Jurny, a leading provider of AI-powered solutions for automating hotel and short-term rental management, announced a new integration with Vrbo today. This enhanced collaboration brings several industry-first features in guest communication and reservation management to the market.

Jurny and Vrbo Partner to Automate Short-Term Rental Management; Unveil Industry’s Most Comprehensive Unified Guest Inbox

Liverpool went five points clear at the top of their Europa League group on Thursday night. Jurgen Klopp’s Reds ran out 5-1 winners over Toulouse in their Group E clash at Anfield. Darwin Nunez got on the scoresheet for Liverpool alongside Diogo Jota,

'Laughable': Sky pundit couldn't believe what he saw from £140k-a-week Liverpool player tonight

There is a lot that you want to do b... With so much going on around you, you are easil... Many hands make the work lighter. Accepting help will get the job done far more quickly. This wil... Many hands make the work lighter. Accepting hel...

Leo - July 23 - August 23 - Jul 23 - Aug 23

With so much going on around you, you are easily distracted. There is a lot that you want to do b... With so much going on around you, you are easil... Many hands make the work lighter. Accepting help will get the job done far more quickly. This wil...

The family of Olympic gymnastics champion Mary Lou Retton says she is making “remarkable” progress in her battle with a rare form of pneumonia that landed her in intensive care. McKenna Kelley ...

Family of Olympic champion gymnast Retton says she is making 'remarkable' progress

Ameren customers have a special offer to help manage their energy bill with the Sensi Smart Thermostat, which pairs with an app on their phone to help them manage their energy use quickly and

Ameren wants to help you manage your energy bills with a free Sensi Smart Thermostat – order by October 31!

Olympic gymnastics champion Mary Lou Retton has pneumonia and is in intensive care in a Texas hospital. Retton's daughter, McKenna Kelley, shared Retton's condition in an Instagram post on Tuesday ...

Olympic gymnastics champion Mary Lou Retton is in intensive care with pneumonia

Easil

easil

Prompt Injection Is What Happens When AI Trusts Too Easily

​​Easily Scale Real-Time Updates with Firestore: A Maintenance-Free Alternative to WebSockets

At HackerNoon, we value stories about building groundbreaking tech. These questions aren’t just about AI mechanics—they’re about the innovation, challenges, and creativity of bringing cutting-edge tools to life, a perfect fit for our community of tech leaders, builders, and future-minded readers. Introduction My name is Aniruth. I work on the storage team at Databricks, where we work on enabling customers to save large amounts of data in an open, scalable format with our Data Intelligence Platform. Specifically, I work on our interoperability efforts with Delta Lake and Apache Iceberg. HackerNoon: What’s the main problem your AI product is designed to solve, and what is effective about your approach to solving it? Aniruth: Databricks unifies data and AI to give customers actionable intelligence—what we call data intelligence. This includes ingesting large amounts of data, ETL, large-scale storage, business intelligence queries, and AI workloads. The techniques used in machine learning in the past decade have been around since the 1980s; the rise of big data made it feasible to run algorithms at scale. Techniques like few shot prompting or RAG rely on high-quality data. Models that have better data win against those with better architectures. Databricks has put significant investment into leading efforts in the data space, pioneering the lakehouse architecture with open data formats and open governance, where customers are able to get the best insights with the best performance from data lakes. What criteria did you use to select the specific AI models for this product, and how does your company make decisions like this? We use AI models in a number of ways in the product - such as Llama 3 for the AI Assistant. We believe in an open data and AI ecosystem and encourage our customers to use any model of their choice. We help make sure customers have end-to-end governance across the AI lifecycle regardless of the models they use, so they can focus on making their models purpose-built for their use cases. How do you ensure your product delivers accurate and unbiased results to users? We have spent significant effort and investment in prioritizing accuracy and unbiased answers for AI usage within our products, and continue to conduct frequent testing. What is your day in the life like as a product manager? The data and AI space is rapidly evolving, so it’s very important to keep up to date. My day can include talking to customers, market analysis, putting together a product requirements document, or preparing marketing materials. My favorite part is getting to make diagrams illustrating how things will work, as it’s pretty fun to transform an idea into a visual. What’s the next big breakthrough in AI that everyone should be watching for? There’s a lot of big breakthroughs coming soon. One that I’m particularly interested in is the hyperpersonalization of content. For the past decade, ads have been fine-tuned to the specific watcher. Some elements of content have been tuned, such as what thumbnail Netflix shows a user, but the actual content (the video itself) has not been. It’ll be interesting to see how directors/producers balance telling the story they want to and the user’s interests. What’s been the biggest challenge you’ve faced in bringing this AI product to market, from concept to launch? I work on large-scale data storage, which can be very confusing to understand. We have various AI optimizations on data, but there’s often questions about when these optimizations are run, how they work, what they don’t cover, etc. With these kinds of questions, it is important to ensure we have clear, consistent messaging about what we are building and why. I’ve found that explaining the cause of limitations resonates very well with customers. How do you envision AI evolving to better understand and respond to human emotions, and what challenges or opportunities do you see in that area? Multimodal models are going to get significantly better in the coming years, which will change our primary mode of interaction with AI. Figuring out human emotion is significantly easier from visual or audio information compared to text. I think there's an opportunity to create more natural interactions in a wider array of scenarios. What performance metrics or KPIs do you track to gauge the success of this AI product? We typically want to see good feedback and usage. I talk to customers pretty often to get a sense of how and why they think about our products, which is key to explain why we see certain trends in metrics. How do you approach designing a product experience that’s not only functional but also engaging and memorable for users? Large-scale data products are notoriously challenging to use. Simple examples are easy to set up, but production workloads typically involve confusing configuration and code. It’s been a high priority for me to build out functionality that customers need, while making the product very simple to use. The future is where any business gets insights from their data easily. In the current world, data-driven business insights are typically restricted to the biggest companies - but even they would prefer a simpler experience. In the long-term, how do you think AI will help the individual reach one’s full potential? For the individual, I’m very excited about AI integrations into hardware. Up to now, we’ve largely seen AI in software applications like websites. There’s many larger applications of building devices that utilize AI, and we’re already starting to see some of those implications in cars and phones. Where do you see the product evolving in the next few years, and what features are you most excited to add? Databricks is on a path towards becoming increasingly simple and more powerful at the same time. There are a lot of efforts we are working on across the board, from making big-scale data and compute easy to work with to improving performance on queries and workflows. Personally, I think we have some exciting features coming soon throughout the product that make workflows way easier with AI. Examples include AI-generated comments on data, AI code suggestions in the notebook editors, and AI interfaces to chat with data (for example, Databricks AI/BI Genie). What’s your perspective on AI’s impact on jobs, and how do you address those concerns in your product strategy? There are concerns over whether AI would reduce the number of jobs. Our products are designed to increase valuable insights, which often come in conjunction with users. For example, with AI/BI Genie, users can create interfaces on their own data. This is a magical experience, where users can ask questions and get answers specific to them. In fact, users can even check the SQL being used to confirm it’s what they’re looking for. This is collaborative with analysts, reducing the time it takes them to go from idea to insight. What user feedback surprised you and led to changes in your roadmap, strategy or product experience? One major surprise for me was the complexity that some of the larger companies have. This introduces requirements into the product that I wouldn’t have considered on my own. A common example is thinking of migration strategies when introducing a new product. Typically, large companies will have either put together existing technologies (commonly open-source software) or have built custom software to solve the problem that our new product offering tackles. It usually takes a bit of time to understand why and how these are put together to ensure we have a solution that covers all possibilities. Would you like to take a stab at answering some of these questions? The link for the template is HERE. Interested in reading the content from all of our writing prompts? Click HERE. At HackerNoon, we value stories about building groundbreaking tech. These questions aren’t just about AI mechanics—they’re about the innovation, challenges, and creativity of bringing cutting-edge tools to life, a perfect fit for our community of tech leaders, builders, and future-minded readers. At HackerNoon, we value stories about building groundbreaking tech. These questions aren’t just about AI mechanics—they’re about the innovation, challenges, and creativity of bringing cutting-edge tools to life, a perfect fit for our community of tech leaders, builders, and future-minded readers. Introduction My name is Aniruth . I work on the storage team at Databricks , where we work on enabling customers to save large amounts of data in an open, scalable format with our Data Intelligence Platform. Specifically, I work on our interoperability efforts with Delta Lake and Apache Iceberg. Aniruth Databricks HackerNoon: What’s the main problem your AI product is designed to solve, and what is effective about your approach to solving it? HackerNoon: Aniruth : Databricks unifies data and AI to give customers actionable intelligence—what we call data intelligence. This includes ingesting large amounts of data, ETL, large-scale storage, business intelligence queries, and AI workloads. The techniques used in machine learning in the past decade have been around since the 1980s; the rise of big data made it feasible to run algorithms at scale. Aniruth Aniruth Techniques like few shot prompting or RAG rely on high-quality data. Models that have better data win against those with better architectures. Databricks has put significant investment into leading efforts in the data space, pioneering the lakehouse architecture with open data formats and open governance, where customers are able to get the best insights with the best performance from data lakes. What criteria did you use to select the specific AI models for this product, and how does your company make decisions like this? We use AI models in a number of ways in the product - such as Llama 3 for the AI Assistant. We believe in an open data and AI ecosystem and encourage our customers to use any model of their choice. We help make sure customers have end-to-end governance across the AI lifecycle regardless of the models they use, so they can focus on making their models purpose-built for their use cases. How do you ensure your product delivers accurate and unbiased results to users? We have spent significant effort and investment in prioritizing accuracy and unbiased answers for AI usage within our products, and continue to conduct frequent testing. What is your day in the life like as a product manager? The data and AI space is rapidly evolving, so it’s very important to keep up to date. My day can include talking to customers, market analysis, putting together a product requirements document, or preparing marketing materials. My favorite part is getting to make diagrams illustrating how things will work, as it’s pretty fun to transform an idea into a visual. What’s the next big breakthrough in AI that everyone should be watching for? There’s a lot of big breakthroughs coming soon. One that I’m particularly interested in is the hyperpersonalization of content. For the past decade, ads have been fine-tuned to the specific watcher. Some elements of content have been tuned, such as what thumbnail Netflix shows a user, but the actual content (the video itself) has not been. It’ll be interesting to see how directors/producers balance telling the story they want to and the user’s interests. What’s been the biggest challenge you’ve faced in bringing this AI product to market, from concept to launch? I work on large-scale data storage, which can be very confusing to understand. We have various AI optimizations on data, but there’s often questions about when these optimizations are run, how they work, what they don’t cover, etc. With these kinds of questions, it is important to ensure we have clear, consistent messaging about what we are building and why. I’ve found that explaining the cause of limitations resonates very well with customers. How do you envision AI evolving to better understand and respond to human emotions, and what challenges or opportunities do you see in that area? Multimodal models are going to get significantly better in the coming years, which will change our primary mode of interaction with AI. Figuring out human emotion is significantly easier from visual or audio information compared to text. I think there's an opportunity to create more natural interactions in a wider array of scenarios. What performance metrics or KPIs do you track to gauge the success of this AI product? We typically want to see good feedback and usage. I talk to customers pretty often to get a sense of how and why they think about our products, which is key to explain why we see certain trends in metrics. How do you approach designing a product experience that’s not only functional but also engaging and memorable for users? Large-scale data products are notoriously challenging to use. Simple examples are easy to set up, but production workloads typically involve confusing configuration and code. It’s been a high priority for me to build out functionality that customers need, while making the product very simple to use. The future is where any business gets insights from their data easily. In the current world, data-driven business insights are typically restricted to the biggest companies - but even they would prefer a simpler experience. In the long-term, how do you think AI will help the individual reach one’s full potential? For the individual, I’m very excited about AI integrations into hardware. Up to now, we’ve largely seen AI in software applications like websites. There’s many larger applications of building devices that utilize AI, and we’re already starting to see some of those implications in cars and phones. Where do you see the product evolving in the next few years, and what features are you most excited to add? Databricks is on a path towards becoming increasingly simple and more powerful at the same time. There are a lot of efforts we are working on across the board, from making big-scale data and compute easy to work with to improving performance on queries and workflows. Personally, I think we have some exciting features coming soon throughout the product that make workflows way easier with AI. Examples include AI-generated comments on data, AI code suggestions in the notebook editors, and AI interfaces to chat with data (for example, Databricks AI/BI Genie). What’s your perspective on AI’s impact on jobs, and how do you address those concerns in your product strategy? There are concerns over whether AI would reduce the number of jobs. Our products are designed to increase valuable insights, which often come in conjunction with users. For example, with AI/BI Genie, users can create interfaces on their own data. This is a magical experience, where users can ask questions and get answers specific to them. In fact, users can even check the SQL being used to confirm it’s what they’re looking for. This is collaborative with analysts, reducing the time it takes them to go from idea to insight. What user feedback surprised you and led to changes in your roadmap, strategy or product experience? One major surprise for me was the complexity that some of the larger companies have. This introduces requirements into the product that I wouldn’t have considered on my own. A common example is thinking of migration strategies when introducing a new product. Typically, large companies will have either put together existing technologies (commonly open-source software) or have built custom software to solve the problem that our new product offering tackles. It usually takes a bit of time to understand why and how these are put together to ensure we have a solution that covers all possibilities. Would you like to take a stab at answering some of these questions? The link for the template is HERE. Interested in reading the content from all of our writing prompts? Click HERE. Would you like to take a stab at answering some of these questions? The link for the template is HERE. Interested in reading the content from all of our writing prompts? Click HERE. The link for the template is HERE. The link for the template is HERE. HERE. HERE.

"The Future is Where Any Business Gets Insights From Their Data Easily" says Aniruth from Databricks

Databricks-dən Aniruth deyir: "Gələcək hər hansı bir biznesin məlumatlarından asanlıqla məlumat əldə etdiyi yerdir"

"El futuro es donde cualquier empresa puede obtener información de sus datos fácilmente", afirma Aniruth de Databricks

“Masa Depan adalah Tempat Setiap Bisnis Mendapatkan Wawasan Dari Data Mereka Dengan Mudah” kata Aniruth dari Databricks

"Il futuro è dove qualsiasi azienda ottiene facilmente informazioni dai propri dati", afferma Aniruth di Databricks

「未来は、どんな企業でもデータから簡単に洞察を得られる時代です」とDatabricksのAniruth氏は語る

„Budúcnosť je tam, kde každá firma ľahko získa informácie zo svojich údajov,“ hovorí Aniruth z Databricks

"Mustaqbalku waa halka uu ganacsi kastaa si sahal ah uga helo xogta xogtooda" ayuu yidhi Aniruth oo ka socda Databricks

"Framtiden är där alla företag enkelt får insikter från sina data" säger Aniruth från Databricks

Анирут аз Databricks мегӯяд: "Оянда он ҷоест, ки ҳама гуна тиҷорат аз маълумоти худ ба осонӣ фаҳмиш мегирад"

"Kelajak - bu har qanday biznes o'z ma'lumotlaridan osongina ma'lumot oladigan joy", deydi Databricks'dan Aniruth

"Ikamva kulapho naliphi na ishishini lifumana ulwazi kwidatha yalo ngokulula" utshilo uAniruth ovela kwiDatabricks.

"Ikusasa Yilapho Noma Yiliphi Ibhizinisi Lithola Ukuqonda Emininingwaneni Yalo Kalula" kusho u-Aniruth ovela ku-Databricks

Ever been in a conversation where you felt like your brain was melting? You know the kind. You’re making a point — calmly, rationally, maybe even with a dash of charm — and suddenly you’re drowning in a tidal wave of nonsense. Personal attacks. Wild conclusions. The type of logic that makes you wonder if the other person recently suffered a head injury. It’s infuriating. It makes you want to flip a table and scream: ‍ ‍ But here’s the thing: Sometimes you can’t avoid these people. Maybe it’s your boss. Maybe it’s a relative you only see at holidays. Maybe it’s the guy in line at the grocery store who thinks he’s the second coming of Aristotle because he read one headline on Facebook. For these moments, you need tools. Not to “win” the argument — because half the time, winning just means wasting more energy — but to keep your sanity and not lose faith in the species. Here are 8 of the sneakiest logic traps people use and how to dismantle them without losing your mind (or your soul). ‍ 1. Ad Hominem The Classic Cheap Shot. Instead of debating your point, they debate you. Because why confront a real argument when they can take a swing at your character? Example: You: “I think this company should adopt more sustainable practices. ”Them: “Oh, coming from the guy who still drives a gas guzzler?” Counter: “Me driving a gas guzzler doesn’t change the fact that sustainability is important. Maybe let’s focus on the idea instead of my car.” Translation: Stop making this about me. My hypocrisy (real or imagined) doesn’t make your argument better. ‍ 2. Straw Man Building a Scarecrow, Then Setting It on Fire. This is when someone takes what you said, twists it into a ridiculous caricature, and then torches that nonsense instead of your actual point. Example: You: “I think remote work has its benefits. ”Them: “So you just want everyone to sit at home in pajamas and never talk to another human again?” Counter: “I didn’t say that. Remote work doesn’t mean social isolation. It means flexibility. Two very different things.” Translation: Don’t shove words in my mouth and then pretend you’re the hero for chewing them up. 3. Slippery Slope The “Everything Will Collapse” Argument. One small change, and suddenly we’re tumbling into the apocalypse. Because why deal with nuance when you can predict doom? Example: You: “We should allow flexible work hours. ”Them: “Yeah, and next thing you know, no one will work at all, and society will crumble.” Counter: “Flexible hours don’t mean people stop working. It means they work smarter. Let’s not jump to Mad Max conclusions.” Translation: Calm down, Nostradamus. One step forward doesn’t always lead to a cliff. 4. Bandwagon Fallacy If Everyone Else Jumped Off a Bridge… This one’s like peer pressure in argument form. Just because everyone believes it doesn’t make it true. Example: Them: “Everyone I know says this diet works, so it must be good for you.” Counter: “A bunch of people believing something doesn’t make it true. Everyone used to think the Earth was flat too.” Translation: Popularity is a terrible judge of truth. Ask any middle school kid. 5. Appeal to Authority Because the “Expert” Said So. Dragging in a big name who has zero relevance to the topic. Example: Them: “My yoga instructor says this is the best investment strategy.” Counter: “Your yoga instructor might know how to do a perfect downward dog, but I don’t think they’re Warren Buffett.” Translation: Cool story. Now let’s hear from someone who knows what they’re talking about. 6. False Dichotomy Pick a Side. There Are Only Two. This is when they boil a complex issue down to two extreme choices. It’s lazy thinking disguised as decisiveness. Example: Them: “You’re either a cat person or a dog person.” Counter: “Or — hear me out — I can like both. Or neither. Because life isn’t a Disney movie.” Translation: There’s more than one flavor of ice cream. Stop making me choose between vanilla and vanilla. ‍ 7. Red Herring Look Over There! They throw in something completely irrelevant to distract you from the actual topic. It’s the argument equivalent of a toddler pointing at a shiny object. Example: You: “I think the camera quality on this phone isn’t great. ”Them: “Yeah, but feel how sleek the design is!” Counter: “The design is nice, but we’re talking about the camera. Stay with me here.” Translation: Stop trying to change the subject. I’m not that easy to distract. ‍ 8. False Cause Two Things Happened, So They Must Be Related. Just because two things happen together doesn’t mean one caused the other. Correlation is not causation. Example: Them: “Every time I wear this shirt, my team wins. It’s my lucky shirt!” Counter: “Or maybe your team is just good at sports. Your shirt isn’t part of the lineup.” Translation: Coincidences happen. Your shirt is not magic. Sorry. How to Survive These Conversations Without Losing Your Mind Stay Calm: Getting emotional is like handing them a free pass to dismiss you. Breathe. Channel your inner stoic.


Listen Hard: Make sure you actually understand what they’re saying before you respond. Misunderstanding them gives them ammo.


Be Prepared: Know these fallacies like the back of your hand. The more you spot them, the less they’ll trip you up.


Keep It Simple: Clear arguments win. Complexity is just noise. Bonus Tip: If the conversation is going nowhere and your sanity is at stake, walk away. NOE ONE ever looked back on their life and thought, “I wish I argued with more idiots.” Final Thought These tools won’t just help you in arguments. They’ll help you avoid the pointless ones. And in a world where arguing online is practically a hobby, that’s a superpower. So go forth, defend your sanity, and maybe — just maybe — help create a world where people think before they speak. Or not. At least you’ll be smarter. Until next time✌️ Ben. ‍ Ever been in a conversation where you felt like your brain was melting? You know the kind. You’re making a point — calmly, rationally, maybe even with a dash of charm — and suddenly you’re drowning in a tidal wave of nonsense. Personal attacks. Wild conclusions. The type of logic that makes you wonder if the other person recently suffered a head injury. It’s infuriating. It makes you want to flip a table and scream: ‍ ‍ But here’s the thing: Sometimes you can’t avoid these people. Maybe it’s your boss. Maybe it’s a relative you only see at holidays. Maybe it’s the guy in line at the grocery store who thinks he’s the second coming of Aristotle because he read one headline on Facebook. For these moments, you need tools. Not to “win” the argument — because half the time, winning just means wasting more energy — but to keep your sanity and not lose faith in the species. Here are 8 of the sneakiest logic traps people use and how to dismantle them without losing your mind (or your soul). 8 of the sneakiest logic 8 of the sneakiest logic ‍ 1. Ad Hominem 1. Ad Hominem The Classic Cheap Shot. The Classic Cheap Shot. Instead of debating your point, they debate you . Because why confront a real argument when they can take a swing at your character? you Example: You: “I think this company should adopt more sustainable practices. ”Them: “Oh, coming from the guy who still drives a gas guzzler?” Example: Counter: “Me driving a gas guzzler doesn’t change the fact that sustainability is important. Maybe let’s focus on the idea instead of my car.” Counter: Translation: Stop making this about me. My hypocrisy (real or imagined) doesn’t make your argument better. Translation: ‍ 2. Straw Man 2. Straw Man Building a Scarecrow, Then Setting It on Fire. Building a Scarecrow, Then Setting It on Fire. This is when someone takes what you said, twists it into a ridiculous caricature, and then torches that nonsense instead of your actual point. Example: You: “I think remote work has its benefits. ”Them: “So you just want everyone to sit at home in pajamas and never talk to another human again?” Example: Counter: “I didn’t say that. Remote work doesn’t mean social isolation. It means flexibility. Two very different things.” Counter: Translation: Don’t shove words in my mouth and then pretend you’re the hero for chewing them up. Translation: 3. Slippery Slope 3. Slippery Slope The “Everything Will Collapse” Argument. The “Everything Will Collapse” Argument. One small change, and suddenly we’re tumbling into the apocalypse. Because why deal with nuance when you can predict doom? Example: You: “We should allow flexible work hours. ”Them: “Yeah, and next thing you know, no one will work at all, and society will crumble.” Example: Counter: “Flexible hours don’t mean people stop working. It means they work smarter. Let’s not jump to Mad Max conclusions.” Counter: Translation: Calm down, Nostradamus. One step forward doesn’t always lead to a cliff. Translation: 4. Bandwagon Fallacy 4. Bandwagon Fallacy If Everyone Else Jumped Off a Bridge… If Everyone Else Jumped Off a Bridge… This one’s like peer pressure in argument form. Just because everyone believes it doesn’t make it true. Example: Them: “Everyone I know says this diet works, so it must be good for you.” Example: Counter: “A bunch of people believing something doesn’t make it true. Everyone used to think the Earth was flat too.” Counter: Translation: Popularity is a terrible judge of truth. Ask any middle school kid. Translation: 5. Appeal to Authority 5. Appeal to Authority Because the “Expert” Said So. Because the “Expert” Said So. Dragging in a big name who has zero relevance to the topic. Example: Them: “My yoga instructor says this is the best investment strategy.” Example: Counter: “Your yoga instructor might know how to do a perfect downward dog, but I don’t think they’re Warren Buffett.” Counter: Translation: Cool story. Now let’s hear from someone who knows what they’re talking about. Translation: 6. False Dichotomy 6. False Dichotomy Pick a Side. There Are Only Two. Pick a Side. There Are Only Two. This is when they boil a complex issue down to two extreme choices. It’s lazy thinking disguised as decisiveness. Example: Them: “You’re either a cat person or a dog person.” Example: Counter: “Or — hear me out — I can like both . Or neither. Because life isn’t a Disney movie.” Counter: both Translation: There’s more than one flavor of ice cream. Stop making me choose between vanilla and vanilla. Translation: ‍ 7. Red Herring 7. Red Herring Look Over There! Look Over There! They throw in something completely irrelevant to distract you from the actual topic. It’s the argument equivalent of a toddler pointing at a shiny object. Example: You: “I think the camera quality on this phone isn’t great. ”Them: “Yeah, but feel how sleek the design is!” Example: Counter: “The design is nice, but we’re talking about the camera. Stay with me here.” Counter: Translation: Stop trying to change the subject. I’m not that easy to distract. Translation: ‍ 8. False Cause 8. False Cause Two Things Happened, So They Must Be Related. Two Things Happened, So They Must Be Related. Just because two things happen together doesn’t mean one caused the other. Correlation is not causation. Example: Them: “Every time I wear this shirt, my team wins. It’s my lucky shirt!” Example: Counter: “Or maybe your team is just good at sports. Your shirt isn’t part of the lineup.” Counter: Translation: Coincidences happen. Your shirt is not magic. Sorry. Translation: How to Survive These Conversations Without Losing Your Mind How to Survive These Conversations Without Losing Your Mind Stay Calm: Getting emotional is like handing them a free pass to dismiss you. Breathe. Channel your inner stoic. Listen Hard: Make sure you actually understand what they’re saying before you respond. Misunderstanding them gives them ammo. Be Prepared: Know these fallacies like the back of your hand. The more you spot them, the less they’ll trip you up. Keep It Simple: Clear arguments win. Complexity is just noise. Stay Calm: Getting emotional is like handing them a free pass to dismiss you. Breathe. Channel your inner stoic. Stay Calm: Getting emotional is like handing them a free pass to dismiss you. Breathe. Channel your inner stoic. Stay Calm: Listen Hard: Make sure you actually understand what they’re saying before you respond. Misunderstanding them gives them ammo. Listen Hard: Make sure you actually understand what they’re saying before you respond. Misunderstanding them gives them ammo. Listen Hard: actually Be Prepared: Know these fallacies like the back of your hand. The more you spot them, the less they’ll trip you up. Be Prepared: Know these fallacies like the back of your hand. The more you spot them, the less they’ll trip you up. Be Prepared: Keep It Simple: Clear arguments win. Complexity is just noise. Keep It Simple: Clear arguments win. Complexity is just noise. Keep It Simple: Bonus Tip: If the conversation is going nowhere and your sanity is at stake, walk away. Bonus Tip: NOE ONE ever looked back on their life and thought, “I wish I argued with more idiots.” “I wish I argued with more idiots.” Final Thought Final Thought These tools won’t just help you in arguments. They’ll help you avoid the pointless ones . avoid the pointless ones avoid And in a world where arguing online is practically a hobby, that’s a superpower. So go forth, defend your sanity, and maybe — just maybe — help create a world where people think before they speak. Or not. At least you’ll be smarter. you’ll Until next time✌️ Ben. ‍

Easily Win Arguments By Avoiding These 8 Logic Traps

Bu 8 Məntiq Tələsindən Qaçaraq Arqumentləri Asanlıqla Qazanın

Guanyeu fàcilment els arguments evitant aquestes 8 trampes lògiques

Gane discusiones fácilmente evitando estas 8 trampas lógicas

Gaña facilmente argumentos evitando estas 8 trampas lóxicas

8つの論理的罠を避けて議論に簡単に勝つ

Лесно победи во аргументи со избегнување на овие 8 логички стапици

Win eenvoudig discussies door deze 8 logische valkuilen te vermijden

Câștigă cu ușurință argumentele evitând aceste 8 capcane logice

Nyora Kuhwina Nharo Nekunzvenga Aya 8 Logic Traps

ካብዞም 8 ሎጂክ መጻወድያታት ብምውጋድ ብቐሊሉ ክትዕ ክትዕወት

Ukuphumelela iingxoxo ngokulula ngokuphepha le migibe esi-8 enengqondo

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In this article, I’ll guide you through C# Testcontainers for MongoDB! I love using Testcontainers for local database work when I don’t want to think about managing the overhead of spinning up and tearing down a database, or managing something in the cloud. MongoDB is no exception here as the C# Testcontainers NuGet package that we have for MongoDB is a dream to work with. In this article, I’ll introduce you to the basics of Testcontainers. From there, I’ll demonstrate how simple it is to interact with your newly created database via the C# MongoDB.Driver NuGet package — almost like you wouldn’t even know there was a container spun up! Let’s dive into it. Overview of C# Testcontainers Testcontainers is an awesome NuGet package that can greatly enhance your testing and local development workflow when building .NET applications. It provides a simple and effective way to manage Docker containers, allowing you to spin up lightweight and isolated instances and have them tear down like nothing ever happened! That means with Testcontainers, you no longer have to worry about setting up and tearing down test databases or dealing with complex configuration files. It handles all the details of container management, making this process feel trivial — I sure wouldn’t want to handle this manually. Some of the key benefits of Testcontainers include: Its ability to provide a consistent and reproducible test environment. You can define the exact service version and configuration that you require, ensuring that your test results are reliable and consistent across different environments. The speed and flexibility it brings to your testing and development workflow. By using lightweight Docker containers, you can quickly spin up and tear down test environments on demand. No configuring cloud or shared resources ahead of time… Just on-the-fly awesomeness. Seamless integration with existing testing frameworks and tools in the DotNet ecosystem, such as Xunit, NUnit, and BenchmarkDotNet. This allows you to easily incorporate containerized service instances into your existing test suites without any major modifications. For this article, we’ll be focusing on how we can use C# Testcontainers for MongoDB in particular. Much of the advice covered here will be applicable to other services, especially databases, but we’ll keep our sights set on MongoDB. Setting Up Testcontainers in C# To get started with Testcontainers in C#, you’ll need to install and configure the necessary dependencies in your project. Follow the steps below to set up Testcontainers for MongoDB in C#. 1. Installing Testcontainers Generally, the first step would be to install the Testcontainers NuGet package in your C# project. You would open the NuGet Package Manager Console, and run the following command: Install-Package Testcontainers However, we need the MongoDB flavor of this package (I am using version 3.8.0 at the time of writing) which includes the base Testcontainers package. You can install it via the package manager user interface or with this command: Install-Package Testcontainers.MonogoDB 2. Configuring Testcontainers for MongoDB Once you have installed Testcontainers, you need to configure it for MongoDB. This involves setting up a MongoDB container with the required settings. Here is an example of how you can configure Testcontainers for MongoDB in C#: using Testcontainers.MongoDb;

MongoDbContainer container = new MongoDbBuilder()
    .WithImage("mongo:latest")
    .Build(); In the above example, we create a new instance of the , and specify the MongoDB image and port binding. The method sets the image for the MongoDB container, so using “mongo:latest” will always pull the latest image. MongoDbBuilder WithImage Optionally, we could use port binding to configure which ports we want to be using explicitly: using Testcontainers.MongoDb;

MongoDbContainer container = new MongoDbBuilder()
    .WithImage("mongo:latest")
    .WithPortBinding(1337, 27017)
    .Build(); The code above uses method binds the container’s port 27017 to the host machine’s port 1337. We’ll see in the upcoming sections that unless you have a specific need to bind ports, you can potentially just leave this be. We can get a dynamic connection string, which is very convenient! WithPortBinding 3. Starting and Stopping the MongoDB Container After configuring Testcontainers for MongoDB, you can start and stop the container as needed. Here are code examples for starting and stopping the container: To start the MongoDB container: await container.StartAsync(); To stop the MongoDB container: await _container.StopAsync(); Remember the MongoDbContainer type can be disposed of, so call to clean up when you’re ready. DisposeAsync Using C# Testcontainers for MongoDB in Practice Now that we’ve seen how to spin up and tear down containers, what are we supposed to do with them? We have plenty of options, and truly, the limit is your own imagination (and time, money, and other resources…)! Most people leverage C# Testcontainers for writing integration or . You can match up Testcontainers with xUnit or NUnit, for example, and write tests that interact with a real database. functional tests For me, I needed to go wire C# Testcontainers for MongoDB up to BenchmarkDotNet so I could ! A similar use case where I needed transient local databases, but not necessarily for a test framework. benchmark inserting records into MongoDB Regardless, we need to be able to connect to these MongoDB database containers from C#, and these next subsections will cover that. Connecting to C# Testcontainers for MongoDB with MongoDB.Driver Let’s assume you read the earlier sections and you have the correct MongoDB package installed for Testcontainers. If you skimmed ahead to this section, go back, read it, and get the right package. Otherwise, you’ll be scratching your head about why you can’t find the right dependencies! You’ll also need to get the MongoDB.Driver NuGet package installed. This is what we’re going to be using to establish a connection to the database container that we spun up. I have other articles you can read if you want more explanation on how MongoDB.Driver works in general: MongoDB in C#: Simplified Guide For Inserting Data MongoDB Filtering in C# – Beginner’s Guide For Easy Filters How To Delete Documents From MongoDB In C# – What You Need To Know How To Update MongoDB Documents in C# – What You Need To Know With the right packages ready to go, we can combine the code we saw earlier with some MongoDB driver code to get things glued together: using MongoDB.Bson;
using MongoDB.Driver;

using Testcontainers.MongoDb;

MongoDbContainer container = new MongoDbBuilder()
    .WithImage("mongo:latest")
    .Build();
await container.StartAsync();
string connectionString = container.GetConnectionString();

MongoClient mongoClient = new MongoClient(connectionString);
IMongoDatabase database = mongoClient.GetDatabase("test");
IMongoCollection<BsonDocument> collection = database.GetCollection<BsonDocument>("test"); In the example above, we can call on the newly spun-up MongoDB container from Testcontainers. The awesome part about this is that no matter how you configure your Testcontainer for MongoDB using the builder pattern we see in this code and earlier in the article, will get you what you need to connect. GetConnectionString() GetConnectionString() Because takes in a connection string as a single argument, it’s trivial to get access to the MongoDB database to start working with it right away! MongoClient Performing CRUD Operations on the MongoDB Testcontainer Now that we have our MongoDB Testcontainer set up and we have a connected to it, we can start performing CRUD operations on it. The connection details provided by the container instance made it a breeze to combine these two things, and truly, we can focus on just the MongoDB.Driver method calls for CRUD. MongoClient Here’s an example of performing a simple CRUD operation: // using code from earlier examples...

// Create
await collection.InsertOneAsync(new BsonDocument()
{
    ["Name"] = "Nick Cosentino",
});

// Read
var filterBuilder = Builders<BsonDocument>.Filter;
var filter = filterBuilder.Eq("Name", "Nick Cosentino");
var results = collection.Find(filter);

// Update
var updateBuilder = Builders<BsonDocument>.Update;
var update = updateBuilder.Set("Name", "Dev Leader");
collection.UpdateOne(filter, update);

// Delete
filter = filterBuilder.Eq("Name", "Dev Leader");
collection.DeleteOne(filter); Using the previous code snippets, we were already up and running with our MongoDB container instance and a . As a result, you can see in the code snippet above that we can directly work with our that we obtained from the prior setup. MongoClient IMongoCollection<BsonDocument> This helps illustrate that once you have the MongoDB docker container up and running via Testcontainers, you don’t have to treat things any special way once you connect to it! Wrapping Up C# Testcontainers for MongoDB To conclude, C# Testcontainers for MongoDB is an incredibly simple way to get a transient MongoDB data store up and running for your testing and development. While this article didn’t focus on specific use cases, hopefully, you have a better understanding of how transparent it feels to layer a MongoDB driver on top of Testcontainers to interact with MongoDB. Given that C# Testcontainers takes the headache out of the setup, teardown, and even connection string management, it’s an easy recommendation from me if you want to get up and running with MongoDB for local usage. If you found this useful and you’re looking for more learning opportunities, consider , and check out my ! subscribing to my free weekly software engineering newsletter free videos on YouTube Also published here

Easily Run Local Databases Using C# Testcontainers for MongoDB

MongoDB-এর জন্য C# টেস্ট কনটেইনার ব্যবহার করে সহজেই স্থানীয় ডেটাবেস চালান

Führen Sie ganz einfach lokale Datenbanken mit C#-Testcontainern für MongoDB aus

Ejecute fácilmente bases de datos locales utilizando C# Testcontainers para MongoDB

Exécutez facilement des bases de données locales à l'aide de C# Testcontainers pour MongoDB

MongoDB के लिए C# टेस्टकंटेनर का उपयोग करके आसानी से स्थानीय डेटाबेस चलाएँ

MongoDB 用の C# Testcontainers を使用してローカル データベースを簡単に実行する

MongoDB용 C# 테스트 컨테이너를 사용하여 로컬 데이터베이스를 쉽게 실행

Execute facilmente bancos de dados locais usando C# Testcontainers para MongoDB

Легко запускайте локальные базы данных с помощью тестовых контейнеров C# для MongoDB

MongoDB için C# Testcontainer'larını Kullanarak Yerel Veritabanlarını Kolayca Çalıştırın

Dễ dàng chạy cơ sở dữ liệu cục bộ bằng C# Testcontainer cho MongoDB

使用 MongoDB 的 C# 测试容器轻松运行本地数据库

Easily Handle Faults And Retries With Polly In C#

Almost any web app that contains data needs a way to represent it succinctly and clearly to the user. However, most Python web development solutions do not provide an easy way to do this purely in . Python Thanks to the plethora of existing React components for creating graphs, an implementation for Python can be created to easily solve this problem. In this post, we’ll walk through how to build a web app purely in Python, which contains graphs that strike a balance between flexibility and ease of use. The graphs are easy to build but also easy to customize to your needs. For this project, we are going to use a new open-source dev tool called Reflex. 1. Getting started with your Python Web App If you face any problems here with installation check out more docs here: https://reflex.dev/docs/getting-started/installation/ $ pip install reflex $ reflex init 2. Creating an Interactive Graph In this example, we will create a live-streaming graph that updates every second with random data. We start by defining some initial data for our chart to use, as well as some imports we will use for the project: from typing import Any, Dict, List
import reflex as rx
import random
import asyncio

data = [
    {"name": "A", "uv": 10, "pv": 110, "amt": 210},
    {"name": "B", "uv": 20, "pv": 120, "amt": 230},
    {"name": "C", "uv": 30, "pv": 120, "amt": 240},
    {"name": "D", "uv": 30, "pv": 130, "amt": 210},
    {"name": "E", "uv": 20, "pv": 140, "amt": 230},
    {"name": "F", "uv": 40, "pv": 170, "amt": 250},
    {"name": "G", "uv": 50, "pv": 190, "amt": 260},
] Here stands for unique visitors, stands for page views and stands for amount. uv pv amt They are arbitrary values that we will use to populate our graph. Next, we define a class that will be used to store the data and update it with an event handler: StreamingState class StreamingState(rx.State):
    data: List[Dict[str, Any]] = data
    stream: bool = False

    def stop_stream(self):
        self.stream = False

    @rx.background
    async def start_stream(self):
        async with self:
            self.stream = True
        while self.stream:
            async with self:
                for i in range(len(self.data)):
                    self.data[i]["uv"] = random.randint(0, 100)
                    self.data[i]["pv"] = random.randint(100, 200)
                    self.data[i]["amt"] = random.randint(200, 300)
            await asyncio.sleep(3) Here, we define a method that will stop the stream when called. We also define a method that will start the stream. We use the decorator to run the method in the background. This allows us to update the data without blocking the UI. stop_stream start_stream @rx.background Remember to use when updating the state in a background task. async with self: Finally, we will define our UI using Reflex's new graphing components. We pass the data from our class to the component and reference the data key we want to use in component. We also add a button to start and stop the stream. StreamingState area_chart area The result is a live updating graph that looks like this: The code for this graph is below: def index():
   return rx.vstack(
     rx.recharts.area_chart(
        rx.recharts.area(
            data_key="pv",
            stroke="#82ca9d",
            fill="#82ca9d",
            type_="natural",
        ),
        rx.recharts.x_axis(
            data_key="name",
        ),
        rx.recharts.y_axis(),
        rx.recharts.legend(),
        data=StreamingState.data,
        width="100%",
        height=400,
    ),
    rx.hstack(
        rx.button(
            "Start Stream",
            on_click=StreamingState.start_stream,
            disabled=StreamingState.stream,
        ),
        rx.button(
            "Stop Stream",
            on_click=StreamingState.stop_stream,
        ),
        width="100%",
    ),
    width="100%",
)

# Add state and page to the app.
app = rx.App()
app.add_page(index)
app.compile() The last three lines define our app, add the graph component to the base route, and then compile our app. Now, we can run it with the terminal command. $ reflex run 3. Multiple Graphs We can add extra components to our chart to show the and data as well. We can also add a an component to show the data when we hover over the chart. area uv amt graphing_tooltip cartesian_grid Keep in mind the child coming first will be displayed in the back, so the order of the components matter. area The code for this graph is below: def index():
  return rx.vstack(
    rx.recharts.area_chart(
        rx.recharts.area(
            data_key="pv",
            fill="#48BB78",
            stroke="#48BB78",
            type_="natural",
        ),
        rx.recharts.area(
            data_key="uv",
            fill="#F56565",
            stroke="#F56565",
            type_="natural",
        ),
        rx.recharts.area(
            data_key="amt",
            fill="#4299E1",
            stroke="#4299E1",
            type_="natural",
        ),
        rx.recharts.x_axis(
            data_key="name",
        ),
        rx.recharts.y_axis(),
        data=StreamingState.data,
        width="90%",
        height=400,
    ),
    rx.hstack(
        rx.button(
            "Start Stream",
            on_click=StreamingState.start_stream,
            is_disabled=StreamingState.stream,
            width="100%",
            color_scheme="green",
        ),
        rx.button(
            "Stop Stream",
            on_click=StreamingState.stop_stream,
            is_disabled=StreamingState.stream == False,
            width="100%",
            color_scheme="red",

        ),
        width="100%",
    )
)

# Add state and page to the app.
app = rx.App()
app.add_page(index)
app.compile() 4. Conclusion So that’s it; in just a few lines of simple code, you’ve created your live streaming graphing web app in pure Python. If you want to learn more about this and how to build graphs with Python check out this . documentation If you have questions, please comment them below or message me on Twitter at or on . Share your app creations on social media and tag me, and I’ll be happy to provide feedback or help retweet! @tgotsman12 LinkedIn Disclaimer: I work as a Founding Engineer at Reflex, where I build the open-source framework.

Python Web Development with Reflex: Creating Dynamic Graphs Easily

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Turn GPT-4 Into Your Expert: Fine-Tuning Large Language Models Easily

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