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U.S. to tell partners they must pick sides in AI race with China
WASHINGTON – The U.S. is preparing to tell dozens of countries they must pick sides in the artificial intelligence race with China, warning they will be excluded from a U.S.-led AI coalition if they also sign up for Beijing’s competing framework, according to a U.S. official and an internal draft reviewed by reporters. Washington last year launched the Pax Silica initiative aimed at securing supply chains for AI models, semiconductors and critical minerals, amid a fierce technology rivalry with Beijing. About two dozen countries have joined, including Kazakhstan, a key potential source of critical minerals that has also joined China’s coalition, as well as close U.S. allies such as Japan, Australia and South Korea.
WASHINGTON – The U.S. is preparing to tell dozens of countries they must pick sides in the artificial intelligence race with China, warning they will be excluded from a U.S.-led AI coalition if they also sign up for Beijing’s competing framework, according to a U.S. official and an internal draft reviewed by reporters. Washington last year launched the Pax Silica initiative aimed at securing supply chains for AI models, semiconductors and critical minerals, amid a fierce technology rivalry with Beijing. About two dozen countries have joined, including Kazakhstan, a key potential source of critical minerals that has also joined China’s coalition, as well as close U.S. allies such as Japan, Australia and South Korea.
The AI Situation in Software Development
Random thoughts about prompting, context windows, compression, and working with AI. You want it to do something you have in mind, and you know how/what to do, or sometimes you don’t. Now there are 3 options: you tell it everything down to the details, every specific thing. Or you just tell it to do something at a high level and expect the thing to understand. Or you can go the middle way. I feel this is the go-to way, explaining the important parts that you think might be difficult for it. You can feed it examples. It’s a faster way to do things, but it depends on the example being close to what you want. All of these are time-consuming. Some you spend time before giving to AI, some after. A common pattern is easy for LLMs to implement, considering they must have seen it before in their training set, for example implementing user auth. A n
Random thoughts about prompting, context windows, compression, and working with AI. You want it to do something you have in mind, and you know how/what to do, or sometimes you don’t. Now there are 3 options: you tell it everything down to the details, every specific thing. Or you just tell it to do something at a high level and expect the thing to understand. Or you can go the middle way. I feel this is the go-to way, explaining the important parts that you think might be difficult for it. You can feed it examples. It’s a faster way to do things, but it depends on the example being close to what you want. All of these are time-consuming. Some you spend time before giving to AI, some after. A common pattern is easy for LLMs to implement, considering they must have seen it before in their training set, for example implementing user auth. A n
ew problem you’re imagining or telling it is of course hard for it and needs hand-holding. Then there’s the context window problem. You can’t just give a 3000-word, 4-page detailed dense spec and expect it to follow everything, and the larger the codebase, the less it can pack everything in, nor are the vast documents you can feed it worthwhile. Not only for writing detailed specs ~ you also want it to summarize patterns, draw conclusions from a large dataset, be it something like analyzing vast amounts of numerical data, for example a historical dataset for a stock. So the cost is on you: you still need to spend the time to write a detailed guide for your project, its goals and its issues, and more importantly the blueprint of the thing you want. Then there’s domain-specific expertise of AI models. You need to pick and choose the right one. As you work on bigger problems and as you integrate AI into your applications, a need for compression arises, packing as much useful information as possible, if not all, into your AI agent to solve a particular problem or to draw a conclusion, make a decision or whatever. I think there will be companies in this space that’ll do this effectively, or the model builders will just solve this once and for all. There must be feedback loops in terms of tests, tooling (purpose-built or otherwise), and refining its approach as the codebase grows large. And ways for improving the signal-to-noise ratio in your codebase. I feel that great explainers or natural teachers find it easy to engage with AI and produce better outputs. Bottom line: you still need to spend time. The implementation time is gone. Now the time you spend has shifted to designing the system upfront, changing assumptions, and refining your dev setup. But implementation is not really gone. I feel I am still implementing in words instead of code.
Secondhand book sales are booming. Is it because of AI?
4 hours ago Something mysterious has been happening in the world of secondhand books. Over the last few months independent booksellers have been observing a strange pattern of purchases which have seen scores of their novels being shipped to far-off warehouses. In a typical week, Stuart Manley, from Barter Books, in Northumberland, would sell two or three thousand books. But one recent single bulk order from a Canadian company equalled what he would expect to move in seven days. He says he has "never seen the like of this after 30 years in the second-hand book trade". Mr Manley is not alone. Booksellers from around the world have been reporting similarly unusual mega orders. They aren't sure what the final destination for their books is. But the suspicion is that they are not being bought for avid overseas rea
4 hours ago Something mysterious has been happening in the world of secondhand books. Over the last few months independent booksellers have been observing a strange pattern of purchases which have seen scores of their novels being shipped to far-off warehouses. In a typical week, Stuart Manley, from Barter Books, in Northumberland, would sell two or three thousand books. But one recent single bulk order from a Canadian company equalled what he would expect to move in seven days. He says he has "never seen the like of this after 30 years in the second-hand book trade". Mr Manley is not alone. Booksellers from around the world have been reporting similarly unusual mega orders. They aren't sure what the final destination for their books is. But the suspicion is that they are not being bought for avid overseas rea
ders but instead something else with a voracious appetite for new information: AI. The idea that the secondhand sales boom is being driven by the explosive growth of AI can be traced back to a court ruling in the US. In 2025, a judge ruled using books purchased in this way to train AI software was not a violation of US copyright law. The decision was the result of a lawsuit brought against AI firm Anthropic by three authors. In his ruling, Judge William Alsup said Anthropic's use of the authors' books was "exceedingly transformative" and therefore allowed under US law. When court documents were unsealed last month it also emerged books were being destroyed in the process of training Anthropic's AI chatbot, Claude. "Claude is trained on a mix of publicly available web data, commercially acquired datasets, and data we generate ourselves," a spokesperson said. They insisted sourcing books for training was a widely used approach across the AI industry. "None of our data acquisition programs buy and destroy rare or antiquarian books," they added. Nonetheless, the idea that books are being pulped is causing unease. David Tobin, runs Walden Books in north London, has also had unusual sales. "In some ways it's very nice to sell some of these titles which haven't been sold for many years, but it would be sad if they are ultimately destroyed," he says. The court documents relating to the Anthropic case revealed the project of ingesting old books was referred to in internal company communications as "Project Panama." The documents indicated the company's aim was to "destructively scan all the books in the world". Destructive scanning is the process of shipping books to locations where they can be digitised at an industrial scale. It includes removing a book's spine so all the pages can be scanned rapidly - and the remains recycled. "A lot of mystery surrounds Project Panama," Manley, from Barter Books, says. "The name is new to me, but the reality of the project is not, and has been the subject of much discussion on the bookseller forums." He does not know that his books are being bought for it or similar projects by other AI firms. But he says it's also difficult to account for the sales, which appear random with "no rhyme or reason." They have varied from obscure Latin texts to cowboy novels. Experts say the diverse subject matter also points to AI, as unusual and rare texts could provide fresh material to improve the training of large language models, the tech which underpins generative AI tools like chatbots. Professor Emily Hudson, intellectual property specialist at Oxford University, says copyright laws in the UK are different to those in the US. "The starting point in the UK is that all these acts of copying - creating the training library and doing the training â require the permission of the copyright owner," she says. For the booksellers, it poses a dilemma. They are uncomfortable with the idea of books being destroyed - even if they admit not every title needs to be saved. "A recent academic text published in only 100 copies, 75 of which are already in libraries, may be very rare on the market - but it is perhaps not such a great loss if one copy is destroyed," says Derek Walker, owner of Edinburgh bookshop McNaughtan's. "But we have, and have sold, books which are for example the only known surviving example of an edition from the 18th century. "It would be a much more significant problem if one like that were to be bought for
destruction, having survived this long." And Manley says recycling books is a good solution for many titles which the public no longer want on their shelves - especially when it comes with a bump in trade. "Some may have ethical concerns about where the books end up and if they're destroyed," he says. "But the world no longer needs five million copies of The Da Vinci Code. "I've had books advertised for 20 years on the web which haven't sold until now". What is AI and how does it work? Sign up for our Tech Decoded newsletter to follow the world's top tech stories and trends. Outside the UK? Sign up here .
Model your business once – for humans and AI alike
nexusx allmonday/nexusx Guide Guide Overview — One Model, Two Graphs Quick Start Data Graph Data Graph GraphQL Mode Auto-Generated Queries GraphQL Pagination Response DTOs Response DTOs Core API Mode Core API Advanced Relationships beyond the ORM Relationships beyond the ORM ER Diagram & Non-ORM Relationships Custom Relationships Virtual Entities Operation Graph Operation Graph UseCase Service UseCase + FastAPI AI Delivery AI Delivery Compose MCP for AI MCP Service (entity-first) MCP & Context Efficiency Beyond One Database Beyond One Database Federation ComposedErManager Tooling Tooling Voyager Visualization ER Diagram Visualization Troubleshooting Articles Articles Design Highlights Clean Architecture Comparison API Reference API Reference GraphQLHandler Core API Cross-Layer Data Flow Relationships & ER Diagram MCP
nexusx allmonday/nexusx Guide Guide Overview — One Model, Two Graphs Quick Start Data Graph Data Graph GraphQL Mode Auto-Generated Queries GraphQL Pagination Response DTOs Response DTOs Core API Mode Core API Advanced Relationships beyond the ORM Relationships beyond the ORM ER Diagram & Non-ORM Relationships Custom Relationships Virtual Entities Operation Graph Operation Graph UseCase Service UseCase + FastAPI AI Delivery AI Delivery Compose MCP for AI MCP Service (entity-first) MCP & Context Efficiency Beyond One Database Beyond One Database Federation ComposedErManager Tooling Tooling Voyager Visualization ER Diagram Visualization Troubleshooting Articles Articles Design Highlights Clean Architecture Comparison API Reference API Reference GraphQLHandler Core API Cross-Layer Data Flow Relationships & ER Diagram MCP
API UseCase API Reference Reference Changelog Migration Guide DTO-first Execution Migration UseCase GraphQL 3.0 Migration Table of contents Run It in 60 Seconds What You'll Get Who Is This For Learning Path Guide (Tutorial Path) Advanced Guides API Reference Back to top
Yadda 3.0.0: BDD in the Age of AI Agents
I’ve just published Yadda 3.0.0 to npm. That means that instead of writing something like: Given a university, The University of Bouvet Island And The University of Bouvet Island offers a degree course in Computer Science with entry requirements of ABB And an A-Level graduate, Steve And Steve has a D in Physics And Steve has a D in Maths When Steve applies to study Computer Science at The University of Bouvet Island Then The University of Bouvet Island rejects the application you can write: The University of Bouvet Island offers a degree course in Computer Science The entry requirements for which are ABB Steve is an A-Level graduate With a D in Physics And a D in Maths When Steve applies to study Computer Science at The University of Bouvet Island They reject his application Both are executable specifications. I find the second considerabl
I’ve just published Yadda 3.0.0 to npm. That means that instead of writing something like: Given a university, The University of Bouvet Island And The University of Bouvet Island offers a degree course in Computer Science with entry requirements of ABB And an A-Level graduate, Steve And Steve has a D in Physics And Steve has a D in Maths When Steve applies to study Computer Science at The University of Bouvet Island Then The University of Bouvet Island rejects the application you can write: The University of Bouvet Island offers a degree course in Computer Science The entry requirements for which are ABB Steve is an A-Level graduate With a D in Physics And a D in Maths When Steve applies to study Computer Science at The University of Bouvet Island They reject his application Both are executable specifications. I find the second considerabl
y easier to read. Most of Yadda 3.0 is a modernisation exercise. All useful, but not especially interesting to write about. There are two things about the release that I think are much more significant. I modernised Yadda using Claude Code with Opus 4.8. The Yadda 3.0 epic , which was itself written by Claude, broke the work into a series of deliberately separated phases: remove obsolete functionality, update the toolchain, perform mechanical formatting separately from behavioural changes, modernise the source, explore API changes, update examples and CI, then finish the metadata, documentation and TypeScript definitions. We planned each phase before implementing it, and then I largely let Claude get on with the work. It made remarkably few mistakes and, more impressively, identified some fairly subtle edge cases that would have been easy to miss during what initially looked like a mechanical modernisation. I made very few interventions. One important factor was that Yadda already had a comprehensive test suite. I also deliberately avoided asking Claude to modify production code and the corresponding tests in the same step. If an agent changes both simultaneously, a green test suite becomes weaker evidence because it is free to change the definition of “correct” at the same time as the implementation. Keeping those changes separate gave Claude a much firmer external constraint. From starting the work to having the package published was roughly a day of elapsed time, and I was doing other things in parallel. At the beginning of this year I wrote about an experiment asking why experiences of vibe coding were so polarised . My conclusion then was that the results depended enormously on how the agent was used. A tightly constrained and supervised Claude could produce extremely good results very quickly. Left to its own devices, it tended towards architectural drift, unnecessary code and operational debt. That was only seven months ago, and the capability has moved on enormously. Even so, saying that Claude can now write this code with very little intervention barely scratches the surface of what is changing. To appreciate where this is going, it helps to stop thinking about a single developer having a conversation with a single coding agent and instead consider several agents working in parallel. There are already several ways to do this. You can simply run multiple Claude Code sessions. Git worktrees let each agent work against an isolated working copy. Tools such as cmux make running a collection of Claude sessions more manageable, while Claude Code Agent View provides another way of seeing what multiple sessions are doing and which ones need attention. All of these let you build significantly faster than working serially, but I fairly quickly hit another limit: my own ability to manage the parallel work. I can comfortably keep three tasks moving at once, and sometimes four or five. Beyond that, I start losing the context of what each agent is doing, which decisions have been made, which task is waiting for me and what I need to review next. At that point, the model is not overloaded and the machine is not overloaded. The bottleneck is the human coordinating the work. I’ve become convinced that good orchestration is the next important layer. I’m not alone in reaching that conclusion. My colleague Marco describes almost exactly this progression in My AI Engineering Journey , moving from AI as autocomplete, through supervised and trusted
agents, to parallel agents where cognitive load becomes the constraint. He is further along this journey than I am, and has responded by building Otto, an orchestration UI around Claude Code and worktrees, before moving on to agent pipelines that coordinate implementation, review, feedback and documentation. The larger point is that AI-assisted software development is still moving extraordinarily quickly. Individual coding capability has improved dramatically, parallel execution is already practical, and the next constraint is increasingly the coordination of all that capability. The tools and approaches for doing so are developing just as quickly, and are now arguably even more important than the model updates. Which brings me back to Yadda. I’ve always thought BDD was valuable for several reasons. Firstly, writing requirements in ordinary language forces you to articulate the domain and, more importantly, encourages you to articulate it consistently. If you write those specifications before writing the implementation, that domain language has a habit of propagating through the codebase. The same concepts start appearing in class and function names, API definitions, database schemas, CSS classes and user interfaces. That gives the codebase a coherence that is surprisingly difficult to achieve retrospectively. Secondly, executable specifications are far more accessible than conventional programmatic tests. A product manager, analyst or domain expert has a realistic chance of understanding: When Steve applies to study Computer Science Then the university rejects his application They are much less likely to extract the same meaning from a Jest test containing fixtures, mocks, builders and assertions. Thirdly, BDD provides a useful abstraction layer for functional tests. The specification describes intent while the step implementation deals with mechanics such as selectors, navigation and browser interaction. This provides some of the same benefits as the Page Object pattern : changes to the user interface can often be absorbed inside the abstraction instead of leaking through hundreds of tests. There has always been a cost, though. BDD tests take longer to write initially. You need to think about the language, create reusable steps, and resist the temptation to write procedural scripts disguised as English. The payoff comes later, through better domain modelling, better communication and more maintainable functional tests. That deferred payoff has always made BDD harder to justify, but I think AI changes the economics. Consider an engineering workflow that is becoming increasingly plausible. Meetings are automatically transcribed and stored as GitHub discussions. Those discussions are analysed and used to update a project wiki. The wiki is mined for requirements and issues. Those issues are then picked up, implemented, reviewed and coordinated by a collection of coding agents. A wiki can tell you what somebody thought the system should do. It can tell you what the system used to do. It can even tell you what an agent inferred that the system ought to do. It cannot, by itself, tell you whether the system actually does it. An executable specification can. That makes BDD much more interesting in an agentic development environment than it was before. The expensive part of BDD was producing and maintaining the specification. AI makes much of that work cheap. A transcript, discussion or requirement can be transformed into a candidate
specification almost trivially, with a human concentrating on whether the language and behaviour are correct rather than typing it all out. Once accepted, that specification becomes more than documentation. It becomes a contract. An implementation agent can use it to understand the required behaviour. A testing agent can use it to determine what needs validating. A reviewing agent can use it to challenge an implementation. CI can continuously verify it. Because it is executable, it remains coupled to the behaviour of the software in a way that a wiki page never can. There is an interesting inversion here. BDD was created partly to make software specifications more useful to humans, but executable specifications may turn out to be even more valuable when much of the software is being written by machines. The natural language gives agents rich domain context, while the executable steps ensure that the specification remains grounded in the behaviour of the system. One other change (added in Yadda v3.1.0) is support for writing feature specifications as GitHub-flavoured Markdown. This makes them easier to read in the repository and, more importantly, allows them to live naturally alongside the project wiki and the other key knowledge artefacts that humans and agents use to understand the system. The same specification can now be written as: # Feature: University applications ## Scenario: Applicant does not meet the entry requirements - The University of Bouvet Island offers a degree course in Computer Science - The entry requirements for which are ABB - Steve is an A-Level graduate - With a D in Physics - And a D in Maths - When Steve applies to study Computer Science at The University of Bouvet Island - They reject his application It remains an executable specification, but when viewed on GitHub it looks and behaves much more like the rest of the project’s documentation. Yadda 3 is available on npm , and the source, documentation and examples are on GitHub .
Adversarial Creation and Detection of AI-Generated Social Bot Content
Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mykola Trokhymovych [ view email ] [v1] Fri, 5 Jun 2026 12:32:47 UTC (1,155 KB) Full-text links: Access Paper: View a PDF of the paper titled Adversarial Creation and Detection of AI-Generated Social Bot Content, by Mykola Trokhymovych and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( Wh
Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mykola Trokhymovych [ view email ] [v1] Fri, 5 Jun 2026 12:32:47 UTC (1,155 KB) Full-text links: Access Paper: View a PDF of the paper titled Adversarial Creation and Detection of AI-Generated Social Bot Content, by Mykola Trokhymovych and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( Wh
at is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .
Switched on Pop’s Nate Sloan and Charlie Harding love fresh vegetables and guitar pedals
As if you needed more reason to love Carly Rae Jepsen’s “ Call Me Maybe ” beyond its pop perfection, it is also, according to lore, the genesis for Switched on Pop , one of the best music podcasts out there. Cohosts Nate Sloan and Charlie Harding obsessively dissect pop songs, from the theory behind their musical choices to their production techniques. If you’re a student of pop music, then this is a must-listen podcast.
As if you needed more reason to love Carly Rae Jepsen’s “ Call Me Maybe ” beyond its pop perfection, it is also, according to lore, the genesis for Switched on Pop , one of the best music podcasts out there. Cohosts Nate Sloan and Charlie Harding obsessively dissect pop songs, from the theory behind their musical choices to their production techniques. If you’re a student of pop music, then this is a must-listen podcast.