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The HTML Dialog Element: Enhancing Accessibility and Ease of Use

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

Dialogs are a common component added to applications, whether on the web or in native applications. Traditionally there has not been a standard way of implementing these on the web, resulting in many ad-hoc implementations that don’t act consistently across different web applications. Often, commonly expected features are missing from dialogs due to the complexity of implementing them.

However, web browsers now offer a standard dialog element.

Why use the dialog element?

The native dialog element streamlines the implementation of dialogs, modals, and other kinds of non-modal dialogs. It does this by implementing many of the features needed by dialogs for you that are already baked into the browser.

This is helpful as it reduces the burden on the developer when making their applications accessible by ensuring that user expectations concerning interaction are met, and it can also potentially simplify the implementation of dialogs in general.

Basic usage

Adding a dialog using the new <dialog> tag can be achieved with just a few lines of code.

<dialog id="example-dialog">
  <button autofocus>Close</button>
  <p>This is a modal with some text!</p>
</dialog>

However, adding the dialog alone won’t do anything to the page. It will show up only once you call the .showModal() method against it.

document.getElementById('example-dialog').showModal();

Then if you want to close it you can call the .close() method on the dialog, or press the escape key to close it, just like most other modals work. Also, note how a backdrop appears that darkens the rest of the page and prevents you from interacting with it. Neat!

Accessibility and focus management

Correctly handling focus is important when making your web applications accessible to all users. Typically you have to move the current focus to the active dialog when showing them, but with the dialog element that’s done for you.

By default, the focus will be set on the first focusable element in the dialog. You can optionally change which element receives focus first by setting the autofocus attribute on the element you want the focus to start on, as seen in the previous example where that attribute was added to the close <button> element.

Using the .showModal() method to open the dialog also implicitly adds the dialog ARIA role to the dialog element. This helps screen readers understand that a modal has appeared and the screen so it can act accordingly.

Adding forms to dialogs

Forms can also be added to dialogs, and there’s even a special method value for them. If you add a <form> element with the method set to dialog then the form will have some different behaviors that differ from the standard get and post form methods.

First off, no external HTTP request will be made with this new method. What will happen instead is that when the form gets submitted, the returnValue property on the form element will be set to the value of the submit button in the form.

So given this example form:

<form id="example-form" method="dialog">
  <input name="value1" type="text" placeholder="text-value1" required />
  <input name="value2" type="text" placeholder="text-value2" />
  <input type="submit" value="Submit" />
</form>

The form element with the example-form id will have its returnValue set to Submit.

In addition to that, the dialog will close immediately after the submit event is done being handled, though not before automatic form validation is done. If this fails then the invalid event will be emitted.

You may have already noticed one caveat to all of this. You might not want the form to close automatically when the submit handler is done running. If you perform an asynchronous request with an API or server you may want to wait for a response and show any errors that occur before dismissing the dialog.

In this case, you can call event.preventDefault() in the submit event listener like so:

exampleForm.addEventListener('submit', (event) => {
  event.preventDefault();
});

Once your desired response comes back from the server, you can close it manually by using the .close() method on the dialog.

Enhancing the backdrop

The backdrop behind the dialog is a mostly translucent gray background by default. However, that backdrop is fully customizable using the ::backdrop pseudo-element. With it, you can set a background-color to any value you want, including gradients, images, etc.

You may also want to make clicking the backdrop dismiss the modal, as this is a commonly implemented feature of them. By default, the &lt;dialog> element doesn’t do this for us. There are a couple of changes that we can make to the dialog to get this working.

First, an event listener is needed so that we know when the user clicks away from the dialog.

dialog.addEventListener('click', (event) => {
  if (event.target === dialog) {
    dialog.close();
  }
});

Alone this event listener looks strange. It appears to dismiss the dialog whenever the dialog is clicked, not the backdrop. That’s the opposite of what we want to do. Unfortunately, you cannot listen for a click event on the backdrop as it is considered to be part of the dialog itself. Adding this event listener by itself will effectively make clicking anywhere on the page dismiss the dialog. To correct for this we need to wrap the contents of the dialog content with another element that will effectively mask the dialog and receive the click instead. A simple

element can do!

<dialog id="example-dialog" class="dialog">
  <div class="dialog-container">
    <p>This is a modal with some text!</p>

    <form id="example-form" method="dialog">
      <input name="value1" type="text" placeholder="text-value1" required />
      <input name="value2" type="text" placeholder="text-value2" />
      <input type="submit" value="Submit" />
    </form>
  </div>
</dialog>

Even this isn’t perfect though as the contents of the div may have elements with margins in them that will push the div down, resulting in clicks close to the edges of the dialog to dismiss it. This can be resolved by adding a couple of styles the the wrapping div that will make the margin stay contained within the wrapper element. The dialog element itself also has some default padding that will exacerbate this issue.

.dialog {
  padding: 0;
}

.dialog-container {
  display: inline-block;
  padding: 1em;
}

The wrapping div can be made into an inline-block element to contain the margin, and by moving the padding from the parent dialog to the wrapper, clicks made in the padded portions of the dialog will now interact with the wrapper element instead ensuring it won’t be dismissed.

Conclusion

Using the dialog element offers significant advantages for creating dialogs and modals by simplifying implementation with reasonable default behavior, enhancing accessibility for users that need assistive technologies such as screen readers by using automatic ARIA role assignment, tailored support for form elements, and flexible styling options.

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The Importance of a Scientific Mindset in Software Engineering: Part 2 (Debugging)

The Importance of a Scientific Mindset in Software Engineering: Part 2 (Debugging) In the first part of my series on the importance of a scientific mindset in software engineering, we explored how the principles of the scientific method can help us evaluate sources and make informed decisions. Now, we will focus on how these principles can help us tackle one of the most crucial and challenging tasks in software engineering: debugging. In software engineering, debugging is often viewed as an art - an intuitive skill honed through experience and trial and error. In a way, it is - the same as a GP, even a very evidence-based one, will likely diagnose most of their patients based on their experience and intuition and not research scientific literature every time; a software engineer will often rely on their experience and intuition to identify and fix common bugs. However, an internist faced with a complex case will likely not be able to rely on their intuition alone and must apply the scientific method to diagnose the patient. Similarly, a software engineer can benefit from using the scientific method to identify and fix the problem when faced with a complex bug. From that perspective, treating engineering challenges like scientific inquiries can transform the way we tackle problems. Rather than resorting to guesswork or gut feelings, we can apply the principles of the scientific method—forming hypotheses, designing controlled experiments, gathering and evaluating evidence—to identify and eliminate bugs systematically. This approach, sometimes referred to as "scientific debugging," reframes debugging from a haphazard process into a structured, disciplined practice. It encourages us to be skeptical, methodical, and transparent in our reasoning. For instance, as Andreas Zeller notes in the book _Why Programs Fail_, the key aspect of scientific debugging is its explicitness: Using the scientific method, you make your assumptions and reasoning explicit, allowing you to understand your assumptions and often reveals hidden clues that can lead to the root cause of the problem on hand. Note: If you'd like to read an excerpt from the book, you can find it on Embedded.com. Scientific Debugging At its core, scientific debugging applies the principles of the scientific method to the process of finding and fixing software defects. Rather than attempting random fixes or relying on intuition, it encourages engineers to move systematically, guided by data, hypotheses, and controlled experimentation. By adopting debugging as a rigorous inquiry, we can reduce guesswork, speed up the resolution process, and ensure that our fixes are based on solid evidence. Just as a scientist begins with a well-defined research question, a software engineer starts by identifying the specific symptom or error condition. For instance, if our users report inconsistencies in the data they see across different parts of the application, our research question could be: _"Under what conditions does the application display outdated or incorrect user data?"_ From there, we can follow a structured debugging process that mirrors the scientific method: - 1. Observe and Define the Problem: First, we need to clearly state the bug's symptoms and the environment in which it occurs. We should isolate whether the issue is deterministic or intermittent and identify any known triggers if possible. Such a structured definition serves as the groundwork for further investigation. - 2. Formulate a Hypothesis: A hypothesis in debugging is a testable explanation for the observed behavior. For instance, you might hypothesize: _"The data inconsistency occurs because a caching layer is serving stale data when certain user profiles are updated."_ The key is that this explanation must be falsifiable; if experiments don't support the hypothesis, it must be refined or discarded. - 3. Collect Evidence and Data: Evidence often includes logs, system metrics, error messages, and runtime traces. Similar to reviewing primary sources in academic research, treat your raw debugging data as crucial evidence. Evaluating these data points can reveal patterns. In our example, such patterns could be whether the bug correlates with specific caching mechanisms, increased memory usage, or database query latency. During this step, it's essential to approach data critically, just as you would analyze the quality and credibility of sources in a research literature review. Don't forget that even logs can be misleading, incomplete, or even incorrect, so cross-referencing multiple sources is key. - 4. Design and Run Experiments: Design minimal, controlled tests to confirm or refute your hypothesis. In our example, you may try disabling or shortening the cache's time-to-live (TTL) to see if more recent data is displayed correctly. By manipulating one variable at a time - such as cache invalidation intervals - you gain clearer insights into causation. Tools such as profilers, debuggers, or specialized test harnesses can help isolate factors and gather precise measurements. - 5. Analyze Results and Refine Hypotheses: If the experiment's outcome doesn't align with your hypothesis, treat it as a stepping stone, not a dead end. Adjust your explanation, form a new hypothesis, or consider additional variables (for example, whether certain API calls bypass caching). Each iteration should bring you closer to a better understanding of the bug's root cause. Remember, the goal is not to prove an initial guess right but to arrive at a verifiable explanation. - 6. Implement and Verify the Fix: Once you're confident in the identified cause, you can implement the fix. Verification doesn't stop at deployment - re-test under the same conditions and, if possible, beyond them. By confirming the fix in a controlled manner, you ensure that the solution is backed by evidence rather than wishful thinking. - Personally, I consider implementing end-to-end tests (e.g., with Playwright) that reproduce the bug and verify the fix to be a crucial part of this step. This both ensures that the bug doesn't reappear in the future due to changes in the codebase and avoids possible imprecisions of manual testing. Now, we can explore these steps in more detail, highlighting how the scientific method can guide us through the debugging process. Establishing Clear Debugging Questions (Formulating a Hypothesis) A hypothesis is a proposed explanation for a phenomenon that can be tested through experimentation. In a debugging context, that phenomenon is the bug or issue you're trying to resolve. Having a clear, falsifiable statement that you can prove or disprove ensures that you stay focused on the real problem rather than jumping haphazardly between possible causes. A properly formulated hypothesis lets you design precise experiments to evaluate whether your explanation holds true. To formulate a hypothesis effectively, you can follow these steps: 1. Clearly Identify the Symptom(s) Before forming any hypothesis, pin down the specific issue users are experiencing. For instance: - "Users intermittently see outdated profile information after updating their accounts." - "Some newly created user profiles don't reflect changes in certain parts of the application." Having a well-defined problem statement keeps your hypothesis focused on the actual issue. Just like a research question in science, the clarity of your symptom definition directly influences the quality of your hypothesis. 2. Draft a Tentative Explanation Next, convert your symptom into a statement that describes a _possible root cause_, such as: - "Data inconsistency occurs because the caching layer isn't invalidating or refreshing user data properly when profiles are updated." - "Stale data is displayed because the cache timeout is too long under certain load conditions." This step makes your assumption about the root cause explicit. As with the scientific method, your hypothesis should be something you can test and either confirm or refute with data or experimentation. 3. Ensure Falsifiability A valid hypothesis must be falsifiable - meaning it can be proven _wrong_. You'll struggle to design meaningful experiments if a hypothesis is too vague or broad. For example: - Not Falsifiable: "Occasionally, the application just shows weird data." - Falsifiable: "Users see stale data when the cache is not invalidated within 30 seconds of profile updates." Making your hypothesis specific enough to fail a test will pave the way for more precise debugging. 4. Align with Available Evidence Match your hypothesis to what you already know - logs, stack traces, metrics, and user reports. For example: - If logs reveal that cache invalidation events aren't firing, form a hypothesis explaining why those events fail or never occur. - If metrics show that data served from the cache is older than the configured TTL, hypothesize about how or why the TTL is being ignored. If your current explanation contradicts existing data, refine your hypothesis until it fits. 5. Plan for Controlled Tests Once you have a testable hypothesis, figure out how you'll attempt to _disprove_ it. This might involve: - Reproducing the environment: Set up a staging/local system that closely mimics production. For instance with the same cache layer configurations. - Varying one condition at a time: For example, only adjust cache invalidation policies or TTLs and then observe how data freshness changes. - Monitoring metrics: In our example, such monitoring would involve tracking user profile updates, cache hits/misses, and response times. These metrics should lead to confirming or rejecting your explanation. These plans become your blueprint for experiments in further debugging stages. Collecting and Evaluating Evidence After formulating a clear, testable hypothesis, the next crucial step is to gather data that can either support or refute it. This mirrors how scientists collect observations in a literature review or initial experiments. 1. Identify "Primary Sources" (Logs, Stack Traces, Code History): - Logs and Stack Traces: These are your direct pieces of evidence - treat them like raw experimental data. For instance, look closely at timestamps, caching-related events (e.g., invalidation triggers), and any error messages related to stale reads. - Code History: Look for related changes in your source control, e.g. using Git bisect. In our example, we would look for changes to caching mechanisms or references to cache libraries in commits, which could pinpoint when the inconsistency was introduced. Sometimes, reverting a commit that altered cache settings helps confirm whether the bug originated there. 2. Corroborate with "Secondary Sources" (Documentation, Q&A Forums): - Documentation: Check official docs for known behavior or configuration details that might differ from your assumptions. - Community Knowledge: Similar issues reported on GitHub or StackOverflow may reveal known pitfalls in a library you're using. 3. Assess Data Quality and Relevance: - Look for Patterns: For instance, does stale data appear only after certain update frequencies or at specific times of day? - Check Environmental Factors: For instance, does the bug happen only with particular deployment setups, container configurations, or memory constraints? - Watch Out for Biases: Avoid seeking only the data that confirms your hypothesis. Look for contradictory logs or metrics that might point to other root causes. You keep your hypothesis grounded in real-world system behavior by treating logs, stack traces, and code history as primary data - akin to raw experimental results. This evidence-first approach reduces guesswork and guides more precise experiments. Designing and Running Experiments With a hypothesis in hand and evidence gathered, it's time to test it through controlled experiments - much like scientists isolate variables to verify or debunk an explanation. 1. Set Up a Reproducible Environment: - Testing Environments: Replicate production conditions as closely as possible. In our example, that would involve ensuring the same caching configuration, library versions, and relevant data sets are in place. - Version Control Branches: Use a dedicated branch to experiment with different settings or configuration, e.g., cache invalidation strategies. This streamlines reverting changes if needed. 2. Control Variables One at a Time: - For instance, if you suspect data inconsistency is tied to cache invalidation events, first adjust only the invalidation timeout and re-test. - Or, if concurrency could be a factor (e.g., multiple requests updating user data simultaneously), test different concurrency levels to see if stale data issues become more pronounced. 3. Measure and Record Outcomes: - Automated Tests: Tests provide a great way to formalize and verify your assumptions. For instance, you could develop tests that intentionally update user profiles and check if the displayed data matches the latest state. - Monitoring Tools: Monitor relevant metrics before, during, and after each experiment. In our example, we might want to track cache hit rates, TTL durations, and query times. - Repeat Trials: Consistency across multiple runs boosts confidence in your findings. 4. Validate Against a Baseline: - If baseline tests manifest normal behavior, but your experimental changes manifest the bug, you've isolated the variable causing the issue. E.g. if the baseline tests show that data is consistently fresh under normal caching conditions but your experimental changes cause stale data. - Conversely, if your change eliminates the buggy behavior, it supports your hypothesis - e.g. that the cache configuration was the root cause. Each experiment outcome is a data point supporting or contradicting your hypothesis. Over time, these data points guide you toward the true cause. Analyzing Results and Iterating In scientific debugging, an unexpected result isn't a failure - it's valuable feedback that brings you closer to the right explanation. 1. Compare Outcomes to the hypothesis. For instance: - Did user data stay consistent after you reduced the cache TTL or fixed invalidation logic? - Did logs show caching events firing as expected, or did they reveal unexpected errors? - Are there only partial improvements that suggest multiple overlapping issues? 2. Incorporate Unexpected Observations: - Sometimes, debugging uncovers side effects - e.g. performance bottlenecks exposed by more frequent cache invalidations. Note these for future work. - If your hypothesis is disproven, revise it. For example, the cache may only be part of the problem, and a separate load balancer setting also needs attention. 3. Avoid Confirmation Bias: - Don't dismiss contrary data. For instance, if you see evidence that updates are fresh in some modules but stale in others, you may have found a more nuanced root cause (e.g., partial cache invalidation). - Consider other credible explanations if your teammates propose them. Test those with the same rigor. 4. Decide If You Need More Data: - If results aren't conclusive, add deeper instrumentation or enable debug modes to capture more detailed logs. - For production-only issues, implement distributed tracing or sampling logs to diagnose real-world usage patterns. 5. Document Each Iteration: - Record the results of each experiment, including any unexpected findings or new hypotheses that arise. - Through iterative experimentation and analysis, each cycle refines your understanding. By letting evidence shape your hypothesis, you ensure that your final conclusion aligns with reality. Implementing and Verifying the Fix Once you've identified the likely culprit - say, a misconfigured or missing cache invalidation policy - the next step is to implement a fix and verify its resilience. 1. Implementing the Change: - Scoped Changes: Adjust just the component pinpointed in your experiments. Avoid large-scale refactoring that might introduce other issues. - Code Reviews: Peer reviews can catch overlooked logic gaps or confirm that your changes align with best practices. 2. Regression Testing: - Re-run the same experiments that initially exposed the issue. In our stale data example, confirm that the data remains fresh under various conditions. - Conduct broader tests - like integration or end-to-end tests - to ensure no new bugs are introduced. 3. Monitoring in Production: - Even with positive test results, real-world scenarios can differ. Monitor logs and metrics (e.g. cache hit rates, user error reports) closely post-deployment. - If the buggy behavior reappears, revisit your hypothesis or consider additional factors, such as unpredicted user behavior. 4. Benchmarking and Performance Checks (If Relevant): - When making changes that affect the frequency of certain processes - such as how often a cache is refreshed - be sure to measure the performance impact. Verify you meet any latency or resource usage requirements. - Keep an eye on the trade-offs: For instance, more frequent cache invalidations might solve stale data but could also raise system load. By systematically verifying your fix - similar to confirming experimental results in research - you ensure that you've addressed the true cause and maintained overall software stability. Documenting the Debugging Process Good science relies on transparency, and so does effective debugging. Thorough documentation guarantees your findings are reproducible and valuable to future team members. 1. Record Your Hypothesis and Experiments: - Keep a concise log of your main hypothesis, the tests you performed, and the outcomes. - A simple markdown file within the repo can capture critical insights without being cumbersome. 2. Highlight Key Evidence and Observations: - Note the logs or metrics that were most instrumental - e.g., seeing repeated stale cache hits 10 minutes after updates. - Document any edge cases discovered along the way. 3. List Follow-Up Actions or Potential Risks: - If you discover additional issues - like memory spikes from more frequent invalidation - note them for future sprints. - Identify parts of the code that might need deeper testing or refactoring to prevent similar issues. 4. Share with Your Team: - Publish your debugging report on an internal wiki or ticket system. A well-documented troubleshooting narrative helps educate other developers. - Encouraging open discussion of the debugging process fosters a culture of continuous learning and collaboration. By paralleling scientific publication practices in your documentation, you establish a knowledge base to guide future debugging efforts and accelerate collective problem-solving. Conclusion Debugging can be as much a rigorous, methodical exercise as an art shaped by intuition and experience. By adopting the principles of scientific inquiry - forming hypotheses, designing controlled experiments, gathering evidence, and transparently documenting your process - you make your debugging approach both systematic and repeatable. The explicitness and structure of scientific debugging offer several benefits: - Better Root-Cause Discovery: Structured, hypothesis-driven debugging sheds light on the _true_ underlying factors causing defects rather than simply masking symptoms. - Informed Decisions: Data and evidence lead the way, minimizing guesswork and reducing the chance of reintroducing similar issues. - Knowledge Sharing: As in scientific research, detailed documentation of methods and outcomes helps others learn from your process and fosters a collaborative culture. Ultimately, whether you are diagnosing an intermittent crash or chasing elusive performance bottlenecks, scientific debugging brings clarity and objectivity to your workflow. By aligning your debugging practices with the scientific method, you build confidence in your solutions and empower your team to tackle complex software challenges with precision and reliability. But most importantly, do not get discouraged by the number of rigorous steps outlined above or by the fact you won't always manage to follow them all religiously. Debugging is a complex and often frustrating process, and it's okay to rely on your intuition and experience when needed. Feel free to adapt the debugging process to your needs and constraints, and as long as you keep the scientific mindset at heart, you'll be on the right track....

“Music and code have a lot in common,” freeCodeCamp’s Jessica Wilkins on what the tech community is doing right to onboard new software engineers cover image

“Music and code have a lot in common,” freeCodeCamp’s Jessica Wilkins on what the tech community is doing right to onboard new software engineers

Before she was a software developer at freeCodeCamp, Jessica Wilkins was a classically trained clarinetist performing across the country. Her days were filled with rehearsals, concerts, and teaching, and she hadn’t considered a tech career until the world changed in 2020. > “When the pandemic hit, most of my gigs were canceled,” she says. “I suddenly had time on my hands and an idea for a site I wanted to build.” That site, a tribute to Black musicians in classical and jazz music, turned into much more than a personal project. It opened the door to a whole new career where her creative instincts and curiosity could thrive just as much as they had in music. Now at freeCodeCamp, Jessica maintains and develops the very JavaScript curriculum that has helped her and millions of developers around the world. We spoke with Jessica about her advice for JavaScript learners, why musicians make great developers, and how inclusive communities are helping more women thrive in tech. Jessica’s Top 3 JavaScript Skill Picks for 2025 If you ask Jessica what it takes to succeed as a JavaScript developer in 2025, she won’t point you straight to the newest library or trend. Instead, she lists three skills that sound simple, but take real time to build: > “Learning how to ask questions and research when you get stuck. Learning how to read error messages. And having a strong foundation in the fundamentals” She says those skills don’t come from shortcuts or shiny tools. They come from building. > “Start with small projects and keep building,” she says. “Books like You Don’t Know JS help you understand the theory, but experience comes from writing and shipping code. You learn a lot by doing.” And don’t forget the people around you. > “Meetups and conferences are amazing,” she adds. “You’ll pick up things faster, get feedback, and make friends who are learning alongside you.” Why So Many Musicians End Up in Tech A musical past like Jessica’s isn’t unheard of in the JavaScript industry. In fact, she’s noticed a surprising number of musicians making the leap into software. > “I think it’s because music and code have a lot in common,” she says. “They both require creativity, pattern recognition, problem-solving… and you can really get into flow when you’re deep in either one.” That crossover between artistry and logic feels like home to people who’ve lived in both worlds. What the Tech Community Is Getting Right Jessica has seen both the challenges and the wins when it comes to supporting women in tech. > “There’s still a lot of toxicity in some corners,” she says. “But the communities that are doing it right—like Women Who Code, Women in Tech, and Virtual Coffee—create safe, supportive spaces to grow and share experiences.” She believes those spaces aren’t just helpful, but they’re essential. > “Having a network makes a huge difference, especially early in your career.” What’s Next for Jessica Wilkins? With a catalog of published articles, open-source projects under her belt, and a growing audience of devs following her journey, Jessica is just getting started. She’s still writing. Still mentoring. Still building. And still proving that creativity doesn’t stop at the orchestra pit—it just finds a new stage. Follow Jessica Wilkins on X and Linkedin to keep up with her work in tech, her musical roots, and whatever she’s building next. Sticker illustration by Jacob Ashley....

CSS Hooks: A new way to style your React apps cover image

CSS Hooks: A new way to style your React apps

In the world of web development, the management of styles has always been a crucial aspect of building modern and responsive user interfaces. With the rise of CSS in JS libraries like Material UI and Chakra, developers have started creating dynamic and reusable styles using JavaScript; however, the performance implications of these libraries have led to the exploration of alternative solutions. One such solution is CSS Hooks, a library that offers a different approach to managing styles in web applications. The Problem with CSS in React and CSS in JS Libraries In React applications, you typically write styles directly with the style prop by applying classes with className, or if you’re using a framework like Material UI or Chakra then you may use the sx attribute, which is more powerful than the baseline attributes. There are some performance concerns in the case of the sx attribute specifically. 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CSS Hooks utilize an interesting trick under the hood to accomplish this, which we will now explore. The Power of CSS Variables and the Fallback Trick As mentioned before, CSS variables are utilized in order to support using advanced dynamic behavior such as pseudo-selectors in our inline styles. CSS variables are core to something known as the “fallback trick”. The gist of how the fallback trick works is to have the pseudo-selector or media query toggle the state of a variable between initial and an empty invalid value, and then we put the value that we actually want to toggle as the “fallback” value in the reference to the CSS variable inside of the inline style itself. Changing this fallback value inside of an inline style essentially allows us to control the values used by the pseudo-selector without having to regenerate linked stylesheets as we only have to change the inline style attribute. This is best explained with an example: ` This plain HTML and CSS code effectively allows us to utilize the focus pseudo-selector on any element that we want using inline styles only. This is what CSS Hooks effectively does in the background when you use dynamic styles. No nasty re-renders and mucking with stylesheets needed! How to use CSS Hooks Now that we know how CSS Hooks fundamentally work, let’s try using them. The library itself is much easier to use than the aforementioned example. Installation and usage of CSS Hooks is easy. Firstly, we must install the @css-hooks/react package into a React project with the package manager of your choice (npm, pnpm yarn, etc). Once that’s done, all that’s left is a little bit of configuration. You have to first use a utility function called createHooks that is exported from the package we just installed. The result of that function returns a couple of variables in an array that can be deconstructed, the first being a stylesheet in the form of a string, and the second being a function. ` I recommend putting this in a separate file that can be imported from multiple other places in the project as this is where we will be configuring hooks across the project. We’re exporting both hooks and css, and both are important. We need to include hooks into the DOM as it contains the styles of the hooks that we’ll be referencing in our components. In your root component you need to add the following tag so that the generated CSS can be used. ` How this is done may vary based on the libraries that you are using. After that’s done, you can integrate CSS Hooks into your components while writing inline styles mostly just like you would do normally, with the only change being the addition of an additional call to the css function that we exported earlier: ` Now that the core boilerplate is set up, your components can now utilize CSS Hooks in theory; however we need to actually add some hooks to start with before we can use them, or else we’ll just be limited to basic styles in the same way that React inline styles are. Make your own hooks We can define our hooks inside of the css-hooks file that we created earlier, the same one that exports the css helper function that gets CSS Hooks working with our components. Hooks are defined in the options parameters that are currently empty. 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Thank you for reading!...

The simplicity of deploying an MCP server on Vercel cover image

The simplicity of deploying an MCP server on Vercel

The current Model Context Protocol (MCP) spec is shifting developers toward lightweight, stateless servers that serve as tool providers for LLM agents. These MCP servers communicate over HTTP, with OAuth handled clientside. Vercel’s infrastructure makes it easy to iterate quickly and ship agentic AI tools without overhead. Example of Lightweight MCP Server Design At This Dot Labs, we built an MCP server that leverages the DocuSign Navigator API. The tools, like `get_agreements`, make a request to the DocuSign API to fetch data and then respond in an LLM-friendly way. ` Before the MCP can request anything, it needs to guide the client on how to kick off OAuth. This involves providing some MCP spec metadata API endpoints that include necessary information about where to obtain authorization tokens and what resources it can access. By understanding these details, the client can seamlessly initiate the OAuth process, ensuring secure and efficient data access. The Oauth flow begins when the user's LLM client makes a request without a valid auth token. In this case they’ll get a 401 response from our server with a WWW-Authenticate header, and then the client will leverage the metadata we exposed to discover the authorization server. Next, the OAuth flow kicks off directly with Docusign as directed by the metadata. Once the client has the token, it passes it in the Authorization header for tool requests to the API. ` This minimal set of API routes enables me to fetch Docusign Navigator data using natural language in my agent chat interface. Deployment Options I deployed this MCP server two different ways: as a Fastify backend and then by Vercel functions. Seeing how simple my Fastify MCP server was, and not really having a plan for deployment yet, I was eager to rewrite it for Vercel. The case for Vercel: * My own familiarity with Next.js API deployment * Fit for architecture * The extremely simple deployment process * Deploy previews (the eternal Vercel customer conversion feature, IMO) Previews of unfamiliar territory Did you know that the MCP spec doesn’t “just work” for use as ChatGPT tooling? Neither did I, and I had to experiment to prove out requirements that I was unfamiliar with. Part of moving fast for me was just deploying Vercel previews right out of the CLI so I could test my API as a Connector in ChatGPT. This was a great workflow for me, and invaluable for the team in code review. Stuff I’m Not Worried About Vercel’s mcp-handler package made setup effortless by abstracting away some of the complexity of implementing the MCP server. It gives you a drop-in way to define tools, setup https-streaming, and handle Oauth. By building on Vercel’s ecosystem, I can focus entirely on shipping my product without worrying about deployment, scaling, or server management. Everything just works. ` A Brief Case for MCP on Next.js Building an API without Next.js on Vercel is straightforward. Though, I’d be happy deploying this as a Next.js app, with the frontend features serving as the documentation, or the tools being a part of your website's agentic capabilities. Overall, this lowers the barrier to building any MCP you want for yourself, and I think that’s cool. Conclusion I'll avoid quoting Vercel documentation in this post. AI tooling is a critical component of this natural language UI, and we just want to ship. I declare Vercel is excellent for stateless MCP servers served over http....

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