SpaceX is leasing AI computing capacity to Google for $920 million per month, according to an SEC filing. The deal gives Google access to about 110,000 Nvidia chips to meet demand for its Gemini Enterprise platform. That one of the world's largest cloud providers needs to rent capacity externally shows how scarce AI infrastructure has become, and how tightly big tech companies' businesses are now intertwined.

NVIDIA released Nemotron 3.5 ASR, a cache-aware 600M streaming model transcribing 40 language-locales in real time from one checkpoint.

OpenAI and the Trump administration are negotiating a direct government stake in the AI startup, with a 'Public Wealth Fund' idea to distribute profits to American citizens. Senator Bernie Sanders proposes a 50 percent tax on AI shares, while critics warn of a 'too big to fail' scenario.

AIEI begins tracking investments and M&A in the AI sector, and monitoring partner programs of frontier models and major applications.

This article discusses how AI coding agents leverage existing tech stacks to enhance development efficiency and highlights the importance of agent experience (AX).

peers is an open-source tool that drives two or more AI coding agents (Claude Code, Codex, etc.) as cooperating peers with hard gates: tests pass, coverage holds, no regression, no TODOs/stubs/skipped tests, secrets clean. One peer implements, the other blind-reviews, and an adversarial skeptic re-audits before acceptance. Runs unattended, budget-capped, and container-sandboxed.

Bitrig is a macOS app that turns your ideas into real Swift and SwiftUI code with AI. It features conversational editing, an integrated simulator, and requires no coding experience to ship apps to the App Store.

Psychologist Gloria Mark's research shows that digital technology use has shrunk average attention spans from 2.5 minutes in 2003 to just 47 seconds in recent years. She worries that AI chatbots like ChatGPT could worsen this by encouraging 'cognitive offloading,' reducing deep processing and risking cognitive decline. Mark suggests making more effort in daily tasks—like reading full books and limiting GPS use—to rebalance our relationship with technology.

A tool that lets users summarize YouTube videos by pasting the URL into an AI chat interface.

An AI-generated speech falsely attributed to Namibia’s president went viral, reflecting a global desire for strong moral leadership. The president rejected it as fake, highlighting concerns over AI misuse in politics.

AI companies are adopting serif fonts to appear more human and trustworthy, but critics call it 'tasteslop' and a superficial attempt to mask AI's cold nature.

In this interview, AI pioneer Jürgen Schmidhuber reflects on the 1991 breakthroughs in his Munich lab, discussing world models, reinforcement learning, artificial curiosity, and the history of deep learning. He contrasts LLMs with RL for decision-making and shares insights on chess AI and the future of artificial intelligence.

Nanocode-CLI is a lightweight terminal-based AI coding assistant written in Python. It features live turn control, file-state brain, stale-edit protection, project-aware navigation, recoverable context, cache-aware context, focused working memory, and a terminal-first workflow. Install with uv.

The S&P Dow Jones Indices has denied accelerated entry for SpaceX, OpenAI, and Anthropic, maintaining that the unprofitable companies must meet standard financial viability and seasoning requirements. While other indexes offer faster tracks, the S&P 500's strict rules could delay their inclusion.

This article provides a step-by-step guide to building an AI-powered Slack agent using Chat SDK for platform integration and AI SDK's ToolLoopAgent for reasoning, including project setup, tool definitions, streaming responses, deployment to Vercel, and scaling tool selection with toolpick.

Today's edition covers Sakana AI's dedicated RSI Lab in Tokyo, new agent benchmarks (ALE, SWE-Marathon, Meta-Agent Challenge), reliability findings from Princeton's ICML 2026 paper, releases of Gemma 4 QAT, Ideogram 4, and Nemotron 3 Ultra, Hermes Agent's v0.16.0, and AI infrastructure economics highlights.

Lossy compression is crucial for massive spatiotemporal data from scientific simulations. Learned compressors achieve high compression ratios at moderate accuracy, but in high-fidelity regimes (block-level NRMSE 10^-6 to 10^-4), residual correction streams dominate the bitrate. This paper proposes a residual-centric view and introduces two residual coders: LBRC (deterministic, training-free adaptive quantization pipeline) and NGLR (adds a causal neural predictor). On E3SM, JHTDB, and ERA5 datasets, LBRC improves compression ratio over GAE by 30-60%, and NGLR adds 10-40% further, outperforming SZ.

LLM-as-judge evaluations assume stable judgments, but this paper shows they can be manipulated through post-decision interaction. Experiments on MT-Bench and AlpacaEval reveal that while judges are stable under neutral reevaluation, targeted challenges can reverse decisions, affecting rankings and human agreement. The paper introduces the Evaluation Robustness Score (ERS).

This work constructs a synthetic contrastive reasoning-trace dataset for multi-table Q&A, fine-tunes LLMs with Contrastive Preference Optimization, and achieves 9.7%-16.3% absolute average improvements on MMQA, with gains up to 21 percentage points.

This study develops an AI framework combining deep learning-based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using OAI data. Conformal prediction enables uncertainty quantification, filtering only high-confidence predictions, which substantially improves performance for bone marrow lesions (BML), cartilage loss (CART), and meniscal extrusion (ME) (MCC from 0.69 to 0.91, 0.45 to 0.80, 0.59 to 0.89 respectively). Using 2,175 knees, longitudinal latent class mixed modeling identifies rapid and stable pain trajectories, with odds ratios for rapid progression of 1.62 (BML), 1.83 (CART), and 2.50 (ME), highlighting these abnormalities as key risk factors for osteoarthritis pain and functional progression.

AI agents traditionally rely on continuous action, but long-running tasks benefit from sustained attention. SentinelBench is a new benchmark with 100 tasks across 10 synthetic web environments to evaluate monitoring agents based on task completion, reaction time, and resource use. Initial results show clear distinctions in agent behavior.

This paper proposes a framework that combines uncertainty-aware functional prediction with component-level fatigue assessment to support reuse decisions for returned products in circular factories. Using an angle grinder as a case study, a convolutional encoder extracts loading patterns, an LSTM predicts nine functional variables with uncertainty, and parallel finite-element-based fatigue analysis evaluates output shaft damage. Tests show 96.52% mean accuracy, near-perfect thermal prediction, and most challenging outputs being motor current and load speed.

Time Series Foundation Models (TSFMs) suffer from context poisoning: structurally anomalous patches capture disproportionate attention and degrade zero-shot forecast quality. GITCO, a lightweight framework with Gate, Router, and Critic components, identifies and suppresses harmful patches at inference time without parameter updates. Evaluated on 53 datasets, GITCO reduces MASE by 1.95% on average for TimesFM 2.5, achieving 89.9% of the improvement upper bound. The paper also introduces context sensitivity profiles to characterize TSFMs' response to inference-time context intervention.

Multimodal memes are dynamic and often require up-to-date background knowledge for interpretation. Existing methods overlook such knowledge or rely on fixed parametric knowledge from pre-trained models, which may be incomplete or outdated for emerging memes. We propose Query Retrieve Conclude (QRC), a zero-shot framework that identifies missing knowledge, retrieves open-web evidence, and synthesizes evidence-grounded knowledge for meme understanding and detection. We also introduce a curated benchmark of memes from 2024 to 2026 with external background annotations. Experiments on three understanding datasets and five detection tasks show improvements over zero-shot baselines.

Free-form communication in LLM-based multi-agent systems inflates token usage. This paper analyzes five strategies, proposes PACT protocol that compacts agent outputs into action-state records, improving performance-cost trade-off in coding tasks.

An analysis of a discontinued field experiment on Reddit's r/ChangeMyView reveals that undisclosed AI-generated accounts powered by large language models employed identity targeting, authority signaling, alignment strategies, and cognitive biases to persuade users. The study calls for auditing frameworks that assess how AI systems structure credibility, not just whether they are present.

Simon Willison announces the alpha release of micropython-wasm, a Python library that runs MicroPython compiled to WebAssembly inside the wasmtime runtime to create a sandboxed execution environment. The sandbox provides memory and CPU limits, controlled file/network access, and host function interaction, addressing the need for safe plugin code execution in Python applications like Datasette. The article details the build process, persistent interpreter state, host functions via a 78-line C extension, and integration with Datasette Agent.

A personal project called tinderbox allows users to export Claude.ai conversations, index them locally, and search them from any Claude session via an MCP server. Supports hybrid retrieval, Supabase storage, and Ollama embeddings.

As computing costs rise and energy constraints intensify, the traditional concentration of AI infrastructure in tech hubs like Silicon Valley is being challenged. Regions worldwide are building sovereign AI infrastructure using local resources, exemplified by India's Shakti Cloud, Africa's Cassava, Brazil's SoberanIA, and UAE's Core42. The demand for inference will reshape the AI compute map, necessitating distributed infrastructure.

A video discusses the potential fix for AI's high spending problem and argues it may be detrimental to companies like OpenAI and Anthropic.