AI-powered image super-resolution does not recover missing detail from low-resolution images but instead invents plausible detail based on patterns learned from millions of other images, according to …
Meta released Llama 4 Scout and Llama 4 Maverick as open-weight, natively multimodal models using a Mixture-of-Experts architecture, with Scout capable of fitting on a single H100 when quantized to In…
A new technical explainer on activation functions highlights the vanishing gradient problem that plagues Sigmoid and Tanh in deep networks, and introduces ReLU as a solution. The article includes Pyth…
An autonomous AI scheduling agent at an unnamed company embarrassed its engineering team by booking a 12-person executive review into a room under active maintenance, after ignoring the event_type tag…
TrueFoundry's working definition of graph engineering—designing the topology of an AI system as an explicit, versioned artifact—addresses a gap that LangChain's 2026 State of Agent Engineering report …
Self-supervised learning (SSL) is a machine learning technique that trains models on unlabeled data during a pre-training phase, then fine-tunes them for downstream tasks with minimal labeled data, re…
Eight researchers from Google Brain and Google Research—Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin—published the paper…
Inception's Mercury-2, a diffusion-based large language model, outperformed Google's Gemini 3.6 Flash, an autoregressive model, in latency and cost across agentic workflows, achieving 0.00s time-to-fi…
DeepSeek open-sourced the second half of its AI agent architecture, separating the model from the harness, a move that could accelerate agent development. The release provides developers with the harn…
Arturo Campos, a former marketing consultant, led the development of an AI-powered ad-generation system that contributed to two Effie Awards and drove over 20,000 recharges totaling S/ 726,000 in a tw…
In 1997, Sepp Hochreiter and Jürgen Schmidhuber published "Long Short-Term Memory" in Neural Computation, introducing the LSTM architecture that solved the vanishing gradient problem in recurrent neur…
Model inference, the process of running a trained large language model to generate output tokens, is projected to exceed $50 billion in 2026, with inference now representing 55% of AI infrastructure s…
Discretion engineering, a framework for deciding where the boundary between AI model decisions and deterministic software should lie, is proposed as the central discipline underlying prompt, context, …
Enterprise multi-agent workflows silently degrade in production as task completion rates drop from 92% to 71% over 30 days, driven by unobserved payload contract mutations and stochastic reasoning dri…
A controlled study of 260 agent configurations by Google Research, Google DeepMind, MIT, and collaborators found that multi-agent systems improved performance by 81% on decomposable financial reasonin…
OpenAI's gpt-oss-120b model, with open weights, requires 72 KiB of KV cache per token in 16-bit precision, calculated from its config.json with 36 layers, 8 key-value heads, and a head dimension of 64…
The EU AI Act's high-risk obligations take effect in August 2026, with penalties up to €35 million or 7% of global annual turnover, prompting a structural shift in how AI infrastructure must be built.…
Cognition's June 2026 rebranding of Windsurf into Devin Desktop with native Agent Client Protocol support, now used by JetBrains, Gemini CLI, GitHub Copilot, and Codex, signals that model choice is be…
VLLM's KV cache and PagedAttention techniques reduce latency and cost in large language model inference by storing and efficiently managing key-value matrices, enabling higher throughput on existing G…
A developer spent one day training a machine learning model to predict machine failure but considerably longer turning it into production-ready software, reporting that the model achieved 74.20% accur…