AI tools precise and unhyped.
AI hardware is built around one dominant operation: multiplying large matrices of numbers, in parallel, over and over. Google's introduction to Cloud TPU defines the chips as custom-developed application-specific integrated circuits for machine learning, and NVIDIA's March 18, 2025 Blackwell…
AI safety is the discipline of preventing harm from AI systems through measurement, mitigation, and governance, and it runs on two tracks. One is public: NIST published its AI Risk Management Framework in January 2023 as a voluntary standard. The other is internal: Google DeepMind's Frontier…
Prompt engineering is the practice of writing and refining the instructions given to a language model, and the major vendors' official guides agree on the discipline before the tricks: define success criteria, test empirically, then iterate on a draft. Anthropic's documentation covers clarity,…
AI benchmarks are standardized task suites with a named publisher and a public scoring method, and they supply most of the evidence behind claims that one model beats another. ARC-AGI-2, launched on March 24, 2025 by ARC Prize, reports that pure large language models score 0 percent on its…
Google DeepMind launched Gemma 4 on April 2, 2026, a family of open models released under the Apache 2.0 license in five parameter sizes from E2B to 31B. Per Google's documentation, the models run on-device rather than only in the cloud, enabling agents that plan and act without a network…