“Exploring Hydrofeminism: Understanding the Body’s Connection to Nature”

“Exploring Hydrofeminism: Understanding the Body’s Connection to Nature”

The Interconnectedness of Water and Feminism

In recent years, there has been a growing recognition of the intricate relationship between water and feminism. This connection is not merely physical or metaphorical, but deeply rooted in historical, social, and environmental contexts.

Historical Context

Throughout history, water has been viewed as a symbol of life, purity, and fertility. In many cultures, women have been associated with water, embodying its fluidity, strength, and resilience. However, women have also been disproportionately affected by water-related issues such as lack of access to clean water, sanitation, and hygiene.

Posthuman Feminist Phenomenology

Hydrofeminism, a branch of ecofeminism, intersects with posthuman feminist phenomenology to understand the intricate connections between the body and the natural world. By acknowledging the fluidity and interconnectedness of all beings, hydrofeminism challenges traditional dualistic views of nature and human existence.

Contemporary Relevance

In today’s world, the impact of climate change, water scarcity, and pollution disproportionately affects marginalized communities, particularly women. By centering the perspectives of women and highlighting their roles as caretakers of water, hydrofeminism offers a unique and valuable insight into how we can address these pressing environmental issues.

As we navigate the complex challenges of the 21st century, it is imperative that we recognize and honor the interconnectedness of water and feminism. By embracing a holistic and inclusive approach to environmental and social justice, we can work towards a more sustainable and equitable future for all.

With research located in hydrofeminism, a posthuman feminist phenomenology that understands the body as essentially linked to the natural world

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“Mastering Complex Widget Interactions with JavaScript APIs in shinytest2”

[This article was first published on jakub::sobolewski, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)


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shinytest2 is powerful for writing Shiny tests effectively, but AppDriver’s methods don’t always cover every interaction you need.

You might discover that the convenient set_inputs() method doesn’t work for everything. Take shiny::selectizeInput with the create option enabled. While set_inputs() will work for selecting existing options, it stumbles when you try to add new ones.

Rather than fighting with high-level testing abstractions, or trying to simulate individual clicks to interact with the widget correctly, the solution lies in understanding how your widgets actually work under the hood.

And that’s where JavaScript APIs become your secret weapon.

Level-up your testing game! Grab your copy of the R testing roadmap.

The Problem with Complex Widget Interactions

The challenge arises because complex widgets like selectize inputs operate through their own JavaScript layer.

When shinytest2 can’t directly interact with them, you might be tempted to simulate what a user would do: open a dropdown, type an option, confirm the selection, close the dropdown. This manual choreography is not only tedious – it’s fragile.

Or you might be tempted to skip writing tests for these interactions altogether – which is even worse.

But if you decide to implement those interactions step by step, each step introduces potential points of failure:

  • Timing issues emerge: has this animation finished?
  • State management becomes unpredictable: is this dropdown already opened?

A change to how the widget renders, and your entire test breaks.

Simplifying interactions with JavaScript APIs

Instead of orchestrating a series of UI actions, you can directly call the widget’s JavaScript API.

For creating new items in shiny::selectizeInput, this means bypassing the UI choreography entirely and using the createItem method:

app <- shinytest2::AppDriver$new(...)

# Use namespaced inputId
app$run_js(sprintf(
  "$('#%s select')[0].selectize.createItem('%s');",
  inputId,
  value
))

# If you're using test selectors - I highly recommend it
app$run_js(sprintf(
  "$('[data-testid=%s] select')[0].selectize.createItem('%s');",
  testid,
  value
))

This approach accomplishes in one API call what would otherwise require multiple sequential actions. There’s no waiting for dropdowns to animate. No typing delays. No confirmation steps. Just direct, instantaneous manipulation of the widget’s state.

Manual interaction would look something like this

This is pseudocode, to illustrate the complexity:

# Find HTML tag to click to trigger opening dropdown
# Find <input> to type new option
# Find HTML tag to click to confirm the new option
# Use HTML tag to close the dropdown

With the API approach, you eliminate orchestration complexity. The code becomes shorter, clearer, and less dependent on implementation details that might change.

We depend on the widget’s own API to handle the internal state correctly, so we’re still exposed to changes in the widget’s implementation, but:

  • Robustness improves dramatically because you’re not relying on UI timing, animation frames, or event sequencing. The widget’s API is a stable contract. When you call createItem(), it works the same way every time, regardless of the surrounding UI state. If the widget’s API changes, you only need to update that single API call in your tests, not a whole series of UI interactions.
  • Clarity increases because your test code expresses intent directly. Instead of a long sequence of UI manipulations, you have a single line that clearly states “create this item.” This makes your tests easier to read and maintain.

But we can make it even better.

Making It Scalable: Functions and AppDriver Extensions

Direct API calls are powerful, but they shouldn’t clutter your test files. When you find yourself using a particular widget manipulation pattern more than once, encapsulate it in a helper function or extend the AppDriver class itself.

This pattern keeps your tests readable while centralizing your widget interaction logic. Instead of repeating JavaScript API calls throughout your test suite, you build a small library of domain-specific testing methods that express intent clearly.

Instead of doing:

app$run_js(
  "$('#module-select select')[0].selectize.createItem('New Option');"
)

You could define a method in an extended AppDriver:

ShinyDriver <- R6::R6Class(
  inherit = shinytest2::AppDriver,
  public = list(
    create_selectize_item = function(inputId, value) {
      self$run_js(sprintf(
        "$('#%s select')[0].selectize.createItem('%s');",
        inputId,
        value
      ))
    }
  )
)

For a deeper exploration of this pattern, including real examples of how to structure an extended AppDriver with custom methods, read BDD Shiny feature testing guide, which demonstrates how to build abstractions that make your tests both simple and maintainable.

How to Discover JavaScript APIs for Widgets

Finding the right JavaScript API calls for your widgets can be straightforward if you know where to look.

Usually, component documentation includes links to which JavaScript libraries power them.

  • shinyWidgets::pickerInput uses Boostrap Select with its own API documented here.
  • shinyWidgets::airDatePicker uses Air Datepicker with its API documented here.

Just head over there and see if the widget exposes methods that let you manipulate it programmatically.


The most reliable path to testing complex widgets isn’t always the highest-level abstraction.

Sometimes the answer lies one level down, in the actual APIs that power these components. By learning to work directly with JavaScript APIs, you gain both flexibility and stability – and your tests become more resilient to the inevitable changes that come with UI development.

Passing JavaScript code in strings might feel crude at first, but with proper encapsulation and abstraction, you can build robust Shiny tests that stand the test of time.

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Continue reading: Simplifying Interactions with Complex Widgets in shinytest2 Using JavaScript APIs

Future and Implications of Simplifying Interactions with Complex Widgets in shinytest2 Using JavaScript APIs

While shinytest2 is a power tool for writing effective Shiny tests, the limitations of its built-in AppDriver methods sometimes lead to the exclusion of certain interactive tests. These limitations can be circumvented using JavaScript APIs, providing a more future-proof solution to testing complex widgets.

The Drawbacks of manual Shiny Tests

Shiny widgets such as selectize inputs function via their own JavaScript layer, making key detailed interactions complex to document during testing. The conventional method of simulating individual user interactions can become extremely tedious and fragile, as well as prone to errors due to timing issues and unpredictable state management. These complexities can even lead to test failures if a widget incurs any changes in rendering.

Simplifying Interactions with JavaScript APIs

JavaScript APIs offer an efficient solution by allowing you to directly call the widget’s functions, instead of replicating a series of UI actions. This strategy offers considerable advantages:

  • You can create interact with widgets in a more direct, less steps-consuming manner without the need for multiple sequential actions or unnecessary user interaction.
  • Code clarity and simplicity is enhanced, reducing dependency on changing implementation details that could create hurdles to testing.
  • Gain robustness, as API calls often represent a stable contract, changes to which would only require update to a single API call in tests.
  • Interactions are independent of the UI timing, animation frames, or event sequencing, thereby reducing the scope for test failure due to rendering changes.

Enhancing Test Scalability with Functions and AppDriver Extensions

Utilizing JavaScript APIs to the fullest extends requires you to maintain code cleanliness. Repeated manipulation patterns must be encapsulated in a helper function or allocated as an extension of the AppDriver class itself. This way, your test suite is uncluttered while you build a library of domain-specific test methods.

Discovering JavaScript APIs for Widgets

Finding the correct JavaScript API calls can be quite straightforward. Component documentations often include links to the JavaScript libraries that power them, you just need to find the right methods to manipulate the widget programmatically.

Actionable Advice

Opt for JavaScript API calls over multi-level UI interactions or high-level testing abstractions when conducting tests for shinytest2. This optimizes time and accuracy in test implementation.

Focus on broadening your understanding of the widget’s working, than investing efforts in simulating manual user interactions. This strategy could save your test from falling apart due to unforeseen changes in rendering.

Maintain clean and uncluttered test files by centralizing widget interaction logic and defining methods in an extended AppDriver. Always encapsulate repeated manipulation patterns in a helper function.

As JavaScript APIs stand the test of time, learning to deploy them efficiently can be beneficial in ensuring your tests are future-proof and resilient in the face of constant UI development changes.

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“The Future of Software Development: Gen AI and Human Expertise”

“The Future of Software Development: Gen AI and Human Expertise”

Gen AI is reshaping the software development lifecycle (SDLC). Faster coding, texting, and documentation. But the fundamental transformation happens when it’s combined with human expertise.

How Gen AI is Transforming Software Development

Artificial Intelligence (AI) has an increasingly significant impact in multiple fields, and software development is no exception. The concept of Generation AI (Gen AI) is reshaping the software development lifecycle (SDLC), accelerating processes like coding, texting, and documentation. However, this transformation becomes fundamental when Gen AI is combined with human expertise.

But what exactly does this mean? And, more importantly, what are the potential long-term implications and possible future developments we can anticipate from this shift? Let’s dive deeper into these topics.

Long-term Implications of Gen AI

Gen AI’s influence today hints at many long-term implications for the SDLC. Considered at its base level, Gen AI offers faster coding, more efficient texting, and streamlined documentation processes. However, its potential goes beyond these improvements. By combining Gen AI with human expertise, we can develop software that is not only more efficient but also vastly more innovative and sensitive to user needs.

“Gen AI’s potential becomes truly groundbreaking when it begins to act not just as a tool, but as a collaborator in the software development process.”

Possible Future Developments

The rise of Gen AI will undoubtedly lead to numerous developments in the future. Machine learning models may continue to evolve, driving even greater efficiency and accuracy in software development tasks. Gen AI could also drive the automation of more complex tasks, beyond coding and documentation. As AI advances, we can expect a more nuanced understanding of human needs and the ability to intuitively respond to user behavior.

Actionable Advice

So, considering these insights, how can businesses effectively respond to these changes? Below are some key pieces of advice:

  1. Invest in AI: The potential of Gen AI in software development is enormous. Businesses should invest in AI technologies and integrate them into their development processes.
  2. Emphasize Training: As AI becomes a more integral part of software development, training in AI technologies should become a priority. This will allow developers to effectively work with AI and leverage its capabilities.
  3. Focus on Collaboration: Gen AI should not replace human developers but rather work alongside them. Emphasizing collaboration between humans and AI will lead to more innovative and user-centered software.
  4. Stay Ahead of the Curve: The rise of Gen AI is inevitable. Staying informed about advancements in AI and being willing to adapt will help businesses stay competitive.

In conclusion, the rise of Gen AI in the realm of software development is an exciting development with significant potential. Leveraged correctly, Gen AI could revolutionize the SDLC, leading to more efficient, innovative, and user-focused software.

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Last month, OpenAI announced a strategic partnership with Nvidia, which enables the AI powerhouse to deploy 10 gigawatts of datacenters based on Nvidia’s formidable GPU hardware. This month, OpenAI unveiled its strategic alliance with AMD, in which the former will deploy 6 gigawatts of the latter’s AMD Instinct GPUs. The agreement also includes AMD stock… Read More »Is OpenAI creating a global compute monopoly?

OpenAI’s Future Impacts and Possible Developments

Last month, OpenAI, one of the leading researchers in the field of Artificial Intelligence technology, made important strategic moves through partnering with Nvidia and AMD. With these partnerships, OpenAI gets to deploy a massive 16 gigawatts of Nvidia and AMD’s robust GPU hardware in their data centers. While bringing a potential shift in AI research landscape, it also sparks a question, Is OpenAI creating a global compute monopoly?

Long-Term Implications

The alliances between OpenAI and the major GPU manufacturers, Nvidia and AMD, indicate a potential shift towards consolidation in the AI research infrastructure. By harnessing such severe computational power, OpenAI may potentially set a high entry barrier for other AI research firms that lack similar resources.

These partnerships could also stimulate further investment in AI research and fuel advancements in technology, thereby potentially accelerating the AI revolution. If managed well, it could lead to game-changing breakthroughs in sectors like healthcare, autonomous driving, and more.

Possible Future Developments

Looking at the pace of OpenAI’s strategic moves, it is plausible that more partnerships between AI research entities and tech heavyweights could follow. This consolidation could lead to an era of ‘superpower’ AI research entities wielding incredible computational capabilities.

Moreover, there is a possibility that OpenAI’s lead in computational power might prompt regulatory scrutiny over potential monopoly fears. Stakeholders may demand a transparent distribution mechanism of AI resources to ensure that advantages are not cornered by a single firm.

Actionable Advice

For AI Research Companies

  • Collaborate: Forging partnerships with hardware manufacturers can enable more significant innovation and remaining competitive.
  • Invest: Increase investment in proprietary infrastructure developments to avoid over-reliance on a single entity.

For Regulatory Bodies

  • Monitor: Keep a close watch on the actions of AI companies that have a large market share to prevent an unhealthy monopoly from forming.
  • Regulate: Create frameworks for fair and transparent distribution of AI resources.

For Investors

  • Research: Keep abreast of the ongoing developments, potential partnerships, and consolidation in the AI industry.
  • Focus: Consider investment opportunities in AI research entities and hardware manufacturers, given the proliferation of AI in various sectors.

In conclusion, OpenAI’s recent partnerships represent a noteworthy shift in the AI research landscape and may lead to significant changes in the industry’s landscape. While there may be concerns about a potential monopoly, smart regulation and competition should ensure a balanced and dynamic AI market.

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“Balancing Automation: How Far is Too Far for Businesses?”

“Balancing Automation: How Far is Too Far for Businesses?”

Navigating the Automation Paradox: Balancing Efficiency and Humanity in Business

The relentless pursuit of efficiency through automation in the business sector is a double-edged sword. On one facet, it promises unparalleled productivity and the reduction of human error, creating an ostensibly streamlined path to economic prosperity. Yet on the other, it presents a profound paradox: the more businesses automate, the greater the risk of alienating the workforce and customers — the very human elements that breathe life into commerce. This analytical lead-in probes deeply into the heart of this modern conundrum, setting the stage for a thoughtful discourse on the extent to which corporations can — and should — leverage automation in their operations.

Promises and Perils of Automation: Unpacking the Implications

In this article, we will explore how businesses can balance the allure of automation with the indispensable value of human touch. As we delve into the topic, we will encounter several critical intersections:

  1. The Drive for Efficiency: Understand how automation has reshaped business models to prioritize efficiency and what this means for the future of work.
  2. Humanity in the Workplace: Examine the implications of reducing human interaction in the workplace and its impact on employee morale, customer satisfaction, and brand loyalty.
  3. The Limits of Automation: Define the boundaries beyond which automation may begin to impair rather than enhance business objectives.
  4. Integrating Automation with a Human-Centric Approach: Discuss strategies for businesses to employ automation without losing sight of the human dimension that fosters innovation and connection.

The Tug-of-War Between Man and Machine

As the tapestry of technology grows ever more complex, businesses grapple with the balance of human and automated labor. The following topics dissect this balance, teasing out the threads of sustainable automation strategies that support rather than supplant the human element.

  • The evolving role of human skills and judgement in an increasingly automated business landscape.
  • Case studies highlighting the successes and failures of automation in various industries.
  • Psychological and social perspectives on the integration of technology into traditionally human-centric roles.

Envisioning a Synergistic Future

The term ‘automation’ need not be synonymous with ‘dehumanization’. Forward-thinking businesses are charting a course that weds technological efficiency with the intrinsic value of human insight. By the end of this article, readers will be equipped with the knowledge to distinguish between the advantageous adoption of automation and its overextension that could potentially hinder growth and human connection within the realm of business.

In this article we examine how far businesses can go with automation without it working against their goals.

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