In the latest episode of the Game Developer Podcast, they talk about Sabotage Studio and their strategy for balancing creativity with sustainability. Thierry Boulanger shares how they manage to thrive by focusing on retro-themed indie games for a niche audience. It seems like a decent approach, but honestly, it’s all a bit repetitive. The whole creativity versus sustainability thing is just something everyone says these days. Anyway, if you're into indie games, you might find it interesting. Or not. #GameDevelopment #IndieGames #RetroGaming #Sustainability #Creativity

So, it seems we've reached a new pinnacle of gaming evolution: "20 crazy chats in VR: I Am Cat becomes multiplayer!" Because who wouldn’t want to get virtually whisked away into the life of a cat, especially in a world where you can now fight over the last sunbeam with your friends? Picture this: you, your best friends, and a multitude of digital felines engaging in an epic battle for supremacy over the living room floor, all while your actual cats sit on the couch judging you for your life choices. Yes, that's right! Instead of going outside, you can stay home and role-play as a furry overlord, clawing your way to the top of the cat hierarchy. Truly, the pinnacle of human achievement. Let’s be real—this is what we’ve all been training for. Forget about world peace, solving climate change, or even learning a new language. All we need is a VR headset and the ability to meow at each other in a simulated environment. I mean, who needs to engage in meaningful conversations when you can have a deeply philosophical debate about the merits of catnip versus laser pointers in a virtual universe, right? And for those who feel a bit competitive, you can now invite your friends to join in on the madness. Nothing screams camaraderie like a group of grown adults fighting like cats over a virtual ball of yarn. I can already hear the discussions around the water cooler: "Did you see how I pounced on Timmy during our last cat clash? Pure feline finesse!" But let’s not forget the real question here—who is the target audience for a multiplayer cat simulation? Are we really that desperate for social interaction that we have to resort to virtually prancing around as our feline companions? Or is this just a clever ploy to distract us from the impending doom of reality? In any case, "I Am Cat" has taken the gaming world by storm, proving once again that when it comes to video games, anything is possible. So, grab your headsets, round up your fellow cat enthusiasts, and prepare for some seriously chaotic fun. Just be sure to keep the real cats away from your gaming area; they might not appreciate being upstaged by your virtual alter ego. Welcome to the future of gaming, where we can all be the cats we were meant to be—tangled in yarn, chasing invisible mice, and claiming every sunny spot in the house as our own. Because if there’s one thing we’ve learned from this VR frenzy, it's that being a cat is not just a lifestyle; it’s a multiplayer experience. #ICatMultiplayer #VRGaming #CrazyCatChats #VirtualReality #GamingCommunity

Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage. #ankur #kothari #qampampa #customer #engagement

Over the past few months, I’ve had the pleasure of testing out the reMarkable Paper Pro. You can read my full review here, but in short, it gets everything right about the note taking experience. Despite being an e-ink tablet, it does get quite pricey. However, there are certainly some fantastic parts of the experience that make it worth comparing to an iPad Air, depending on what you’re looking for in a note taking device for school, work, or whatever else. Updated June 15th to reflect reMarkable’s new post-tariff pricing. Overview Since the reMarkable Paper Pro comes in at with the reMarkable Marker Plus included, it likely makes most sense to compare this against Apple’s iPad Air 11-inch. That comes in at without an Apple Pencil, and adding in the Apple Pencil Pro will run you an additional The equivalent iPad setup will run you more than the reMarkable Paper Pro. Given the fact that iPad Air‘s regularly go on sale, it’d be fair to say they’re roughly on the same playing field. So, for a reMarkable Paper Pro setup, versus for a comparable iPad Air setup. Which is better for you? Obviously, the iPad Air has one key advantage: It runs iOS, has millions of apps available, can browse the web, play games, stream TV shows/movies, and much more. To some, that might end the comparison and make the iPad a clear winner, but I disagree. Yes, if you want your tablet to do all of those things for you, the iPad Air is a no brainer. At the end of the day, the iPad Air is a general purpose tablet that’ll do a lot more for you. However, if you also have a laptop to accompany your tablet, I’d argue that the iPad Air may fall into a category of slight redundance. Most things you’d want to do on the iPad can be done on a laptop, excluding any sort of touchscreen/stylus reliant features. iPads are great, and if you want that – you should pick that. However, I have an alternative argument to offer… The reMarkable Paper Pro does one thing really well: note taking. At first thought, you might think: why would I pay so much for a device that only does one thing? Well, that’s because it does that one thing really well. There’s also a second side to this argument: focus. It’s much easier to focus on what you’re doing when the device isn’t capable of anything else. If you’re taking notes while studying, you could easily see a notification or have the temptation to check notification center. Or, if you’re reading an e-book, you could easily choose to swipe up and get into another app. The best thing about the reMarkable Paper Pro is that you can’t easily get lost in the world of modern technology, while still having important technological features like cloud backup of your notes. Plus, you don’t have to worry about carrying around physical paper. One last thing – the reMarkable Paper Pro also has rubber feet on the back, so if you place it down flat on a table caseless, you don’t have to worry about scratching it up. Spec comparison Here’s a quick rundown of all of the key specs between the two devices. reMarkable Paper Pro‘s strengths definitely lie in battery, form factor, and stylus. iPad has some rather neat features with the Apple Pencil Pro, and also clears in the display category. Both devices also offer keyboards for typed notes, though only the iPad offers a trackpad. Display– 10.9-inch LCD display– Glossy glass– 2360 × 1640 at 264 ppi– 11.8-inch Color e-ink display– Paper-feeling textured glass– 2160 × 1620 at 229 ppiHardware– 6.1mm thin– Anodized aluminum coating– Weighs 461g w/o Pencil Pro– 5.1mm thin– Textured aluminum edges– Weighs 360g w/ Marker attachedStylus– Magnetically charges from device– Supports tilt/pressure sensitivity– Low latency– Matte plastic build– Squeeze features, double tap gestures– Magnetically charges from device– Supports tilt/pressure sensitivity– Ultra-low latency– Premium textured aluminum build– Built in eraser on the bottomBattery life– Up to 10 hours of web browsing– Recharges to 100% in 2-3 hrs– Up to 14 days of typical usage– Fast charges to 90% in 90 minsPrice–for iPad Air–for Pencil Pro– bundled with Marker Plus Wrap up All in all, I’m not going to try to convince anyone that wanted to buy an iPad that they should buy a reMarkable Paper Pro. You can’t beat the fact that the iPad Air will do a lot more, for roughly the same cost. But, if you’re not buying this to be a primary computing device, I’d argue that the reMarkable Paper Pro is a worthy alternative, especially if you really just want something you can zone in on. The reMarkable Paper Pro feels a lot nicer to write on, has substantially longer battery life, and really masters a minimalist form of digital note taking. Buy M3 iPad Air on Amazon: Buy reMarkable Paper Pro on Amazon: What do you think of these two tablets? Let us know in the comments. My favorite Apple accessory recommendations: Follow Michael: X/Twitter, Bluesky, Instagram Add 9to5Mac to your Google News feed.  FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel #ipad #air #remarkable #paper #pro

One stock recently impacted by a whirlwind of volatility is Block—the fintech powerhouse behind Square, Cash App, Tidal Music, and more. The company’s COO and CFO, Amrita Ahuja, shares how her team is using new AI tools to find opportunity amid disruption and reach customers left behind by traditional financial systems. Ahuja also shares lessons from the video game industry and discusses Gen Z’s surprising approach to money management.   This is an abridged transcript of an interview from Rapid Response, hosted by Robert Safian, former editor-in-chief of Fast Company. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. As a leader, when you’re looking at all of this volatility—the tariffs, consumer sentiment’s been unclear, the stock market’s been all over the place. You guys had a huge one-day drop in early May, and it quickly bounced back. How do you make sense of all these external factors? Yeah, our focus is on what we can control. And ultimately, the thing that we are laser-focused on for our business is product velocity. How quickly can we start small with something, launch something for our customers, and then test and iterate and learn so that ultimately, that something that we’ve launched scales into an important product? I’ll give you an example. Cash App Borrow, which is a product where our customers can get access to a line of credit, often that bridges them from paycheck to paycheck. We know so many Americans are living paycheck to paycheck. That’s a product that we launched about three years ago and have now scaled to serve 9 million actives with billion in credit supply to our customers in a span of a couple short years. The more we can be out testing and launching product at a pace, the more we know we are ultimately delivering value to our customers, and the right things will happen from a stock perspective. Block is a financial services provider. You have Square, the point-of-sale system; the digital wallet Cash App, which you mentioned, which competes with Venmo and Robinhood; and a bunch of others. Then you’ve got the buy-now, pay-later leader Afterpay. You chair Square Financial Services, which is Block’s chartered bank. But you’ve said that in the fintech world, Block is only a little bit fin—that comparatively, it’s more tech. Can you explain what you mean by that? What we think is unique about us is our ability as a technology company to completely change innovation in the space, such that we can help solve systemic issues across credit, payments, commerce, and banking. What that means ultimately is we use technologies like AI and machine learning and data science, and we use these technologies in a unique way, in a way that’s different from a traditional bank. We are able to underwrite those who are often frankly forgotten by the traditional financial ecosystems. Our Square Loans product has almost triple the rate of women-owned businesses that we underwrite. Fifty-eight percent of our loans go to women-owned businesses versus 20% for the industry average. For that Cash App Borrow product I was talking about, 70% of those actives, the 9 million actives that we underwrote, fell below 580 as a FICO score. That’s considered a poor FICO score, and yet 97% of repayments are made on time. And this is because we have unique access to data and these technology and tools which can help us uniquely underwrite this often forgotten customer base. Yeah. I mean, credit—sometimes it’s been blamed for financial excesses. But access to credit is also, as you say, an advantage that’s not available to everyone. Do you have a philosophy between those poles—between risk and opportunity? Or is what you’re saying is that the tech you have allows you to avoid that risk? That’s right. Let’s start with how do the current systems work? It works using inferior data, frankly. It’s more limited data. It’s outdated. Sometimes it’s inaccurate. And it ignores things like someone’s cash flows, the stability of your income, your savings rate, how money moves through your accounts, or how you use alternative forms of credit—like buy now, pay later, which we have in our ecosystem through Afterpay. We have a lot of these signals for our 57 million monthly actives on the Cash App side and for the 4 million small businesses on the Square side, and those, frankly, billions of transaction data points that we have on any given day paired with new technologies. And we intend to continue to be on the forefront of AI, machine learning, and data science to be able to empower more people into the economy. The combination of the superior data and the technologies is what we believe ultimately helps expand access. You have a financial background, but not in the financial services industry. Before Block, you were a video game developer at Activision. Are financial businesses and video games similar? Are there things that are similar about them? There are. There actually are some things that are similar, I will say. There are many things that are unique to each industry. Each industry is incredibly complex. You find that when big technology companies try to do gaming. They’ve taken over the world in many different ways, but they can’t always crack the nut on putting out a great game. Similarly, some of the largest technology companies have dabbled in fintech but haven’t been able to go as deep, so they’re both very nuanced and complex industries. I would say another similarity is that design really matters. Industrial design, the design of products, the interface of products, is absolutely mission-critical to a great game, and it’s absolutely mission-critical to the simplicity and accessibility of our products, be it on Square or Cash App. And then maybe the third thing that I would say is that when I was in gaming, at least the business models were rapidly changing from an intermediary distribution mechanism, like releasing a game once and then selling it through a retailer, to an always-on, direct-to-consumer connection. And similarly with banking, people don’t want to bank from 9 to 5, six days a week. They want 24/7 access to their money and the ability to, again, grow their financial livelihood, move their money around seamlessly. So, some similarities are there in that shift to an intermediary model or a slower model to an always-on, direct-to-consumer connection. Part of your target audience or your target customer base at Block are Gen Z folks. Did you learn things at Activision about Gen Z that has been useful? Are there things that businesses misunderstand about younger generations still? What we’ve learned is that Gen Z, millennial customers, aren’t going to do things the way their parents did. Some of our stats show that 63% of Gen Z customers have moved away from traditional credit cards, and over 80% are skeptical of them. Which means they’re not using a credit card to manage expenses; they’re using a debit card, but then layering on on a transaction-by-transaction basis. Or again, using tools like buy now, pay later, or Cash App Borrow, the means in which they’re managing their consistent cash flows. So that’s an example of how things are changing, and you’ve got to get up to speed with how the next generation of customers expects to manage their money. #blocks #cfo #explains #gen #surprising

Reading Time: 6 minutes What is marketing without data? Assumptions. Guesses. Fluff. For Chapter 6 of our book, “The Customer Engagement Book: Adapt or Die,” we spoke with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, to explore how engagement data can truly inform critical business decisions.  Alec discusses the different types of customer behaviors that matter most, how to separate meaningful information from the rest, and the role of systems that learn over time to create tailored customer experiences. This interview provides insights into using data for real-time actions and shaping the future of marketing. Prepare to learn about AI decision-making and how a focus on data is changing how we engage with customers.   Alec Haase Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? It’s a culmination of everything. Behavioral signals — the actual conversions and micro-conversions that users take within your product or website. Obviously, that’s things like purchases. But there are also other behavioral signals marketers should be using and thinking about. Things like micro-conversions — maybe that’s shopping for a product, clicking to learn more about a product, or visiting a certain page on your website. Behind that, you also need to have all your user data to tie that to. So I know someone took said action; I can follow up with them in email or out on paid social. I need the user identifiers to do that. 2. How do you distinguish between data that is actionable versus data that is just noise? Data that’s actionable includes the conversions and micro-conversions — very clear instances of “someone did this.” I can react to or measure those. What’s becoming a bit of a challenge for marketers is understanding that there’s other data that is valuable for machine learning or reinforcement learning models, things like tags on the types of products customers are interacting with. Maybe there’s category information about that product, or color information. That would otherwise look like noise to the average marketer. But behind the scenes, it can be used for reinforcement learning. There is definitely the “clear-cut” actionable data, but marketers shouldn’t be quick to classify things as noise because the rise in machine learning and reinforcement learning will make that data more valuable. 3. How can customer engagement data be used to identify and prioritize new business opportunities? At Hightouch, we don’t necessarily think about retroactive analysis. We have a system where we have customer engagement data firing in that we then have real-time scores reacting to. An interesting example is when you have machine learning and reinforcement learning models running. In the pet retailer example I gave you, the system is able to figure out what to prioritize. The concept of reinforcement learning is not a marketer making rules to say, “I know this type of thing works well on this type of audience.” It’s the machine itself using the data to determine what attribute responds well to which offer, recommendation, or marketing campaign. 4. How can marketers ensure their use of customer engagement data aligns with the broader business objectives? It starts with the objectives. It’s starting with the desired outcome and working your way back. That whole flip of the paradigm is starting with outcomes and letting the system optimize. What are you trying to drive, and then back into the types of experiences that can make that happen? There’s personalization. When we talk about data-driven experiences and personalization, Spotify Wrapped is the North Star. For Spotify Wrapped, you want to drive customer stickiness and create a brand. To make that happen, you want to send a personalized email. What components do you want in that email? Maybe it’s top five songs, top five artists, and then you can back into the actual event data you need to make that happen. 5. What role does engagement data play in influencing cross-functional decisions such as those in product development, sales, or customer service? For product development, it’s product analytics — knowing what features users are using, or seeing in heat maps where users are clicking. Sales is similar. We’re using behavioral signals like what types of content they’re reading on the site to help inform what they would be interested in — the types of products or the types of use cases. For customer service, you can look at errors they’ve run into in the past or specific purchases they’ve made, so that when you’re helping them the next time they engage with you, you know exactly what their past behaviors were and what products they could be calling about. 6. What are some challenges marketers face when trying to translate customer engagement data into actionable insights? Access to data is one challenge. You might not know what data you have because marketers historically may not have been used to the systems where data is stored. Historically, that’s been pretty siloed away from them. Rich behavioral data and other data across the business was stored somewhere else. Now, as more companies embrace the data warehouse at the center of their business, it gives everyone a true single place where data can be stored. Marketers are working more with data teams, understanding more about the data they have, and using that data to power downstream use cases, personalization, reinforcement learning, or general business insights. 7. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? As a marketer, I think proof is key. The best thing is if you’ve actually run a test. “I think we should do this. I ran a small test, and it’s showing that this is actually proving out.” Being able to clearly explain and justify your reasoning with data is super important. 8. What technology or tools have you found most effective for gathering and analyzing customer engagement data? Any type of behavioral event collection, specifically ones that write to the cloud data warehouse, is the critical component. Your data team is operating off the data warehouse. Having an event collection product that stores data in that central spot is really important if you want to use the other data when making recommendations. You want to get everything into the data warehouse where it can be used both for insights and for putting into action. For Spotify Wrapped, you want to collect behavioral event signals like songs listened to or concerts attended, writing to the warehouse so that you can get insights back — how many songs were played this year, projections for next month — but then you can also use those behavioral events in downstream platforms to fire off personalized emails with product recommendations or Spotify Wrapped-style experiences. 9. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? What we’re excited about is the concept of AI Decisioning — having AI agents actually using customer data to train their own models and decision-making to create personalized experiences. We’re sitting on top of all this behavioral data, engagement data, and user attributes, and our system is learning from all of that to make the best decisions across downstream systems. Whether that’s as simple as driving a loyalty program and figuring out what emails to send or what on-site experiences to show, or exposing insights that might lead you to completely change your business strategy, we see engagement data as the fuel to the engine of reinforcement learning, machine learning, AI agents, this whole next wave of Martech that’s just now coming. But it all starts with having the data to train those systems. I think that behavioral data is the fuel of modern Martech, and that only holds more true as Martech platforms adopt these decisioning and AI capabilities, because they’re only as good as the data that’s training the models.     This interview Q&A was hosted with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Alec Haase Q&A: Customer Engagement Book Interview appeared first on MoEngage. #alec #haase #qampampa #customer #engagement

There are a few things an interior designer wouldn’t dare put in a bathroom. Carpet? Definitely not. Only overhead lighting? Design blasphemy. But there is one feature that finds its way into the bathroom all the time—rarely questioned, though maybe it should be—and that’s artwork. We get it: who doesn’t want to add a little personality to a space that otherwise is quite functional? Still, design fans are often split on the addition, especially when it comes to certain types of art. Related StoriesAn oil painting resting above a clawfoot bathtub or a framed graphic print next to a mirror infuses your bathroom with warmth and storytelling, a very necessary addition to a space that's often centered around pure function. “In a bathroom, where surfaces tend to be hard and the layout driven by function, a thoughtful piece can shift the entire ambience,” shares interior designer Linette Dai. “It brings dimension to the everyday.”According to designer Ali Milch, art can transform the entire experience from “routine to restorative.” But, is it the bathroom the bestplace to put a favorite photo or heirloom painting? With moisture in the mix and potential for it being in the “splash zone”, you need to be considerate of the art you bring in and where it’s placed. To help guide your curation, we chatted with interior designers and experts on how to integrate art into your space in a way that is both beautiful and bathroom-appropriate.Be Wary of HumidityMaybe this one is obvious, but when placing art in the bathroom, be sure to look for materials that aren’t prone to water damage. “We recommend framing art with a sealed backing and UV-protective acrylic instead of glass, which is both lighter and more resistant to moisture—an important consideration in steamy bathrooms,” Cathy Glazer, founder of Artfully Walls, shares. “Plus, acrylic is much safer than glass if dropped, especially on hard tile floors, as it won’t shatter.”Dai agrees that acrylic is the way to go when putting framed works into the bathroom, “I usually recommend acrylic glazing to avoid moisture damage. For humid environments, prints or photography mounted directly on aluminum or face-mounted under acrylic are durable and beautiful.”Make It Your Creative CanvasCourtsey of Ali MilchUnless you have a sprawling space, chances are your bathroom’s square footage is limited. Rather than viewing this as a constraint, think about it as an opportunity to get creative. “Because they’re smaller and more self-contained,invite experimentation—think unexpected pieces, playful themes, or striking colors,” shares Glazer. “Art helps turn the bathroom into a moment of surprise and style.”“It doesn’t have to feel stuffy or overly formal,” Milch adds. “In a recent Tribeca project, we installed a kitschy iMessage bubble with the text ‘I love you too’ on the wall facing the entry. It’s a lighthearted, personal touch.”While it’s fun to get whimsical with your bathroom art, Dai also suggests still approaching it with a curated eye and saving anything that is precious or too high-maintenance for the powder room. “In full baths, I tend to be more selective based on how the space is ventilated and used day-to-day,” she shares. “Powder rooms, on the other hand, offer more freedom. That’s where I love incorporating oil paintings. They bring soul and a sense of history, and can make even the smallest space feel elevated.”Keep Materials And Size In MindAnother material worth considering adding? Ceramics. “Ceramic pieces also work beautifully, especially when there’s open shelving or decorative niches to display them,” shares Milch. Be wary of larger-scale sculptures, as they could potentially be slightly disruptive to the space. “Any type of artwork can work in a bathroom depending on the spatial allowances, but the typical bathroom is suited to wall hangings versus sculptures,” says Sarah Latham of L Interiors.And don’t forget to be mindful of scale. “As for size, I always opt for larger pieces in smaller spaces, it may feel counter-intuitive, but it makes a tight space feel larger,” Anastasia Casey of The Interior Collective shares. “I look for works that complement the finishes and palette without overwhelming it.”Let It Set The ToneCourtesy of Annie SloanArtwork in the bathroom doesn’t just decorate it; it can define it. “In bathrooms, there’s often less visual competition—no bold furniture or patterned textiles—so the art naturally becomes more of a focal point,” Dai adds. “That’s why the mood it sets matters so much. I think more intentionally about subject matter—what someone will see up close, often in moments of solitude,” shares Dai. Whether it’s a serene landscape photo or storied painting, don’t underestimate what a piece of art can do for the most utilitarian room in the house. With the right materials and placement, it can hold its own—moisture and all—while adding a design moment and feels considered and unexpected.Follow House Beautiful on Instagram and TikTok. #hanging #art #bathroom #not #gross

Pictured Above: Designer Amber Lewis balances New England charm with old-world sophistication with a half Calacatta Vagli marble backsplash in the kitchen of this Martha's Vineyard home. To backsplash or not to backsplash? That is the question. Or is it? Because if anyone’s ever told you “you shouldn’t do anything halfway,” they clearly haven’t heard of the half backsplash. This twist on a design mainstay makes a compelling case for stopping short. So maybe the real question is: to backsplash or to half backsplash?Lately, we’ve seen more and more designers going for the latter. “A trend these days is to use 1/2 or 2/3 stone backsplashes with a six- to nine-inch ledge,” says designer Jennifer Gilmer. “This is typically used behind a range and adds interest as well as softening the overall look.” It’s not just aesthetic—it’s strategic functionality. “The ledge is useful for salt and pepper shakers, olive oil, and other items,” she adds. Ahead, we break down everything to know about half backsplashes and why this kitchen trend is gaining traction in the design world.Related StoriesWhat Is a Half Backsplash?Lisa PetroleMagnolia’s director of styling, Ashley Maddox, enlisted the help of designer Hilary Walker to create her midcentury-modern dream home in Waco, Texas. Complete with walnut kitchen cabinetry topped with a Topzstone countertop continued into a partial backsplash.“A half backsplash or 1/3 backsplash is when the material stops at a point on the wall determined by the design,” explains designer Isabella Patrick. This makes it distinct from a “built-out or existing element, such as upper cabinets, a ceiling, soffit, or some other inherent element of the space.” In other words, it’s intentional, not just the result of running out of tile.Courtesy of JN Interior SpacesTaking the ceiling height into consideration, JN Interior Spaces decided a half backsplash would be suitable for this sleek, modern kitchen.While traditional backsplashes typically reach the bottom of upper cabinetry or span the entire wall, partial backsplashes usually stop somewhere around four to 25 inches up, depending on the look you’re going for.And while it may sound like a design compromise, it’s actually quite the opposite.Related StoryWhy Designers Are Loving the Half-Height LookOpting for a half backsplash is a clever way to balance proportion, budget, and visual interest. “If the design does not have upper cabinets, we would opt for a half backsplash to create visual interest,” Patrick says. “A full wall of the same tile or stone could overwhelm the space and seem like an afterthought.”Shannon Dupre/DD RepsIsabella Patrick experimented with this concept in her own kitchen, mixing materials for a more layered half backsplash look.Instead, Patrick often mixes materials—like running Cambria quartzite up from the counter to a ledge, then switching to Fireclay tile above. “This is a great example of how a singular material would have overwhelmed the space but also may have felt like an afterthought,” she explains. “Mixing materials and adding in details and personal touches is what good design is.”Another bonus? It lets the rest of the kitchen sing. “In another design, we eliminated the upper cabinets in favor of a more open and airy look so that the windows were not blocked—and so you were not walking right into a side view of cabinetry,” Patrick says. “No upper cabinets also makes the kitchen feel more of a transitional space and decorative, especially since it opens right into a dining room.”krafty_photos
copyright 2021This kitchen from JN Interior Spaces proves that a partial backsplash can still make a big impact. They chose to use an iridescent, almost-patina tile in this Wyoming kitchen.For Jill Najinigier of JN Interior Spaces, the choice is just as much about form as it is function. “It's all about how the backsplash interacts with the architecture,” she explains. “Wall height, windows, the shape of the hood, upper cabinets, or open shelves—where do they start and terminate?”In one standout project, Najinigier used a luminous tile just tall enough to tuck under a tapered plaster hood, topped with a narrow stone ledge carved from the same slab as the counter. The result? “Clean lines that make a stunning statement.”Mixing materials and adding in details and personal touches is what good design is.It’s Decorative and FunctionalHeather TalbertDesigner Kate Pearce installed a statement-making marble backsplash. Bringing it only halfway up allows its beauty to be appreciated while giving the other aesthetic elements in the space room to breathe.Don’t underestimate what that ledge can do. Designer Kate Pearce swears by hers: “I love my little five-inch-deep marble shelf that allows me to style some vintage kitchenware in the space,” she says. “And I think the shelfis exactly what gives the kitchen an approachable feel—versus having a full backsplash of marble, which would have given the space a more serious vibe.”Stylish ProductionsPrioritizing visually continuity, Italian designer Federica Asack of Masseria Chic used the same leathered sandstone, a natural material that will develop a wonderful patina, for both the counters and the backsplash.Designer Federica Asack of Masseria Chic used a leathered sandstone for both her countertop and half backsplash, adding a ledge that’s just deep enough to style. “It allows for a splash-free decorating opportunity to layer artwork and favorite objects,” she says.Designer Molly Watson agrees: “The simple shelf is just deep enough for some special items to be on display,” she notes of a project where carrying the countertop stone up the wall helped keep things visually calm and scaled to the space. Related StoryThe Verdict on Half BacksplashesErin Kelly"Keeping materials simple in this kitchen was important for scale," says designer Molly Watson. "Carrying the countertop up the wall as a backsplash allowed the space to feel larger."Half backsplashes are having a major design moment, but not just because they’re practical. They’re a blank canvas for creativity. From floating ledges and mixed materials to budget-conscious decisions that don’t skimp on style, they’re a smartway to make your kitchen feel lighter, livelier, and totally considered.So, go ahead—do it halfway.Follow House Beautiful on Instagram and TikTok. #why #half #backsplashes #are #taking

The Inefficiency of Static Chain-of-Thought Reasoning in LRMs Recent LRMs achieve top performance by using detailed CoT reasoning to solve complex tasks. However, many simple tasks they handle could be solved by smaller models with fewer tokens, making such elaborate reasoning unnecessary. This echoes human thinking, where we use fast, intuitive responses for easy problems and slower, analytical thinking for complex ones. While LRMs mimic slow, logical reasoning, they generate significantly longer outputs, thereby increasing computational cost. Current methods for reducing reasoning steps lack flexibility, limiting models to a single fixed reasoning style. There is a growing need for adaptive reasoning that adjusts effort according to task difficulty.  Limitations of Existing Training-Based and Training-Free Approaches Recent research on improving reasoning efficiency in LRMs can be categorized into two main areas: training-based and training-free methods. Training strategies often use reinforcement learning or fine-tuning to limit token usage or adjust reasoning depth, but they tend to follow fixed patterns without flexibility. Training-free approaches utilize prompt engineering or pattern detection to shorten outputs during inference; however, they also lack adaptability. More recent work focuses on variable-length reasoning, where models adjust reasoning depth based on task complexity. Others study “overthinking,” where models over-reason unnecessarily. However, few methods enable dynamic switching between quick and thorough reasoning—something this paper addresses directly.  Introducing OThink-R1: Dynamic Fast/Slow Reasoning Framework Researchers from Zhejiang University and OPPO have developed OThink-R1, a new approach that enables LRMs to switch between fast and slow thinking smartly, much like humans do. By analyzing reasoning patterns, they identified which steps are essential and which are redundant. With help from another model acting as a judge, they trained LRMs to adapt their reasoning style based on task complexity. Their method reduces unnecessary reasoning by over 23% without losing accuracy. Using a loss function and fine-tuned datasets, OThink-R1 outperforms previous models in both efficiency and performance on various math and question-answering tasks.  System Architecture: Reasoning Pruning and Dual-Reference Optimization The OThink-R1 framework helps LRMs dynamically switch between fast and slow thinking. First, it identifies when LRMs include unnecessary reasoning, like overexplaining or double-checking, versus when detailed steps are truly essential. Using this, it builds a curated training dataset by pruning redundant reasoning and retaining valuable logic. Then, during fine-tuning, a special loss function balances both reasoning styles. This dual-reference loss compares the model’s outputs with both fast and slow thinking variants, encouraging flexibility. As a result, OThink-R1 can adaptively choose the most efficient reasoning path for each problem while preserving accuracy and logical depth.  Empirical Evaluation and Comparative Performance The OThink-R1 model was tested on simpler QA and math tasks to evaluate its ability to switch between fast and slow reasoning. Using datasets like OpenBookQA, CommonsenseQA, ASDIV, and GSM8K, the model demonstrated strong performance, generating fewer tokens while maintaining or improving accuracy. Compared to baselines such as NoThinking and DualFormer, OThink-R1 demonstrated a better balance between efficiency and effectiveness. Ablation studies confirmed the importance of pruning, KL constraints, and LLM-Judge in achieving optimal results. A case study illustrated that unnecessary reasoning can lead to overthinking and reduced accuracy, highlighting OThink-R1’s strength in adaptive reasoning.  Conclusion: Towards Scalable and Efficient Hybrid Reasoning Systems In conclusion, OThink-R1 is a large reasoning model that adaptively switches between fast and slow thinking modes to improve both efficiency and performance. It addresses the issue of unnecessarily complex reasoning in large models by analyzing and classifying reasoning steps as either essential or redundant. By pruning the redundant ones while maintaining logical accuracy, OThink-R1 reduces unnecessary computation. It also introduces a dual-reference KL-divergence loss to strengthen hybrid reasoning. Tested on math and QA tasks, it cuts down reasoning redundancy by 23% without sacrificing accuracy, showing promise for building more adaptive, scalable, and efficient AI reasoning systems in the future.  Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Building AI-Powered Applications Using the Plan → Files → Code Workflow in TinyDevSana Hassanhttps://www.marktechpost.com/author/sana-hassan/MemOS: A Memory-Centric Operating System for Evolving and Adaptive Large Language ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Google AI Unveils a Hybrid AI-Physics Model for Accurate Regional Climate Risk Forecasts with Better Uncertainty AssessmentSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Run Multiple AI Coding Agents in Parallel with Container-Use from Dagger #othinkr1 #dualmode #reasoning #framework #cut

In a previous article, we looked at some practical examples of how to code SVG by hand. In that guide, we covered the basics of the SVG elements rect, circle, ellipse, line, polyline, and polygon. This time around, we are going to tackle a more advanced topic, the absolute powerhouse of SVG elements: path. Don’t get me wrong; I still stand by my point that image paths are better drawn in vector programs than coded. But when it comes to technical drawings and data visualizations, the path element unlocks a wide array of possibilities and opens up the world of hand-coded SVGs. The path syntax can be really complex. We’re going to tackle it in two separate parts. In this first installment, we’re learning all about straight and angular paths. In the second part, we’ll make lines bend, twist, and turn. Required Knowledge And Guide Structure Note: If you are unfamiliar with the basics of SVG, such as the subject of viewBox and the basic syntax of the simple elements, I recommend reading my guide before diving into this one. You should also familiarize yourself with <text> if you want to understand each line of code in the examples. Before we get started, I want to quickly recap how I code SVG using JavaScript. I don’t like dealing with numbers and math, and reading SVG Code with numbers filled into every attribute makes me lose all understanding of it. By giving coordinates names and having all my math easy to parse and write out, I have a much better time with this type of code, and I think you will, too. The goal of this article is more about understanding path syntax than it is about doing placement or how to leverage loops and other more basic things. So, I will not run you through the entire setup of each example. I’ll instead share snippets of the code, but they may be slightly adjusted from the CodePen or simplified to make this article easier to read. However, if there are specific questions about code that are not part of the text in the CodePen demos, the comment section is open. To keep this all framework-agnostic, the code is written in vanilla JavaScript. Setting Up For Success As the path element relies on our understanding of some of the coordinates we plug into the commands, I think it is a lot easier if we have a bit of visual orientation. So, all of the examples will be coded on top of a visual representation of a traditional viewBox setup with the origin in the top-left corner, then moves diagonally down to. The command is: M10 10 L100 100. The blue line is horizontal. It starts atand should end at. We could use the L command, but we’d have to write 55 again. So, instead, we write M10 55 H100, and then SVG knows to look back at the y value of M for the y value of H. It’s the same thing for the green line, but when we use the V command, SVG knows to refer back to the x value of M for the x value of V. If we compare the resulting horizontal path with the same implementation in a <line> element, we may Notice how much more efficient path can be, and Remove quite a bit of meaning for anyone who doesn’t speak path. Because, as we look at these strings, one of them is called “line”. And while the rest doesn’t mean anything out of context, the line definitely conjures a specific image in our heads. <path d="M 10 55 H 100" /> <line x1="10" y1="55" x2="100" y2="55" /> Making Polygons And Polylines With Z In the previous section, we learned how path can behave like <line>, which is pretty cool. But it can do more. It can also act like polyline and polygon. Remember, how those two basically work the same, but polygon connects the first and last point, while polyline does not? The path element can do the same thing. There is a separate command to close the path with a line, which is the Z command. const polyline2Points = M${start.x} ${start.y} L${p1.x} ${p1.y} L${p2.x} ${p2.y}; const polygon2Points = M${start.x} ${start.y} L${p1.x} ${p1.y} L${p2.x} ${p2.y} Z; So, let’s see this in action and create a repeating triangle shape. Every odd time, it’s open, and every even time, it’s closed. Pretty neat! See the Pen Alternating Trianglesby Myriam. When it comes to comparing path versus polygon and polyline, the other tags tell us about their names, but I would argue that fewer people know what a polygon is versus what a line is. The argument to use these two tags over path for legibility is weak, in my opinion, and I guess you’d probably agree that this looks like equal levels of meaningless string given to an SVG element. <path d="M0 0 L86.6 50 L0 100 Z" /> <polygon points="0,0 86.6,50 0,100" /> <path d="M0 0 L86.6 50 L0 100" /> <polyline points="0,0 86.6,50 0,100" /> Relative Commands: m, l, h, v All of the line commands exist in absolute and relative versions. The difference is that the relative commands are lowercase, e.g., m, l, h, and v. The relative commands are always relative to the last point, so instead of declaring an x value, you’re declaring a dx value, saying this is how many units you’re moving. Before we look at the example visually, I want you to look at the following three-line commands. Try not to look at the CodePen beforehand. const lines =; As I mentioned, I hate looking at numbers without meaning, but there is one number whose meaning is pretty constant in most contexts: 0. Seeing a 0 in combination with a command I just learned means relative manages to instantly tell me that nothing is happening. Seeing l 0 20 by itself tells me that this line only moves along one axis instead of two. And looking at that entire blue path command, the repeated 20 value gives me a sense that the shape might have some regularity to it. The first path does a bit of that by repeating 10 and 30. But the third? As someone who can’t do math in my head, that third string gives me nothing. Now, you might be surprised, but they all draw the same shape, just in different places. See the Pen SVG Compound Pathsby Myriam. So, how valuable is it that we can recognize the regularity in the blue path? Not very, in my opinion. In some cases, going with the relative value is easier than an absolute one. In other cases, the absolute is king. Neither is better nor worse. And, in all cases, that previous example would be much more efficient if it were set up with a variable for the gap, a variable for the shape size, and a function to generate the path definition that’s called from within a loop so it can take in the index to properly calculate the start point. Jumping Points: How To Make Compound Paths Another very useful thing is something you don’t see visually in the previous CodePen, but it relates to the grid and its code. I snuck in a grid drawing update. With the method used in earlier examples, using line to draw the grid, the above CodePen would’ve rendered the grid with 14 separate elements. If you go and inspect the final code of that last CodePen, you’ll notice that there is just a single path element within the .grid group. It looks like this, which is not fun to look at but holds the secret to how it’s possible: <path d="M0 0 H110 M0 10 H110 M0 20 H110 M0 30 H110 M0 0 V45 M10 0 V45 M20 0 V45 M30 0 V45 M40 0 V45 M50 0 V45 M60 0 V45 M70 0 V45 M80 0 V45 M90 0 V45" stroke="currentColor" stroke-width="0.2" fill="none"></path> If we take a close look, we may notice that there are multiple M commands. This is the magic of compound paths. Since the M/m commands don’t actually draw and just place the cursor, a path can have jumps. So, whenever we have multiple paths that share common styling and don’t need to have separate interactions, we can just chain them together to make our code shorter. Coming Up Next Armed with this knowledge, we’re now able to replace line, polyline, and polygon with path commands and combine them in compound paths. But there is so much more to uncover because path doesn’t just offer foreign-language versions of lines but also gives us the option to code circles and ellipses that have open space and can sometimes also bend, twist, and turn. We’ll refer to those as curves and arcs, and discuss them more explicitly in the next article. Further Reading On SmashingMag “Mastering SVG Arcs,” Akshay Gupta “Accessible SVGs: Perfect Patterns For Screen Reader Users,” Carie Fisher “Easy SVG Customization And Animation: A Practical Guide,” Adrian Bece “Magical SVG Techniques,” Cosima Mielke #decoding #svg #ampltcodeampgtpathampltcodeampgt #element #line