Google’s Gemini 4 Argon is in limited testing with trusted cyber defenders before a wider release. We explain the one-million-token output and what independent testers measured, how strong the DeepSWE coding record is, what the model costs per task against GPT-6 Astra and Claude, why defenders get it first, and what developers should test.
University of Miami researchers treat a chatbot’s answer probabilities like a small quantum system, nudge it, and measure how unstable it is. We explain the ICLR 2026 method, recheck its worked example (which contradicts one of its own sentences), and set out its cost, its limits and what it means for businesses using AI.
A University of Amsterdam study in PNAS Nexus asked 21 large language models to judge people who helped, or refused to help, others with good or bad reputations. The models agreed on good people and split on bad ones. We explain the four social norms, the model-by-model figures, what the norms would do to cooperation, and what organisations should test.
A world-first study from JAIST and Waseda University tested ChatGPT’s urban design advice for health against four ethical criteria. It avoided harm in all 180 answers and treated poorer areas fairly, but community participation and human oversight dropped sharply under a budget constraint. We explain the findings and how planners should use AI advice.
Johns Hopkins researchers took 427 real workplace writing prompts, rewrote each into a women-associated and a men-associated version, and ran both through GPT-4, Llama, Mistral and Gemma. Every model returned shorter, plainer, less formal documents for the women-coded phrasing. Adding a male or female sign-off name changed nothing. Linear probes decode the linguistic register at 0.988 accuracy by layer 5 while name gender reaches only 0.717, and activation patching puts the causal weight in layers 0 to 7. This article covers the method, the per-model results, the two controls that rule out mirroring, and why the authors say users cannot fix it themselves.
Elon Musk says Grok 4.8 is a 2.5-trillion-parameter model trained with xAI’s new C++ software stack and will start reinforcement learning this week. We check the claim against every parameter count and stack post Musk has made, the delayed Grok 4.7, Grok 4.6’s published benchmarks, and what a 2.5T model means for memory, speed and price.
Tencent open-sourced Hy4 preview on 28 August 2026 under Apache 2.0: a 770B Mixture-of-Experts model that activates just 49B parameters per token and reads a one-million-token context window. This breakdown covers the full architecture, from 256 routed experts per layer to Gated DeepSeek Sparse Attention and the built-in speculative decoding layer; every benchmark figure Tencent published, including the 163-expert blind evaluation against GLM 5.3 and Kimi K3; the API price list against GPT-5.6 Sol; the eight-GPU serving recipes; and the four caveats worth naming before any of it reaches production.
DeepSeek V4 is the MIT-licensed open-weight family that replaced the never-released R2, pairing a one-million-token context window with sub-dollar output pricing. This complete guide covers the V4 Pro 0813 and V4 Flash 0731 GA builds, their official benchmarks, the peak/off-peak billing change landing on 16 August 2026, and a practical framework for choosing between the two models.
Why AI Agents Give Inconsistent Results is explained by the complete agent system, including instructions, retrieved information, memory, tools, permissions, orchestration, and runtime conditions.
AI Coding Assistants have rapidly transformed modern software engineering by helping developers write code faster, review pull requests more efficiently, automate repetitive tasks, and improve software quality throughout the development lifecycle. As artificial intelligence becomes increasingly integrated into professional development environments, platforms such as GitHub and GitLab are evolving beyond traditional source code repositories into […]
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