VRTogether

vrtogether

Virtual reality (VR) may soon replace pictures as the standard visualization tool for online property listings. How will this technology impact buyers and sellers while reshaping the real estate market? The Emerging Trend of VR Home Tours in Real Estate VR technology enables you to tour every room in a property and speak face-to-face with the rental agent without leaving the comfort of your living room couch. This might be why its global market value in the real estate sector will increase to $2.6 billion in 2025, up from $800 million in 2020. One of the biggest advantages of virtual home tours is accessibility. It doesn’t matter if a property is under construction or occupied by the owners — with VR, you can walk through it anyway. You can even schedule an assisted walkthrough with a rental or listing agent to get insider information on the property, even if you’re both on opposite ends of the country. If you plan on moving into a fixer-upper, you can use VR technology to visualize the property’s potential by overlaying renovations or design changes onto the real-world model. These features make this type of tour one of the most personal and immersive — which is why it’s catching on so quickly. With interest rates rising — the interest rate on 30-year mortgages increased from an average of 3.02% in 2020 to 5.8%-6.1% in 2024 — real estate demand is slowing. This is where VR comes in. It has the unique opportunity to benefit buyers and sellers by making touring more efficient and getting properties off the market faster. The Technical Advancements Making It All Possible While VR technology is fundamental to a virtual home tour, a handful of other innovations make it possible. The first is high-resolution 3D imaging, where a 360-degree camera scans the property. The seller then stitches the images together with specialized software to make a realistic virtual replica for tours. The second innovation making VR home tours possible is the Internet of Things (IoT). Sellers can use interconnected sensors to feed real-time data into the virtual world to make it more dynamic. Alternatively, they can use internet-connected edge devices to reduce the computational and bandwidth-related strain on end users. Advances in controllers and motion sensors have made interactivity possible. Instead of sitting through a prerecorded guided tour, you can move through the property at your own pace. Also, you can trigger animations — like lowering the blinds or opening the cabinets — with a flick of your wrist. Considering consumer applications makeup 53% of the VR market, the number of technological advancements directed toward real estate shouldn’t surprise you. There’s a lot of money in researching and developing software, design, and hosting platforms for these applications. The Implications of VR-Driven Real Estate Showings VR technology makes home tours more accessible, broadening the pool of interested parties. According to experts, they reach more potential buyers than the standard approach. They don’t just attract more people — they’re also more engaging. Usually, you only can see a property’s layout when looking at its listing online. If you’re lucky, you’ll also get many high-quality pictures. However, even those don’t give you a good idea of how it feels to be in the house. VR engages you more because it places you in an immersive, lifelike replica where you have agency and control. While the buyer-side implications of virtual home tours might be more noticeable, the seller-side ones are more impactful. Properties using VR real estate showings have selling prices 7.25% higher than the benchmark. Heightened buyer engagement and broadened accessibility lead to a competitive advantage. The process moves more quickly since virtual tours don’t require in-person assistance or scheduling. Some experts claim properties using VR real estate showings spend 20%-30% fewer days on the market. As a result, agents can take on more listings at once. Potential Challenges Buyers and Sellers Face Using VR Although VR home tours benefit buyers and sellers, they also often come with challenges — with the lack of VR headset accessibility being one of the most significant. While virtual viewings are still possible without one, immersive experiences are only possible with mid- or high-end equipment. Properties in low-income locations are more likely to be impacted by this issue. Another challenge buyers and sellers face is agent accessibility. While virtual home tours don’t need an assistant, many serious buyers want one so they can ask questions. However, the increased attention VR listings get can complicate scheduling. The last potential challenge revolves around the accuracy of virtual property replicas. Even though developing one costs less than $300 on average, some sellers might be unable to afford to redo theirs every time they renovate or change something. As a result, the tour might not resemble its real-life counterpart — making buyers feel misled. The Future Outlook of VR Home Tours in Real Estate Sellers can use VR to track eye movement, location data, and interaction information to see where buyers come from and which properties they’re most interested in. This way, they can optimize their marketing, demand forecasting, pricing, and risk assessment strategies. At the same time, buyers can remotely walk through properties regardless of location and conditions. VR technology’s impact on accessibility and engagement will likely transform what the standard property sale looks like. VR technology has the potential to permanently transform the real estate market, becoming the go-to choice for sellers and prospective buyers. As its popularity increases, it might become standard to include it alongside layout and property pictures and come to be something people expect to see. Virtual reality (VR) may soon replace pictures as the standard visualization tool for online property listings. How will this technology impact buyers and sellers while reshaping the real estate market? The Emerging Trend of VR Home Tours in Real Estate The Emerging Trend of VR Home Tours in Real Estate VR technology enables you to tour every room in a property and speak face-to-face with the rental agent without leaving the comfort of your living room couch. This might be why its global market value in the real estate sector will increase to $2.6 billion in 2025, up from $800 million in 2020. will increase to $2.6 billion will increase to $2.6 billion One of the biggest advantages of virtual home tours is accessibility. It doesn’t matter if a property is under construction or occupied by the owners — with VR, you can walk through it anyway. You can even schedule an assisted walkthrough with a rental or listing agent to get insider information on the property, even if you’re both on opposite ends of the country. If you plan on moving into a fixer-upper, you can use VR technology to visualize the property’s potential by overlaying renovations or design changes onto the real-world model. These features make this type of tour one of the most personal and immersive — which is why it’s catching on so quickly. With interest rates rising — the interest rate on 30-year mortgages increased from an average of 3.02% in 2020 to 5.8%-6.1% in 2024 — real estate demand is slowing. This is where VR comes in. It has the unique opportunity to benefit buyers and sellers by making touring more efficient and getting properties off the market faster. increased from an average of 3.02% increased from an average of 3.02% The Technical Advancements Making It All Possible While VR technology is fundamental to a virtual home tour, a handful of other innovations make it possible. The first is high-resolution 3D imaging, where a 360-degree camera scans the property. The seller then stitches the images together with specialized software to make a realistic virtual replica for tours. The second innovation making VR home tours possible is the Internet of Things (IoT). Sellers can use interconnected sensors to feed real-time data into the virtual world to make it more dynamic. Alternatively, they can use internet-connected edge devices to reduce the computational and bandwidth-related strain on end users. Advances in controllers and motion sensors have made interactivity possible. Instead of sitting through a prerecorded guided tour, you can move through the property at your own pace. Also, you can trigger animations — like lowering the blinds or opening the cabinets — with a flick of your wrist. Considering consumer applications makeup 53% of the VR market , the number of technological advancements directed toward real estate shouldn’t surprise you. There’s a lot of money in researching and developing software, design, and hosting platforms for these applications. makeup 53% of the VR market makeup 53% of the VR market The Implications of VR-Driven Real Estate Showings VR technology makes home tours more accessible, broadening the pool of interested parties. According to experts, they reach more potential buyers than the standard approach. They don’t just attract more people — they’re also more engaging. reach more potential buyers reach more potential buyers Usually, you only can see a property’s layout when looking at its listing online. If you’re lucky, you’ll also get many high-quality pictures. However, even those don’t give you a good idea of how it feels to be in the house. VR engages you more because it places you in an immersive, lifelike replica where you have agency and control. While the buyer-side implications of virtual home tours might be more noticeable, the seller-side ones are more impactful. Properties using VR real estate showings have selling prices 7.25% higher than the benchmark. Heightened buyer engagement and broadened accessibility lead to a competitive advantage. have selling prices 7.25% higher have selling prices 7.25% higher The process moves more quickly since virtual tours don’t require in-person assistance or scheduling. Some experts claim properties using VR real estate showings spend 20%-30% fewer days on the market. As a result, agents can take on more listings at once. spend 20%-30% fewer days spend 20%-30% fewer days Potential Challenges Buyers and Sellers Face Using VR Although VR home tours benefit buyers and sellers, they also often come with challenges — with the lack of VR headset accessibility being one of the most significant. While virtual viewings are still possible without one, immersive experiences are only possible with mid- or high-end equipment. Properties in low-income locations are more likely to be impacted by this issue. Another challenge buyers and sellers face is agent accessibility. While virtual home tours don’t need an assistant, many serious buyers want one so they can ask questions. However, the increased attention VR listings get can complicate scheduling. The last potential challenge revolves around the accuracy of virtual property replicas. Even though developing one costs less than $300 on average, some sellers might be unable to afford to redo theirs every time they renovate or change something. As a result, the tour might not resemble its real-life counterpart — making buyers feel misled. costs less than $300 costs less than $300 The Future Outlook of VR Home Tours in Real Estate Sellers can use VR to track eye movement, location data, and interaction information to see where buyers come from and which properties they’re most interested in. This way, they can optimize their marketing, demand forecasting, pricing, and risk assessment strategies. At the same time, buyers can remotely walk through properties regardless of location and conditions. VR technology’s impact on accessibility and engagement will likely transform what the standard property sale looks like. VR technology has the potential to permanently transform the real estate market, becoming the go-to choice for sellers and prospective buyers. As its popularity increases, it might become standard to include it alongside layout and property pictures and come to be something people expect to see.

VR Home Tours: The Future of Real Estate Showings

Once, I had a back-and-forth discussion with a coworker about the correct way to use @Component and @StepScope annotations in a Spring-based Java application. We debated whether using both annotations was necessary or if it led to redundancy or unexpected behavior. In this article, I'll share insights from that discussion, clarify the topic, share examples, and provide references to Spring documentation to help guide you through the correct approach. Problem Statement Consider the following scenario: you have a class that requires both Spring's @Component annotation and @StepScope from Spring Batch. The @Component annotation registers your class as a Spring-managed bean, while @StepScope ensures that the bean is instantiated with a lifecycle tied to a specific step in your batch job. The combination may look like this: @Component
@StepScope
public class MyStepScopedComponent {
    // Class implementation
} The question is: Is using @Component with @StepScope redundant or problematic? Does it make sense to use both annotations, or should you use a different approach? Understanding the Annotations @Component: This annotation is part of the core Spring framework. It marks a class as a Spring-managed bean, which means it will be automatically discovered and registered in the Spring application context through component scanning. Typically, @Component beans are singleton-scoped by default, meaning they are created once and used throughout the application's lifecycle.
@StepScope: This is a Spring Batch annotation that specifies the bean should have a scope that is tied to the lifecycle of a batch step. Unlike a singleton bean, a step-scoped bean is instantiated each time a step is executed. This allows it to access resources like job parameters and step-specific data. Is the Combination Acceptable? Yes, using @Component and @StepScope together is acceptable, but there are some important considerations: Bean Lifecycle and Proxy Creation: The @StepScope annotation changes the lifecycle of the bean from the default singleton to one that is created specifically for each step execution. When @StepScope is used, Spring creates a proxy for the bean, meaning that the actual bean instance is created when the step starts, not at application startup.
Avoid Lifecycle Conflicts: Using @Component implies that the bean is part of component scanning and might be treated as a singleton by default. @StepScope changes that lifecycle to step scope. This duality can sometimes create confusion or unexpected behavior if not properly handled. To avoid these issues: Use Lazy Injection: If you inject a step-scoped bean into a singleton-scoped bean, use @Lazy to ensure that the bean is not prematurely instantiated, which can cause problems.
@Component
public class MySingletonBean {
    private final MyStepScopedComponent myStepScopedComponent;

    public MySingletonBean(@Lazy MyStepScopedComponent myStepScopedComponent) {
        this.myStepScopedComponent = myStepScopedComponent;
    }
}



Consider Refactoring to @Bean and @StepScope: If your step-scoped bean requires constructor injection or complex configuration, you might be better off defining it in a configuration class using @Bean.
@Configuration
public class BatchConfiguration {
    @Bean
    @StepScope
    public MyStepScopedComponent myStepScopedComponent() {
        return new MyStepScopedComponent();
    }
} Best Practices Use @StepScope on the Bean Definition Only: When defining your step-scoped bean using @Bean in a @Configuration class, it gives you more control over initialization and prevents scope conflicts.
Avoid Direct Field Injection: Prefer constructor injection with @Lazy to ensure proper lifecycle handling.
Test Thoroughly: Scoping issues often arise from improper initialization timing. Ensure you test thoroughly to confirm that your job parameters are correctly injected when the step starts. Example with Proper Scope Management Here is an example where a bean is scoped to a batch step but defined in a configuration class for better clarity and control: @Configuration
public class BatchJobConfig {
    @Bean
    @StepScope
    public MyStepScopedComponent myStepScopedBean(@Value("#{jobParameters['inputFileName']}") String inputFileName) {
        return new MyStepScopedComponent(inputFileName);
    }
} In this example, the @StepScope ensures that a new instance of MyStepScopedComponent is created for each step execution, and the inputFileName job parameter is injected correctly. References and Resources To better understand the nuances of using @StepScope and its interaction with other Spring annotations, I recommend checking the following references: Spring Batch Step Scope Documentation provides details about the usage and lifecycle of @StepScope.
Delft Stack's Guide on Step Scope in Spring Batch offers a practical explanation of how to configure step-scoped beans, including examples in XML and Java configurations. Conclusion Using @Component and @StepScope together is technically allowed, but understanding their differences and potential conflicts is key to using them effectively. If you're unsure, consider defining your step-scoped beans in a @Configuration class with @Bean and @StepScope for better lifecycle management and clarity. For any questions or further discussion, feel free to comment below or refer to the linked documentation. Once, I had a back-and-forth discussion with a coworker about the correct way to use @Component and @StepScope annotations in a Spring-based Java application. We debated whether using both annotations was necessary or if it led to redundancy or unexpected behavior. In this article, I'll share insights from that discussion, clarify the topic, share examples, and provide references to Spring documentation to help guide you through the correct approach. @Component @StepScope Problem Statement Consider the following scenario: you have a class that requires both Spring's @Component annotation and @StepScope from Spring Batch. The @Component annotation registers your class as a Spring-managed bean, while @StepScope ensures that the bean is instantiated with a lifecycle tied to a specific step in your batch job. The combination may look like this: @Component @StepScope @Component @StepScope @Component
@StepScope
public class MyStepScopedComponent {
    // Class implementation
} @Component
@StepScope
public class MyStepScopedComponent {
    // Class implementation
} The question is: Is using @Component with @StepScope redundant or problematic? Does it make sense to use both annotations, or should you use a different approach? Is using @Component with @StepScope redundant or problematic? Understanding the Annotations @Component: This annotation is part of the core Spring framework. It marks a class as a Spring-managed bean, which means it will be automatically discovered and registered in the Spring application context through component scanning. Typically, @Component beans are singleton-scoped by default, meaning they are created once and used throughout the application's lifecycle. @StepScope: This is a Spring Batch annotation that specifies the bean should have a scope that is tied to the lifecycle of a batch step. Unlike a singleton bean, a step-scoped bean is instantiated each time a step is executed. This allows it to access resources like job parameters and step-specific data. @Component : This annotation is part of the core Spring framework. It marks a class as a Spring-managed bean, which means it will be automatically discovered and registered in the Spring application context through component scanning. Typically, @Component beans are singleton-scoped by default, meaning they are created once and used throughout the application's lifecycle. @Component @Component singleton-scoped @StepScope : This is a Spring Batch annotation that specifies the bean should have a scope that is tied to the lifecycle of a batch step. Unlike a singleton bean, a step-scoped bean is instantiated each time a step is executed . This allows it to access resources like job parameters and step-specific data. @StepScope instantiated each time a step is executed Is the Combination Acceptable? Yes , using @Component and @StepScope together is acceptable, but there are some important considerations: Yes @Component @StepScope Bean Lifecycle and Proxy Creation: The @StepScope annotation changes the lifecycle of the bean from the default singleton to one that is created specifically for each step execution. When @StepScope is used, Spring creates a proxy for the bean, meaning that the actual bean instance is created when the step starts, not at application startup. Avoid Lifecycle Conflicts: Using @Component implies that the bean is part of component scanning and might be treated as a singleton by default. @StepScope changes that lifecycle to step scope. This duality can sometimes create confusion or unexpected behavior if not properly handled. Bean Lifecycle and Proxy Creation : The @StepScope annotation changes the lifecycle of the bean from the default singleton to one that is created specifically for each step execution. When @StepScope is used, Spring creates a proxy for the bean, meaning that the actual bean instance is created when the step starts, not at application startup. Bean Lifecycle and Proxy Creation @StepScope @StepScope Avoid Lifecycle Conflicts : Using @Component implies that the bean is part of component scanning and might be treated as a singleton by default. @StepScope changes that lifecycle to step scope. This duality can sometimes create confusion or unexpected behavior if not properly handled. Avoid Lifecycle Conflicts @Component @StepScope To avoid these issues : To avoid these issues Use Lazy Injection: If you inject a step-scoped bean into a singleton-scoped bean, use @Lazy to ensure that the bean is not prematurely instantiated, which can cause problems.
@Component
public class MySingletonBean {
    private final MyStepScopedComponent myStepScopedComponent;

    public MySingletonBean(@Lazy MyStepScopedComponent myStepScopedComponent) {
        this.myStepScopedComponent = myStepScopedComponent;
    }
} Consider Refactoring to @Bean and @StepScope: If your step-scoped bean requires constructor injection or complex configuration, you might be better off defining it in a configuration class using @Bean.
@Configuration
public class BatchConfiguration {
    @Bean
    @StepScope
    public MyStepScopedComponent myStepScopedComponent() {
        return new MyStepScopedComponent();
    }
} Use Lazy Injection: If you inject a step-scoped bean into a singleton-scoped bean, use @Lazy to ensure that the bean is not prematurely instantiated, which can cause problems. @Component
public class MySingletonBean {
    private final MyStepScopedComponent myStepScopedComponent;

    public MySingletonBean(@Lazy MyStepScopedComponent myStepScopedComponent) {
        this.myStepScopedComponent = myStepScopedComponent;
    }
} Use Lazy Injection : If you inject a step-scoped bean into a singleton-scoped bean, use @Lazy to ensure that the bean is not prematurely instantiated, which can cause problems. Use Lazy Injection @Lazy @Component
public class MySingletonBean {
    private final MyStepScopedComponent myStepScopedComponent;

    public MySingletonBean(@Lazy MyStepScopedComponent myStepScopedComponent) {
        this.myStepScopedComponent = myStepScopedComponent;
    }
} @Component
public class MySingletonBean {
    private final MyStepScopedComponent myStepScopedComponent;

    public MySingletonBean(@Lazy MyStepScopedComponent myStepScopedComponent) {
        this.myStepScopedComponent = myStepScopedComponent;
    }
} Consider Refactoring to @Bean and @StepScope: If your step-scoped bean requires constructor injection or complex configuration, you might be better off defining it in a configuration class using @Bean. @Configuration
public class BatchConfiguration {
    @Bean
    @StepScope
    public MyStepScopedComponent myStepScopedComponent() {
        return new MyStepScopedComponent();
    }
} Consider Refactoring to @Bean and @StepScope : If your step-scoped bean requires constructor injection or complex configuration, you might be better off defining it in a configuration class using @Bean . Consider Refactoring to @Bean and @StepScope @Bean @Configuration
public class BatchConfiguration {
    @Bean
    @StepScope
    public MyStepScopedComponent myStepScopedComponent() {
        return new MyStepScopedComponent();
    }
} @Configuration
public class BatchConfiguration {
    @Bean
    @StepScope
    public MyStepScopedComponent myStepScopedComponent() {
        return new MyStepScopedComponent();
    }
} Best Practices Use @StepScope on the Bean Definition Only: When defining your step-scoped bean using @Bean in a @Configuration class, it gives you more control over initialization and prevents scope conflicts. Avoid Direct Field Injection: Prefer constructor injection with @Lazy to ensure proper lifecycle handling. Test Thoroughly: Scoping issues often arise from improper initialization timing. Ensure you test thoroughly to confirm that your job parameters are correctly injected when the step starts. Use @StepScope on the Bean Definition Only : When defining your step-scoped bean using @Bean in a @Configuration class, it gives you more control over initialization and prevents scope conflicts. Use @StepScope on the Bean Definition Only @Bean @Configuration more control over initialization Avoid Direct Field Injection : Prefer constructor injection with @Lazy to ensure proper lifecycle handling. Avoid Direct Field Injection @Lazy Test Thoroughly : Scoping issues often arise from improper initialization timing. Ensure you test thoroughly to confirm that your job parameters are correctly injected when the step starts. Test Thoroughly Example with Proper Scope Management Here is an example where a bean is scoped to a batch step but defined in a configuration class for better clarity and control: @Configuration
public class BatchJobConfig {
    @Bean
    @StepScope
    public MyStepScopedComponent myStepScopedBean(@Value("#{jobParameters['inputFileName']}") String inputFileName) {
        return new MyStepScopedComponent(inputFileName);
    }
} @Configuration
public class BatchJobConfig {
    @Bean
    @StepScope
    public MyStepScopedComponent myStepScopedBean(@Value("#{jobParameters['inputFileName']}") String inputFileName) {
        return new MyStepScopedComponent(inputFileName);
    }
} In this example, the @StepScope ensures that a new instance of MyStepScopedComponent is created for each step execution, and the inputFileName job parameter is injected correctly. @StepScope MyStepScopedComponent inputFileName References and Resources To better understand the nuances of using @StepScope and its interaction with other Spring annotations, I recommend checking the following references: @StepScope Spring Batch Step Scope Documentation provides details about the usage and lifecycle of @StepScope. Delft Stack's Guide on Step Scope in Spring Batch offers a practical explanation of how to configure step-scoped beans, including examples in XML and Java configurations. Spring Batch Step Scope Documentation provides details about the usage and lifecycle of @StepScope. Spring Batch Step Scope Documentation @StepScope. Delft Stack's Guide on Step Scope in Spring Batch offers a practical explanation of how to configure step-scoped beans, including examples in XML and Java configurations. Delft Stack's Guide on Step Scope in Spring Batch Conclusion Using @Component and @StepScope together is technically allowed, but understanding their differences and potential conflicts is key to using them effectively. If you're unsure, consider defining your step-scoped beans in a @Configuration class with @Bean and @StepScope for better lifecycle management and clarity. @Component @StepScope @Configuration @Bean @StepScope For any questions or further discussion, feel free to comment below or refer to the linked documentation.

Using @Component and @StepScope Together in Java Spring Batch

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 8 The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Interpretation is the process through which a cognitive agent assigns meaning to experience. In the case of ELIZA (or PARRY) “experience” is merely textual interaction with the user. Weizenbaum recognized that ELIZA has no interpretive structures or processes at all. It is, however, worth making a connection between interpretation, and the focus described above on recursion and lists, and language, because this connects all of the threads of research that are described here. To create a completely tied-together coherent explication of this complex knot of concepts would require much more space than is available at the moment, so we will merely touch the topic at a few points which will hopefully be sufficient to give the reader the sense of the whole. First, observe that at the simplest level, a sentence is a “merely” list of words. However, as pointed out by Chomsky, human language is fundamentally recursive, as can easily be seen in either the understanding or production of novel, potentially infinitely deeply nested sentences that may be hard to understand, but which, like this one, are nonetheless grammatical and sensible, for example: “The rat that the cat that the dog chased killed ate the malt.”[20, p. 286] Second, observe that a list (perhaps representing a sentence) is a special case of a graph, and, moreover, a nested list (perhaps representing a nested sentence, like the example above) is just a tree, which is a specific kind of graph. Indeed, Chomsky’s underlying grammars are graphs as well, where elements link to other elements. And even without going into that depth, it is obvious that language is graph-structured, not merely tree-structured even on the surface, through, for example, pronouns which refer to other constituents of the sentences, creating edges in the graph. Third, observe that the belief structures that are used in most AI to represent beliefs are generally graphs, wherein symbols, which might represent concepts, are linked to one another by edges, which might represent relationships between concepts.[8] Fourth, observe that graph (or tree or list) traversal is a naturally recursive process, proceeding from vertex to vertex along the edges of the graph, and that many of the core algorithms of classical “symbolic” AI rely upon various versions of efficient graph (or tree) traversal.[9] Finally (fifth), observe that extended conversations (discourse), is analogous to individual sentences in that at the surface it is linear (list-like), but even going one level down one finds that real discourse contains explicit connections (e.g., “...before you said...”), and, again analogous to our story of interpretation, as well at both the levels of semantic and pragmatic connections that connect parts of the conversation to one another. Given these glosses, it can be seen (roughly) that interpretation is a process – indeed, a computable recursive function, precisely in Turing’s sense – that transforms one sort of graph structure, the surface structure of sentences, and indeed of whole discussions, to another – the meaning – and then back again, into the next turn of the conversation.[10] Although ELIZA only engages in the shallowest such recursive operations in transforming input sentences into output sentences directly in accord with its script, Weizenbaum was explicitly aware that ELIZA’s users – and indeed the users of any AI (or for that matter every human everywhere all the time) – were engaged in interpretation in all is complexity and glory. Putting this more clearly, the users of an AI are interpreting the program as intelligent, and they are led – or perhaps mis-led – to this interpretation by virtue of the program putting forward an appearance of intelligence, or what Weizenbaum called “the illusion of understanding” in his conversation with McCorduck, and, as we shall soon see, in the ELIZA paper itself. It is critical to understand that this “illusion” does not arise through triggering some sort of abnormal cognitive error. Quite to the contrary, it relies – as does all magic – upon the perfectly normal, continuous, and central cognitive process of interpretation that humans are engaged in all the time in everything they do. Without the continuous cognitive process of interpretation, we could not operate, and indeed, we would not be cognitive agents at all. Mistaken interpretation is a common and normal feature of cognition, and is usually easily corrected, if becomes relevant at all.[11] Armed with this insight, we come, finally, to ELIZA itself. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. [8] In AIs built out of ANNs the concepts and their inter-relationships are not so concisely represented – usually being diffused across the network, however, the networks themselves are still graphs (indeed, the term “network” is just a synonym for “graph”), and the analytic bases for the construction and analysis of ANNs rest firmly on graph theory. [9] The operation of ANNs relies upon matrix multiplication rather than upon graph traversal, however these are closely related, and, indeed, one commonly implements graph traversal via matrix multiplication. Furthermore, advanced users of ANNs are coming to the realization that in order to understand what the ANN is doing, and to guide it “intelligently” we are likely to end up relying upon more classical sorts of algorithms, resulting in a hybrid of ANN and symbolic AI. [10] NN-based AIs, especially modern Large Language Models (LLMs) usually operate wordby-word (more precisely, token by token), generating the next “most likely” word (token) as result of bubbling the context – i.e., the whole previous interaction (including the LLM’s own outputs) back through the network in what is called a “recurrent” pattern. These are more like ELIZA than they are like the AIs built by Colby, Schank, etc. Instead of an author creating a script, the scripts for LLMs a created by transforming enormous amounts of language, usually scraped from the web, into the incredibly complex graphs describing how words relate to other words in their context, but they have no explicit representation of meaning, aside from the nest of interrelationships burned into and buried in the network. As a result, when engaged in discourse, LLMs act striking like ELIZA in that they can briefly maintain the appearance of understanding but once one attempts to carry on the conversation in a new direction, or refer (directly or indirectly) to previous conversational context, they are essentially as lost as ELIZA, and engage in what might be most charitably described as grammatically correct confabulation. [11] Uncorrected mistaken interpretation maybe be said to be the core problem in some of the cognitive impairments studied by Colby and other psychiatrists. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 8 The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Interpretation is the process through which a cognitive agent assigns meaning to experience. In the case of ELIZA (or PARRY) “experience” is merely textual interaction with the user. Weizenbaum recognized that ELIZA has no interpretive structures or processes at all. It is, however, worth making a connection between interpretation, and the focus described above on recursion and lists, and language, because this connects all of the threads of research that are described here. To create a completely tied-together coherent explication of this complex knot of concepts would require much more space than is available at the moment, so we will merely touch the topic at a few points which will hopefully be sufficient to give the reader the sense of the whole. First, observe that at the simplest level, a sentence is a “merely” list of words. However, as pointed out by Chomsky, human language is fundamentally recursive, as can easily be seen in either the understanding or production of novel, potentially infinitely deeply nested sentences that may be hard to understand, but which, like this one, are nonetheless grammatical and sensible, for example: “The rat that the cat that the dog chased killed ate the malt.”[20, p. 286] Second, observe that a list (perhaps representing a sentence) is a special case of a graph, and, moreover, a nested list (perhaps representing a nested sentence, like the example above) is just a tree, which is a specific kind of graph. Indeed, Chomsky’s underlying grammars are graphs as well, where elements link to other elements. And even without going into that depth, it is obvious that language is graph-structured, not merely tree-structured even on the surface, through, for example, pronouns which refer to other constituents of the sentences, creating edges in the graph. Third, observe that the belief structures that are used in most AI to represent beliefs are generally graphs, wherein symbols, which might represent concepts, are linked to one another by edges, which might represent relationships between concepts.[8] Fourth, observe that graph (or tree or list) traversal is a naturally recursive process, proceeding from vertex to vertex along the edges of the graph, and that many of the core algorithms of classical “symbolic” AI rely upon various versions of efficient graph (or tree) traversal.[9] Finally (fifth), observe that extended conversations (discourse), is analogous to individual sentences in that at the surface it is linear (list-like), but even going one level down one finds that real discourse contains explicit connections (e.g., “...before you said...”), and, again analogous to our story of interpretation, as well at both the levels of semantic and pragmatic connections that connect parts of the conversation to one another. Given these glosses, it can be seen (roughly) that interpretation is a process – indeed, a computable recursive function, precisely in Turing’s sense – that transforms one sort of graph structure, the surface structure of sentences, and indeed of whole discussions, to another – the meaning – and then back again, into the next turn of the conversation.[10] Although ELIZA only engages in the shallowest such recursive operations in transforming input sentences into output sentences directly in accord with its script, Weizenbaum was explicitly aware that ELIZA’s users – and indeed the users of any AI (or for that matter every human everywhere all the time) – were engaged in interpretation in all is complexity and glory. Putting this more clearly, the users of an AI are interpreting the program as intelligent, and they are led – or perhaps mis-led – to this interpretation by virtue of the program putting forward an appearance of intelligence, or what Weizenbaum called “the illusion of understanding” in his conversation with McCorduck, and, as we shall soon see, in the ELIZA paper itself. appearance It is critical to understand that this “illusion” does not arise through triggering some sort of abnormal cognitive error. Quite to the contrary, it relies – as does all magic – upon the perfectly normal, continuous, and central cognitive process of interpretation that humans are engaged in all the time in everything they do. Without the continuous cognitive process of interpretation, we could not operate, and indeed, we would not be cognitive agents at all. Mistaken interpretation is a common and normal feature of cognition, and is usually easily corrected, if becomes relevant at all.[11] Armed with this insight, we come, finally, to ELIZA itself. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [8] In AIs built out of ANNs the concepts and their inter-relationships are not so concisely represented – usually being diffused across the network, however, the networks themselves are still graphs (indeed, the term “network” is just a synonym for “graph”), and the analytic bases for the construction and analysis of ANNs rest firmly on graph theory. [9] The operation of ANNs relies upon matrix multiplication rather than upon graph traversal, however these are closely related, and, indeed, one commonly implements graph traversal via matrix multiplication. Furthermore, advanced users of ANNs are coming to the realization that in order to understand what the ANN is doing, and to guide it “intelligently” we are likely to end up relying upon more classical sorts of algorithms, resulting in a hybrid of ANN and symbolic AI. [10] NN-based AIs, especially modern Large Language Models (LLMs) usually operate wordby-word (more precisely, token by token), generating the next “most likely” word (token) as result of bubbling the context – i.e., the whole previous interaction (including the LLM’s own outputs) back through the network in what is called a “recurrent” pattern. These are more like ELIZA than they are like the AIs built by Colby, Schank, etc. Instead of an author creating a script, the scripts for LLMs a created by transforming enormous amounts of language, usually scraped from the web, into the incredibly complex graphs describing how words relate to other words in their context, but they have no explicit representation of meaning, aside from the nest of interrelationships burned into and buried in the network. As a result, when engaged in discourse, LLMs act striking like ELIZA in that they can briefly maintain the appearance of understanding but once one attempts to carry on the conversation in a new direction, or refer (directly or indirectly) to previous conversational context, they are essentially as lost as ELIZA, and engage in what might be most charitably described as grammatically correct confabulation. [11] Uncorrected mistaken interpretation maybe be said to be the core problem in some of the cognitive impairments studied by Colby and other psychiatrists.

The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion

Apache Kafka Architecture 101: Internal Components and How They Work Together

**LISBON, Portugal, May 9th, 2024/Chainwire/--**Azuro, the leading liquidity layer for onchain predictions, and Chiliz, the world’s premier sports and entertainment blockchain, are partnering up to accelerate the adoption of onchain sport prediction markets. These efforts will allow millions of fans worldwide to further support and cheer for their favorite teams and athletes through fair and transparent prediction apps. Chiliz Chain is renowned for its blockchain solutions that transform fan engagement in sports through features like fan tokens and interactive engagement platforms. Through its flagship venture Socios.com, the network has expanded significantly to support more than 80 sport-related fan tokens and 2M+ ecosystem users. Team-specific fan tokens for the best football clubs, including FC Barcelona, Inter Milan, Juventus, Paris Saint-Germain, Manchester City, and many more, are all currently supported by Chiliz Chain through Socios. The network leverages a Proof of Staked Authority (PoSA) consensus algorithm which allows smart contract developers, validators, stakers, and other stakeholders to actively interact with the chain. Chiliz Chain is built to power sports and entertainment applications with a focus on brand IP and project use-case integrity. For Chiliz, partnering with Azuro represents a step toward tapping into the rapidly growing onchain prediction markets by the hand of the de facto infrastructure layer for sports prediction applications across EVM chains. Its innovative liquidity pool design, advanced tooling and oracle solutions allow apps to launch without upfront investment or running costs, enabling them to focus entirely on user acquisition, engagement and monetization from minute one. Azuro’s robust platform, currently supports over 25 active applications with transaction volumes surpassing $290 million, offers an ideal environment for Chiliz to expand its offerings in the sports and entertainment sectors. As part of this collaboration, Chiliz will be integrated into the Azuro Protocol, adding to the existing supported chains such as Polygon and Gnosis Chain. This integration will enable Chiliz to expand its reach within sports-focused prediction applications. New and existing builders relying on Azuro infrastructure will be able to connect their apps to the Chiliz Chain and allow their users to place transactions with the blockchain’s native token CHZ. "Integrating with Azuro marks a pivotal move for Chiliz Chain to capitalize on the rapidly expanding onchain prediction market," said Alexandre Dreyfus, CEO of Chiliz and Socios.com. "Leveraging Azuro's advanced infrastructure, we're empowering developers to innovate within the sports and gaming sectors." Rossen, a core contributor at Azuro, stated, “The onchain prediction markets have always been strongly tied to the sports and entertainment sector. Working alongside Chiliz Chain, the leading blockchain in this segment, feels like an obvious step for us. Azuro’s robust infrastructure will help expand Chiliz Chain’s usability within the fast-growing onchain prediction markets.” Looking ahead, Azuro and Chiliz will be soon launching a co-sponsored grants program aimed at incentivizing app developers to build on their combined infrastructure. These initiatives, including bounties for the first apps, are designed to fuel further adoption of onchain prediction markets, and aligned with Azuro’s vision for setting new transparency and fairness standards. About Azuro Azuro is the onchain predictions layer. It consists of modular tooling, oracle and liquidity solutions for EVM chains to host powerful prediction and gaming apps. With its unique infrastructure layer approach Azuro makes on-chain predictions and gaming portable and composable. It allows anyone to engage and monetize users by building apps, integrations, and products quickly, permissionlessly and with zero upfront or running costs. For more information or press inquiries, please contact team@azuro.org Website: https://azuro.org/
Docs: https://gem.azuro.org
Github: https://github.com/Azuro-protocol
Onchain activity: https://dune.com/azuro/stats About Chiliz Chiliz is the home of SportFi, where sports brands meet decentralized opportunities. Its infrastructure is driven by the Chiliz Chain, the first Layer-1 EVM compatible blockchain for sports and entertainment which now has a diverse ecosystem, fuelled by Chiliz native token CHZ. Its primary product on the Chiliz Chain, Socios.com, hosts a global community of over two million users, which makes it the largest non-finance/trading-centric Web3 consumer-facing product globally by user base. It hase partnerships with countless brands, including the giants of world sport such as FC Barcelona, Paris Saint-Germain, Manchester City, Juventus, S.S.C Napoli, Inter Milan, AC Milan, Arsenal, Atlético Madrid, A.S. Roma and major F1 teams. Website: https://www.chiliz.com/ Contact Azuro team team@azuro.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **LISBON, Portugal, May 9th, 2024/Chainwire/--**Azuro, the leading liquidity layer for onchain predictions, and Chiliz, the world’s premier sports and entertainment blockchain, are partnering up to accelerate the adoption of onchain sport prediction markets. These efforts will allow millions of fans worldwide to further support and cheer for their favorite teams and athletes through fair and transparent prediction apps. Chiliz Chain is renowned for its blockchain solutions that transform fan engagement in sports through features like fan tokens and interactive engagement platforms. Through its flagship venture Socios.com, the network has expanded significantly to support more than 80 sport-related fan tokens and 2M+ ecosystem users. Team-specific fan tokens for the best football clubs, including FC Barcelona, Inter Milan, Juventus, Paris Saint-Germain, Manchester City, and many more, are all currently supported by Chiliz Chain through Socios. The network leverages a Proof of Staked Authority (PoSA) consensus algorithm which allows smart contract developers, validators, stakers, and other stakeholders to actively interact with the chain. Chiliz Chain is built to power sports and entertainment applications with a focus on brand IP and project use-case integrity. For Chiliz, partnering with Azuro represents a step toward tapping into the rapidly growing onchain prediction markets by the hand of the de facto infrastructure layer for sports prediction applications across EVM chains. Its innovative liquidity pool design, advanced tooling and oracle solutions allow apps to launch without upfront investment or running costs, enabling them to focus entirely on user acquisition, engagement and monetization from minute one. Azuro’s robust platform, currently supports over 25 active applications with transaction volumes surpassing $290 million, offers an ideal environment for Chiliz to expand its offerings in the sports and entertainment sectors. As part of this collaboration, Chiliz will be integrated into the Azuro Protocol, adding to the existing supported chains such as Polygon and Gnosis Chain. This integration will enable Chiliz to expand its reach within sports-focused prediction applications. New and existing builders relying on Azuro infrastructure will be able to connect their apps to the Chiliz Chain and allow their users to place transactions with the blockchain’s native token CHZ. "Integrating with Azuro marks a pivotal move for Chiliz Chain to capitalize on the rapidly expanding onchain prediction market," said Alexandre Dreyfus, CEO of Chiliz and Socios.com. "Leveraging Azuro's advanced infrastructure, we're empowering developers to innovate within the sports and gaming sectors." Rossen, a core contributor at Azuro, stated, “The onchain prediction markets have always been strongly tied to the sports and entertainment sector. Working alongside Chiliz Chain, the leading blockchain in this segment, feels like an obvious step for us. Azuro’s robust infrastructure will help expand Chiliz Chain’s usability within the fast-growing onchain prediction markets.” Looking ahead, Azuro and Chiliz will be soon launching a co-sponsored grants program aimed at incentivizing app developers to build on their combined infrastructure. These initiatives, including bounties for the first apps, are designed to fuel further adoption of onchain prediction markets, and aligned with Azuro’s vision for setting new transparency and fairness standards. About Azuro Azuro is the onchain predictions layer. It consists of modular tooling, oracle and liquidity solutions for EVM chains to host powerful prediction and gaming apps. With its unique infrastructure layer approach Azuro makes on-chain predictions and gaming portable and composable. It allows anyone to engage and monetize users by building apps, integrations, and products quickly, permissionlessly and with zero upfront or running costs. For more information or press inquiries, please contact team@azuro.org Website: https://azuro.org/ Docs: https://gem.azuro.org Github: https://github.com/Azuro-protocol Onchain activity: https://dune.com/azuro/stats Website: https://azuro.org/ https://azuro.org/ https://azuro.org/ Docs: https://gem.azuro.org https://gem.azuro.org https://gem.azuro.org Github: https://github.com/Azuro-protocol https://github.com/Azuro-protocol https://github.com/Azuro-protocol Onchain activity: https://dune.com/azuro/stats https://dune.com/azuro/stats https://dune.com/azuro/stats About Chiliz Chiliz is the home of SportFi, where sports brands meet decentralized opportunities. Its infrastructure is driven by the Chiliz Chain, the first Layer-1 EVM compatible blockchain for sports and entertainment which now has a diverse ecosystem, fuelled by Chiliz native token CHZ. Its primary product on the Chiliz Chain, Socios.com, hosts a global community of over two million users, which makes it the largest non-finance/trading-centric Web3 consumer-facing product globally by user base. It hase partnerships with countless brands, including the giants of world sport such as FC Barcelona, Paris Saint-Germain, Manchester City, Juventus, S.S.C Napoli, Inter Milan, AC Milan, Arsenal, Atlético Madrid, A.S. Roma and major F1 teams. Website: https://www.chiliz.com/ Website: https://www.chiliz.com/ https://www.chiliz.com/ https://www.chiliz.com/ Contact Azuro team team@azuro.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Azuro And Chiliz Working Together To Boost Adoption Of Onchain Sport Prediction Markets

GEORGE TOWN, Grand Cayman, November 22nd, 2024/Chainwire/--Zircuit, the chain where innovation meets security, today announced its ZRC Token Launch on Monday, November 25th—a key step in building a thriving, decentralized ecosystem. ZRC serves as the foundation of Zircuit’s architecture, enabling participants to receive additional rewards, participate in network app fair launches, and drive its growth. As the ecosystem’s cornerstone, ZRC aligns incentives across developers and users, fostering active collaboration and innovation. The ZRC launch follows a series of notable achievements for Zircuit, including the successful rollout of Mainnet, a $2 billion TVL ecosystem, the groundbreaking EIGEN fairdrop with over 190,000 participants, the liquidity hub launch, and strategic investment from Binance Labs, Pantera, and other strategic partners. Together, these milestones underscore Zircuit’s position as a leader in decentralized finance and staking. “The ZRC token is more than a milestone; it’s a gateway to the decentralized future we’ve been building at Zircuit,” said Martin Derka, co-founder of Zircuit. “By aligning incentives across our ecosystem, ZRC empowers developers and users to shape the network collaboratively.” Designed with transparency and accessibility in mind, the ZRC Token Launch ensures participants can seamlessly engage with staking partners through Seasons 1-3 of Zircuit’s ecosystem. This includes staking through the Liquidity Hub, offering the potential to earn rewards. Zircuit protects users from hacks through its built-in, automated AI techniques that guard users against smart contract exploits and malicious actors. This system automatically guards against smart contract exploits and malicious actors, making Zircuit one of the safest blockchain platforms available. As the safest chain for DeFi and staking, Zircuit is the premier liquidity hub for various assets, including ETH, BTC, LSTs, and LRTs, while providing robust security guarantees. Zircuit’s strong infrastructure allows users to earn competitive yields natively, combining safety with attractive returns. Users can explore the ZRC token’s role in Zircuit’s ecosystem by engaging with staking and reward opportunities through the Liquidity Hub. For more information, users can visit zircuit.com and follow Zircuit on Twitter/X at @ZircuitL2. About Zircuit Zircuit: Where innovation meets security, designed for everyone. Zircuit offers developers powerful features while giving users peace of mind. Designed by a team of web3 security veterans and PhDs, Zircuit combines high performance with unmatched security. Experience the safest chain for DeFi and staking. To learn more about Zircuit, visit zircuit.com, and follow us on Twitter/X @ZircuitL2. Contact Head of Communications Jennifer Zheng Zircuit jen@zircuit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here GEORGE TOWN, Grand Cayman, November 22nd, 2024/Chainwire/--Zircuit, the chain where innovation meets security, today announced its ZRC Token Launch on Monday, November 25th—a key step in building a thriving, decentralized ecosystem. ZRC serves as the foundation of Zircuit’s architecture, enabling participants to receive additional rewards, participate in network app fair launches, and drive its growth. As the ecosystem’s cornerstone, ZRC aligns incentives across developers and users, fostering active collaboration and innovation. The ZRC launch follows a series of notable achievements for Zircuit, including the successful rollout of Mainnet, a $2 billion TVL ecosystem, the groundbreaking EIGEN fairdrop with over 190,000 participants, the liquidity hub launch, and strategic investment from Binance Labs, Pantera, and other strategic partners. Together, these milestones underscore Zircuit’s position as a leader in decentralized finance and staking. “The ZRC token is more than a milestone; it’s a gateway to the decentralized future we’ve been building at Zircuit,” said Martin Derka, co-founder of Zircuit. “By aligning incentives across our ecosystem, ZRC empowers developers and users to shape the network collaboratively.” “The ZRC token is more than a milestone; it’s a gateway to the decentralized future we’ve been building at Zircuit,” said Martin Derka, co-founder of Zircuit. “By aligning incentives across our ecosystem, ZRC empowers developers and users to shape the network collaboratively.” Designed with transparency and accessibility in mind, the ZRC Token Launch ensures participants can seamlessly engage with staking partners through Seasons 1-3 of Zircuit’s ecosystem. This includes staking through the Liquidity Hub, offering the potential to earn rewards. Zircuit protects users from hacks through its built-in, automated AI techniques that guard users against smart contract exploits and malicious actors. This system automatically guards against smart contract exploits and malicious actors, making Zircuit one of the safest blockchain platforms available. As the safest chain for DeFi and staking, Zircuit is the premier liquidity hub for various assets, including ETH, BTC, LSTs, and LRTs, while providing robust security guarantees. Zircuit’s strong infrastructure allows users to earn competitive yields natively, combining safety with attractive returns. Users can explore the ZRC token’s role in Zircuit’s ecosystem by engaging with staking and reward opportunities through the Liquidity Hub . For more information, users can visit zircuit.com and follow Zircuit on Twitter/X at @ZircuitL2 . Liquidity Hub Liquidity Hub zircuit.com zircuit.com @ZircuitL2 @ZircuitL2 About Zircuit Zircuit: Where innovation meets security, designed for everyone. Zircuit offers developers powerful features while giving users peace of mind. Designed by a team of web3 security veterans and PhDs, Zircuit combines high performance with unmatched security. Experience the safest chain for DeFi and staking. To learn more about Zircuit, visit zircuit.com , and follow us on Twitter/X @ZircuitL2 . zircuit.com zircuit.com @ZircuitL2 @ZircuitL2 Contact Head of Communications Jennifer Zheng Zircuit jen@zircuit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Zircuit Launches ZRC Token: Pioneering The Next Era Of Decentralized Finance

Zircuit, ZRC Tokenini işə salır: Mərkəzləşdirilməmiş Maliyyənin Növbəti Erasının Pioneri

Zircuit пуска ZRC токен: Пионер в следващата ера на децентрализираните финанси

Zircuit lanza el token ZRC: pionero de la próxima era de las finanzas descentralizadas

Zircuit julkaisee ZRC Tokenin: Hajautetun rahoituksen seuraavan aikakauden uraauurtava

Zircuit lanza o token ZRC: pioneiro na próxima era das finanzas descentralizadas

ज़िरकुइट ने ZRC टोकन लॉन्च किया: विकेंद्रीकृत वित्त के अगले युग की शुरुआत

Zircuit が ZRC トークンをリリース: 分散型金融の次世代を切り開く

Zircuit dia namoaka ZRC Token: Mpisava lalana amin'ny vanim-potoana manaraka amin'ny fitantanam-bola

Zircuit wprowadza token ZRC: pionier nowej ery zdecentralizowanych finansów

Zircuit uvádza na trh ZRC Token: Priekopník ďalšej éry decentralizovaných financií

Inilunsad ng Zircuit ang ZRC Token: Pangunguna sa Susunod na Panahon ng Desentralisadong Pananalapi

Zircuit, ZRC Token'ı Piyasaya Sürüyor: Merkezi Olmayan Finansın Yeni Dönemine Öncülük Ediyor

No, You Don't Need a Highly Specialized Team to Build Your SaaS Application 

As a web developer working across both backend (.NET Core, Django) and frontend (mainly React), having the right tools makes all the difference in my daily workflow. Whether it's managing packages, improving terminal experience, or organizing my work, these tools help keep everything running smoothly. Here are my top 10 must-have Mac tools that I rely on every day. 1. Homebrew Homebrew is a package manager that makes installing software on a Mac super easy. It handles the setup and installation of command-line tools, libraries, and other essential software. For developers, Homebrew is especially useful because it removes the hassle of tracking dependencies or updating software manually. Instead, Homebrew does it all with just a few commands. Using Homebrew saves a lot of time, especially if you often need different tools for various projects. With Homebrew, you can easily install, update, and manage packages. It keeps everything in one place, which makes maintaining your development environment much simpler. 2. Oh My Zsh Oh My Zsh is an open-source, community-driven framework that enhances the Zsh shell, making it more powerful and easier to use. It’s packed with themes, plugins, and handy shortcuts. For anyone who spends a lot of time in the terminal, Oh My Zsh is a fantastic way to speed up your workflow. With plugins that support Git commands, auto-completion, and even syntax highlighting, Oh My Zsh turns the terminal into a much more friendly and efficient place. It’s highly customizable, so you can tweak it to match your preferences, making your terminal experience both visually appealing and productive. 3. Shottr Shottr is a screenshot tool specifically designed for developers and designers. It allows you to quickly capture parts of your screen, annotate, and share them with others. Shottr has tools for blurring out sensitive data and for measuring pixel distances, which is incredibly helpful when dealing with UI designs. This tool is particularly lightweight, meaning it won’t slow down your Mac. It’s designed with speed and simplicity in mind, making it ideal for capturing errors, UI details, or any part of the screen that you might need for documentation or collaboration. 4. VS Code Visual Studio Code (VS Code) is a lightweight but powerful code editor that has become a favorite among developers. With support for almost every programming language and a vast library of extensions, VS Code offers a highly customizable development environment. Whether you're working on frontend or backend code, it has everything you need. VS Code’s debugging tools, integrated terminal, and Git support make it ideal for all stages of development. You can also personalize it with themes, snippets, and extensions to enhance your productivity. It’s free, open-source, and highly efficient for both small and large projects. 5. Podman Podman is a container management tool, similar to Docker, but with some key differences. Podman doesn’t require a daemon, making it easier to run containers without root privileges. It’s perfect for developers working with containerized applications who want a lightweight, flexible solution. With Podman, you can manage and run containers directly from the command line, giving you control over your containerized environments. It’s compatible with Docker commands, so if you’ve used Docker before, the transition is simple. For Mac developers working with cloud-native applications, Podman is a great choice. 6. Termius Termius is a modern SSH client that makes remote access easy and secure. With support for SSH, Mosh, and Telnet, Termius lets you connect to remote servers with ease. The app is cross-platform and synchronizes your connections, keys, and settings across devices, so you can access your servers from any device. Termius also has a clean, user-friendly interface that simplifies remote management. It supports multi-tab sessions, making it easy to work with multiple servers at once. It’s a highly secure and efficient tool for managing remote environments, especially if you regularly connect to different servers. 7. Obsidian Obsidian is a note-taking tool designed for developers and creatives alike. It uses Markdown to create and organize notes, making it easy to write down ideas, keep track of code snippets, or document processes. One of Obsidian's key features is its graph view, which visually links notes to show relationships between ideas. Obsidian is especially helpful for organizing thoughts and tracking knowledge across projects. For developers, it’s great for keeping track of troubleshooting steps, project notes, and to-dos. Obsidian is fully offline, so your data stays private and accessible only to you. 8. Postman Postman is an API testing tool that allows you to create, test, and document APIs efficiently. With Postman, you can set up requests, check responses, and even automate testing processes. It supports multiple authentication methods, making it easy to interact with different APIs and check how they perform. Postman also helps with team collaboration by allowing you to share collections of API calls. This is especially helpful for frontend and backend developers working together, as it keeps everyone on the same page. It’s a straightforward tool that makes API testing and documentation much easier. 9. Amethyst Amethyst is a window manager for Mac that helps organize your workspace by automatically arranging open windows. It supports tiling, floating, and fullscreen layouts, allowing you to customize the way applications are displayed on your screen. Amethyst is especially useful if you work with multiple applications and monitors. With Amethyst, you can stay organized without constantly resizing and moving windows around. It helps maintain a clean and efficient workspace, saving time and reducing clutter on your desktop. For developers juggling multiple windows, it’s an essential tool to keep everything in order. 10. Arc Browser Arc is a browser built specifically for organizing web-based workspaces. It lets you organize tabs into groups, making it easy to keep track of different projects, research, or tasks without getting overwhelmed. Arc also has a unique sidebar layout, which makes it easy to switch between tabs and focus on what matters. Arc’s clean and minimal design is perfect for developers who need a distraction-free browser. It supports extensions, so you can customize it just like other browsers, but its organization features are what set it apart. It’s ideal for keeping your online workspaces tidy and focused. Each of these tools has a unique role in my workflow as a web developer, helping me stay organized, work more efficiently, and keep my Mac environment streamlined. If you’re looking to enhance your setup, these apps are a great place to start. As a web developer working across both backend (.NET Core, Django) and frontend (mainly React), having the right tools makes all the difference in my daily workflow. Whether it's managing packages, improving terminal experience, or organizing my work, these tools help keep everything running smoothly. Here are my top 10 must-have Mac tools that I rely on every day. 1. Homebrew Homebrew is a package manager that makes installing software on a Mac super easy. It handles the setup and installation of command-line tools, libraries, and other essential software. For developers, Homebrew is especially useful because it removes the hassle of tracking dependencies or updating software manually. Instead, Homebrew does it all with just a few commands. Homebrew Using Homebrew saves a lot of time, especially if you often need different tools for various projects. With Homebrew, you can easily install, update, and manage packages. It keeps everything in one place, which makes maintaining your development environment much simpler. 2. Oh My Zsh Oh My Zsh i s an open-source, community-driven framework that enhances the Zsh shell, making it more powerful and easier to use. It’s packed with themes, plugins, and handy shortcuts. For anyone who spends a lot of time in the terminal, Oh My Zsh is a fantastic way to speed up your workflow. Oh My Zsh i With plugins that support Git commands, auto-completion, and even syntax highlighting, Oh My Zsh turns the terminal into a much more friendly and efficient place. It’s highly customizable, so you can tweak it to match your preferences, making your terminal experience both visually appealing and productive. 3. Shottr Shottr is a screenshot tool specifically designed for developers and designers. It allows you to quickly capture parts of your screen, annotate, and share them with others. Shottr has tools for blurring out sensitive data and for measuring pixel distances, which is incredibly helpful when dealing with UI designs. This tool is particularly lightweight, meaning it won’t slow down your Mac. It’s designed with speed and simplicity in mind, making it ideal for capturing errors, UI details, or any part of the screen that you might need for documentation or collaboration. 4. VS Code Visual Studio Code ( VS Code) is a lightweight but powerful code editor that has become a favorite among developers. With support for almost every programming language and a vast library of extensions, VS Code offers a highly customizable development environment. Whether you're working on frontend or backend code, it has everything you need. Visual Studio Code ( VS Code’s debugging tools, integrated terminal, and Git support make it ideal for all stages of development. You can also personalize it with themes, snippets, and extensions to enhance your productivity. It’s free, open-source, and highly efficient for both small and large projects. 5. Podman Podman is a container management tool, similar to Docker, but with some key differences. Podman doesn’t require a daemon, making it easier to run containers without root privileges. It’s perfect for developers working with containerized applications who want a lightweight, flexible solution. With Podman, you can manage and run containers directly from the command line , giving you control over your containerized environments. It’s compatible with Docker commands, so if you’ve used Docker before, the transition is simple. For Mac developers working with cloud-native applications, Podman is a great choice. command line 6. Termius Termius is a modern SSH client that makes remote access easy and secure. With support for SSH , Mosh, and Telnet, Termius lets you connect to remote servers with ease. The app is cross-platform and synchronizes your connections, keys, and settings across devices, so you can access your servers from any device. SSH Termius also has a clean, user-friendly interface that simplifies remote management. It supports multi-tab sessions, making it easy to work with multiple servers at once. It’s a highly secure and efficient tool for managing remote environments, especially if you regularly connect to different servers. 7. Obsidian Obsidian is a note-taking tool designed for developers and creatives alike. It uses Markdown to create and organize notes, making it easy to write down ideas, keep track of code snippets, or document processes. One of Obsidian's key features is its graph view, which visually links notes to show relationships between ideas. Obsidian Obsidian is especially helpful for organizing thoughts and tracking knowledge across projects. For developers, it’s great for keeping track of troubleshooting steps, project notes, and to-dos. Obsidian is fully offline, so your data stays private and accessible only to you. 8. Postman Postman is an API testing tool that allows you to create, test, and document APIs efficiently. With Postman, you can set up requests, check responses, and even automate testing processes. It supports multiple authentication methods, making it easy to interact with different APIs and check how they perform. Postman APIs Postman also helps with team collaboration by allowing you to share collections of API calls. This is especially helpful for frontend and backend developers working together, as it keeps everyone on the same page. It’s a straightforward tool that makes API testing and documentation much easier. 9. Amethyst Amethyst is a window manager for Mac that helps organize your workspace by automatically arranging open windows. It supports tiling, floating, and fullscreen layouts, allowing you to customize the way applications are displayed on your screen. Amethyst is especially useful if you work with multiple applications and monitors. With Amethyst, you can stay organized without constantly resizing and moving windows around. It helps maintain a clean and efficient workspace, saving time and reducing clutter on your desktop. For developers juggling multiple windows, it’s an essential tool to keep everything in order. 10. Arc Browser Arc is a browser built specifically for organizing web-based workspaces. It lets you organize tabs into groups, making it easy to keep track of different projects, research, or tasks without getting overwhelmed. Arc also has a unique sidebar layout, which makes it easy to switch between tabs and focus on what matters. Arc’s clean and minimal design is perfect for developers who need a distraction-free browser. It supports extensions, so you can customize it just like other browsers, but its organization features are what set it apart. It’s ideal for keeping your online workspaces tidy and focused. Each of these tools has a unique role in my workflow as a web developer, helping me stay organized, work more efficiently, and keep my Mac environment streamlined. If you’re looking to enhance your setup, these apps are a great place to start.

Ever Tool I Use to Be a Giga Web Developer on Mac 

AI is evolving rapidly, and its influence in enterprise environments is undeniable. We’ve seen the rise of AI agents capable of handling specific tasks, from automating data entry to providing customer support. But with this explosion of specialized agents comes a new challenge: how to manage their collective actions and ensure they work together seamlessly. Enter the orchestrator agent, an emerging concept poised to shape the future of enterprise AI. These agents don’t replace task-specific bots, they elevate them, coordinating workflows, integrating systems, and ensuring collaboration between humans and machines. Let’s explore why orchestrator agents are essential and how they might transform the way businesses leverage AI in the years to come. Why Orchestrator Agents Are Emerging Now The rapid adoption of AI agents has brought incredible opportunities but also introduced new complexities. Today, most AI systems operate in silos, focusing narrowly on their tasks without understanding the broader business context. This fragmentation creates inefficiencies and limits their potential. As companies deploy more AI-driven solutions, the need for a higher-level system to manage and harmonize these agents becomes apparent. Orchestrator agents are emerging as the answer to this challenge. They act as meta-managers, ensuring that individual agents collaborate effectively while adapting dynamically to the needs of the enterprise. https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true What Orchestrator Agents Could Bring to the Table Although orchestrator agents are still in their infancy, their potential impact is enormous. Here are some of the capabilities they might offer as they develop: 1. Intelligent Workflow Management In the future, orchestrator agents will enable businesses to design and execute complex workflows that involve multiple AI agents and human operators. They will understand the sequence of tasks needed to achieve a goal and ensure that each agent contributes effectively. For example, an orchestrator agent might: Assign a customer inquiry to a chatbot for initial triage.
Escalate the case to a human representative if the chatbot cannot resolve the issue.
Follow up with a satisfaction survey sent by another specialized agent. This seamless handoff between agents and humans will optimize operations while maintaining a high level of service. 2. Advanced Integration Across Systems As AI ecosystems grow, integration will become even more critical. Orchestrator agents will serve as the central hub, connecting disparate systems, agents, and data sources. They’ll ensure that no agent operates in isolation and that all tools contribute to a unified strategy. Future orchestrator agents could: Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise.
Enable real-time data sharing between agents, preventing duplication and enhancing decision-making.
Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. 3. Enhanced Reasoning and Decision-Making Unlike task-specific agents, orchestrator agents will rely heavily on reasoning capabilities. They’ll use advanced AI models to simulate scenarios, weigh options, and make informed decisions. This ability to "think" at a higher level will allow them to adapt to changing circumstances, ensuring workflows remain efficient even as variables shift. For instance, if an AI agent encounters an unexpected error, the orchestrator could assess the situation, identify an alternative path, and trigger the next best step. 4. Human-AI Collaboration Orchestrator agents won’t just automate workflows; they’ll also make them more transparent and collaborative. By providing intuitive interfaces, these agents will allow employees to: Visualize the progress of tasks.
Understand how decisions are being made.
Provide feedback to improve the system over time. In the future, orchestrator agents might also enable natural language interactions, letting employees adjust workflows or intervene directly through simple commands. The Road Ahead While orchestrator agents hold immense promise, they are still in the early stages of development. Many companies are experimenting with basic versions of these systems, but the full realization of their potential lies ahead. Here’s what the future could look like: Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems.
The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Conclusion The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Orchestrator agents represent more than just a solution, they symbolize the next step in how humans and AI systems collaborate. By ensuring that agents, systems, and people work together harmoniously, these meta-managers could redefine efficiency and innovation across industries. The road ahead is filled with questions: How will these agents evolve? What challenges will emerge as they scale? And, most importantly, how can enterprises prepare to harness their full potential? One thing is certain: orchestrator agents are a glimpse into the future of enterprise AI, and the journey has only just begun. AI is evolving rapidly, and its influence in enterprise environments is undeniable. We’ve seen the rise of AI agents capable of handling specific tasks, from automating data entry to providing customer support. But with this explosion of specialized agents comes a new challenge: how to manage their collective actions and ensure they work together seamlessly. Enter the orchestrator agent, an emerging concept poised to shape the future of enterprise AI. These agents don’t replace task-specific bots, they elevate them, coordinating workflows, integrating systems, and ensuring collaboration between humans and machines. Let’s explore why orchestrator agents are essential and how they might transform the way businesses leverage AI in the years to come. Why Orchestrator Agents Are Emerging Now Why Orchestrator Agents Are Emerging Now The rapid adoption of AI agents has brought incredible opportunities but also introduced new complexities. Today, most AI systems operate in silos, focusing narrowly on their tasks without understanding the broader business context. This fragmentation creates inefficiencies and limits their potential. As companies deploy more AI-driven solutions, the need for a higher-level system to manage and harmonize these agents becomes apparent. Orchestrator agents are emerging as the answer to this challenge. They act as meta-managers, ensuring that individual agents collaborate effectively while adapting dynamically to the needs of the enterprise. https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true What Orchestrator Agents Could Bring to the Table What Orchestrator Agents Could Bring to the Table Although orchestrator agents are still in their infancy, their potential impact is enormous. Here are some of the capabilities they might offer as they develop: 1. Intelligent Workflow Management In the future, orchestrator agents will enable businesses to design and execute complex workflows that involve multiple AI agents and human operators. They will understand the sequence of tasks needed to achieve a goal and ensure that each agent contributes effectively. For example, an orchestrator agent might: Assign a customer inquiry to a chatbot for initial triage. Escalate the case to a human representative if the chatbot cannot resolve the issue. Follow up with a satisfaction survey sent by another specialized agent. Assign a customer inquiry to a chatbot for initial triage. Escalate the case to a human representative if the chatbot cannot resolve the issue. Follow up with a satisfaction survey sent by another specialized agent. This seamless handoff between agents and humans will optimize operations while maintaining a high level of service. 2. Advanced Integration Across Systems 2. Advanced Integration Across Systems As AI ecosystems grow, integration will become even more critical. Orchestrator agents will serve as the central hub, connecting disparate systems, agents, and data sources. They’ll ensure that no agent operates in isolation and that all tools contribute to a unified strategy. Future orchestrator agents could: Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise. Enable real-time data sharing between agents, preventing duplication and enhancing decision-making. Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise. Enable real-time data sharing between agents, preventing duplication and enhancing decision-making. Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. 3. Enhanced Reasoning and Decision-Making 3. Enhanced Reasoning and Decision-Making Unlike task-specific agents, orchestrator agents will rely heavily on reasoning capabilities. They’ll use advanced AI models to simulate scenarios, weigh options, and make informed decisions. This ability to "think" at a higher level will allow them to adapt to changing circumstances, ensuring workflows remain efficient even as variables shift. For instance, if an AI agent encounters an unexpected error, the orchestrator could assess the situation, identify an alternative path, and trigger the next best step. 4. Human-AI Collaboration 4. Human-AI Collaboration Orchestrator agents won’t just automate workflows; they’ll also make them more transparent and collaborative. By providing intuitive interfaces, these agents will allow employees to: Visualize the progress of tasks. Understand how decisions are being made. Provide feedback to improve the system over time. Visualize the progress of tasks. Understand how decisions are being made. Provide feedback to improve the system over time. In the future, orchestrator agents might also enable natural language interactions, letting employees adjust workflows or intervene directly through simple commands. The Road Ahead The Road Ahead While orchestrator agents hold immense promise, they are still in the early stages of development. Many companies are experimenting with basic versions of these systems, but the full realization of their potential lies ahead. Here’s what the future could look like: Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Greater Autonomy: Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Deeper Learning: Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems.
The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems. The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems. Wider Adoption: The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Conclusion The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Orchestrator agents represent more than just a solution, they symbolize the next step in how humans and AI systems collaborate. By ensuring that agents, systems, and people work together harmoniously, these meta-managers could redefine efficiency and innovation across industries. The road ahead is filled with questions: How will these agents evolve? What challenges will emerge as they scale? And, most importantly, how can enterprises prepare to harness their full potential? One thing is certain: orchestrator agents are a glimpse into the future of enterprise AI, and the journey has only just begun.

Orchestrator Agents: The Next Evolution in Enterprise AI

What Do Product Managers Do? I get this question several times. From aspiring Product Managers, professionals who want to make a move to product management, students, friends, and even my mom (as she struggles to explain to others what her daughter does). As glorified as it may sound, and as most definitions would describe a Product Manager as being the ‘CEO of the product!’, I think it is the most misunderstood interpretation of the level of direct authority this role possesses. Of course, as a PM, you get to shape the product vision and strategy to drive it toward success, but as they say, it takes a village, and as I like to put it, a product manager is like a shepherd, who will drive the herd toward taking products to greener pastures. And although the role varies in different organizations, In simple terms, A product manager helps develop product strategy, partners with executives/stakeholders on how to deliver and take it to market and manages people/resources to execute on that vision. I can understand why it has taken so long for people to get familiar with this new role, as the title ‘Product Manager’ did not exist until perhaps a decade ago. It is basically a ‘jack of all trades’ kind of a role, which varies according to the company size, the user audience, the industry, and the stage at which the product is in its lifecycle. More than the roles and responsibilities, I would like to elaborate on some of the core competencies that a PM possesses and skills he or she applies, in their day-to-day jobs. Core Competencies for a PM These are the skills that differentiate a good PM from a stellar PM: Ability to Define a Vision: Any product starts with setting a vision, that is in line with the overall vision for the company. A mission statement about what you want your product to be. A product manager sets the direction for where the product is going and what it will deliver in the future. For example, Google’s vision is “to organize the world’s information and make it universally accessible and useful.” Slack’s product vision is “transforming work, building better communication, and creating greater transparency.” This is not simply a tagline or a marketing statement; it is a culture that every person working at these companies abides by. Setting a strategic vision requires a solid understanding of the business, the company goals, and the customer’s needs. Understanding Product-Market Fit: This is one of the most critical pieces in terms of knowledge areas for a PM, as it involves understanding the market, defining the problem statement, and then building a product that solves this problem. ‘Achieving product-market fit’ from the beginning and iterating on it throughout the product development process is key to being a successful product manager. Business Modeling: While the vision tells you why the product should be made, the business case tells you how the product will create value, monetize on that value, and be a sustainable growing business. For this, a PM sets short-term and long-term goals, defines the growth path, and the revenue streams, and identifies the resources to achieve the product vision. I like Alex Osterwalder’s Business Model Canvas as a template to put together some of these details. Experimentation and Hypothesis Testing: You must have heard of taking a ‘data-driven approach’ to making decisions. Well, there is no dearth today of tools and mechanisms for product managers to gather data and feedback. The main issue is to sift through this data and identify the metrics that really matter. One of the skills of a PM is to frame a hypothesis for a particular observation, run experiments to test the hypothesis, and then effectively measure the outcome by deriving KPIs that objectively validate the assumptions and will eventually help drive decisions. Taking the example of Slack in their early days, they were trying to prove its utility to users. One of the hypotheses was ‘eliminating the use of email’. The team started asking prospective clients to spend an entire day without email, only using Slack for all communications. The experiment was a success and the Slack team was able to demonstrate how effective the tool was in breaking people away from their email and enabling productivity. Prioritization Ask any PM what is that one competency that he or she is forever trying to master, and the answer most often will be prioritization skills! You have a great set of ideas and requests gathered from users, sales, product support, developers, etc. Evaluating these against the right set of criteria to know what to work on first, is according to me the most important skill that needs to be developed and honed over time. There are many prioritization frameworks and techniques, including the RICE method, ICE scoring, and the KANO method, to name a few. As a good prioritization example, Slack focused on a few things that were most important to their product vision: Search: The team knew that users needed to not worry about labeling and storing documents and had to focus their roadmap to build this capability, one that Gmail and Google, had set high standards for. Synchronization: The problem with every other enterprise productivity tool was the need for the user to be able to pick up from where he left off after switching devices. This is what became one of Slack’s biggest differentiators. Simple file-sharing: Slack wanted to make it super easy to drag-drop files and paste images which was in line with its focus on building an intuitive UI.
Although these features may seem rudimentary, Slack identified and prioritized these, as an attempt to build something that would be an important change for the audience who were used to cumbersome experiences through other competitive tools. Design Thinking A PM typically works very closely with a product designer to ensure the features presented to the user, serve the functionality in the way they are designed for. In order to design experiences and user interfaces, a PM should have a thorough understanding of the user journey and how users will consume the product capabilities. As a good resource, I found the Lean UX helpful in sharing techniques to incorporate design into agile product development. Metrics and Measurement More often than not, PMs are faced with the challenge of weighing the information assimilated from a variety of sources in order to make product decisions. As you build your hypothesis and begin to test, you will need to understand the opportunities for feedback, what to measure, and how to measure. Being proficient in these metrics measurement methodologies is a core skill to develop as a PM. This includes qualitative testing (e.g., usability testing or session monitoring) or quantitative (traffic analysis or user engagement tracking). There are also other forms of comparative measurement such as A/B testing, multi-variate testing, etc. Sticking to the same example of Slack, when they were tackling their next set of goals, performed a bunch of qualitative interviews with their largest accounts to test the hypothesis that they weren’t serving those larger teams as well as they could. Some of the questions asked were — How do you use Slack? Who uses it within the organization? What do they use it for? What other tools do you use, and how does Slack fit into that? Using the feedback gathered, Slack then made several changes to its feature set for larger teams (e.g., creating a different set of default notifications to avoid ping fatigue) Besides these core skills, there are other personal or as I call them, fundamental proficiencies that truly differentiate a good PM from an expert, seasoned product person. These include leadership, planning, cross-functional collaboration, stakeholder management, and relationship building which are as vital to becoming a successful product manager as the core competencies covered above. What Do Product Managers Do? I get this question several times. From aspiring Product Managers, professionals who want to make a move to product management, students, friends, and even my mom (as she struggles to explain to others what her daughter does). As glorified as it may sound, and as most definitions would describe a Product Manager as being the ‘CEO of the product!’, I think it is the most misunderstood interpretation of the level of direct authority this role possesses. Of course, as a PM, you get to shape the product vision and strategy to drive it toward success, but as they say, it takes a village, and as I like to put it, a product manager is like a shepherd, who will drive the herd toward taking products to greener pastures. And although the role varies in different organizations, In simple terms, A product manager helps develop product strategy, partners with executives/stakeholders on how to deliver and take it to market and manages people/resources to execute on that vision. A product manager helps develop product strategy, partners with executives/stakeholders on how to deliver and take it to market and manages people/resources to execute on that vision. I can understand why it has taken so long for people to get familiar with this new role, as the title ‘Product Manager’ did not exist until perhaps a decade ago. It is basically a ‘jack of all trades’ kind of a role, which varies according to the company size, the user audience, the industry, and the stage at which the product is in its lifecycle. More than the roles and responsibilities, I would like to elaborate on some of the core competencies that a PM possesses and skills he or she applies, in their day-to-day jobs. Core Competencies for a PM These are the skills that differentiate a good PM from a stellar PM: Ability to Define a Vision: Ability to Define a Vision: Any product starts with setting a vision, that is in line with the overall vision for the company. A mission statement about what you want your product to be. A product manager sets the direction for where the product is going and what it will deliver in the future. For example, Google’s vision is “to organize the world’s information and make it universally accessible and useful.” Slack’s product vision is “transforming work, building better communication, and creating greater transparency.” This is not simply a tagline or a marketing statement; it is a culture that every person working at these companies abides by. Setting a strategic vision requires a solid understanding of the business, the company goals, and the customer’s needs. Understanding Product-Market Fit : Understanding Product-Market Fit This is one of the most critical pieces in terms of knowledge areas for a PM, as it involves understanding the market, defining the problem statement, and then building a product that solves this problem. ‘Achieving product-market fit’ from the beginning and iterating on it throughout the product development process is key to being a successful product manager. Business Modeling : Business Modeling While the vision tells you why the product should be made, the business case tells you how the product will create value, monetize on that value, and be a sustainable growing business. For this, a PM sets short-term and long-term goals, defines the growth path, and the revenue streams, and identifies the resources to achieve the product vision. I like Alex Osterwalder’s Business Model Canvas as a template to put together some of these details. Business Model Canvas Experimentation and Hypothesis Testing: Experimentation and Hypothesis Testing: You must have heard of taking a ‘data-driven approach’ to making decisions. Well, there is no dearth today of tools and mechanisms for product managers to gather data and feedback. The main issue is to sift through this data and identify the metrics that really matter. One of the skills of a PM is to frame a hypothesis for a particular observation, run experiments to test the hypothesis, and then effectively measure the outcome by deriving KPIs that objectively validate the assumptions and will eventually help drive decisions. Taking the example of Slack in their early days, they were trying to prove its utility to users. One of the hypotheses was ‘eliminating the use of email’. The team started asking prospective clients to spend an entire day without email, only using Slack for all communications. The experiment was a success and the Slack team was able to demonstrate how effective the tool was in breaking people away from their email and enabling productivity. Prioritization Prioritization Ask any PM what is that one competency that he or she is forever trying to master, and the answer most often will be prioritization skills! You have a great set of ideas and requests gathered from users, sales, product support, developers, etc. Evaluating these against the right set of criteria to know what to work on first, is according to me the most important skill that needs to be developed and honed over time. There are many prioritization frameworks and techniques, including the RICE method , ICE scoring , and the KANO method , to name a few. RICE method ICE scoring KANO method As a good prioritization example, Slack focused on a few things that were most important to their product vision: Search: The team knew that users needed to not worry about labeling and storing documents and had to focus their roadmap to build this capability, one that Gmail and Google, had set high standards for. Search: The team knew that users needed to not worry about labeling and storing documents and had to focus their roadmap to build this capability, one that Gmail and Google, had set high standards for. Search: Synchronization: The problem with every other enterprise productivity tool was the need for the user to be able to pick up from where he left off after switching devices. This is what became one of Slack’s biggest differentiators. Synchronization: The problem with every other enterprise productivity tool was the need for the user to be able to pick up from where he left off after switching devices. This is what became one of Slack’s biggest differentiators. Synchronization: Simple file-sharing: Slack wanted to make it super easy to drag-drop files and paste images which was in line with its focus on building an intuitive UI.
Although these features may seem rudimentary, Slack identified and prioritized these, as an attempt to build something that would be an important change for the audience who were used to cumbersome experiences through other competitive tools. Simple file-sharing: Slack wanted to make it super easy to drag-drop files and paste images which was in line with its focus on building an intuitive UI. Although these features may seem rudimentary, Slack identified and prioritized these, as an attempt to build something that would be an important change for the audience who were used to cumbersome experiences through other competitive tools. Simple file-sharing: Slack wanted to make it super easy to drag-drop files and paste images which was in line with its focus on building an intuitive UI. Simple file-sharing: Although these features may seem rudimentary, Slack identified and prioritized these, as an attempt to build something that would be an important change for the audience who were used to cumbersome experiences through other competitive tools. Design Thinking Design Thinking A PM typically works very closely with a product designer to ensure the features presented to the user, serve the functionality in the way they are designed for. In order to design experiences and user interfaces, a PM should have a thorough understanding of the user journey and how users will consume the product capabilities. As a good resource, I found the Lean UX helpful in sharing techniques to incorporate design into agile product development. Metrics and Measurement Metrics and Measurement More often than not, PMs are faced with the challenge of weighing the information assimilated from a variety of sources in order to make product decisions. As you build your hypothesis and begin to test, you will need to understand the opportunities for feedback, what to measure, and how to measure. Being proficient in these metrics measurement methodologies is a core skill to develop as a PM. This includes qualitative testing (e.g., usability testing or session monitoring) or quantitative (traffic analysis or user engagement tracking). There are also other forms of comparative measurement such as A/B testing, multi-variate testing, etc. Sticking to the same example of Slack, when they were tackling their next set of goals, performed a bunch of qualitative interviews with their largest accounts to test the hypothesis that they weren’t serving those larger teams as well as they could. Some of the questions asked were — How do you use Slack? Who uses it within the organization? What do they use it for? What other tools do you use, and how does Slack fit into that? Using the feedback gathered, Slack then made several changes to its feature set for larger teams (e.g., creating a different set of default notifications to avoid ping fatigue) Besides these core skills, there are other personal or as I call them, fundamental proficiencies that truly differentiate a good PM from an expert, seasoned product person. These include leadership, planning, cross-functional collaboration, stakeholder management, and relationship building which are as vital to becoming a successful product manager as the core competencies covered above.

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