost involved—think emotional, physical, social costs of taking the risk and not just financial impact. To determine if the risk is worth your time and energy, ask these questions: What will you gain if you take the risk? What will you lose if you don’t take the risk? What’s the cost to you and others of taking the risk and not achieving the desired outcome? Does your work environment promote risk taking or hinders it? Once you have thought through the questions above, answer these next: What you may lose from taking the risk substantially outweighs your gain or the other way round? What challenges do you foresee if your work environment does not encourage risk taking? What kind of support would you need to create a plan and put it into action? Don’t take the risk if it isn’t worth it. You have limited time. Better make it count elsewhere. Everyone's time is limited. What matters most is to focus on what matters most. — Roy Bennett Is the decision reversible or irreversible? In a newsletter to his shareholders, Jeff Bezos, the founder of Amazon.com, described two types of decisions – irreversible (type 1) and reversible decisions (type 2). He compares irreversible decisions to “one-way doors” and says that such decisions should be made methodically, carefully and slowly with great deliberation and consultation “If you walk through the door and don’t like what you see on the other side, you can’t get back to where you were before.” However, he says most decisions in an organization aren’t like that. They are changeable and reversible. They are two-way doors “If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. These decisions can and should be made quickly by high judgment individuals or small groups.” What about the risk you plan to take—is it reversible or irreversible? If the risk is reversible and worth it, just go ahead and get started on it. Make the decision, don’t overthink and move fast. For example: If you’re planning to quit your job to start your own company, there’s definitely some risk involved. But the risk isn’t huge and it’s also reversible—you can easily find another job if your company fails or things don’t work out. However, if it’s irreversible, better to slow down and gather more information because once an irreversible decision is put to action it can’t be easily undone. The cost to get the information you need to reduce uncertainty will be worth your time and effort. For example: The decision to announce that the company will be 100% remote and employees can work from anywhere in the world shouldn’t be taken lightly. Once you roll out this policy and it doesn’t work out, getting your employees back to office won’t be easy. Be wary of analysis-paralysis though. Delaying the risk to make a more informed decision shouldn’t lead to putting it off endlessly. Once you have sufficient data at hand or your intuition tells you it’s time to move forward, the biggest mistake will be waiting. How can I stack the odds in my favor? What are the default odds of accomplishing your goal? Are you satisfied with the default odds? What changes can you make or what steps can you take to stack the odds in your favor? Stacking the odds in your favor involves purposefully taking steps to increase the likelihood of achieving your objectives. It involves being prepared to face the unknowns and challenges as they arise. You need to think about what can increase your chances of success and what might reduce it. To do this, identify: What can go wrong? What’s the absolute worst that can happen? What’s the likelihood of it happening? What’s its impact? What is in place to prevent it or how prepared are you to handle it? What steps can you proactively take to reduce it or avoid it entirely? You don’t have to act on every possibility upfront. But being prepared with a plan to deal with them when they arise can tremendously increase your chances of success. You also have to treat every failure as feedback. If you take a risk and fail, it’s not the end of the world. You can’t back out and give up. Learn from your failure and identify what changes you can make to turn it around and do better next time. When you take risks you learn that there will be times when you succeed and there will be times when you fail, and both are equally important. — Ellen DeGeneres What’s within my control? When taking a risk, despite your best efforts, things may not turn out the way you anticipated. Being prepared to handle the unknowns does not guarantee success. Many externals beyond your control can lead to an unexpected outcome. Think about everything that can go wrong and place it in one of the categories: Circle of influence: these are the things over which you can exercise your control and bring about a change. Circle of concern: these are the things you have no control over and focusing your energy here is a waste of your time. Proactive people focus their efforts in the Circle of Influence. They work on the things they can do something about. The nature of their energy is positive, enlarging and magnifying, causing their Circle of Influence to increase - Stephen Covey He adds “Reactive people, on the other hand, focus their efforts in the Circle of Concern. They focus on the weakness of other people, the problems in the environment, and circumstances over which they have no control. Their focus results in blaming and accusing attitudes, reactive language, and increased feelings of victimization. The negative energy generated by that focus, combined with neglect in areas they could do something about, causes their Circle of Influence to shrink.” When you take the risk, focus on the process, not the outcome. Remember you can only control the process. You can’t control the outcome. Working on the process will enlarge your circle of influence enabling you to navigate the risk strategically while obsessing about the outcome will only waste your thinking cycles, add to stress and anxiety without creating any value. Do everything you can to succeed but also be mentally prepared to accept the outcome whatever it may be. Don’t fret. Don’t complain. Remember you did your absolute best and simply move on. How can I take more risks? The science of habit building says that anything done repeatedly and consistently becomes effortless and turns into a habit. This happens because our brain is constantly looking for ways to save effort to allow our minds to ramp down more often. This makes it convert any sequence of actions done consistently into an automatic routine and it’s at the root of how habits are formed. Habits never really disappear. They’re encoded into the structures of our brain, and that’s a huge advantage for us, because it would be awful if we had to relearn how to drive after every vacation. The problem is that your brain can’t tell the difference between bad and good habits, and so if you have a bad one, it’s always lurking there, waiting for the right cues and rewards - Charles Duhigg Want to take more risks? Leverage the power of habit building. The more risks you take, the better will be your tolerance for unknowns and uncertainty. Taking more and more risks will build the mental muscle to do it more often. You will not only get better at taking risks but also at identifying which risks to take and how to deal with the outcome when it doesn’t work out in your favor. Taking risks will soon become your second nature. Risks that earlier brought fear, self-doubt and anxiety, will no longer seem scary. You’ll be ready to make bold decisions and embrace more risky propositions. Building the ability to take risks is a huge career advantage. Don’t let fear and self-doubt hold you back from realizing your true potential. Start small. Take incremental risks. Slowly build the courage to take bigger and better risks. To live a life of excellence, you will have to take risks. You will have to step into new territory and climb new mountains. If you’re up to something that’s as big as you are, it’s going to be scary. If it feels perfectly safe, you are probably underachieving. To leave your mark in the world, you will have to stand someplace you’ve never been willing to stand before. And you will have to have the courage to aspire to excellence. — Debbie Ford Summary Risk taking is not limited to a few with special talents and abilities. Anyone can build the mental muscle to take risks with courage, experience and practice. When fear guides your thinking, you become risk averse. Avoiding risks keeps you safe, but it also limits your learning and growth. Taking risks without a proper decision criteria is foolish. Take only those risks that are worth your time and attention. If the decision to take the risk is reversible, make a quick decision and move fast, but if it’s irreversible, slow down and be more deliberate. Once you decide to take the risk, proactively identify the obstacles that might get in the way and take steps to stack odds of succeeding in your favor. When you don’t get the expected outcome, identify what’s within your control and act on it instead of wasting time and energy on things beyond your control. Use the power of your brain to your advantage. The more risks you take, the easier it will be for your brain to make risk-taking automatic. This story was previously published . Follow me on or here for more stories. here LinkedIn

How to Become Better at Risk Taking

ঝুঁকি গ্রহণে কীভাবে আরও ভাল হওয়া যায়

Wie man risikofreudiger wird

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Comment devenir meilleur en prenant des risques

जोखिम उठाने में बेहतर कैसे बनें

リスクテイクが上手になる方法

위험을 더 잘 감수하는 방법

Como se tornar melhor na tomada de riscos

Как стать лучше, принимая риск

Risk Alma Konusunda Nasıl Daha İyi Olunur?

Làm thế nào để trở nên giỏi hơn khi chấp nhận rủi ro

如何更好地承担风险

I was recently asked about my thoughts on performance management and to share the resources I find helpful. So, as we wrap up another performance management (PM) season, I’d like to reiterate what I’ve mentioned privately: it’s crucial to reflect on the inherent shortcomings of most PM processes, especially as they pertain to engineering culture. The key point is the importance of acknowledging and embracing the subjectivity at the heart of performance management. One of the most compelling arguments comes from “Nine Lies About Work” by Marcus Buckingham and Ashley Goodall. They assert, “People can reliably rate their own experience” but are notably unreliable at rating others. They explain, “Your rating of a team member on something called ‘performance’ is unreliable because your definition of performance is unique to you” (Buckingham & Goodall, 2019). This challenges traditional, often rigid, performance evaluation models and suggests a more introspective approach. Similarly, “No Rules Rules” by Reed Hastings and Erin Meyer presents the intriguing concept of “The Keeper Test.” It’s a straightforward yet powerful tool: “If a person on your team were to quit tomorrow, would you try to change their mind? Or would you accept their resignation, perhaps with a little relief? If the latter, you should give them a severance package now, and look for a star, someone you would fight to keep” (Hastings & Meyer, 2020). This approach simplifies performance assessment to a singular, yet profound question, focusing on the real value an individual brings to the team. “Working Backwards” by Colin Bryar and Bill Carr, though not directly addressing PM, offers valuable insights into creating environments where performance is naturally high. Amazon’s emphasis on clarity, autonomy, and direct, candid communication is a testament to creating a culture where high performance is a byproduct of the work environment itself. In most large corporate settings managing low performance consumes disproportionate time, energy, and resources. The goal should be to cultivate high-performing teams where excellence is the norm, not the exception. As ’s Research-Driven Engineering Leadership blog aptly notes, measuring engineering productivity requires a nuanced understanding of what performance means in a technical and creative domain (RDEL, 2023). Quotient In conclusion, embracing the subjectivity of performance management can lead to more genuine, effective, and meaningful assessments. By focusing on individual experiences, aligning personal goals with team missions, and empowering team members with context and autonomy, organizations can create a dynamic, responsive, and ultimately more effective approach to managing performance. References: Buckingham, M., & Goodall, A. (2019). Nine Lies About Work: A Freethinking Leader’s Guide to the Real World. Hastings, R., & Meyer, E. (2020). No Rules Rules: Netflix and the Culture of Reinvention. Bryar, C., & Carr, B. (2021). Working Backwards: Insights, Stories, and Secrets from Inside Amazon. RDEL. (2023). Research-Driven Engineering Leadership: The Most Popular Posts of the Year. [ ]. Blog Post

Why Embracing Subjectivity Is a Better Approach to Performance Management

In recent years, the concept of tokenization has been gaining significant traction in the financial and real estate sectors. Real-world asset tokenization involves converting physical assets, such as real estate or art, into asset-referenced tokens (ART) that can be bought and sold on blockchain-based platforms. However, as with any financial innovation, compliance with existing legislation is paramount to ensure a secure and transparent ecosystem for all stakeholders. In this article, we will delve deeper into the world of real-world asset tokenization, focusing particularly on the legislative landscape in different regions and how tokenization companies ensure compliance. Understanding Tokenization and Tokenizers 1.1. Tokenization Explained Tokenization is a transformative process that converts tangible real-world assets into digital tokens that can be traded and owned on blockchain platforms. This innovative approach provides a multitude of benefits by allowing assets like real estate, art, and commodities to be divided into smaller, more affordable units. This fractionalization enables a broader spectrum of investors to participate, resulting in increased liquidity and access to high-value assets. Tokenizers, the companies responsible for managing the technical, legal, and regulatory aspects of asset tokenization, are instrumental in ensuring that the tokenization process aligns with the relevant legal frameworks and maintains utmost transparency and security. Tokenizers are at the forefront of integrating these benefits into the broader financial landscape. 1.2. The Benefits of Tokenization The advantages of tokenization extend beyond mere convenience and accessibility. Here are some key benefits: Liquidity: Tokenization facilitates easy buying and selling of assets, which improves liquidity in traditionally illiquid markets. Fractional Ownership: High-value assets become more accessible as they are divided into smaller units, making ownership possible for a wider range of investors. Cost Efficiency: The use of blockchain technology reduces transaction costs and minimizes intermediaries, resulting in a cost-effective investment ecosystem. Transparency: Blockchain's transparency and immutability ensure secure and efficient trading, reducing the risk of fraud and enhancing trust among investors. Legislation Overview in Different Regions 2.1. The European Union (EU) The European Union imposes a comprehensive regulatory framework for tokenization companies. Key regulations include Markets in Financial Instruments Directive 2014 (MiFID II), Markets in Financial Instruments (MiFIR),  the 6th Anti‑Money Laundering Directive (AMLD6), the Markets in Crypto-Assets Regulation (MiCA), and the Basel Extension. These regulations necessitate tokenization companies to implement robust customer identification and due diligence measures. Compliance with Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements is essential to ensure financial security and transparency. By adhering to these standards, tokenization companies operating in the EU can foster trust and maintain the integrity of the financial system. III 2.2. The United States In the United States, the Securities and Exchange Commission (SEC) plays a pivotal role in regulating tokenization. The SEC employs the Howey Test to determine whether a token is classified as a security. This classification holds profound implications for tokenization companies, as it dictates the necessity for registration and compliance with securities laws. Operating within the intricate regulatory landscape of the United States requires a deep understanding of the legal intricacies and a commitment to compliance. 2.3. Asia Asia offers a diverse regulatory landscape, with varying approaches to tokenization: Singapore has instituted the Payment Services Act, which particularly regulates digital payment tokens. Tokenization companies in Singapore must comply with the act's requirements to maintain a foothold in this burgeoning financial hub. Japan has taken a clear stance on security token offerings (STOs). Tokenization companies in Japan are guided by specific guidelines, which include strict regulatory standards and reporting requirements. Operating across the Asian region presents unique challenges as tokenization companies navigate a multitude of regulatory frameworks, necessitating adaptability and an understanding of local compliance requirements. Compliance Strategies and Practices How do tokenizers comply right now? The existing solution that most tokenizers use is working with KYC providers. In this process, users are required to provide their passport information to a KYC provider, which then verifies the identity of the user and crypto wallet. Once verified, the RWA tokenizer whitelists the user's wallet, granting them access to the tokenization services. In some cases, the provider gives users’ data to the tokenizer, in some cases it doesn’t. However, this method has notable drawbacks. Firstly, the tokenization platform must consistently update and maintain the whitelist, which demands a considerable investment of time and effort and can be challenging to scale efficiently. When a company manually adds and removes the wallets from its whitelist, it leaves a lot of space for human error. Additionally, the centralized storage of passport information poses a security risk, as there is a higher likelihood of data leaks, potentially compromising users' sensitive information. It doesn’t matter whether the KYC provider or the tokenizer stores users’ data—if it’s stored on a centralised server, there’s a good chance it will be leaked at some point. The third significant downside to the current approach of RWA tokenizers implementing KYC compliance is the lack of integration with other platforms. Users who tokenize their assets through a particular RWA tokenizer find limitations in utilizing those tokens elsewhere. This lack of interoperability restricts users from leveraging their tokenized assets in various financial services, such as lending platforms. Essentially, users are confined to the ecosystem of the specific tokenization platform they initially engaged with, limiting the versatility and potential utility of their tokenized assets. As we see, the current method is very ineffective and will slow down the growth of the RWA tokenization sphere. However, with the next-generation KYC providers, the situation may change. Instead of whitelisting wallets for certain tokenizers only, KYC providers can issue something called “verifiable credentials” that can be used to prove certain information without having to disclose the information itself. Conclusion In summary, real-world asset tokenization has already revolutionized blockchain finance, offering increased liquidity, fractional ownership, cost efficiency, and transparency. However, current KYC compliance methods, involving wallet whitelisting through partnerships with KYC providers, present significant drawbacks. Collaboration between regulatory bodies, tokenization companies, and advanced KYC solutions will be crucial for realizing the full potential of real-world asset tokenization in a secure financial landscape. Also published . here

Tokenize it Compliantly — Where is Better to Tokenize Your RWA Assets?

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