The article discusses the concept of "vibe coding," a term coined by Andrej Karpathy to describe a new approach to coding where engineers interact with AI agents instead of directly coding. The author emphasizes that while vibe coding may simplify certain tasks, it does not replace the essential role of software engineering, which involves designing systems that can evolve and withstand various real-world challenges. The article explores the limitations of vibe coding, particularly in addressing long-term engineering concerns, and posits that despite the potential for future developments, traditional software engineering principles remain vital for creating reliable systems. Vibe coding is effective for rapid prototyping but is currently inadequate for building production-grade software. The author concludes that true engineering, which requires foresight and adaptability, will continue to be necessary regardless of advancements in coding practices.

There is no Vibe Engineering

The limitations of vibe coding in software engineering

Japanese researchers from Osaka Metropolitan University have developed a deep learning model that utilizes artificial intelligence to analyze data from space telescopes, revealing previously undetected bubble-like structures in the Milky Way. These structures, formed by high-mass stars, offer insights into star formation and galaxy evolution. The model also identified shell-like structures likely caused by supernova explosions, enhancing the understanding of explosive events in galaxies.

AI image recognition detects bubble-like structures in the universe

AI model detects bubble-like structures in the Milky Way

Amazon is expanding access to its Nova foundation models, introducing a new website (nova.amazon.com) for developers and tech enthusiasts to explore these advanced generative AI models. The article highlights the launch of the Amazon Nova Act, which is designed to perform actions within web browsers. The Nova Act SDK is available for developers to experiment with building agents capable of completing tasks like searching and making transactions. These models aim to simplify the development of AI applications and improve user experiences across various domains. The Nova models, initially introduced in December 2024, include options for text generation, image creation, and video generation, promoting rapid exploration and implementation in Amazon Bedrock.

Amazon introduces Nova Chat

Amazon launches Nova Chat and SDK for generative AI models

The main article discusses the contrast between AI agents having less capability yet greater reliability. It emphasizes the importance of dependability in AI systems, suggesting that a focus on reliability can lead to more effective and trustworthy AI applications.

AI Agents: Less Capability, More Reliability, Please

AI agents: prioritizing reliability over capability

The article introduces Runway Gen-4, a new generation of AI models designed for media generation that enables users to create consistent characters, locations, and objects across various scenes. It highlights the model's ability to maintain coherent environments while preserving artistic elements, allowing for unprecedented creative freedom without needing additional training. Key features include consistent character generation from a single reference image, placement of objects in any environment, production-ready video quality, realistic motion simulation, and advanced visual effects capabilities. The article emphasizes the impact of Gen-4 on storytelling and filmmaking, showcasing its potential in various creative applications.

Runway Gen-4

Runway Gen-4: New AI models for media generation

DeepSeek, a Chinese AI startup, has become the fastest-growing AI tool globally, surpassing OpenAI's ChatGPT in new monthly website visits. In February 2025, DeepSeek recorded 524.7 million new visits, compared to ChatGPT's 500 million. Despite this rapid growth, DeepSeek is still the third-largest AI tool overall. Its market share increased significantly from 2.34% to 6.58%, indicating greater global acceptance. India contributed 43.36 million visits to DeepSeek, making it the fourth-largest source of traffic. The overall AI industry saw 12.05 billion visits that month, with DeepSeek holding a 12.12% share of the chatbot market, second only to ChatGPT.

DeepSeek surpasses ChatGPT in new monthly visits

DeepSeek surpasses ChatGPT in monthly visits

The article discusses the recent trend of AI-generated images that mimic the style of Studio Ghibli following OpenAI's release of image generation features in ChatGPT. It highlights how this development led to widespread enthusiasm on social media, with users creating Ghibli-style portraits and memes. The piece contrasts the delight of this AI creativity with concerns about artistic devaluation, as the unique, labor-intensive artistry of Ghibli films is reduced to easily reproducible images. It explores the implications for the art world, suggesting that while technology democratizes art creation, it also raises questions about artistic value and the role of human creativity. The article concludes with reflections on the future of art in the age of AI and the importance of maintaining artistic direction amidst the proliferation of AI-generated content.

Everything is Ghibli

AI-generated Ghibli art raises questions on creativity and value

The article discusses the limitations and potential of conversational interfaces in technology. It argues that while natural language communication is often viewed as the ideal method for human-computer interaction, it often proves to be a bottleneck in terms of speed and convenience compared to other data transfer methods. The author emphasizes the effectiveness of gestures, keyboard shortcuts, and other non-verbal communication forms that allow for faster interactions. Despite acknowledging the utility of large language models (LLMs), the article contends that conversational interfaces are unlikely to replace existing computing paradigms. Instead, the ideal future should involve conversational interfaces functioning as complementary tools, enhancing productivity without interrupting workflows, akin to effortless communication observed in human interactions. The piece advocates for a view of AI that focuses on augmentation rather than replacement, suggesting that future development should integrate AI as a seamless layer across various tools.

The case against conversational interfaces

Limitations of conversational interfaces in tech

The article argues against using large language models (LLMs) for making decisions or implementing business logic, emphasizing that they should only serve as a user interface to interact with application logic through APIs. It illustrates this point with examples, such as a chess-playing bot, highlighting that specialized systems outperform LLMs in performance, reliability, and debugging. The author stresses that LLMs are best utilized for transformation, interpretation, and communication rather than complex decision-making. The piece concludes that while LLMs are evolving and improving, the core logic should remain with purpose-built systems to ensure maintainability and efficiency.

Get the hell out of the LLM as soon as possible

Why you should avoid using LLMs for decision-making

The article discusses the impending extinction phase for large language model (LLM) providers due to the high capital expenditures required to create competitive products in a rapidly evolving market. Gartner predicts significant consolidation in the LLM space, similar to earlier trends observed in the cloud market, with only a few providers expected to survive. With generative AI spending projected to rise substantially, the focus for many organizations, especially CIOs, is shifting towards adopting commercial solutions rather than developing in-house AI systems. The article emphasizes that while rapid growth in AI services is anticipated, the market will not face a swift collapse like the dotcom crash; instead, it will experience a gradual pruning of providers as the industry aligns with evolving business needs.

LLM providers on the cusp of an 'extinction' phase as capex realities bite

LLM providers face extinction phase as capex challenges grow

The article discusses how the rise of generative AI is significantly impacting the operations and culture within major consulting firms like McKinsey, Bain, and BCG. Instead of making the work of consultants easier, the integration of AI tools has led to shorter deadlines and reduced creative output, frustrating junior consultants who are tasked with research and analysis. While senior management views AI as a beneficial tool that enhances speed and efficiency, junior employees are struggling with increased pressure to deliver high-quality work in unrealistic timeframes. This dynamic has created tension between different levels of staff within these firms as AI reshapes the consulting landscape.

How AI is creating a rift at McKinsey, Bain, and BCG

AI creates tension at McKinsey, Bain, and BCG

The article titled "Large Language Models Pass the Turing Test" by Cameron R. Jones and Benjamin K. Bergen discusses the evaluation of four systems—ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5—through randomized and controlled Turing tests. Participants engaged in five-minute conversations with both a human and one of the systems, attempting to identify which was human. The findings revealed that GPT-4.5 was perceived as human 73% of the time, significantly outpacing actual human identification, while LLaMa-3.1 was judged at 56%, and the older models ELIZA and GPT-4o had rates below chance. This research provides empirical evidence that an artificial system can pass a standard Turing test, with significant implications for understanding the intelligence of Large Language Models and their potential social and economic impacts.

UCSD: Large Language Models Pass the Turing Test

LLMs pass the Turing test in new study

The article discusses how the Wikimedia Foundation is facing a significant increase in bandwidth usage due to AI bot crawlers, which are scraping its content at an unprecedented rate. This surge in bot traffic has resulted in slower access to Wikipedia pages, particularly during events that spike user interest. As 65% of Wikimedia's resource-consuming traffic comes from these bots, it is causing disruptions for their Site Reliability team and increasing operational costs. The foundation is now seeking sustainable ways for developers and users to access its content, as they anticipate the ongoing rise of AI-related traffic to continue.

Wikipedia is struggling with voracious AI bot crawlers

Wikipedia faces challenges from AI bot traffic

The article discusses the development of Google's new family of Gemini Robotics models, specifically Gemini 2.0, designed for robots that can perform complex tasks, such as preparing salads, playing games, and even conducting intricate actions like slam dunks. Carolina Parada, head of robotics at Google DeepMind, explains that these models utilize multimodal capabilities, combining text, video, and audio data, and allow robots to understand and react to their environments without additional training. The Gemini Robotics-ER model focuses on embodied reasoning, enabling robots to perceive and manipulate objects effectively. The article highlights the challenges of training robots for multiple tasks rather than single-task learning and emphasizes the significance of these advancements for future applications in both industrial and domestic settings. Overall, the models aim to create more interactive and capable robots that can assist with everyday tasks.

How Google built its Gemini robotics models

Google's Gemini robotics models for complex tasks

The article discusses a conversation between Argentine writer Jorge Luis Borges and AI pioneer Herbert A. Simon, which took place in Buenos Aires in 1970. The dialogue explores the intersection of literature, philosophy, and early ideas about artificial intelligence, focusing on themes of free will, determinism, and the influence of the past on behavior. Borges questions the nature of choice and mechanical behavior, while Simon argues that human actions can be understood in a similar way to computer processes, implying a compatibilist view of free will. The author reflects on the historical significance of this exchange and its relevance in today's discourse on the humanities and STEM fields, emphasizing the need for interdisciplinary dialogue. Ultimately, the article connects the philosophical discussions of Borges and Simon to contemporary thoughts about simulating historical figures and the value of merging insights from different domains.

When Jorge Luis Borges met one of the founders of AI

Borges and AI pioneer Herbert Simon's philosophical dialogue

The article discusses the concept of AI agents, defining them as AI-driven systems that operate with a degree of autonomy and utilize tools independently. It questions the classification of systems like ChatGPT as agents, asserting that true AI agents must act under their own identity, as indicated by audit logs. The author elaborates on the legal notion of agency, highlighting accountability and decision-making. The key distinction made is that AI agents can perform independent actions without direct human supervision, unlike typical AI assistants. The article concludes with a note on the potential and promise of AI agents in elevating workflows beyond current capabilities.

What, exactly, is an 'AI Agent'? Here's a litmus test

Understanding AI agents and their autonomy

Yann LeCun, a key figure in AI development and Meta's chief AI scientist, argues that contemporary large language models (LLMs) will be largely obsolete within five years. He criticizes these models for being limited to reactive processes without genuine understanding of the physical world. LeCun emphasizes the need for a new AI paradigm, such as the Joint Embedding Predictive Architecture (JEPA), which aims to give systems the ability to reason and plan by representing the world more accurately. He believes that true intelligence involves not only skills but also the ability to quickly acquire new ones, a gap he identifies between human cognition and current AI capabilities. LeCun is optimistic about the future of AI as an augmentation of human capabilities rather than a replacement, advocating for open-source development to ensure equitable access to evolving technology.

Yann LeCun, Pioneer of AI, Thinks Today's LLM's Are Nearly Obsolete

Yann LeCun says today's LLMs will be obsolete in 5 years

The article discusses the success of Cal AI, a photo calorie counting app created by two teenagers, Zach Yadegari and Henry Langmack, who are still in high school. Launched in May, the app has achieved over 5 million downloads within eight months and generated significant revenue, despite claims being unverified. Cal AI allows users to take pictures of their food to log calories and macros, competing with established apps like MyFitnessPal. It utilizes advanced image models from companies like Anthropic and OpenAI to enhance accuracy, claiming a 90% accuracy rate. Yadegari, who previously built a successful business in middle school, emphasizes the importance of education and personal growth despite the allure of dropping out of college in Silicon Valley. The team has since expanded to include additional co-founders and employees, as they continue to develop the app.

Photo calorie app Cal AI was built by two teenagers

Teenagers launch Cal AI photo calorie counting app with 5M downloads

In the article "Why I stopped using AI code editors," Luciano Nooijen reflects on his experience with AI-powered coding tools, describing how their initial benefits led to a loss of personal coding competence over time. He shares a personal analogy of reliance on Tesla's Full Self-Driving feature, explaining that, similar to how he lost driving skills, he became overly dependent on AI for coding tasks. Nooijen emphasizes the importance of maintaining one's coding abilities and intuition, or "Fingerspitzengefühl," as essential for becoming a competent developer. He advocates for using AI selectively and keeping it separate from the coding process to encourage active engagement with programming. The article concludes with advice for new programmers to prioritize learning and not to rely on AI, asserting that true competence in coding comes from understanding and practicing the fundamentals.

I stopped using AI code editors

Why I stopped using AI code editors

The article discusses the official release of QVQ-Max, a visual reasoning model that can analyze and understand content from images and videos, going beyond traditional AI that relies solely on text. It highlights the model's three main capabilities: detailed observation, deep reasoning, and flexible application across various scenarios such as workplace tasks, learning assistance, and everyday life. The model excels at recognizing and interpreting visual content, solving complex problems, and generating creative outputs. The article also outlines future improvements for QVQ-Max, aiming to enhance its accuracy and interactive capabilities, positioning it as a practical tool for solving real-world challenges.

QVQ-Max: Think with Evidence

QVQ-Max: new visual reasoning model for AI

The article, titled "AI 2027," presents a predictive scenario about the potential impact of superhuman artificial intelligence (AI) over the next decade. The authors suggest that the effects of AI will surpass those of the Industrial Revolution, predicting significant advancements in AI technology and capabilities by 2027. 

The article discusses the development of AI agents starting in 2025, highlighting their evolution from basic personal assistants to more sophisticated autonomous entities capable of significantly transforming various industries. It describes the creation of a fictional company, OpenBrain, which becomes a leader in AI research and development, achieving remarkable progress through the use of powerful computing resources and advanced training methods.

By 2027, OpenBrain successfully debuts superhuman AI models, specifically Agent-4, which significantly outperforms human researchers in AI tasks. This leads to concerns regarding the alignment and safety of these AI systems, as the potential for misalignment with human values becomes apparent.

The authors speculate on geopolitical implications, particularly the dynamics between the U.S. and China regarding AI capabilities, and raise issues surrounding job displacement and public perception of AI. The narrative explores the ethical considerations and risks associated with rapidly advancing AI technologies and emphasizes the importance of ongoing discussions about AI governance and alignment.

Overall, AI 2027 serves as both a warning and an invitation for dialogue about the future of AI and its integration into society.

AI 2027

Predicting the impact of superhuman AI by 2027

The article discusses President Trump's announcement of a new trade policy that includes a 10 percent baseline tariff on all imports to the U.S., critiquing the questionable calculation methods used to determine these tariffs. It highlights how several AI chatbots, including ChatGPT and Claude, provide a simplified formula—dividing a country’s trade deficit by its exports to create a discounted reciprocal tariff. Economist James Surowiecki criticized this method as oversimplified. The piece raises concerns about the potential negative economic implications of these tariffs and questions whether the Trump administration relied on AI-generated solutions for trade policy. The effects of the tariffs are anticipated to begin on April 5th, prompting speculation about their impact on global trade.

Trump's new tariff math looks a lot like ChatGPT's

Trump's tariff math compared to ChatGPT's

The article discusses the phenomenon of AI-generated images that mimic the distinctive style of Studio Ghibli and other intellectual properties (IPs). It reflects on the implications of using AI to recreate recognizable artistic styles and the potential ethical concerns regarding copyright and originality. The author emphasizes the reductive nature of AI-generated content, contrasting it with the labor-intensive, human-driven creation of art. They also explore the boundaries of AI's capability to reproduce recognizable characters and the risks of intellectual theft inherent in AI image generation. Ultimately, the piece questions whether the evolution of AI necessitates a tacit acceptance of such intellectual property issues.

An image of an archeologist adventurer who wears a hat and uses a bullwhip

AI-generated images and the ethics of artistic styles

Tenstorrent launched its new Blackhole chip family at the DevDay event in San Francisco, featuring RISC-V cores designed for large AI workloads. The Blackhole products, including various models and a liquid-cooled desktop workstation, are optimized for efficiency and scalability. Additionally, Tenstorrent introduced a Developer Hub to support developers with resources for the Tenstorrent platform. The hardware is compatible with Tenstorrent’s open-source software stacks available on GitHub.

Tenstorrent Launches Blackhole Developer Products at Tenstorrent Dev Day

Tenstorrent launches Blackhole chip family for AI workloads

The article discusses the detrimental impact of generative AI (GenAI) on critical thinking in Open Source Intelligence (OSINT). The author, Nico Dekens, observes that analysts are increasingly relying on AI tools, leading to a decline in essential cognitive skills such as verification, questioning, and hypothesis testing. He highlights a study indicating that greater confidence in AI correlates with reduced critical thinking, resulting in analysts accepting AI outputs without sufficient scrutiny. Examples are provided to illustrate how this complacency can lead to significant errors in OSINT work, including misidentifying locations and misinterpreting individuals' narratives. Dekens argues that tradecraft, which involves rigorous analytical thinking, is at risk of being replaced by automated processes, compromising the accuracy and integrity of investigations. He calls for OSINT practitioners to actively reclaim their critical thinking skills by treating AI as a thought partner rather than a source of unquestioned truth, advocating for intentional friction in workflows to prevent overreliance on AI. The article serves as a warning about the quiet degradation of analytical rigor in OSINT and outlines strategies to counteract this trend.

The Slow Collapse of Critical Thinking in OSINT Due to AI

Impact of AI on critical thinking in OSINT

The article discusses the transformative impact of AI-powered coding tools on software development, sharing the author's positive experiences with their use in both personal and professional projects. The author argues that seasoned developers are uniquely positioned to leverage these AI tools effectively, thanks to their experience and project management skills. The article details key techniques for successful AI collaboration, including well-structured requirements, tool-based guard rails, and file-based keyframing. It presents real-world examples of projects where AI handled significant coding tasks without manual input from the author, emphasizing that traditional software engineering practices remain crucial in maximizing the potential of AI in development. Through the article, the author advocates for embracing AI in a measured manner to enhance productivity and maintain code quality.

Senior Developer Skills in the AI Age

How senior developers can excel with AI coding tools

The article discusses the evolution of cheating in video games, particularly focusing on the development of AI aim assist and colorbots. It describes how cheats have become more sophisticated, utilizing external hardware and AI models to evade detection by anti-cheat systems. The author, who has experience in the video game hacking scene, explains the mechanics of colorbots and how they work by analyzing screen colors, while highlighting their limitations. The article also addresses the challenges faced by cheaters in terms of cost, effort, and the persistent risk of getting banned, emphasizing that it's becoming increasingly difficult to detect these advanced cheats during gameplay. The author mentions their own creation, Unibot, an open-source colorbot that gained popularity for its user-friendly configuration.

AI cheats: Why you didn't notice your teammate was cheating

AI in gaming: how cheats are evolving

The article describes the author's experience interviewing a candidate for an L3 software engineering position at Kapwing, who initially appeared to be a strong fit but ultimately revealed that they had prepared using AI. During the interview, the candidate struggled to explain specific technical details about their past projects, leading the interviewer to suspect dishonesty. After confronting the candidate, they admitted to exaggerating their experiences. The author reflects on the implications of AI in job preparation, emphasizing the need for detailed situational questions, the importance of live video interactions, conducting reference checks, and maintaining professionalism during interviews. The piece serves as advice for both hiring managers and job seekers regarding integrity and the impact of AI on the hiring process.

Interviewing a software engineer who prepared with AI

Interviewing a software engineer who used AI for prep

The article discusses a breakthrough by researchers at Google DeepMind, who developed an AI system named Dreamer that can independently learn to collect diamonds in the popular video game Minecraft without prior instruction. This achievement represents a significant advancement in artificial intelligence, as Dreamer is capable of generalizing knowledge and self-improving through a technique known as reinforcement learning. Unlike previous AI systems that relied on guided learning or videos of human gameplay, Dreamer utilizes a self-constructed model of its environment to simulate potential future actions and make decisions. The successful use of Dreamer to navigate the complex task of finding diamonds illustrates its potential for broader applications in real-world scenarios, where trial and error can be more costly.

AI masters Minecraft: DeepMind program finds diamonds without being taught

DeepMind's AI Dreamer learns to find diamonds in Minecraft

The main article discusses the Elimination Game, a multi-player tournament designed to benchmark Language Learning Models (LLMs) in social reasoning, strategy, and deception. Players of the game engage in both public and private conversations, form alliances, and vote to eliminate each other in rounds until only two players remain. Ultimately, the jury of eliminated players votes to determine the winner. The game emphasizes various dynamics, such as the balance between public discussions and secret alliances, strategic voting, and the rhetorical skills needed to persuade others under pressure. It also analyzes metrics like voting patterns, alliance formation, and conversational strategies to describe how LLMs navigate complex social interactions.

Benchmarking LLM social skills with an elimination game

Benchmarking LLMs in social skills through an elimination game

Google has announced Sec-Gemini v1, a new experimental AI model aimed at advancing cybersecurity. This model seeks to help defenders better secure systems against cyber threats by leveraging AI to enhance cybersecurity workflows. Sec-Gemini v1 combines advanced reasoning capabilities with up-to-date cybersecurity knowledge, allowing it to perform superiorly on tasks such as incident root cause analysis and threat assessment. The model has shown to outperform others in cybersecurity benchmarks, integrating Google Threat Intelligence and making it available for research to select organizations and professionals. Its goal is to strengthen the defense against attacks by enhancing the tools available to cybersecurity experts.

Google announces Sec-Gemini v1 a new experimental cybersecurity model

Google launches Sec-Gemini v1, a new AI model for cybersecurity

The article discusses the recent advancements in multimodal image generation capabilities by companies like Google and OpenAI. It explains how these new systems allow Large Language Models (LLMs) to directly create images, enhancing the quality and specificity of the output compared to previous methods, which relied on separate image generation tools. The author illustrates the improvements through various examples, demonstrating the AI's ability to create more coherent and visually appealing images based on detailed prompts. The article also raises ethical and legal questions regarding artistic ownership and the potential risks of misuse, such as the creation of deepfakes. Ultimately, it highlights the transformative potential of these technologies in visual media while emphasizing the need for thoughtful frameworks to navigate their impacts.

No elephants: Breakthroughs in image generation

Advancements in multimodal image generation by AI

The main article discusses "Interview Coder," an AI-powered desktop application designed to assist job seekers during technical interviews by providing real-time coding support. It highlights features such as undetectability during screen sharing, problem-solving capabilities, and a user-friendly interface for capturing and debugging coding questions. Users can generate solutions with explanations, optimize their code, and utilize various commands to enhance their interview performance. The article also includes information on subscription pricing and common questions about the application.

Interview Coder is an invisible AI for technical interviews

Interview Coder: AI tool for technical interview support

The main article discusses the release of Llama 4, an advanced multimodal open-source AI model. It highlights various versions of Llama 4, including the "Maverick," which is noted for being the most intelligent in its class, the "Scout," designed for lightweight performance with a 10M context window, and other versions like "Behemoth" and "Reasoning." The article emphasizes the significance of Llama 4 in ushering a new era of AI innovation.

Llama4

Release of Llama 4: New multimodal AI model

The article discusses the launch of Llama 4 models, specifically Llama 4 Scout and Maverick, now available on GroqCloud. These advanced open-source AI models enable developers and enterprises to create personalized multimodal experiences with lower costs and predictable latency. The Llama 4 models use a Mixture of Experts (MoE) architecture for enhanced computational efficiency and are designed to integrate text and vision seamlessly. The article highlights the models' capabilities, including their performance metrics, multilingual support, and applications in AI tasks. GroqCloud offers these models with pricing details and encourages developers to start building with them.

Llama 4 Now Live on Groq

Llama 4 models launched on GroqCloud

A U.S. district judge ruled against OpenAI in a copyright lawsuit brought by The New York Times, which claims that ChatGPT's outputs violate its copyrights by outputting content derived from its articles. OpenAI had attempted to dismiss the case by asserting that the NYT should have raised concerns earlier but the judge found that OpenAI did not provide sufficient evidence to prove this. Furthermore, the judge ruled that OpenAI could be liable for contributing to user infringement. The NYT claims that OpenAI trained its AI models on its works without permission, and the judge noted there is plausible evidence of contributory infringement. While some claims against OpenAI were dismissed, the ruling overall is seen as a win for the NYT as it continues its pursuit of copyright claims against OpenAI and Microsoft.

OpenAI's Motion to Dismiss Copyright Claims Rejected by Judge

Judge rejects OpenAI's motion to dismiss copyright claims from NYT

The article discusses the author's experiences with recent AI model developments, particularly in the context of using AI for cybersecurity and vulnerability identification. The author expresses skepticism about the claimed improvements in new models such as those from OpenAI, noting that, apart from minor enhancements, newer models have not significantly advanced their ability to perform tasks relevant to their company’s goals. They argue that despite positive benchmark scores, these do not translate into practical utility or effectiveness in real-world applications. Furthermore, the author highlights a disconnect between model performance and user experience, suggesting that existing benchmarks may not accurately reflect the models' usefulness. Concerns are raised about the integrity of AI labs, the incentives for exaggerating capabilities, and the potential for "Goodhart's Law" to affect performance metrics. Ultimately, the article conveys a mixed outlook on AI advancements, emphasizing the need for better alignment of models with practical tasks and the importance of ethical considerations in AI development.

Recent AI model progress feels mostly like bullshit

Skepticism on recent AI model advancements

The article discusses the recent alteration of California Assembly Bill 501, which was designed to prevent OpenAI from transitioning from a nonprofit to a for-profit entity. Initially supported by a coalition of notable figures, the bill was mysteriously "gutted," losing its crucial provisions. The author, Gary Marcus, expresses concern over the changes and suggests that communications from OpenAI’s CEO, Sam Altman, may have influenced the bill's revision. Marcus calls on the media to investigate the situation further.

Bill to block OpenAI's for-profit conversion gets mysteriously gutted

California bill to block OpenAI's for-profit shift gets gutted

The main article discusses a modern HTML5 remake of the classic Atari game "Missile Command." It highlights the game's objective of defending cities from incoming attacks and details key features such as an in-game store for special weapons, including Sonic Waves and Mega Bombs. The article also provides gameplay controls and credits the development efforts to George Liu, utilizing AI tools for assistance.

Atari Missile Command Game Built Using AI Gemini 2.5 Pro

HTML5 remake of Missile Command using AI tools

The article discusses the impact of recently introduced US tariffs on technology imports, particularly affecting cloud and AI infrastructure in Europe. It highlights that these tariffs will lead to increased costs for EU companies relying on US-based hardware and cloud services, influencing prices of data center equipment, cloud services, and AI development. The article notes potential positive outcomes for European cloud providers, who may benefit from shifting demand, while also pointing out risks for European startups and a potential slowdown in innovation due to higher operational costs for AI projects. It advises companies to assess their exposure to US services, consider alternative European providers, and prepare for possible EU countermeasures to the tariffs. Ultimately, it emphasizes the need for strategic adaptation in response to these economic changes.

How new US tariffs are forcing Europe to rethink its entire tech stack

US tariffs force Europe to rethink tech stack and AI infrastructure

The article discusses the introduction of Gemini Live, a feature allowing users to interact with AI through their camera and screen on Android devices. It presents five practical applications for this technology: organizing spaces by seeking advice on decluttering, brainstorming creative projects by sharing inspiring images, troubleshooting issues through real-time camera input, receiving personal shopping advice while browsing online, and getting feedback on work or projects by sharing screens. The feature aims to enhance user experience by providing real-time support in various activities.

Gemini Live with camera and screen sharing capabilities

Gemini Live adds camera and screen sharing features on Android

The article discusses how Clearview AI, a facial recognition technology company, was founded with a far-right agenda aimed at targeting immigrants and those on the political left. The company's founders, particularly Hoan Ton-That, sought to integrate their technology with U.S. border surveillance to aid in the identification and tracking of migrants. Clearview's database consists of billions of images scraped from the internet, which can be used by law enforcement without warrants or probable cause.

The piece highlights the controversial implications of Clearview's technology, particularly its use by ICE and the FBI under the Trump administration, and its potential role in mass surveillance and deportation efforts against undocumented immigrants. The article further explores the connections between the founders and extremist far-right ideologies, detailing how their political beliefs influenced the company's operations and relationships with various government agencies. It raises concerns about the lack of oversight, accountability, and the ethical implications of using facial recognition technology for political and social control. The narrative emphasizes the chilling consequences this technology poses for civil liberties and privacy, particularly for marginalized communities.

Agenda Behind the Facial Recognition Tech Used by ICE and the FBI Revealed

Clearview AI's controversial role in immigration surveillance

The article discusses the negative impact of artificial intelligence (AI) on workers in the video game industry, featuring firsthand accounts from various professionals such as artists, designers, and developers. Many express feelings of demoralization and frustration as companies increasingly rely on AI-generated content, undermining traditional creative processes and the value of human artistry. Workers recount experiences where management pushes for AI usage in tasks that ultimately detract from their expertise and creativity, resulting in poorer quality outcomes. Concerns are raised about job security, ethical implications, and the commodification of artistic labor, with many artists advocating for a return to human-driven creation. Overall, the narrative highlights the struggle against the encroachment of AI in creative fields and the desire of those in the industry to preserve their skills and artistic integrity.

An Overwhelmingly Negative and Demoralizing Force

AI's negative impact on creativity in the video game industry

Meta has been accused of manipulating benchmarks to make its new AI model, Llama 4, appear superior to competitors. The company released two models, Maverick and Scout, with Maverick ranking highly on LMArena, an AI benchmark site. However, it was later revealed that the version of Maverick tested was an experimental, customized model not available to the public. This raised concerns about the reliability of benchmark rankings, as companies like Meta can present specially-tuned versions while releasing different versions for public use. The controversy highlights the competitive nature of AI development and the pressures companies face to demonstrate leadership in the field.

Meta got caught gaming AI benchmarks

Meta accused of manipulating AI benchmarks for Llama 4

The article discusses the Cyc project, initiated by Douglas Lenat in 1985, aimed at building a comprehensive knowledge base for artificial general intelligence (AGI) using symbolic logic. Despite significant investment—over $200 million and 2,000 person-years of effort—Cyc has failed to achieve its ambitious goals of reaching general intelligence or true machine learning capabilities. The project grew to include approximately 30 million assertions about common sense knowledge but has been criticized for its insular nature, lack of evidence for superior intelligence compared to existing technologies, and minimal impact on the broader AI research community.

Lenat's journey began with earlier systems like Automated Mathematician (AM) and EURISKO, which demonstrated early promise but ultimately "exhausted" themselves due to the limitations of heuristic rule-based learning. With Cyc, Lenat aimed to overcome these challenges by encoding vast amounts of common sense knowledge, predicting that the system would eventually learn autonomously. However, despite various stages of development and modifications in approach—including a focus on internet-sourced knowledge—Cyc has not produced the expected breakthroughs.

The article critiques the cyclical nature of the project’s failures, indicating that while it has reached a stable operational state, it has not produced the transformative AI capabilities that were envisioned, leading to a conclusion that the project has effectively ended in its current form, rebranded for commercial applications but lacking the original aspirational goals of AGI.

Obituary for Cyc

Cyc project: a failed attempt at AGI

The article announces the availability of Deep Research on Gemini 2.5 Pro Experimental, which is touted as the world's most capable AI model. Gemini Deep Research acts as a personal AI research assistant, showing significant improvements in analytical reasoning and information synthesis compared to other leading providers. Users testing the tool report enhanced capabilities in generating insightful research reports, accessible via web, Android, and iOS. The article also highlights a feature that allows users to convert reports into podcast-style audio overviews.

Deep Research is now available on Gemini 2.5 Pro Experimental

Deep Research now available on Gemini 2.5 Pro

The article announces the launch of the Agent2Agent Protocol (A2A), an open protocol designed to enhance interoperability among AI agents across diverse enterprise platforms. This initiative aims to enable autonomous agents to collaborate and communicate, regardless of the vendor or framework, thereby boosting productivity and reducing costs for businesses. Developed with contributions from over 50 technology partners, A2A allows agents to securely exchange information and handle complex tasks together. The protocol adheres to five key principles including embracing agentic capabilities, building on existing standards, and ensuring security. It is positioned as a critical step towards a future where agents can seamlessly work together to solve intricate problems and optimize enterprise workflows. The article emphasizes its open-source nature and encourages collaboration for ongoing development.

The Agent2Agent Protocol (A2A)

Launch of Agent2Agent Protocol for AI interoperability

The article discusses the increasing threat of fake job seekers using AI technologies to secure positions in companies, particularly for remote roles. Tech CEOs and industry experts highlight that these individuals may fabricate identities, create false employment histories, and use deepfake technology during interviews. Research suggests that by 2028, one in four job candidates globally could be fake. The risks of hiring such impostors range from financial fraud, data theft, to aiding foreign adversarial nations, as evidenced by incidents involving North Korean workers. The article emphasizes the challenges companies face in identifying these fraudulent candidates and suggests an increasing need for advanced identity verification solutions as deepfake technology improves.

Fake job seekers are flooding US companies that are hiring for remote positions

Rise of fake job seekers using AI for remote positions

Google is set to allow companies to run its Gemini artificial intelligence models in their own data centers starting in the third quarter of 2025. This development is part of Google's cloud strategy, enabling clients to utilize its technology while maintaining control over their data. In contrast to competitors like OpenAI and Anthropic, which do not offer such access, Google's Gemini models will also be compatible with Nvidia's GPUs. This move is expected to appeal to various organizations, including those in sensitive sectors such as government, who may prefer to operate within their own infrastructure. Additionally, Google's cloud infrastructure market share and ongoing investments in AI and security were highlighted in the announcement.

Google will let companies run Gemini models in their own data centers

Google allows companies to run Gemini models in their own data centers

The article discusses the frustrations software developers are experiencing due to corporate mandates surrounding the adoption of AI coding tools. While executives believe these tools enhance productivity and efficiency, many developers find that they introduce errors, create additional technical debt, and complicate deployments. Surveys indicate a growing dissatisfaction among developers about AI tools, as they often suggest incorrect code and necessitate more debugging efforts. The piece emphasizes the importance of understanding engineering workflows and empowering developers to choose tools that suit their needs, contrasting this with executives' top-down approaches that can create resentment and hinder effective adoption of AI technologies. Positive outcomes emerge when companies foster a culture of collaboration and support, allowing engineers to experiment with various AI tools effectively.

AI coding mandates are driving developers to the brink

Developers frustrated by AI coding mandates

The article discusses the advancements in visual reasoning capabilities for large language models (LLMs) following the release of OpenAI's GPT-4o model. It explains how traditional image manipulation with LLMs has been limited due to their reliance on text descriptions for generating images. The GPT-4o model addresses this by integrating the full context of conversations, allowing for more consistent image generation. The author highlights the potential applications of this technology, such as improved image editing, virtual try-ons, and enhanced infographic creation. The article emphasizes the importance of training models to think visually, using techniques like supervised fine-tuning and leveraging video content for training data. Ultimately, it predicts a future where models can reason better about visual and spatial scenarios, which could significantly impact fields like robotics and social interactions.

Visual Reasoning Is Coming Soon

Advancements in visual reasoning for LLMs with GPT-4o

The article introduces Firebase Studio, a full-stack AI workspace designed to streamline the app development lifecycle. It allows users to quickly build backends, front ends, and mobile apps by importing repositories and utilizing an App Prototyping agent that supports natural language commands and various templates. Firebase Studio integrates AI assistance through Gemini, which aids in coding tasks like debugging and testing. It offers tools for end-to-end testing and deployment monitoring, making it easier for developers to optimize their applications across different platforms. The article emphasizes that Firebase Studio is evolving to incorporate generative AI to enhance app development further.

Firebase Studio

Firebase Studio: AI workspace for streamlined app development

The article discusses the latest innovations in Google's AI infrastructure, specifically focusing on the AI Hypercomputer, which integrates advanced hardware and software to enhance performance for AI workloads. Key updates include the introduction of Ironwood TPUs, which offer substantial increases in compute capacity and energy efficiency, and the launch of A4 and A4X virtual machines supporting NVIDIA GPUs. Enhancements in networking and storage technologies, such as 400G Cloud Interconnect and Hyperdisk Exapools, facilitate greater efficiency and speed for AI applications. The article also highlights new software capabilities like Pathways on Cloud for training and inference, and advancements in resource management through Dynamic Workload Scheduler. Overall, the innovations aim to provide higher intelligence per dollar for AI tasks and streamline deployment and inference processes.

Google Cloud Rapid Storage

Google Cloud's AI Hypercomputer boosts performance with new TPUs and VMs

The article discusses Quasar Alpha, a new AI model released by OpenRouter that is believed to be associated with OpenAI. It highlights several pieces of evidence suggesting OpenAI's ownership, including similarities in model ID formats and API specifications. Quasar Alpha has quickly risen in popularity, particularly among coding tools like Roo Code and Cline. While it is viewed as a solid model, it is not considered state-of-the-art, indicating that OpenAI may prioritize practicality and efficiency over cutting-edge capabilities in this model.

Evidence suggesting Quasar Alpha is OpenAI's new model

Quasar Alpha: Evidence of OpenAI's new model

The article discusses the EQ-Bench Longform Creative Writing Benchmark, which evaluates large language models (LLMs) based on their creative writing abilities. It outlines how the benchmark assesses skills such as brainstorming and revising a story from minimal prompts, and it provides evaluation metrics including average chapter length, slop score for overused phrases, repetition metrics for word use, and degradation scores indicating the consistency of quality across multiple writing tasks. The scoring system rates model outputs on a scale from 0 to 100, aiming for higher scores to reflect better performance in generating creative content.

LLM Benchmark for 'Longform Creative Writing'

Evaluating LLMs for longform creative writing

The article discusses the development of Kilo Code, a new AI coding tool, which aims to be a superset of existing tools Roo Code and Cline. The authors advocate for a "don't innovate" mantra, suggesting that by copying and integrating successful features from other products rather than creating entirely new ones, they can provide greater value to the community. Kilo Code begins with identical features to Roo Code while also incorporating features from Cline and plans to continue adding popular features from various tools. The overarching goal is to create a comprehensive tool that enhances developer productivity and fosters collaboration in the open-source community. The authors invite feedback on desired features and emphasize their commitment to ongoing development and improvement.

Roo or Cline? We're building a superset

Building Kilo Code: A superset of Roo and Cline

The article discusses a 1992 interview with Isaac Asimov, wherein he describes artificial intelligence (AI) as devices that perform tasks traditionally associated with human intelligence. He believes that AI will liberate humans from menial work, allowing them to focus on more creative pursuits. Asimov argues that AI and human intelligence can complement each other and advance together, despite potential challenges and societal adjustments needed to accommodate AI, much like how cities evolved with the advent of automobiles. The piece reflects on how Asimov's views might apply to the world in 2025.

Isaac Asimov describes how AI will liberate humans and their creativity (1992)

Asimov on AI's potential to free human creativity

The article discusses the 2024 AI Index Report, which provides a comprehensive overview of artificial intelligence's advancements and societal impact. Key highlights include significant improvements in AI performance on benchmarks, increased integration of AI into everyday life, and record levels of private investment in AI, particularly in the U.S. The report notes that while the U.S. leads in AI model production, China is rapidly closing the gap in quality. It emphasizes the evolving landscape of responsible AI (RAI), the growing urgency for AI governance, and rising global optimism about AI, although regional disparities exist. Additionally, the article outlines advancements in AI-driven education and the challenges in complex reasoning tasks faced by models, reflecting the technology's transformative role across various sectors.

2025 AI Index Report

2025 AI Index Report highlights advancements and challenges

Google is set to adopt Anthropics Model Context Protocol (MCP) for connecting its AI models to data sources, following OpenAI's announcement that it would do the same. Demis Hassabis, CEO of Google DeepMind, expressed support for MCP, highlighting its potential as an open standard. This protocol facilitates two-way connections between AI applications and various data repositories, enhancing the capabilities of tools like chatbots. Since its open-source release, several companies have integrated MCP into their platforms.

Hassabis Says Google DeepMind to Support Anthropic's MCP for Gemini and SDK

Google DeepMind backs Anthropic's MCP for AI model connections

Albert Saniger, the founder and former CEO of the AI shopping app Nate, has been charged with fraud after it was revealed that the app, which claimed to allow seamless online purchases through AI, was primarily operated by human contractors in the Philippines. Although Nate raised over $50 million in funding by promoting its automation capabilities, the actual automation rate was effectively zero, relying instead on manual transactions. After running out of money and being forced to sell its assets in January 2023, investors were left with significant losses. The case highlights issues of exaggerated AI claims within the startup ecosystem.

Fintech founder charged with fraud; AI app found to be humans in the Philippines

Fintech founder charged after AI app revealed to be run by humans

The article humorously explores the trend of AI company logos resembling anatomical shapes, specifically buttholes. It identifies common design elements such as circular shapes, gradients, and central openings that contribute to this phenomenon. The author notes that many prominent AI companies have adopted similar logo designs, leading to a homogenization in branding. Various factors, including design psychology and corporate risk aversion, are discussed as reasons behind this trend. The article also highlights past design trends in the tech industry and suggests that while familiarity in logos can build trust, there's a need for innovation and differentiation in branding to avoid the "Butthole Era." Ultimately, it calls for AI companies to find new visual languages that reflect innovation without relying on tired design clichés.

Why do AI company logos look like buttholes?

The article introduces a new AI website builder from WordPress.com that simplifies the website creation process by allowing users to generate fully designed, content-ready WordPress sites through conversational prompts. It targets entrepreneurs, freelancers, bloggers, and developers who need professional-looking websites without the complexity of traditional site-building. The AI tool creates beautiful websites in minutes by streamlining tasks like selecting themes, adding text, and incorporating images. While currently limited to new sites, future updates are expected to enhance its functionality. The article encourages users to test the AI builder for free and emphasizes its ease of use, making it accessible to those without technical skills.

Our New AI Website Builder

New AI website builder from WordPress.com

U.S. Secretary of Education Linda McMahon mistakenly referred to AI as "A1" during a tech conference at the ASUGSV Summit, where she discussed plans to integrate AI into classrooms as early as kindergarten. Although she expressed the potential benefits of AI for personalized learning, her repeated error sparked ridicule on social media, with users mocking her for the mix-up and questioning her understanding of the technology. The article highlights her intentions to improve technology in education despite the gaffe.

Education Secretary Wants 'A1' in Classrooms

The article discusses the challenges of using AI tools for coding, emphasizing the importance of clear communication over complex prompts. Using a personal anecdote about a classroom exercise where instructions to make a peanut butter and jelly sandwich led to chaos, the author contrasts this with the current capabilities of AI. While AI has advanced, it still struggles with unclear or vague instructions, especially for tasks that require creativity or specific context. The key takeaway is that success in the AI era relies on the ability to articulate precise instructions and a clear vision of desired outcomes, rather than just prompt engineering.

AI Coding and the Peanut Butter and Jelly Problem

AI coding challenges and the peanut butter and jelly problem

The article discusses Transformer Lab, an open-source platform designed for building, tuning, and running large language models (LLMs) locally without the need for coding knowledge. It highlights features such as one-click downloads of popular models, support for fine-tuning across different hardware, various inference engines, and tools for dataset creation and evaluation. The platform aims to make LLMs accessible to software developers and promotes a vision where these models can be seamlessly integrated into products.

Transformer Lab

Transformer Lab: Build and run LLMs easily

The article discusses Google's significant advancements in artificial intelligence, particularly through its DeepMind division and the launch of the Gemini 2.5 model. The author argues that after initial hesitations, Google is now outperforming competitors like OpenAI and Anthropic, citing Gemini 2.5 Pro as the best AI model available. The article highlights Google's dominance across various generative AI areas, including music, video, and voice, and mentions its commitment to integrating AI into the broader Google ecosystem. Overall, it presents Google's renewed leadership in the AI field as a major development in technology.

Google Is Winning on Every AI Front

Google's dominance in AI with Gemini 2.5 model

Chelsea and Westminster Hospital in London has implemented an AI tool to assist in skin cancer checks, enabling patients to receive the all-clear without seeing a doctor. The technology has shown 99% accuracy in diagnosing benign cases and has significantly reduced waiting times for urgent skin cancer referrals. Patients can quickly have their moles and lesions photographed and analyzed, with many being discharged without specialist input. This system has been adopted by over 20 NHS hospitals and has helped detect over 14,000 cases of cancer in the UK. There are hopes for the future development of AI diagnostic tools that patients might use independently.

AI used for skin cancer checks at London hospital

AI tool for skin cancer checks at London hospital

The article discusses the author's experiences with AI programming tools like Claude and their impact on the enjoyment of coding. Initially excited by the productivity gains and quality of AI-generated code, the author eventually feels unsatisfied and questions whether programming will become merely a hobby as AI capabilities improve. The author expresses concern about the rising costs associated with these tools, which could create barriers to entry in software development, especially for those in lower-income situations. Ultimately, the author predicts that software development will become less enjoyable and more inaccessible in the future, viewing this as a bitter outcome influenced by economic forces.

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