06/02 — 2025 by abduzeedo Explore how andstudio created a unified, nature-inspired branding and visual identity for Mana Hotels, blending luxury with authentic connection. Hey fellow creatives! Ever dive into a project that just clicks? Where the core idea shines through every design choice? That’s exactly what happened with Mana Hotels' unified brand identity, a beautiful piece of work by andstudio. It's a masterclass in how thoughtful branding and visual identity can truly elevate an experience. Mana Hotels isn't just another luxury escape. It’s an apartment-style hotel and wellness retreat, redefining relaxation by blending nature with well-being. Think seaside tranquility in Palanga or spa serenity in Druskininkai. The challenge for andstudio was clear: how to create a unified brand identity that resonates across diverse locations and audiences, while still feeling authentic and personalized. The Sun's Embrace: A Core Inspiration The design team at andstudio found their anchor in the rhythm of the sun. This natural cycle, from dawn to twilight, became the central inspiration. It's a smart move. The sun-inspired logo isn't just pretty; it represents vitality, blending luxury with approachability. This focus on a natural, constant element grounds the entire visual language. Color Palettes: Reflecting Local Soul One of the standout elements in this branding and visual identity project is the use of distinct color palettes for each location. For Palanga, the branding reflects the sunlit coast, evoking a bright, airy feel. In contrast, Druskininkai's palette captures the shift from day to night, aligning with the spa's focus on tranquility and restful sleep. You can see this subtle yet impactful differentiation in the visual identity artifacts. It’s a clever way to maintain unity while celebrating local character. Beyond the Logo: A Holistic Experience The branding extends far beyond a simple logo. It’s a comprehensive system that includes motion design, signage, and stationery. Look at the subtle branding on the stationery, like the "Hey Hepy, see you at the pool!" note, or the refined packaging elements. Even the robes feature the Mana logo. This consistent visual language, paired with authentic photography of peaceful moments, highlights a retreat centered on genuine human connection and relaxation. It’s about creating an overall soothing and luxurious experience that nurtures mind, body, and soul. Mana’s approach to branding and visual identity truly emphasizes human connection and a deep understanding of modern-day stresses. They’ve successfully bridged the gap between a service-oriented approach and a family-oriented atmosphere, creating a unique value proposition. This project by andstudio reminds us that great design isn't just about aesthetics; it's about understanding the core essence of a brand and translating that into a cohesive, meaningful visual story. It's about creating a feeling, an experience, and a connection that resonates deeply with the audience. Want to see more of their inspiring work? Check out andstudio's portfolio directly on Behance. Branding and visual identity artifacts Tags branding

Local Large Language Models can convert massive DataFrames to presentable Markdown reports — here's how. The post LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries appeared first on Towards Data Science.

Key Takeaways Dell and Nvidia will together provide architecture for the next set of supercomputers, named Doudna, for the US Department of Energy. Dell will focus more on sustainable hardware and cooling systems, while Nvidia will provide its AI architecture, including the Vera Rubin AI chips. The US Department of Energy wants to focus more on the sustainable development of AI, hence the choice of environment-conscious companies. The US Department of Energy said that Dell’s next batch of supercomputers will be delivered with Nvidia’s ‘Vera Rubin’ AI chips, marking the beginning of a new era of AI dominance in research. The said systems are expected to be 10x faster than the current batch of supercomputers, which HP provided. The supercomputer will be named ‘Doudna,’ after Jennifer Doudna, a Nobel Prize winner who made key contributions in CRISPR gene-editing. Supercomputers have been instrumental in key scientific discoveries in the last few decades and also played a big role in the design and maintenance of the U.S. nuclear weapons arsenal. And now, with the introduction of artificial intelligence, we’re heading towards a new decade of faster and more efficient scientific research. It (supercomputers) is the foundation of scientific discovery for our country. It is also a foundation for economic and technological leadership. And with that, national security – Nvidia CEO, Jensen Huang Dell Going All in on AI This isn’t the first time Dell and Nvidia have come together to develop newer AI solutions. Back in March 2024, Dell announced the Dell AI Factory with NVIDIA, an end-to-end enterprise AI solution designed for businesses.  This joint venture used Dell’s infrastructure, such as servers, storage, and networking, combined with NVIDIA’s AI architecture and technologies such as GPUs, DPUs, and AI software. Image Credit – Dell For instance, the Dell PowerEdge server uses NVIDIA’s full AI stack to provide enterprises with solutions required for a wide range of AI applications, including speech recognition, cybersecurity, recommendation systems, and language-based services. The demand for Dell’s AI servers has also increased, reaching $12.1B in the first quarter of 2025, with a total backlog of $14.4B, which suggests a strong future demand and order book. The company has set a bold profit forecast between $28.5B and $29.5B, as against the analysts’ prediction of $ 25.05 B. With Doudna, Dell is well-positioned to lead the next generation of supercomputers in AI research, invention, and discoveries. Focus on Energy Efficiency Seagate has warned about the unprecedented increase in demand for AI data storage in the coming few years, which is a significant challenge to the sustainability of AI data centers. Global data volume is expected to increase threefold by 2028.  Image Credit – DIGITIMES Asian The data storage industry currently produces only 1-2 zettabytes (1 zettabyte equals 1 trillion gigabytes) of storage annually, which is much lower than what would be required in the next 4-5 years. At the same time, Goldman Sachs predicts that power requirements will also go up by 165% by 2030 due to increasing demand for AI data centers. This calls for a more sustainable approach for the supercomputing industry as well.  Dell will use its proprietary technologies, such as Direct Liquid Cooling, the PowerCool eRDHx, and Smart Flow design in the Doudna, ensuring energy efficiency. Direct Liquid Cooling (DLC) increased computing density by supporting more cores per rack, which reduces cooling costs by as much as 45%. Dell’s PowerCool eRDHx is a self-contained airflow design that can capture 100% of the heat generated by IT systems. This reduces the dependency on expensive chillers, as eRDHx can work in usual temperatures of 32 and 36 degrees Celsius, leading to 60% savings in cooling energy costs. Lastly, the Dell Smart Flow design improves airflow within IT components and reduces the fan power by 52%. This leads to better performance with fewer cooling requirements. Besides this, Dell plans to incorporate Leak Sense Technology. If a coolant leak occurs, the system’s leak sensor will log an alert in the iDRAC system so that swift action can be taken. As per a report titled ‘Energy and AI’ by the IEA, the data center electricity demand will increase to 945 terawatt-hours (TWh) by 2030. For comparison, this is more than the total electricity consumption of Japan today. The US alone will consume more electricity in 2030 for processing data than for manufacturing all energy-intensive goods combined, including aluminum, steel, cement, and chemicals. Therefore, the need to develop sustainable AI data centers and supercomputers cannot be highlighted enough. Dell’s technology-focused, sustainable approach can be a pivotal point in how efficiently we use AI in the next decade. The US Department of Energy’s choice of Dell also seems to be a conscious move to shift towards companies that give importance to sustainability and can vouch for the long-term viability of research-intensive AI setups. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

Looker_Studio - stock.adobe.com News Analysis of job vacancies shows earnings boost for AI skills Even when parts of a job are being automated, those who know how to work with artificial intelligence tools can expect higher salaries By Cliff Saran, Managing Editor Published: 03 Jun 2025 7:00 UK workers with skills in artificial intelligence (AI) appear to earn 11% more on average, even in sectors where AI is automating parts of their existing job functions. Workers in sectors exposed to AI, where the technology can be deployed for some tasks, are more productive and command higher salaries, according to PwC’s 2025 Global AI Jobs Barometer. The study, which was based on an analysis of almost one billion job adverts, found that wages are rising twice as fast in industries most exposed to AI. From a skills perspective, PwC reported that AI is changing the skills required of job applicants. According to PwC, to succeed in the workplace, candidates are more likely to need experience in using AI tools and the ability to demonstrate critical thinking and collaboration. Phillippa O’Connor, chief people officer at PwC UK, noted that while degrees are still important for many jobs, a reduction in degree requirements suggests employers are looking at a broader range of measures to assess skills and potential. In occupations most exposed to AI, PwC noted that the skills sought by employers are changing 59% faster than in occupations least exposed to AI. “AI is reshaping the jobs market – lowering barriers to entry in some areas, while raising the bar on the skills required in others,” O’Connor added. Those with the right AI skills are being rewarded with higher salaries. In fact, PwC found that wages are growing twice as fast in AI-exposed industries. This includes jobs that are classed as “automatable”, which means they contain some tasks that can readily be automated. The highest premiums are attached to occupations requiring AI skills, with an average premium in 2024 of 11% for UK workers in these roles.   AI is reshaping the jobs market – lowering barriers to entry in some areas, while raising the bar on the skills required in others Phillippa O’Connor PwC UK PwC’s analysis shows that sectors exposed to AI experience three times higher growth in the revenue generated by each employee. It also reported that growth in revenue per employee for AI-exposed industries surged when large language models (LLMs) such as generative AI (GenAI) became mainstream. Revenue growth per employee has nearly quadrupled in industries most exposed to AI, such as software, rising from 7% between 2018 and 2022, to 27% between 2018 and 2024. In contrast, revenue growth per employee in industries least exposed to AI, such as mining and hospitality, fell slightly, from 10% between 2018 and 2022, to 9% between 2018 and 2024. However, since 2018, job postings for occupations with greater exposure to AI have grown at a slower pace than those with lower exposure – and this gap is widening. Umang Paw, chief technology officer (CTO) at PwC UK, said: “There are still many unknowns about AI’s potential. AI can provide stardust to those ready to adapt, but risks leaving others behind.” Paw believes there needs to be a concerted effort to expand access to technology and training to ensure the benefits of AI are widely shared. “In the intelligence age, the fusion of AI with technologies like real-time data analytics – and businesses broadening their products and services – will create new industries and fresh job opportunities,” Paw added. Read more about AI skills AWS addresses the skills barrier holding back enterprises: The AWS Summit in London saw the public cloud giant appoint itself to take on the task of skilling up hundreds of thousands of UK people in using AI technologies. Could generative AI help to fill the skills gap in engineering: The role of artificial intelligence and machine learning in society continues to be hotly debated as the tools promise to revolutionise our lives, but how will they affect the engineering sector? In The Current Issue: UK government outlines plan to surveil migrants with eVisa data Why we must reform the Computer Misuse Act: A cyber pro speaks out Download Current Issue What to expect from Aera Technology AeraHUB 25 – CW Developer Network NTT IOWN all-photonics ‘saves Princess Miku’ from dragon – CW Developer Network View All Blogs

Modern software engineering faces growing challenges in accurately retrieving and understanding code across diverse programming languages and large-scale codebases. Existing embedding models often struggle to capture the deep semantics of code, resulting in poor performance in tasks such as code search, RAG, and semantic analysis. These limitations hinder developers’ ability to efficiently locate relevant code snippets, reuse components, and manage large projects effectively. As software systems grow increasingly complex, there is a pressing need for more effective, language-agnostic representations of code that can power reliable and high-quality retrieval and reasoning across a wide range of development tasks.  Mistral AI has introduced Codestral Embed, a specialized embedding model built specifically for code-related tasks. Designed to handle real-world code more effectively than existing solutions, it enables powerful retrieval capabilities across large codebases. What sets it apart is its flexibility—users can adjust embedding dimensions and precision levels to balance performance with storage efficiency. Even at lower dimensions, such as 256 with int8 precision, Codestral Embed reportedly surpasses top models from competitors like OpenAI, Cohere, and Voyage, offering high retrieval quality at a reduced storage cost. Beyond basic retrieval, Codestral Embed supports a wide range of developer-focused applications. These include code completion, explanation, editing, semantic search, and duplicate detection. The model can also help organize and analyze repositories by clustering code based on functionality or structure, eliminating the need for manual supervision. This makes it particularly useful for tasks like understanding architectural patterns, categorizing code, or supporting automated documentation, ultimately helping developers work more efficiently with large and complex codebases.  Codestral Embed is tailored for understanding and retrieving code efficiently, especially in large-scale development environments. It powers retrieval-augmented generation by quickly fetching relevant context for tasks like code completion, editing, and explanation—ideal for use in coding assistants and agent-based tools. Developers can also perform semantic code searches using natural language or code queries to find relevant snippets. Its ability to detect similar or duplicated code helps with reuse, policy enforcement, and cleaning up redundancy. Additionally, it can cluster code by functionality or structure, making it useful for repository analysis, spotting architectural patterns, and enhancing documentation workflows.  Codestral Embed is a specialized embedding model designed to enhance code retrieval and semantic analysis tasks. It surpasses existing models, such as OpenAI’s and Cohere’s, in benchmarks like SWE-Bench Lite and CodeSearchNet. The model offers customizable embedding dimensions and precision levels, allowing users to effectively balance performance and storage needs. Key applications include retrieval-augmented generation, semantic code search, duplicate detection, and code clustering. Available via API at $0.15 per million tokens, with a 50% discount for batch processing, Codestral Embed supports various output formats and dimensions, catering to diverse development workflows. In conclusion, Codestral Embed offers customizable embedding dimensions and precisions, enabling developers to strike a balance between performance and storage efficiency. Benchmark evaluations indicate that Codestral Embed surpasses existing models like OpenAI’s and Cohere’s in various code-related tasks, including retrieval-augmented generation and semantic code search. Its applications span from identifying duplicate code segments to facilitating semantic clustering for code analytics. Available through Mistral’s API, Codestral Embed provides a flexible and efficient solution for developers seeking advanced code understanding capabilities.  vides valuable insights for the community. Check out the Technical details. 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 95k+ 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/Off-Policy Reinforcement Learning RL with KL Divergence Yields Superior Reasoning in Large Language ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/This AI Paper from Microsoft Introduces WINA: A Training-Free Sparse Activation Framework for Efficient Large Language Model InferenceSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Apple and Duke Researchers Present a Reinforcement Learning Approach That Enables LLMs to Provide Intermediate Answers, Enhancing Speed and AccuracySana Hassanhttps://www.marktechpost.com/author/sana-hassan/National University of Singapore Researchers Introduce Dimple: A Discrete Diffusion Multimodal Language Model for Efficient and Controllable Text Generation

Many don’t like it, but (almost) everybody agrees it’s the future.“Vibe Coding” is everywhere. Tools and game engines are implementing AI-assisted coding, vibe coding interest skyrocketed on Google search, on social media, everybody claims to build apps and games in minutes, while the comment section gets flooded with angry developers calling out the pile of garbage code that will never be shipped.A screenshot from Andrej Karpathy with the original “definition” of Vibe CodingBUT, how do professionals feel about it?This is what I will cover in this article. We will look at:How people react to the term vibe coding,How their attitude differs based on who they are and their professional experienceThe reason for their stance towards “vibe coding” (with direct quotes)How they feel about the impact “vibe coding” will have in the next 5 yearsIt all started with this survey on LinkedIn. I have always been curious about how technology can support creatives and I believe that the only way to get a deeper understanding is to go beyond buzzwords and ask the hard questions. That’s why for over a year, I’ve been conducting weekly interviews with both the founders developing these tools and the creatives utilising them. If you want to learn their journeys, I’ve gathered their insights and experiences on my blog called XR AI Spotlight.Driven by the same motives and curious about people’s feelings about “vibe coding”, I asked a simple question: How does the term “Vibe Coding” make you feel?Original LinkedIn poll by Gabriele RomagnoliIn just three days, the poll collected 139 votes and it was clear that most responders didn’t have a good “vibe” about it. The remaining half was equally split between excitement and no specific feeling.But who are these people? What is their professional background? Why did they respond the way they did?Curious, I created a more comprehensive survey and sent it to everyone who voted on the LinkedIn poll.The survey had four questions:Select what describes you best: developers, creative, non-creative professionalHow many years of experience do you have? 1–5, 6–10, 11–15 or 16+Explain why the term “vibe coding” makes you feel excited/neutral/dismissive?Do you think “vibe coding” will become more relevant in the next 5 years?: It’s the future, only in niche use cases, unlikely, no idea)In a few days, I collected 62 replies and started digging into the findings, and that’s when I finally started understanding who took part in the initial poll.The audienceWhen characterising the audience, I refrained from adding too many options because I just wanted to understand:If the people responding were the ones making stuffWhat percentage of makers were creatives and what developersI was happy to see that only 8% of respondents were non-creative professionals and the remaining 92% were actual makers who have more “skin in the game“ with almost a 50/50 split between creatives and developers. There was also a good spread in the degree of professional experience of the respondents, but that’s where things started to get surprising.Respondents are mostly “makers” and show a good variety in professional experienceWhen creating 2 groups with people who have more or less than 10 years of experience, it is clear that less experienced professionals skew more towards a neutral or negative stance than the more experienced group.Experienced professionals are more positive and open to vibe codingThis might be because senior professionals see AI as a tool to accelerate their workflows, while more juniors perceive it as a competitor or threat.I then took out the non-professional creatives and looked at the attitude of these 2 groups. Not surprisingly, fewer creatives than developers have a negative attitude towards “vibe coding” (47% for developers Vs 37% for creatives), but the percentage of creatives and developers who have a positive attitude stays almost constant. This means that creatives have a more indecisive or neutral stance than developers.Creatives have a more positive attitude to vibe coding than developersWhat are people saying about “vibe coding”?As part of the survey, everybody had the chance to add a few sentences explaining their stance. This was not a compulsory field, but to my surprise, only 3 of the 62 left it empty (thanks everybody). Before getting into the sentiment analysis, I noticed something quite interesting while filtering the data. People with a negative attitude had much more to say, and their responses were significantly longer than the other group. They wrote an average of 59 words while the others barely 37 and I think is a good indication of the emotional investment of people who want to articulate and explain their point. Let’s now look at what the different groups of people replied.😍 Patterns in Positive Responses to “Vibe Coding”Positive responders often embraced vibe coding as a way to break free from rigid programming structures and instead explore, improvise, and experiment creatively.“It puts no pressure on it being perfect or thorough.”“Pursuing the vibe, trying what works and then adapt.”“Coding can be geeky and laborious… ‘vibing’ is quite nice.”This perspective repositions code not as rigid infrastructure, but something that favors creativity and playfulness over precision.Several answers point to vibe coding as a democratizing force opening up coding to a broader audience, who want to build without going through the traditional gatekeeping of engineering culture.“For every person complaining… there are ten who are dabbling in code and programming, building stuff without permission.”“Bridges creative with technical perfectly, thus creating potential for independence.”This group often used words like “freedom,” “reframing,” and “revolution.”.😑 Patterns in Neutral Responses to “Vibe Coding”As shown in the initial LinkedIn poll, 27% of respondents expressed mixed feelings. When going through their responses, they recognised potential and were open to experimentation but they also had lingering doubts about the name, seriousness, and future usefulness.“It’s still a hype or buzzword.”“I have mixed feelings of fascination and scepticism.”“Unsure about further developments.”They were on the fence and were often enthusiastic about the capability, but wary of the framing.Neutral responders also acknowledged that complex, polished, or production-level work still requires traditional approaches and framed vibe coding as an early-stage assistant, not a full solution.“Nice tool, but not more than autocomplete on steroids.”“Helps get setup quickly… but critical thinking is still a human job.”“Great for prototyping, not enough to finalize product.”Some respondents were indifferent to the term itself, viewing it more as a label or meme than a paradigm shift. For them, it doesn’t change the substance of what’s happening.“At the end of the day they are just words. Are you able to accomplish what’s needed?”“I think it’s been around forever, just now with a new name.”These voices grounded the discussion in the terminology and I think they bring up a very important point that leads to the polarisation of a lot of the conversations around “vibe coding”.🤮 Patterns in Negative Responses to “Vibe Coding”Many respondents expressed concern that vibe coding implies a casual, unstructured approach to coding. This was often linked to fears about poor code quality, bugs, and security issues.“Feels like building a house without knowing how electricity and water systems work.”“Without fundamental knowledge… you quickly lose control over the output.”The term was also seen as dismissive or diminishing the value of skilled developers. It really rubbed people the wrong way, especially those with professional experience.“It downplays the skill and intention behind writing a functional, efficient program.”“Vibe coding implies not understanding what the AI does but still micromanaging it.”Like for “neutral” respondents, there’s a strong mistrust around how the term is used (especially on social media) where it’s seen as fueling unrealistic expectations or being pushed by non-experts.“Used to promote coding without knowledge.”“Just another overhyped term like NFTs or memecoins.”“It feels like a joke that went too far.”Ultimately, I decided to compare attitudes that are excited (positive) and accepting (neutral) of vibe coding vs. those that reject or criticise it. After all, even among people who were neutral, there was a general acceptance that vibe coding has its place. Many saw it as a useful tool for things like prototyping, creative exploration, or simply making it easier to get started. What really stood out, though, was the absence of fear that was very prominent in the “negative” group and saw vibe coding as a threat to software quality or professional identity.People in the neutral and positive groups generally see potential. They view it as useful for prototyping, creative exploration, or making coding more accessible, but they still recognise the need for structure in complex systems. In contrast, the negative group rejects the concept outright, and not just the name, but what it stands for: a more casual, less rigorous approach to coding. Their opinion is often rooted in defending software engineering as a disciplined craft… and probably their job.😍 “As long as you understand the result and the process, AI can write and fix scripts much faster than humans can.”🤮 “It’s a joke. It started as a joke… but to me doesn’t encapsulate actual AI co-engineering.”On the topic of skill and control, the neutral and positive group sees AI as a helpful assistant, assuming that a human is still guiding the process. They mention refining and reviewing as normal parts of the workflow. The negative group sees more danger, fearing that vibe coding gives a false sense of competence. They describe it as producing buggy or shallow results, often in the hands of inexperienced users.😑 “Critical thinking is still a human job… but vibe coding helps with fast results.”🤮“Vibe-Coding takes away the very features of a good developer… logical thinking and orchestration are crucial.”Culturally, the divide is clear. The positive and neutral voices often embrace vibe coding as part of a broader shift, welcoming new types of creators and perspectives. They tend to come from design or interdisciplinary backgrounds and are more comfortable with playful language. On the other hand, the negative group associates the term with hype and cringe, criticising it as disrespectful to those who’ve spent years honing their technical skills.😍“It’s about playful, relaxed creation — for the love of making something.”🤮Creating a lot of unsafe bloatware with no proper planning.”What’s the future of “Vibe Coding”?The responses to the last question were probably the most surprising to me. I was expecting that the big scepticism towards vibe coding would align with the scepticism on its future, but that was not the case. 90% of people still see “vibe coding” becoming more relevant overall or in niche use cases.Vibe coding is here to stayOut of curiosity, I also went back to see if there was any difference based on professional experience, and that’s where we see the more experienced audience being more conservative. Only 30% of more senior Vs 50% of less experienced professionals see vibe coding playing a role in niche use cases and 13 % Vs only 3% of more experienced users don’t see vibe coding becoming more relevant at all.More experienced professionals are less likely to think Vibe Coding is the futureThere are still many open questions. What is “vibe coding” really? For whom is it? What can you do with it?To answer these questions, I decided to start a new survey you can find here. If you would like to further contribute to this research, I encourage you to participate and in case you are interested, I will share the results with you as well.The more I read or learn about this, I feel “Vibe Coding” is like the “Metaverse”:Some people hate it, some people love it.Everybody means something differentIn one form or another, it is here to stay.What professionals really think about “Vibe Coding” was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Editor’s note: This blog, originally published on October 28, 2024, has been updated. Financial losses from worldwide credit card transaction fraud are projected to reach more than $403 billion over the next decade. The new NVIDIA AI Blueprint for financial fraud detection can help combat this burgeoning epidemic — using accelerated data processing and advanced algorithms to improve AI’s ability to detect and prevent credit card transaction fraud. Launched this week at the Money20/20 financial services conference, the blueprint provides a reference example for financial institutions to identify subtle patterns and anomalies in transaction data based on user behavior to improve accuracy and reduce false positives compared with traditional methods. It shows developers how to build a financial fraud detection workflow by providing reference code, deployment tools and a reference architecture. Companies can streamline the migration of their fraud detection workflows from traditional compute to accelerated compute using the NVIDIA AI Enterprise software platform and NVIDIA accelerated computing. The NVIDIA AI Blueprint is available for customers to run on Amazon Web Services, with availability coming soon on Dell Technologies and Hewlett Packard Enterprise. Customers can also use the blueprint through service offerings from NVIDIA partners including Cloudera, EXL, Infosys and SHI International. Businesses embracing comprehensive machine learning (ML) tools and strategies can observe up to an estimated 40% improvement in fraud detection accuracy, boosting their ability to identify and stop fraudsters faster and mitigate harm. As such, leading financial organizations like American Express and Capital One have been using AI to build proprietary solutions that mitigate fraud and enhance customer protection. The new AI Blueprint accelerates model training and inference, and demonstrates how these components can be wrapped into a single, easy-to-use software offering, powered by NVIDIA AI. Currently optimized for credit card transaction fraud, the blueprint could be adapted for use cases such as new account fraud, account takeover and money laundering. Using Accelerated Computing and Graph Neural Networks for Fraud Detection Traditional data science pipelines lack the compute acceleration to handle the massive data volumes required for effective fraud detection. ML models like XGBoost are effective for detecting anomalies in individual transactions but fall short when fraud involves complex networks of linked accounts and devices. Helping address these gaps, NVIDIA RAPIDS — part of the NVIDIA CUDA-X collection of microservices, libraries, tools and technologies — enables payment companies to speed up data processing and transform raw data into powerful features at scale. These companies can fuel their AI models and integrate them with graph neural networks (GNNs) to uncover hidden, large-scale fraud patterns by analyzing relationships across different transactions, users and devices. The use of gradient-boosted decision trees — a type of ML algorithm — tapping into libraries such as XGBoost, has long been the standard for fraud detection. The new AI Blueprint for financial fraud detection enhances the XGBoost ML model with NVIDIA CUDA-X Data Science libraries including GNNs to generate embeddings that can be used as additional features to help reduce false positives. The GNN embeddings are fed into XGBoost to create and train a model that can then be orchestrated. In addition, NVIDIA Dynamo-Triton, formerly NVIDIA Triton Inference Server, boosts real-time inferencing while optimizing AI model throughput, latency and utilization. NVIDIA CUDA-X Data Science and Dynamo-Triton are included with NVIDIA AI Enterprise. Leading Financial Services Organizations Adopt AI During a time when many large North American financial institutions are reporting online or mobile fraud losses continue to increase, AI is helping to combat this trend. American Express, which began using AI to fight fraud in 2010, leverages fraud detection algorithms to monitor all customer transactions globally in real time, generating fraud decisions in just milliseconds. Using a combination of advanced algorithms, one of which tapped into the NVIDIA AI platform, American Express enhanced model accuracy, advancing the company’s ability to better fight fraud. European digital bank bunq uses generative AI and large language models to help detect fraud and money laundering. Its AI-powered transaction-monitoring system achieved nearly 100x faster model training speeds with NVIDIA accelerated computing. BNY announced in March 2024 that it became the first major bank to deploy an NVIDIA DGX SuperPOD with DGX H100 systems, which will help build solutions that support fraud detection and other use cases. And now, systems integrators, software vendors and cloud service providers can integrate the new NVIDIA blueprint for fraud detection to boost their financial services applications and help keep customers’ money, identities and digital accounts safe. Explore the NVIDIA AI Blueprint for financial fraud detection and read this NVIDIA Technical Blog on supercharging fraud detection with GNNs. Learn more about AI for fraud detection by visiting the AI Summit at Money20/20, running this week in Amsterdam. See notice regarding software product information.

Three days from now, the future of AI walks through the doors of TechCrunch Sessions: AI at UC Berkeley’s Zellerbach Hall. This Thursday, June 5, TC Sessions: AI brings together the minds shaping what’s next. Builders, dreamers, and disciplined think