The best way to build meaningful customer experiences, on every touchpoint of the customer's lifecycle. Crisp allows you to interact with your new & existing customers, on any channel. From sales to customer support, Crisp is made to keep your workflow simple. With its quickly evolving set of tools, Crisp is leading businesses through the customer-driven revolution by using conversations to unlock hypergrowth. Customers use Crisp to provide a modern customer lifecycle experience for potential customers, generate more qualified leads, enhance customer help, and improve customer satisfaction leading to a global increase in revenue.

Crisp

The emergence of remarkable technologies like the Internet, the iPhone, and Bitcoin have left us all in constant search of "the next big thing."Is it Uber? Airbnb? Everything-as-a-service? Tesla? Gartner formalized this frenzied questioning with the " ." hype cycle One trend Gartner notes is the rise of the "augmented human," which, besides improving the human body with biochips and AI, includes biotech, like CRISPR. CRISPR is a gene editing tool, combining a guide RNA (molecular GPS) with a Cas endonuclease (molecular scissors) to let us finely target and manipulate genes at will. Infinite Possibility Because there are many kinds of , there are many applications for CRISPR, including , , , and . Cas endonucleases COVID-19 testing HIV diagnostics autism treatments so much more The sheer versatility of a precise gene manipulation tool like CRISPR means the sky is the limit. One of Silicon Valley's most marquee brands, YCombinator, writes about how genetically engineered ocean phytoplankton could . Yes, CRISPR can be used here . solve climate change as well Infinite Risk? Like Uncle Ben said, with great power comes great responsibility. While clichés like this are thrown around constantly, whether it's in AI or even blockchain, the reality is that CRISPR truly is a turning point for humanity. There's a nearly-universal of CRISPR for non-therapeautic uses, like creating designer babies: denouncement "In our view, genome editing in human embryos using current technologies could have unpredictable effects on future generations... This makes it dangerous and ethically unacceptable." The more politically correct term is "genetically modified children," which is accomplished through human germline editing, or changing heritable DNA (sperm and eggs, or embryos). If scientists edit heritable DNA, it's something we can't take back. This is in sharp contrast to most other technologies out there. For instance, if you unintentionally create a biased AI algorithm, there will be and potentially , but the algorithm can be rolled-back and modified. a ton of backlash fear You can't roll-back a line of genetically-modified humans (or any other animal, for that matter). Once they're out of the lab, genetically modified organisms are free to reproduce, and continue passing on manipulated genes, with unforeseeable consequences. The solution seems fairly straightforward: A moratorium on heritable DNA editing, while allowing somatic DNA editing (not sperm and egg cells). However, we're not out of the water just yet. A Fight to Control CRISPR With so much at stake, many parties are engaged in vicious legal battles to . patent CRISPR Since the first commercialization of CRISPR Cas9 in 2012, hundreds upon hundreds of patent applications have been made, leading to many disputes and overlaps in claimed IP. This is a sign that, while an incredibly powerful technology, we're still in the early days of the CRISPR field. By comparison, the AI and blockchain industries, for instance, are not wholly embroiled in patent battles over technological control. Instead, we're seeing and being used around us already. blockchain AI The Future of CRISPR Once the legal battles over CRISPR settle down, rapid progress will be inevitable. Even now, we're seeing tremendous developments being made. While CRISPR now is mostly limited to university labs and scientists, this technology is being " ," or more simply, it's becoming more and more accessible. democratized To become a part of that democratization and help advance the field for everyone, not just those at the top, it's up to readers like you to . learn more about CRISPR

Why CRISPR Deserves the Hype

Casting the Tablets: CRISPR

It’s now been 18 months since we started mabl with an effort to help make test automation easier for software teams around the world. As we’ve come this far, we’ve tried to , engaging them in our channel, with user surveys, and meeting them at conferences throughout the world. All of this engagement has helped shaped the mabl product into what it is today with dozens of customers adopting it as their primary automated testing service. stay as close as possible to our users Friends of mabl slack Even with all of these endeavors, we believe there’s more we can do for software teams and the testing community broadly than just listening closely and building a product. We decided it was time to: engage with teams more actively broaden the scope of the feedback from the community back into the mabl product build further awareness of the trends within testing teams around continuous delivery. 1) 2) 3) That’s why we’re super excited to welcome Lisa Crispin to the mabl team. Many of you have been curious what Lisa’s title would be and what she’d be doing so I thought I’d share some more details. What’s Lisa’s role at mabl? At the heart of Lisa’s focus will be what she’s been doing for a lifetime — publicly sharing her experience and learnings with the testing community through public speaking opportunities at conferences like , conference, and other Testing events around the world. Despite working at mabl, she will not focus on mabl, but instead continue to focus on agile, , and how teams adopting these trends can improve their testing processes. We’ll also leverage Lisa’s public speaking experience at other smaller events including Meetups and to extend her reach to audiences who don’t get to extend to the larger conferences. Agile Testing Days CAST continuous delivery DevOpsDays Lisa’s reputation has been established as a practitioner in the testing community, especially as it relates to Agile development practices, and that’s one focus she’ll continue to have. We are offering Lisa’s expertise to mabl clients throughout the world to both learn from her experience in how to make testing more effective but for Lisa to also learn from their experiences on what makes effective software delivery. This will be a two-way learning experience so both client and Lisa will draw value from one another in an effort to improve testing process and quality for users globally. Community Engagement As we’ve learned through the key features we’ve built already, much of our roadmap is shaped by our users. , File Upload, and items in tests have all been suggestions from our users. However these are only the tip of the iceberg of features we could be building into mabl. Data-driven testing Asserting on Not Present Lisa will engage with not only mabl users, but with the community broadly to solicit feedback and help prioritize the features which make automated testing easier. To date, we’ve done this with the extra time our team has had, but Lisa will engage more closely with our users and the community to truly understand the use cases and challenges they have today. She’ll explore the broader themes that we could build into mabl, and also dive deeper into where machine learning could help with some of the more manual tasks testers have today. Lisa’s goal here is to accelerate innovation in the test automation lifecycle. To kick things off, she’s hosting her first mabl webinar next month. Lisa will help you learn the language of DevOps and continuous delivery so that you can communicate better with your team. You’ll see examples to better understand continuous delivery and continuous deployment pipelines. Lisa will introduce some tools and techniques that can help your team embrace a DevOps culture, and learn how testers can help build bridges between “Dev”, “Ops” and the business stakeholders. You’ll learn that there IS a “test” in “DevOps” — what we’re calling DevTestOps. Moving Forward As you may be able to tell, we’ve put a lot of thought into how Lisa can help the testing community while also driving innovation in test automation. If you have suggestions for other ways Lisa could be helpful to you, we’d love to hear your feedback! Lisa is now officially on board and her title is but we just refer to her as a member of the mabl team. Feel free to find her on or . Testing Advocate, Slack Twitter Originally published at www.mabl.com .

Lisa Crispin teams up with mabl to drive innovation in test automation

Baptiste is the CEO and co-founder of Crisp, an easy and effective way to chat with your website visitors. Davis Baer : What’s your background, and what are you working on? : My name is Baptiste Jamin, and I’m 24, and I’m CEO at Crisp. My passion for hacking things started when I was 5: My brother taught me how to boot our family’s Windows 3.1 computer using DOS. Then, when I was 12 I started coding by joining open source projects as a developer. We made indie video games, 2D games engines. At this time, Github didn’t exist and it’s crazy how we made projects as teenagers just by searching for knowledge on internet. This how my passion with entrepreneurship came. Baptiste Jamin Then, after being fired in High School because I hacked the network system, I joined a networking school. I also joined Orange the biggest ISP as a developer as a part-time job, did some freelancing, and joined few startups as a developer. During this time, I also did few side projects: Some succeeded, others failed. Later, I met Valerian, my now co-founder at Crisp. When I met him, he was working on an open source messaging solution using XMPP which was used by NATO and 60K users. On my end, I was working on trash mail system, which was popular in France also. From our previous side projects and startup experiences, we truly felt it was crucial to communicate with customers to succeed instartups, and at this time, most tools were outdated, complex and expensive for small companies. So we made it easy for startups and small companies to chat with their customers and to unify all their customer relationship in one place. They can add our chat on their website, manage all their customer email, Facebook Messenger queries, from our tool. Now, around 70K websites are using Crisp, around 60 million people are chatting every month on the platform. What went into building the initial product? In 2015, we kickstarted Crisp in three months in my cofounder’s flat in France. First, we designing the platform on Sketch, and then literally coded the platform in a few weeks, using a micro-service architecture and an AngularJS app. It was my first time ever working with this new co-founder. Then we started getting our first users. The fun story about this, is that a good friend of mine wanted wanted to try the platform, and it was password-locked. He asked me to remove the password lock. So I did. A day later, we discovered that around 20 users were using the product and were happy with it! This friend had been sent an angry email because they didn’t have any customer support chat, and they wanted to use Crisp to solve this. A bit later, one of those customers posted us on ProductHunt, and this is how our growth started. The first Crisp release. At this time the product was free, and I guess it helped to get an initial traction and to learn about the market. When you are free, customers are also a bit more tolerant about the lack of features on the product. Being free also forced us to optimize everything, because at this time we didn’t have any money. For instance I had to sell my car and few other things I had so I could focus on Crisp full-time. By optimizing, we had to optimize each dollar, and we coded internal tools like our own help-center, status page, financial tools. The funny thing is our customer loved those tools, and asked us to release these as products later. We also had to go really fast because the competition was also moving quickly, so we had to implement a ton of new features in a small amount of time. We were a really small team(two founders — developers), and thanks to all our previous experience we were able to move fast. We also chose to bootstrap instead of fundraise. Thanks to this decision, we are 100% independent. How have you attracted users and grown Crisp? After we got our first traction thanks to my friend and ProductHunt, we did a lot of growth hacking. We had to optimize our landing page and our app to convert visitors to long term users. For instance we discovered that when people implemented Crisp onto their website after signing up, 51% were still using the platform after 10 weeks. So we doubled down on how to make people install Crisp on their website. It was a big technical and UX challenge. What helped a lot as well, was the “Powered by Crisp” logo under our chatbox that amplified the growth as well. Finally, the most important advice to entrepreneurs is to focus on customers and customers support. This is the most important thing to grow. Thanks to customers, you will learn how to improve your product. What’s your business model, and how have you grown your revenue? As explained earlier, we started by being free. The free customers helped us to know what were the most imported paid features to build. A year after launching Crisp, we released Crisp 2.0 on ProductHunt with our new paid model with the new paid features (we still have a free plan). In a few days, we had a huge success. Crisp has a very international base, so we had to implement most of payment providers out there. This is why we are using both Stripe and PayPal as payment options. The key of our success is we adapted our pricing for startups, and we have a plan for each situation, from new startups to succeeding companies. What are your goals for the future? Currently we are working on a new release on Crisp that should be out there soon. The goal is to double our annual revenue and customers in the next few year. Personally, we will also start being sedentary again, after being nomads for so long. Crisp is still a small team (we are now 5), and bootstrapping our company allowed us to be nomads.Over the last few years, we moved to several different locations. Sometimes we are with each other, sometimes we are not. I also lived with my cofounder for several months. What are the biggest challenges you’ve faced and obstacles you’ve overcome? If you had to start over, what would you do differently? The biggest challenge was clearing how to build a big platform in a small amount of time. If I started Crisp again, I wouldn’t change anything, even mistakes can lead to luck, and that’s what happened. Young entrepreneurs who are doing their first project don’t even realize how it helps to know nothing (yet) about the subject you are working on. Have you found anything particularly helpful or advantageous? We had some help and knowledge about The Family. I would say it’s the French YC, which we were a part of. It helped us to meet interesting people and to focus on growth. For instance, I didn’t even know what SaaS meant before meeting people from The Family. What’s your advice for entrepreneurs who are just starting out? The best advice is to work hard, and to build to the best of your abilities. I also think that young entrepreneurs should build side projects to get some experiences before building a startup. You can build really successful projects, that are not startups, but that could be in the future. I think it’s what happened to Crisp. Where can we go to learn more? You can learn more about Crisp on and by . crisp.chat following Crisp on twitter Don’t hesitate to comment this post if you want to know more about us. This interview is brought to you by , a tool to schedule and automatically repeat your posts on Facebook, Instagram, Twitter, Pinterest, LinkedIn, and Google My Business OneUp

Founder Interviews: Baptiste Jamin of Crisp

Loss of Amazon Rainforest Resilience: Author Contributions

“Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat, the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life, so even well-preserved samples from over 1,000 years ago aren’t complete. AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually, and finding similarities in DNA sequences is a similar task. Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal, so they’re ideal mothers to grow a baby mammoth. After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers. “Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat , the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. call AI an extinction-level threat call AI an extinction-level threat How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. shortens the time spent in genome sequencing shortens the time spent in genome sequencing Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life , so even well-preserved samples from over 1,000 years ago aren’t complete. has a 521-year half-life has a 521-year half-life AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually , and finding similarities in DNA sequences is a similar task. saves Netflix $1 billion annually saves Netflix $1 billion annually Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal , so they’re ideal mothers to grow a baby mammoth. the longest pregnancy of any mammal the longest pregnancy of any mammal After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. resurrect the woolly mammoth by 2027 resurrect the woolly mammoth by 2027 Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. successfully cloned a black-footed ferret successfully cloned a black-footed ferret Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. could improve global health could improve global health Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers.

The Role of AI in Resurrecting Extinct Species

বিলুপ্ত প্রজাতির পুনরুত্থানে এআই-এর ভূমিকা

Die Rolle der KI bei der Wiederbelebung ausgestorbener Arten

El papel de la IA en la resurrección de especies extintas

Le rôle de l’IA dans la résurrection des espèces disparues

विलुप्त प्रजातियों को पुनर्जीवित करने में एआई की भूमिका

絶滅した種の復活におけるAIの役割

멸종된 종을 부활시키는 데 있어 AI의 역할

O papel da IA na ressurreição de espécies extintas

Роль ИИ в воскрешении вымерших видов

Soyu Tükenmiş Türlerin Diriltilmesinde Yapay Zekanın Rolü

Vai trò của AI trong việc hồi sinh các loài đã tuyệt chủng

人工智能在复活灭绝物种中的作用

HEPT MOVES ON

You know what’s cooler than using an image when building a website? How about making something with HTML and CSS that looks and acts like an image? And you know what, this is super easy now with container queries. In this post, we’re going to make an ad, that looks like an image, with HTML and CSS. Then we’re going to make it act like an image as it gets squished, expanded, and moved to other locations within the document. Ok, let’s check it out! https://youtu.be/5ugwp7HlpGs?embedable=true Ok, so we have this Vans ad in the sidebar of the site that we are building, and it’s currently an image. This makes it difficult to edit; a designer would need to update it and provide us with a new image if something needed to change. Also, we’d probably need multiple versions to use with the source set so that it will look crisp on both high-density and low-resolution displays. So, we’re tasked with converting it to HTML; how can we do this? Some Downsides to Using Images and Viewport Units Well, we could probably use viewport units, but we’d need to add extra code if we were to put the ad in different locations where its dimensions would be different since everything would be based off the viewport and not the container dimensions. So, we could pull it off, but it could get a little messy. Instead, we could use container queries and container query units. Container queries are a little like media queries but based off the dimensions of any given container in the page instead of the overall viewport. Setting up a Container With the CSS Property container-type Ok, let’s check out what we’re starting with. So, it looks pretty good right here but, how does it do as it responds? Uh, the text and borders don’t change size, so it needs some love cause it’s pretty broken as it stands. Now, one thing we’re already doing here is, we’re using an which allows the container to respond as an image would so, that’s all good. aspect-ratio figure {
    aspect-ratio: 1 / 1.5;
} We just need to change all the dimensions for the content to make it respond correctly too. The first step here is to provide a container to monitor our dimensions. This will be our figure element. To use container queries here, we need to define what is known as a containment context. We can do this with the property. For this example, we can use a value of inline size. container-type figure {
    ...
    container-type: inline-size;
} This is going to set up a container that will size things based off its inline size which, in this case, will be the width of the figure. Alright, so now we have a container to monitor, so we’re now free to use container query units. And, there’s lots to choose from. We can use any of these unit values: cqw cqh cqi cqb cqmin cqmax Here, we’re going to use which, I believe, stands for container query inline. One unit is equal to one percent of the width of the container. Ok, that’s all we need; from here, we’re just setting units as needed. cqi cqi Here, we have a couple of custom properties that are used on several elements within this ad. figure {
    ...
    --frameInset: 0.5rem;
    --frameStyle: solid 0.25rem currentColor;
} Let’s start with the amount this frame is inset from the outer edge of the container. Let’s make it three . cqi figure {
    ...
    --frameInset: 3cqi;
} Next, let’s set the thickness of the borders here. In this case, I’m going to use the function to prevent the borders from ever-shrinking under one pixel, but I want them to be dynamic as long as they are larger than that one-pixel value. max So, the first value is one pixel, then the second is the dynamic value. Let’s make it one . cqi figure {
    ...
    --frameStyle: solid max(1px, 1cqi) currentColor;
} Now, for the element, which is the main title, the "Vans" text, let’s make it twenty-five . And, for the space underneath the title, let’s make it three . strong cqi cqi strong {
    font-size: 25cqi;
    padding-bottom: 3cqi;
} Now, let’s move to the "Off the Wall" subtitle. It should be about half the size of the main title, so let’s try twelve . And, for the space above the text, let’s go with three again. cqi cqi em {
    font-size: 12cqi;
    padding-top: 3cqi;
} Alright, for the last piece, the "Since 1966" label, let’s go with a font size of six . And, for the space above and below, we’re using the logical property for which takes up to two values. The first value is the value above the text, and the second value is for the space below. cqi padding-block So, let’s go with two above. And, in our calculation, we’ll leave the as is, but we’ll go with five of additional space. cqi --frameInset cqi time {
    font-size: 6cqi;
    padding-block: 2cqi calc(5cqi + var(--frameInset));
} Ok cool, this looks good. Let’s take a look at how it responds. As we squeeze it, everything is properly responding uniformly like we wanted. It looks a lot like an image. It looks pretty good even when it’s small, and you can see that the borders never shrink below one pixel. Then, when we get into really narrow widths it gets wider, and everything still looks great. How cool is this? One set of styles, and it just works all the way through. As we scale it back and forth, it looks great. Now, what’s even more cool is that we can take our ad mark-up, move it to or main column region here, and everything continues to work just like it would if it were an image. <header><h1>A Brief History of Vans</h1></header>
<div class="content">
    <main>
        <figure>
            <svg viewBox="0 0 256 256">
                <path d="M90.4,67.9C88,69,87.1,70.6,83,80c-4.4,10.2-4.8,12.3-3.1,15"/>
                <path d="M97.4,95.2c-0.6,0.6-1.2,1.7-1.2,2.3s0"/>
            </svg>
            <figcaption>
                <strong>Vans</strong>
                <em>Off the Wall</em>
                <time>Since 1966</time>
            </figcaption>
        </figure>
        ...
    </main>
</div> This is just so nice and so cool. Remember, there’s only one set of styles to pull all of this off. And, zero media queries were involved. There’s quite a bit more to container queries as a whole, so be on the lookout for future posts where I cover different aspects. Want to See It in Action? Check out the demo code and examples of these techniques in the Codepen example below. If you have any questions or thoughts, don’t hesitate to leave a comment. https://codepen.io/brianmtreese/pen/poGegXB?embedable=true

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