Center for the Alignment of AI Alignment Centers

The Center for the Alignment of AI Alignment Centers (CA AAC) claims to be the world's first AI alignment alignment center, aiming to coordinate and align the efforts of numerous AI research centers and institutes to solve the AI alignment problem. The organization pokes fun at the complexity and chaos of the AI research landscape, while also highlighting the importance of addressing the potential risks and challenges associated with advanced artificial intelligence.

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The rise of async AI programming

Developers are starting to adopt a new workflow, referred to as "async programming," where they describe problems clearly and let AI tools solve them in the background, allowing for simultaneous work on multiple complex problems. This approach requires clear problem definitions, automated verification, and human-driven code review, enabling developers to focus on high-level tasks and letting AI handle routine implementation, ultimately changing the way developers work and optimizing their time.

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AI's $344B 'language model' bet looks fragile

Silicon Valley is betting heavily on large language models (LLMs), a type of artificial intelligence, with the world's four largest tech firms set to spend $344 billion on AI this year, primarily on data centers to train and run LLMs like ChatGPT. However, this investment may be fragile as it relies on a single technique, predicting tokens in a sequence, which may not be sustainable in the long term.

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DeepCodeBench: Real-World Codebase Understanding by Q&A Benchmarking

Qodo has created a new benchmark dataset of real-world questions derived from large, complex code repositories to support the development of code retrieval and understanding systems. The dataset, generated from pull requests in open-source repositories, contains 1,144 questions that require deep retrieval and reflection of realistic developer workflows, and has been used to evaluate the performance of various language models, including Qodo's Deep Research agent, Codex, and Claude Code.

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Four Fallacies of Modern AI

The author has developed a framework to navigate the hype and doubt surrounding Artificial Intelligence by rejecting extremes and focusing on reason and evidence, using computer scientist Melanie Mitchell's concept of four foundational fallacies as a diagnostic tool. These fallacies, including the assumption that narrow AI feats are incremental steps towards human-level AI and the mis-calibration of progress due to Moravec's Paradox, help to rigorously test grand claims about AI against the complexities of reality.

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Mathematical research with GPT-5: a Malliavin-Stein experiment

GPT-5 was tested to see if it could extend a known qualitative fourth-moment theorem to a quantitative formulation with explicit convergence rates in Gaussian and Poisson settings, an open problem that had not been addressed in existing literature. The experiment and its results, which assess GPT-5's ability to go beyond known results in central limit theorems, are documented and discussed in the present paper.

ArXiv

XML Prompting Revolution: Math Proofs for Guaranteed LLM Stability

Researchers have developed a logic-first approach to XML prompting for large language models, which combines grammar-constrained decoding, fixed-point semantics, and human-AI interaction loops to produce parseable outputs. This framework, grounded in mathematical proofs, enables convergent guidance and guarantees well-formedness while preserving task performance, with potential applications in human-AI interaction and task automation.

ArXiv

Refrag: Rethinking RAG Based Decoding

Large Language Models (LLMs) that use retrieval-augmented generation (RAG) face significant system latency and memory demands due to long-context inputs, but most computations over the context can be eliminated without impacting performance. The proposed REFRAG framework tackles this issue by compressing, sensing, and expanding the context, resulting in a 30.85% acceleration in time-to-first-token and enabling larger context sizes without loss in perplexity or accuracy.

ArXiv

EnvX: Agentize Everything with Agentic AI

EnvX is a framework that uses Agentic AI to transform GitHub repositories into intelligent, autonomous agents that can interact with each other and perform tasks through natural language interaction. By automating the process of understanding, initializing, and operationalizing repository functionality, EnvX achieves a high execution completion rate and task pass rate, outperforming existing frameworks and enabling greater accessibility and collaboration within the open-source ecosystem.

ArXiv

A tech-law measurement and analysis of event listeners for wiretapping

Researchers investigated the use of JavaScript event listeners by third-party trackers to intercept keystrokes on websites, finding that 38.52% of sampled websites used this technique and at least 3.18% transmitted intercepted information to third-party servers, potentially violating US wiretapping laws. The study highlights a potentially significant gap in the enforcement of older laws related to electronic communication interception, which could lead to meaningful changes in the web tracking landscape if further legal research confirms the illegality of these practices.

ArXiv

LLM-optimizer: Benchmark and optimize LLM inference across frameworks with ease

Llm-optimizer is a Python tool that benchmarks and optimizes the inference performance of open-source large language models (LLMs), allowing users to find the optimal setup for their use case and apply performance constraints. The tool supports benchmarking with frameworks like SGLang and vLLM, and provides features such as performance estimation, interactive visualization, and custom server commands to help users optimize their LLM inference.

Repo

Show HN: Uniprof – Universal CPU profiler for humans and AI agents

Uniprof is a tool that simplifies CPU profiling for various platforms and languages, allowing users to profile applications without code changes or added dependencies. It supports multiple languages, including Python, Node.js, Ruby, PHP, JVM, and .NET, and can run in both container and host modes, with the ability to automatically detect the appropriate profiler and transform output into a single format for analysis.

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Linting for Your Docs

Alexandria is a context engineering platform that aims to make maintaining code context as easy as linting, with tools such as the Alexandria CLI, Memory Palace VS Code extension, and Alexandria Web. The platform can be easily set up and tried out using a few simple commands, and offers various benefits for agents, developers, and teams, with more information available in its documentation and website.

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Pydantic AI Gateway

Pydantic AI Gateway (PAIG) is an open-source AI gateway that offers excellent integration with Pydantic AI and Logfire, as well as features like API key delegation, cost limiting, and caching. PAIG can be self-hosted on CloudFlare workers or used as a hosted service with a convenient UI and API, allowing users to configure and deploy it with ease.

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Show HN: AionUi v1.2 – GUI to Boost Gemini CLI with Multi-Agent

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