The article discusses how the author, Simon Willison, effectively utilizes Large Language Models (LLMs) to assist in coding. It addresses common frustrations developers face when using these models, emphasizing that while LLMs can significantly enhance productivity in coding, they require proper usage and context management to yield good results. Key recommendations include setting realistic expectations, being mindful of the models' training cut-off dates, providing clear instructions, and treating the interaction as a conversation. The author shares personal experiences and detailed techniques, such as using LLMs for initial research, managing context, and validating the generated code. Additionally, he introduces the concept of "vibe-coding," which involves a relaxed, exploratory approach to coding with LLMs. Ultimately, the article highlights the importance of human oversight and expertise when leveraging LLMs for programming tasks, and how these tools can accelerate learning and project development.

Here's how I use LLMs to help me write code

Using LLMs to enhance coding productivity

The article discusses the dangers of using AI-generated voice evidence in court, highlighting the inadequacy of current authentication standards under Federal Rule of Evidence 901. It presents a case where an individual almost fell victim to an AI voice scam, emphasizing that the technological advancements in voice cloning have made it difficult to distinguish between real and AI-generated voices. The authors argue that the authentication process should be revised, allowing judges discretion to exclude evidence even if a witness claims familiarity with the voice. They recommend modifying the rules to transition from mandatory to permissive standards for voice evidence authentication, ensuring courts can better evaluate the reliability of such evidence in light of AI advancements.

AI-Generated Voice Evidence Poses Dangers in Court

Risks of AI-generated voice evidence in court

The article discusses the introduction of "Learning to Drive" (L2D), the world's largest open-source multimodal dataset aimed at enhancing spatial intelligence in the automotive domain. Developed by Yaak in collaboration with the LeRobot team, L2D aims to support the robotics AI community by providing over 1 petabyte of data collected from 60 electric vehicles operated by driving instructors and learner drivers in 30 German cities over three years. The dataset captures a diversity of driving scenarios and includes expert and student driving policies, accompanied by natural language instructions for various driving tasks. The goal of L2D is to facilitate end-to-end learning for self-driving models, addressing the current limitations in existing self-driving datasets. The article emphasizes the potential for L2D to unlock significant advancements in AI for robotics and autonomous driving.

Open-sourcing 5,000hrs of self-driving dataset

Open-sourcing 5,000 hours of self-driving dataset for AI

The article discusses Mayo Clinic's innovative approach to mitigating AI hallucinations in healthcare applications, particularly using a technique called Reverse RAG (Retrieval-Augmented Generation). This method involves linking extracted information back to its original data sources, effectively eliminating inaccuracies in data retrieval. The clinic has successfully implemented this process in handling electronic health records and streamlining discharge summaries, significantly reducing the time required for data processing. Additionally, Mayo Clinic is exploring AI in more complex areas, including genomics and imaging, aiming to enhance diagnostic capabilities and personalize patient care. The challenges of validating AI in clinical environments remain, but there is optimism about its transformative potential in healthcare.

Mayo Clinic's secret weapon against AI hallucinations: Reverse RAG in action

Mayo Clinic tackles AI hallucinations with Reverse RAG

The main article celebrates the milestone of comma.ai selling its 10,000th comma 3X unit, marking the company's first product to surpass 5-digit sales. It reflects on the challenges of hardware development and the progress made since the launch of their first product, the panda, in 2017. The article discusses improvements in manufacturing, supply chain, and unit economics, highlighting optimism for 2025 as a significant year for the company, with plans to expand their office, data center, and manufacturing capacity. It also mentions the company's strong position in the automotive tech sector, having the second-largest fleet after Tesla, which will enhance data processing and training capabilities.

Happy 10k Day

comma.ai celebrates selling 10,000th comma 3X unit

The article discusses the author's experience using Cursor, an AI tool for coding, and offers tips on how to maximize its effectiveness while avoiding common pitfalls. The author emphasizes keeping cursor rules minimal, providing context for the code, and being cautious with the AI's outputs. They suggest regularly reviewing and understanding key parts of the code, manually refactoring as needed, and using AI to assist with coding rather than relying on it entirely. The article concludes by encouraging experimentation with AI tools, while also acknowledging the potential drawbacks for junior developers and the influence of the tech stack on the AI's performance.

I use Cursor daily - here's how I avoid the garbage parts

Tips for using Cursor AI tool effectively

The article discusses the introduction of Gemini Robotics, a new AI model by Google DeepMind based on Gemini 2.0, designed to enhance robotics capabilities by integrating AI into the physical world. It highlights two main models: Gemini Robotics, a vision-language-action model for controlling robots, and Gemini Robotics-ER, focused on advanced spatial understanding and embodied reasoning. These models aim to enable robots to perform a wider range of real-world tasks, characterized by generality, interactivity, and dexterity. The article emphasizes the importance of safety in robotics, detailing efforts to ensure robots can operate safely in dynamic environments while addressing societal implications through responsible AI development. Collaborations with partners like Apptronik and the release of new datasets for evaluating robotic safety are also mentioned.

Gemini Robotics brings AI into the physical world

Gemini Robotics: Google's AI model for real-world robotics

The article discusses the introduction of native image generation capabilities in Gemini 2.0 Flash, a feature now available for developers to experiment with. It highlights the model's strengths, including multimodal output that combines text and images, conversational image editing, enhanced world understanding for realistic imagery, and improved text rendering. Developers can use the Gemini API to create images and interactive stories, with the goal of refining the model further based on user feedback.

Experiment with Gemini 2.0 Flash native image generation

Gemini 2.0 Flash adds native image generation features

The article discusses the cultural divide between traditional mathematics and AI research, as observed during the 2025 Joint Mathematics Meeting (JMM). The author reflects on the contrasting values and approaches of mathematicians, who prioritize understanding and openness, and AI researchers, who focus on practical applications and often operate in more secretive environments. Key differences include the motivations behind research, with mathematicians pursuing knowledge for its own sake, while AI researchers aim to create commercially viable technologies. The article emphasizes the need for better collaboration between the two fields, exploring potential areas for cooperation, such as literature management and theorem verification, while advocating for the preservation of mathematical culture amid technological advances. Ultimately, the author expresses optimism about the collaborative possibilities that can enhance both fields without diminishing the human element and understanding that underpin mathematical research.

The Cultural Divide Between Mathematics and AI

Cultural divide between mathematics and AI research

The article discusses Apple's recent announcement regarding the delay of more personalized Siri features under the Apple Intelligence umbrella, which were expected to launch soon. The author expresses frustration at having missed early warning signs about these features' unpreparedness and examines Apple's failure to demonstrate these functionalities at previous events, branding it as a significant credibility issue for the company. The author argues that Apple prioritized a compelling narrative around their AI capabilities over the reality of their product readiness. This approach is seen as reminiscent of past failures in Apple's history when they overpromised and underdelivered, leading to a tarnished reputation. Ultimately, the article critiques Apple's leadership decisions and emphasizes the importance of accountability and integrity in maintaining the company's credibility.

Something Is Rotten in the State of Cupertino

Apple's Siri features delay raises credibility concerns

OpenAI has requested the Trump administration to provide relief from increasing state regulations on artificial intelligence (AI) if AI companies agree to voluntarily share their models with the federal government. In a 15-page policy suggestion, OpenAI expressed concerns that the multitude of pending state AI bills could hinder technological progress in the U.S., especially amidst competition from China. OpenAI proposed that the U.S. AI Safety Institute act as a liaison between the government and AI companies, offering liability protections and exemptions from state regulations in exchange for collaboration on reviewing models. The company also emphasized the need for federal support in AI infrastructure, copyright reform, and access to government-held data to maintain U.S. leadership in AI development.

OpenAI Asks White House for Relief from State AI Rules

OpenAI seeks relief from state AI regulations from White House

The article discusses the introduction of Command A, a high-performance, scalable language model that reportedly outperforms GPT-4o and DeepSeek-V3 in enterprise tasks while demonstrating greater efficiency.

Command A: Max performance, minimal compute – 256k context window

Command A: High-performance language model with 256k context window

The article discusses Hopsworks' migration from AWS to OVHCloud, emphasizing how this transition led to a 62% reduction in costs. Hopsworks, a platform for developing and operating AI systems, sought to decrease expenses, particularly concerning network egress charges prevalent with AWS. They found OVHCloud to offer managed Kubernetes, S3 compatible storage, and other services at a significantly lower cost. The migration process involved a 24-hour maintenance window during which Hopsworks backed up data and transitioned to OVHCloud with minimal disruption to users. By Q4 2024, the migration was complete, resulting in a resilient infrastructure while maintaining serverless services in North America for user latency. The move to OVHCloud also established a partnership aimed at developing a sovereign AI platform in Europe.

Migrating from AWS to a European Cloud – How We Cut Costs by 62%

How Hopsworks cut costs by 62% migrating to OVHCloud

The article discusses how ProPublica utilizes artificial intelligence (AI) responsibly in its investigative journalism, specifically in analyzing a database of grants released by Senator Ted Cruz. The database, which claimed to highlight "woke" grants from the National Science Foundation, was examined by ProPublica reporters using AI to identify themes relating to diversity, equity, and inclusion. The analysis revealed that many flagged grants were unrelated to the social themes cited by Cruz. The article emphasizes the importance of human oversight while using AI as a tool for data analysis in reporting, showcasing previous successful applications of AI in various investigations conducted by ProPublica.

How ProPublica Uses AI in Its Investigations

ProPublica's use of AI in investigative journalism

Amazon is changing its privacy policy for Echo devices, requiring users' voice recordings to be sent to the cloud for processing starting March 28, 2025. Previously, users could opt to store voice requests locally, but this option will be eliminated as Amazon rolls out new generative AI capabilities for Alexa. The decision raises privacy concerns, especially given Amazon's history of mishandling voice recordings and allowing employees to listen to them. Users will have to choose between granting Amazon access to their voice recordings or losing functionality like the Voice ID feature, which requires saved recordings to work effectively. This shift is seen as a move to enhance Alexa's capabilities while prioritizing the monetization of the service over user privacy.

Everything you say to your Echo will be sent to Amazon starting on March 28

Amazon to send Echo voice recordings to the cloud starting March 28

The article discusses the challenges faced by professionals navigating the job market in 2025, particularly in the context of the transitioning technology landscape dominated by foundational models and large language models (LLMs). It highlights how previous management skills are becoming less relevant, leading to dissatisfaction among senior leaders who are struggling to adapt. The author notes that many individuals, especially those seeking senior roles, feel left behind as companies prioritize different skill sets. The market dynamics are also shifting, with reduced funding and hiring, resulting in increased competition for desirable positions. The piece emphasizes that current job seekers should not take difficulties personally, and suggests that making the best of their current situations may be more beneficial than trying to wait out the cycle. Overall, it provides a realistic appraisal of the job market and the need for adaptation in a rapidly changing professional environment.

Career Advice in 2025

Navigating the job market in 2025 with LLMs

The article discusses the competitive landscape in chipmaking, particularly highlighting ASML, a Dutch company that produces advanced lithography tools essential for creating cutting-edge AI chips. It emphasizes the significance of ASML's latest machine, a massive and sophisticated device valued at around $350 million, which is crucial for the future of artificial intelligence. The article implies that overtaking ASML in this technology will be a challenging task for competitors.

The race is on to build the most complex machine

ASML's advanced machine crucial for AI chipmaking competition

The article discusses the third edition of "Artificial Intelligence Foundations of Computational Agents" by David L. Poole and Alan K. Mackworth, which presents a comprehensive introduction to the science of artificial intelligence (AI). The book serves as a textbook accessible to both undergraduate and graduate students, providing a synthesis of AI as a formal science and engineering discipline. It emphasizes the link between theory and practice, integrates modern machine learning concepts, and includes discussions on ethical and societal impacts of AI. The third edition features new chapters on deep learning and causality, along with a focus on the responsible design and implementation of AI technologies. The book is supported by online resources, AIPython implementations, and instructor materials, making it a valuable resource for students, educators, and practitioners in the field of AI.

Artificial Intelligence: Foundations of Computational Agents

Overview of AI textbook: Foundations of Computational Agents

The article discusses the gradual loss of historical internet content as web pages disappear over time. It emphasizes the importance of the Internet Archive in preserving digital history, likening it to a sacred space. The author reflects on the cultural significance of early internet subcultures, personal blogs, scientific papers, and digital art that are at risk of being lost forever. The piece suggests that while preservation efforts may be challenging and economically unviable, large language models (LLMs) can serve as a means to compress and retain some information, even if imperfectly. The author advocates for efforts to ensure that the weights of publicly released LLMs are preserved, and that the Internet Archive is included in their training sets.

Big LLMs weights are a piece of history

Preserving digital history with LLM weights

The article discusses the challenges posed by artificial intelligence (AI) training on open knowledge resources, particularly those licensed under Creative Commons. It highlights the frustrations of creators whose freely shared works are being exploited by large tech companies without fair compensation or respect for the original contributions. The author argues that while the open access movement aims to provide equitable access to knowledge, the rising trend of restrictive licensing and paywalls in response to AI misuse threatens the very essence of open knowledge. The article advocates for establishing fair terms and compensation for the use of creators’ works, emphasizing the need for legal frameworks that protect creators’ rights while maintaining the accessibility of knowledge for all. The future of free and open access depends on collaboration between AI companies and knowledge repositories to ensure that all can benefit from shared resources.

"Wait, not like that": Free and open access in the age of generative AI

Challenges of open access in the age of generative AI

The article discusses the drawbacks of relying on AI tools, specifically LLMs (Large Language Models), in software development. The author argues that while these tools can enhance productivity, they may also lead to a decline in developers' skills and knowledge retention. By depending on LLMs for coding tasks, developers risk forgetting fundamental concepts and the satisfaction of problem-solving independently. The piece highlights the idea of "Copilot Lag," where developers become reliant on AI suggestions, hindering self-sufficiency. Although LLMs can be used effectively for research and learning if approached with skepticism, the author emphasizes the importance of understanding the underlying concepts rather than simply accepting AI-generated code. Overall, the article advocates for a balanced use of AI in development, encouraging developers to maintain their critical thinking and learning processes.

AI Is Making Developers Dumb

The downside of AI tools for developers

The article discusses a leaked Apple meeting revealing the company's struggles with Siri and artificial intelligence features. Senior director Robby Walker acknowledged delays in delivering AI capabilities initially promised for Siri, with uncertainty about their inclusion in the upcoming iOS 19. Walker expressed concern for employees feeling frustrated and noted tensions between the Siri team and marketing over unready features that were previously highlighted, leading to unrealistic customer expectations. He emphasized the importance of delivering a more robust Siri experience, acknowledging quality issues that have contributed to the delays. Senior executives are taking accountability for the situation as Apple faces criticism for its slow progress.

Leaked Apple meeting shows how dire the Siri situation is

Apple's Siri struggles revealed in leaked meeting

Microsoft is introducing a paywall for certain features in its iconic apps Notepad and Paint, requiring users to have an active Microsoft 365 subscription to access these features. Notably, new AI functions in both apps, such as AI rewriting and image generation capabilities in Paint, will only be available to subscribers. Users can still utilize the basic functions of Notepad and Paint without a subscription, but will miss out on these new offerings. This shift marks a significant change, as Microsoft had previously provided full access to these applications without a paywall.

Microsoft paywalling features in Notepad and Paint

Microsoft adds paywall for AI features in Notepad and Paint

The article discusses the frustrations of the author, Drew DeVault, with the impact of aggressive LLM (large language model) crawlers and externalization of costs on their work at SourceHut. DeVault highlights how these crawlers, alongside past experiences with cryptocurrency miners and resource abuse by larger tech companies, have significantly disrupted their operations, leading to increased outages and delays in critical tasks. They express a strong disdain for the implications of LLMs and similar technologies, urging the tech community to reconsider their approaches and priorities, and imploring developers to cease their work on these technologies altogether due to their negative societal impacts.

Please stop externalizing your costs directly into my face

Frustrations with LLM crawlers and externalized costs

The article discusses the evolving landscape of artificial intelligence (AI), emphasizing that the model itself has become the primary product, rather than just an application layer built on top of it. It highlights the stalling of generalist scaling in AI training, the improved efficiencies through techniques like reinforcement learning, and the necessity for model providers to adapt their strategies in light of economic realities. The text elaborates on new models like OpenAI's DeepResearch and Anthropic's Claude 3.7, which represent a shift towards models capable of autonomous processes and end-to-end task execution. It argues that traditional workflows are being challenged by these advanced models that can redesign processes for greater efficiency. The article raises concerns about the current investment environment, suggesting that many are ignoring the value of model training while betting on applications. Overall, it points towards an impending disruption in the AI sector, where model providers are likely to capture more value at the expense of wrappers and applications built on their models.

The Model Is the Product

The model is the product in AI's evolving landscape

The article discusses the capabilities and limitations of artificial intelligence (AI) in generating system architecture diagrams. It examines three use cases: creating generic diagrams, whiteboarding proposed systems, and diagramming real-life systems. The author illustrates the strengths and weaknesses of AI-generated diagrams, noting that while AI can produce good starting points for whiteboarding, it struggles with detailing existing systems from source code. Challenges include a lack of training data, complex code analysis, and difficulty understanding system intent. Ultimately, the article concludes that while AI can assist in diagramming, it cannot replace the need for human expertise and investigation in generating accurate and informative system diagrams.

Diagrams AI can, and cannot, generate

AI's strengths and weaknesses in generating system diagrams

The article discusses the challenges faced by SourceHut, an open-source git-hosting service, due to excessive demands from AI crawlers, particularly those used for training large language models (LLMs). SourceHut has reported disruptions in their services and has implemented mitigation strategies, such as deploying a tar pit system to trap these crawlers, though this may affect user access to some pages. They have also blocked several cloud providers that generated high volumes of bot traffic. Over the past two years, AI crawlers have increasingly burdened various online services, leading to concerns about bandwidth overload. While some AI companies have pledged to respect robots.txt files to manage crawling behavior, reports of abusive crawling persist. The article highlights the complications arising from these issues, including the spoofing of user-agent strings and the challenge of distinguishing between legitimate and problematic bot traffic.

AI crawlers haven't learned to play nice with websites

Challenges of AI crawlers on websites like SourceHut

The article discusses the upcoming keynote by NVIDIA CEO Jensen Huang at the GTC 2025 conference, which will focus on advancements in agentic AI, robotics, and accelerated computing. The event will take place from March 17-21, 2025, in San Jose, CA, with various sessions, workshops, and opportunities for networking and engagement within the tech community.

Jensen Huang – Nvidia GTC 2025 Keynote

Nvidia CEO Jensen Huang to keynote GTC 2025 on agentic AI and robotics

Amazon has announced that beginning March 28, 2025, it will discontinue on-device voice processing for its Alexa-enabled Echo devices, pushing all voice commands to the cloud for processing. Although Amazon has not made a formal announcement, emails have confirmed the change, which affects users who had opted for the "Do Not Send Voice Recordings" feature. This decision is driven by the need for processing power to support new generative AI features that cannot run on existing Echo hardware. As a result, privacy-conscious users will automatically lose the ability to keep their voice recordings on-device, potentially limiting the personalization features available to them. Despite concerns about user privacy, Amazon claims that their system maintains privacy and security, while also promoting the advancement of AI capabilities through cloud processing.

Amazon to kill off local Alexa processing, all voice requests shipped to cloud

Amazon moves Alexa voice processing to the cloud

The article discusses the Federal Trade Commission's (FTC) recent removal of business guidance posts critical of major tech companies, including Amazon and Microsoft, that were published during the Biden administration. Over 300 blogs were deleted, which included important information on consumer protection related to artificial intelligence and privacy violations. Critics argue that these removals raise compliance concerns and enable tech companies to operate with less regulatory oversight. The article also touches on the shift in FTC leadership under President Trump, with new chair Andrew Ferguson focusing on reducing regulations and emphasizing different issues, like conservative speech censorship on social media.

FTC Removes Posts Critical of Amazon, Microsoft, and AI Companies

FTC removes posts critical of Amazon, Microsoft, and AI companies

The article discusses the launch of Cloudflare's AI Labyrinth, a new approach to mitigate the impact of misbehaving bots and AI crawlers that ignore "no crawl" directives. By using AI-generated content, the system slows down and distracts these crawlers without alerting them to being blocked. When unauthorized bot activity is detected, an AI-generated series of linked pages are presented, effectively wasting the crawlers' resources and serving as a next-generation honeypot to identify and track malicious bots. The AI Labyrinth is easy to enable for Cloudflare customers and aims to improve bot detection capabilities while maintaining the normal user experience on websites. The article also mentions ongoing efforts to enhance the stealth and integration of this system into existing website structures.

Trapping misbehaving bots in an AI Labyrinth

Cloudflare launches AI Labyrinth to trap misbehaving bots

The article provides a beginner-friendly tutorial on understanding how LLM (Large Language Model) agents operate as simple graphs. It aims to demystify the technical complexities associated with AI agents by explaining their workings in plain language. The guide covers the basic components of an agent system, likening nodes to cooking stations in a kitchen and the flow to a recipe guiding the process. It details how agents make decisions based on user input, actions, and gathered information, ultimately illustrating the process of building a simple research assistant that can search for information and provide answers. The tutorial emphasizes that LLM agents consist of decision nodes, action nodes, and loops that facilitate their functionality, making them accessible even to those without a strong technical background.

LLM Agents Are Simply Graph – Tutorial for Dummies

Beginner's guide to LLM agents as simple graphs

The article discusses FutureHouse, a nonprofit research institute in San Francisco focused on automating scientific discovery using AI. The institute has developed several crow-themed tools, such as ChemCrow for chemical reactions and WikiCrow for compiling protein information. FutureHouse aims to let AI read and reason about biological data, with a ten-year mission to create semi-autonomous AIs that can explore new research directions. The co-founders, Sam Rodriques and Andrew White, discuss the challenges of creating AI scientists, including building infrastructure and ensuring reliability of information. They emphasize the potential of AI to improve scientific reproducibility and outline their future plans for integrating AI more deeply into scientific research.

The Humans Building AI Scientists

FutureHouse: Nonprofit aims to create AI scientists

SoftBank Group Corp. has announced its acquisition of Ampere Computing, a semiconductor design company, in an all-cash deal valued at $6.5 billion. Ampere will continue to operate as a subsidiary under its own name. The acquisition aims to enhance SoftBank's capabilities in AI infrastructure and high-performance computing, aligning with the company's vision for advancing artificial intelligence. The transaction is subject to regulatory approvals and is expected to close in the second half of 2025. Ampere, founded in 2018, focuses on energy-efficient AI compute and has a diverse range of products for cloud workloads.

SoftBank Group to Acquire Ampere Computing for 6.5B

SoftBank to acquire Ampere Computing for $6.5B

The article discusses the growing issue of AI companies employing aggressive web scraping techniques that negatively impact Free and Open Source Software (FOSS) infrastructure. It highlights incidents where AI crawlers overwhelmed platforms like SourceHut and KDE GitLab, causing outages and disrupting work for developers. The author references discussions among sysadmins about the challenges of distinguishing between human users and bots, as well as the difficulty in mitigating these attacks. Various projects have resorted to drastic measures, such as implementing Anubis, a proof-of-work challenge, to filter out bots. The article also touches on the rise of AI-generated bug reports, which complicate the workload for developers due to their misleading nature. Overall, the piece underscores the disproportionate burden these issues place on the FOSS community, which lacks the same resources as commercial entities.

FOSS infrastructure is under attack by AI companies

AI companies attacking FOSS infrastructure with web scraping

The article expresses concern about the increasing threats to the unauthenticated web due to the aggressive scraping practices of LLM and AI companies. It discusses how these companies have begun targeting not only larger websites with protections but also smaller project platforms, raising fears of widespread authentication requirements and challenges. The author suggests that users of cloud infrastructure should set billing limits to avoid unexpected costs from these abuses and highlights the difficulties of seeking compensation for damages caused by anonymous abusers.

I fear for the unauthenticated web

Concerns over unauthenticated web and AI scraping practices

OpenAI is facing a privacy complaint in Europe from the advocacy group Noyb due to ChatGPT generating false and defamatory information about an individual, Arve Hjalmar Holmen, who was incorrectly identified as having been convicted of child murder. The complaint highlights concerns under the EU's General Data Protection Regulation (GDPR), which requires personal data to be accurate, and notes that OpenAI lacks an effective way for individuals to correct incorrect information generated by the AI. This issue is compounded by recent updates that have led to the AI hallucinating false details. Noyb argues that even a disclaimer about potential errors does not fulfill OpenAI's legal responsibilities under GDPR. The case aims to prompt regulators to address the risks posed by AI-generated misinformation, with similar complaints against ChatGPT indicating this is not an isolated incident.

ChatGPT hit with privacy complaint over defamatory hallucinations

OpenAI faces privacy complaint over ChatGPT's false information

The article announces that Claude, an AI model developed by Anthropic, can now search the web to provide more up-to-date and relevant responses. This feature allows Claude to access the latest events and information, enhancing its accuracy, and it includes direct citations for easy fact-checking. The article highlights various applications of this web search capability, such as helping sales teams analyze industry trends, assisting financial analysts with market data, aiding researchers in developing proposals, and enabling shoppers to compare product information. Currently, web search is available in feature preview for paid users in the U.S., with plans to expand access to free users and other countries soon.

Claude can now search the web

Claude AI model adds web search feature

The main article discusses how OpenAI utilizes the open-source Ory Hydra to authenticate over 400 million weekly active users. It outlines the evolution of Ory Hydra from its inception as a Go-based alternative to Keycloak to its current status as a leading open-source OAuth2 server. The article highlights the simplification of the authentication process through the removal of user management features, allowing for easy integration into existing infrastructures. It emphasizes Ory Hydra's performance optimizations, achieving high throughput for authentication flows, thus catering to the demands of the world's busiest websites. Overall, the article showcases Ory Hydra's reliable technology, scalability, and the importance of community involvement in its development.

OpenAI uses open source Ory to authenticate over 400M weekly active users

OpenAI uses Ory Hydra for authenticating 400M users weekly

Google has introduced Gemma 3, its latest AI model, which is touted as the most powerful model that can run on a single GPU. This iteration builds on the previous open Gemma AI models and is designed for developers to create AI applications that can function across a range of devices. Gemma 3 can interpret not only text but also images and short videos, supporting over 35 languages. Google claims that it outperforms competitors like Facebook's Llama and OpenAI in terms of performance on a single GPU setup. The model features enhancements like a better vision encoder and a new image safety classifier to filter inappropriate content. Despite its advanced capabilities, Google has maintained restrictions on the model's use, continuing to promote it through various programs, including credits for academic research.

Google calls Gemma 3 the most powerful AI model you can run on one GPU

Google's Gemma 3: Most powerful AI model for a single GPU

Apple is undergoing a significant executive reshuffle to improve its artificial intelligence initiatives, particularly related to Siri. CEO Tim Cook has lost confidence in John Giannandrea, the current head of AI, and has appointed Mike Rockwell, known for his work on the Vision Pro headset, to oversee Siri. This management change aims to address months of delays in AI features and improve Siri's performance, as the company struggles to keep up with its competitors in the AI space. Despite challenges, Rockwell's experience could help integrate AI more effectively into Apple's future products. Giannandrea will remain with the company, overseeing other AI-related responsibilities. The changes reflect Apple's urgent need to revitalize Siri and its AI capabilities, especially after setbacks in rolling out promised features.

Apple shuffles AI executive ranks in bid to turn around Siri

Apple reshuffles AI execs to improve Siri performance

The article discusses a study exploring the alignment between how large language models (LLMs) and the human brain process natural language. Researchers found that neural activity in the human brain correlates with internal representations created by LLMs during everyday conversations. Specifically, they examined the Whisper speech-to-text model, revealing that its embeddings align with neural patterns in brain areas responsible for speech comprehension and production. The study highlights shared computational principles, such as predicting the next word in a sequence, between LLMs and the human brain, but also notes significant differences in their architectures. The findings suggest that deep learning models can provide insights into the neural code of language processing, with future goals aimed at developing biologically inspired neural networks enhanced for real-world information processing.

Deciphering language processing in the human brain through LLM representations

Study links LLM representations to human brain language processing

The article discusses the ethical implications and legal challenges surrounding Meta's use of pirated books from Library Genesis (LibGen) to train its AI model, Llama 3. It highlights how Meta sought to acquire high-quality texts quickly and inexpensively, ultimately resorting to downloading data from LibGen due to the slow and costly legal licensing process. The article reveals that both Meta and OpenAI have used LibGen datasets for AI training, raising questions about copyright infringement and fair use. Internal communications from Meta indicate awareness of the legal risks involved in torrenting content from LibGen. It also explores the broader issues of knowledge accessibility and the impact of generative AI on the value of original creative work, suggesting a tension between making literature and information available and the potential undermining of the rights and reputations of authors.

Meta pirated books to train its AI

Meta's use of pirated books for training Llama 3 raises ethical concerns

The article "Revenge of the Junior Developer" by Steve Yegge discusses the evolving landscape of software development driven by AI, focusing on the concept of "vibe coding," where AI tools handle most coding tasks. Yegge outlines various waves of coding methodologies, from traditional coding to the emerging use of coding agents, which allow developers to work more efficiently without traditional manual coding. He emphasizes that junior developers are quicker to adopt these new AI tools compared to their senior counterparts, who may resist change due to fear of job security. The piece highlights the necessity for developers to embrace AI and coding agents to remain competitive in a rapidly transforming industry, predicting significant changes in job roles and productivity in the near future. Ultimately, he advocates for adapting to these advancements to harness their full potential.

Revenge of the Junior Developer

How AI is changing the role of junior developers

The article argues that the primary economic value of AI will stem from broad automation across various sectors, rather than being concentrated in research and development (R&D) as is often assumed. It critiques the prevalent notion that AI's primary impact on the economy will be through speeding up R&D processes, highlighting that R&D's contribution to overall productivity growth is smaller than generally believed. The authors assert that most economic value will come from automating widespread labor tasks, as this has a greater potential for enhancing productivity and economic growth. Furthermore, they argue that fully automating R&D is complex due to the unique capabilities required for scientific research, which include more than just abstract reasoning. Consequently, they predict that AI will first automate numerous conventional jobs before significantly transforming R&D, leading to a broader and more visible shift in the economy driven by automation across various industries, rather than a narrow focus on R&D tasks.

Most AI value will come from broad automation, not from R&D

AI's economic value will come from automation, not R&D

The article discusses the concept of "Vibe Coding," a term that describes a trend where individuals rely heavily on large language models (LLMs) to develop software without a deep understanding of coding fundamentals. This phenomenon has emerged due to the increasing accessibility of LLMs and tools that allow users to describe their ideas in natural language. While these models offer the thrill of quickly generating functional prototypes, the article highlights their limitations in producing reliable, secure, and production-quality software. Mistakes frequently occur, such as poor code practices and inability to learn from context. The article concludes that while LLM agents can enhance productivity for skilled developers, they cannot yet replace the nuanced work needed from experienced professionals to create durable software solutions. Ultimately, Vibe Coding can only take users partway toward functional software, emphasizing the necessity of human intervention for quality work.

"Vibe Coding" vs. Reality

Vibe coding vs. reality: the limits of LLMs in software development

The article discusses the sentencing of Paul Roberts, CEO of the now-defunct ad-tech firm Kubient, who was convicted of fraud related to inflating the company’s revenue to support its initial public offering (IPO). Roberts created fake revenue statements amounting to $1.3 million and directed employees to generate fraudulent reports about Kubient's ad fraud detection technology. This scheme involved billing a partner company for service that was never rendered, leading to significant legal repercussions for Roberts and other executives. Kubient, which claimed to be combating digital ad fraud, is now undergoing liquidation, and Roberts faces a year in prison followed by post-release supervision.

CEO of AI ad-tech firm pledging "world free of fraud" sentenced for fraud

AI ad-tech CEO sentenced for fraud scheme

The article discusses the evolution of recommendation systems and search technologies, focusing on the integration of large language models (LLMs) and multimodal content to enhance performance. It outlines various innovative approaches, such as hybrid architectures that incorporate content understanding with behavioral modeling to address challenges like cold-start and long-tail item recommendations. Specific examples include YouTube's use of semantic IDs to improve efficiency and user engagement, M3CSR's multimodal embeddings for better alignment with user behavior, and FLIP's technique of aligning ID-based recommendation models with LLMs. The article also highlights the utilization of LLMs for data generation and quality improvement across different applications, including Bing, Spotify, and Yelp, showcasing results like enhanced prediction accuracy and engagement rates. Furthermore, it explores training methods inspired by LLMs, such as knowledge distillation, scaling laws, and low-rank adaptations, demonstrating their effectiveness in boosting the quality and performance of recommendation systems. The concluding remarks indicate that while initial research faced challenges, recent industry implementations exhibit promising benefits from LLM enhancements in search and recommendation systems.

Improving Recommendation Systems & Search in the Age of LLMs

Enhancing recommendation systems with LLMs

The article discusses the impact of ChatGPT and AI on high school English education, particularly focusing on writing instruction. The author, John Warner, argues that while ChatGPT can generate written content, it lacks true understanding and meaning, often producing generic results that reflect predetermined patterns rather than genuine creativity. He critiques traditional writing courses that rely on rigid structures like the five-paragraph essay, suggesting these methods do not adequately prepare students for real-world writing and often disengage them from the learning process. Warner calls for a shift in teaching practices to value the writing process and engage students in meaningful ways, rather than emphasizing superficial metrics like grades. He emphasizes the importance of nurturing students' desire to learn and communicate, advocating for a more human-centered approach to writing in the classroom that can coexist with the technological advancements of AI.

ChatGPT can't kill anything worth preserving

Rethinking high school writing in the age of ChatGPT

The article discusses Richard Sutton's concept from "The Bitter Lesson," which posits that AI systems relying on raw computing power will consistently outperform those built on intricate human-designed rules. The author reflects on various approaches to AI, particularly in customer service automation, highlighting a shift from rule-based systems to those leveraging extensive computing resources. By employing Reinforcement Learning (RL) agents, which explore multiple problem-solving paths through computation, organizations can discover more effective solutions that wouldn't arise from strict programming. The key takeaway is that instead of focusing on complex algorithms, AI engineers should prioritize building scalable systems that effectively utilize computational power, as this will be the competitive advantage in future AI development.

Bitter Lesson is about AI agents

AI agents and the Bitter Lesson by Richard Sutton

IBM's CEO Arvind Krishna expressed skepticism about the idea that AI will soon replace programmers, estimating that only about 20-30% of code, rather than 90% as predicted by some, could be written by AI in the near future. He emphasized that AI is a tool that will enhance programmer productivity and ultimately contribute to local economic growth through global trade and the influx of international talent. Krishna also discussed the limitations of current AI technologies, noting that while they can improve output quality, they are unlikely to lead to significant scientific breakthroughs compared to advancements in quantum computing. He argued that AI learning is based on existing knowledge and isn't poised to reach the level of artificial general intelligence.

IBM's CEO doesn't think AI will replace programmers anytime soon

IBM CEO on AI's role in programming and productivity

The article "The Software Engineering Identity Crisis" by Annie Vella explores the profound impact of AI coding assistants on the identity and role of software engineers. It argues that engineers are transitioning from creators and builders of code to overseers and managers of AI systems, which poses an identity crisis as it challenges the intrinsic joy and pride derived from crafting solutions. Vella discusses the shift in skills required, emphasizing the emergence of "prompt engineering" and communication skills over traditional coding mastery. While AI tools enhance productivity, they also raise concerns about code quality and the evolving nature of engineering work. The author calls for engineers to adapt by balancing technical skills with oversight responsibilities while preserving their essence as creators. Ultimately, the article stresses the importance of reclaiming a broader understanding of software engineering that encompasses problem-solving, user needs, and overall system design, rather than merely writing code.

The Software Engineering Identity Crisis

AI coding assistants and the software engineering identity crisis

The article discusses the launch and features of the Qwen2.5-VL-32B model, which belongs to the Qwen2.5-VL series of models. It emphasizes improvements made through reinforcement learning, including enhanced alignment of responses with human preferences, improved accuracy in solving complex mathematical problems, and better image understanding and reasoning capabilities. The performance of the Qwen2.5-VL-32B model is compared against state-of-the-art models, demonstrating competitive advantages, particularly in multimodal tasks and user experience assessments. The article also provides demo cases that showcase the model's reasoning capabilities in both mathematical and visual tasks, while hinting at future research directions focused on long-term reasoning processes in visual models.

Qwen2.5-VL-32B: Smarter and Lighter

Launch of Qwen2.5-VL-32B model with improved capabilities

The article announces the launch of ARC-AGI-2 and the ARC Prize 2025, aiming to advance research towards artificial general intelligence (AGI). It emphasizes the importance of effective benchmarks that distinguish human capabilities from those of AI, with ARC-AGI-2 designed to be significantly more challenging for AI systems while remaining relatively easy for humans. The new benchmark addresses critical gaps in AI reasoning, including symbolic interpretation, compositional reasoning, and contextual rule application. The ARC Prize competition encourages open-source collaboration to drive innovation, offering substantial prizes for breakthroughs that achieve high performance on the ARC-AGI-2 tasks. The article underscores the need for novel ideas in the pursuit of AGI, inviting researchers to participate in the competition and contribute to the field.

Arc-AGI-2 and ARC Prize 2025

Launch of ARC-AGI-2 and ARC Prize 2025 for AGI research

The article discusses new research indicating that heavy usage of chatbots, particularly ChatGPT, is associated with increased feelings of loneliness and decreased social interaction. Studies from MIT Media Lab and OpenAI analyzed millions of conversations and surveyed users, revealing that while many users have a neutral relationship with the chatbot, "power users" are more at risk of developing emotional dependence and loneliness. The findings suggest that these users seek emotional bonds with chatbots as a replacement for human connections, raising concerns about the mental health implications. The article emphasizes the need for chatbot developers to prioritize user well-being and design chatbots in a way that minimizes potential negative effects, alongside suggestions for lawmakers to regulate exploitative business models that could exacerbate these issues. Overall, while chatbots can provide benefits, their impact on mental health needs careful consideration.

Chatbots could spark the next big mental health crisis

Chatbots and the risk of a mental health crisis

The article discusses Qodo's decision to use LangGraph for building their AI coding assistant. Initially, Qodo relied on structured workflows with earlier LLMs for coding tasks, but with the advancements brought by newer models, they aimed for a more dynamic and flexible approach. LangGraph's graph-based framework allows for opinionated workflows while maintaining adaptability, enabling the creation of agents that can transition between structured flows and open-ended tasks. The article highlights the importance of a coherent interface, reusability of components across workflows, and built-in state management offered by LangGraph. Despite its benefits, the article points out challenges with documentation and testing of LLM-driven systems that Qodo hopes will improve over time.

We chose LangGraph to build our coding agent

Why Qodo chose LangGraph for their AI coding agent

The article "Bubble Trouble" discusses the potential risks associated with the booming investment in artificial intelligence (AI), particularly focusing on OpenAI and the challenges it faces amidst rising competition. It highlights the launch of OpenAI's ambitious Project Stargate and the significant investment plans in AI infrastructure, which reflect a broader trend of inflated valuations and spending in the tech sector. The emergence of a competing AI model, DeepSeek R1, threatens OpenAI's market position and highlights the unsustainable financial practices prevalent in Silicon Valley, where venture capitalists are heavily invested in a speculative AI bubble. The article warns that if this bubble bursts, it could have severe repercussions for both the tech industry and the broader economy, echoing historical financial crises triggered by unsustainable growth and optimism.

An AI bubble threatens Silicon Valley, and all of us

AI bubble risks in Silicon Valley and OpenAI's challenges

The article introduces Gemini 2.5, Google's most advanced AI model designed for complex problem-solving. It highlights the capabilities of Gemini 2.5 and its experimental version, Gemini 2.5 Pro, which excels in reasoning and coding tasks. The article notes that these models show significant improvements over previous versions, achieving high scores on various benchmarks, including math and science assessments. Gemini 2.5 Pro features advanced reasoning abilities and can create executable code from simple prompts. The model also supports multimodal input, allowing it to work with diverse data types. Developers can experiment with the new model in Google AI Studio, with plans for its broader release soon.

Gemini 2.5: Our most intelligent AI model

Introducing Gemini 2.5: Google's latest AI model

The article discusses the negative impact of AI bots on Open Access websites and digital libraries. It describes how AI companies, seeking quality data for training large language models, are aggressively targeting the resources of libraries, archives, and scholarly publishers. As these institutions struggle to maintain access to quality information, they face unprecedented challenges from malicious bots that overwhelm their systems, leading to outages and significant losses of accessible content. The author highlights discussions from a recent conference where technologists shared strategies to combat these bots, expressing frustration over the wasted resources and potential future implications for accessing scholarly work, as good information may increasingly be locked behind paywalls.

AI bots are destroying Open Access

AI bots threaten Open Access resources

The article discusses the increasing dominance of AI crawlers in web traffic, particularly their impact on open-source projects. Developers have reported that these aggressive bots overwhelm servers, leading to instability and downtime, akin to persistent DDoS attacks. Measures to block these crawlers have largely failed due to their ability to spoof user agents and cycle through IP addresses. Some projects have had to implement significant defensive measures, such as requiring users to solve computational puzzles to access their sites. This crisis is straining community-maintained infrastructure, driving up bandwidth costs, and consuming valuable developer resources, as many of the crawlers are also submitting erroneous AI-generated bug reports. The article highlights the broader implications of AI firms extracting data without collaboration or compensation, risking the sustainability of open-source resources. New tools and defensive strategies are emerging, but the ongoing tension between AI companies and open-source maintainers poses significant challenges for the digital ecosystem.

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