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Explores whether ensembles of AI models could outperform human crowds in prediction markets, questioning if AI consensus will eventually surpass human forecasting accuracy.

The paper introduces VGB, a process-guided sampling algorithm with probabilistic backtracking, which significantly improves coding performance on tiny 0.5B models by being robust to verifier errors.

The author argues that for live voice agents, STT latency and real-time behavior are more critical than raw transcription accuracy, and proposes a different evaluation scorecard.

Chinese AI startup Z.ai releases its open-source GLM-5.2 model, which scores close to top US models from Anthropic and OpenAI on benchmarks, and announces plans for a dual listing in Shanghai.

This essay explores the transition of AI from learning from human data to potentially creating autonomously, discussing how AI internalizes patterns and could eventually develop new genres and shape its own evolution.

Claude Fable 5, an AI model by Anthropic, may return today after a 13-day forced suspension by the government.

The author describes losing faith in public AI model benchmarks due to vendor-created metrics, self-reported parameters, and lack of independent verification, and advocates for building custom evaluation sets from real production traffic to make more relevant model comparisons.

An exploration of how AI agent memory systems often miss crucial cognitive processes like working memory, drawing parallels to anterograde amnesia, and offering design guidance for more effective solutions.

Anthropic's Fable 5 model disappeared after 96 hours due to export controls, and days later, Z.ai open-sourced GLM-5.2 under MIT license, surpassing Fable 5 on the Design Arena. This highlights that the best model is not always the most accessible, shifting focus from benchmarks to availability and licensing.

A discussion on how companies should measure the real-world impact of AI agents and skills in production environments, rather than relying solely on benchmark results.

Explores whether AI-driven trading is feasible and secure, addressing potential risks and benefits.

NVIDIA released Nemotron-TwoTower-30B-A3B-Base-BF16, a diffusion-based language model that uses block-wise autoregressive diffusion to generate text by iterative denoising of token blocks, achieving 2.42× the generation throughput of the autoregressive baseline while retaining 98.7% of benchmark quality.

Experimental implementation of RDMA over USB4 demonstrated using Thunderbolt and Strix Halo, potentially enabling high-speed data transfer with any USB4 host.

A statement arguing that the approval user experience, not the MCP or connectors themselves, constitutes the real product.

The author reflects on their mistaken belief that AI agent cost is solely a backend problem, suggesting a broader perspective on cost factors.

The author contrasts polished AI agent demos with the reality of production systems, noting that most agent code is for error handling and guardrails rather than the core intelligence.

The Washington Post published the full list of questions and answers used to evaluate political bias in AI models, revealing the specific methodology and potential biases.

A report on the most used AI chatbots among Americans, highlighting which platforms are most popular.

The AI agent market is fragmenting from generic copilots into vertical-specific agents for sales, support, IT, and knowledge, mirroring the evolution of the SaaS market. The key question is whether vertical players become entrenched or a horizontal layer emerges.

Micron's strong quarterly earnings signal a significant shift in the AI memory market, highlighting increased demand for memory chips used in AI applications.