February 2026

Large model inference container – latest capabilities and performance enhancements

Large model inference container – latest capabilities and performance enhancements

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Modern large language model (LLM) deployments face an escalating cost and performance challenge driven by token count growth. Token count, which is directly related to word count, image size, and other input factors, determines both computational requirements and costs. Longer contexts translate to higher expenses per inference request. This challenge has intensified as frontier models now support up to 10 million tokens to accommodate growing context demands from Retrieval Augmented Generation (RAG) systems and coding agents that require […]

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LEAP 5: The World’s Most Attended Technology Event Returns to Riyadh, Taking Global Tech Into New Worlds

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As the global technology ecosystem searches for its next frontier in growth, investment and collaboration, Riyadh will once again take centre stage as LEAP returns for its fifth edition from 13 to 16 April 2026. The award-winning global technology event will convene the world’s most influential technology leaders, policymakers, investors and founders to explore how innovation is reshaping economies, industries and society on a global scale. Co-organised by the Ministry of Communications and Information Technology (MCIT), the Saudi […]

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[D] Dissertation uses ANNs–what do I do with all the training data?

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Hi. I’m currently finishing up my PhD in which I leaned on ANNs to help make some predictions. Throughout the work I ran several series of ANNs, and I’m at the point where I’m button up my appendices, and I don’t know what to do with training data for the preliminary or failed NNs. Right now, my training appendices are just pages upon pages of tables, and they will be longer than my main document before I’m done. […]

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Google reveals Nano Banana 2 AI image model, coming to Gemini today

Google reveals Nano Banana 2 AI image model, coming to Gemini today

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The last year has been big for Google’s AI efforts. Its rapid-fire model releases have brought it to parity with the likes of OpenAI and Anthropic and, in some cases, pushed it into the lead. The Nano Banana image generator was emblematic of that trend when it debuted last year, and subsequent updates only made it better. Now, Google has announced yet another update to its image model with Nano Banana 2, which is available starting today. Nano […]

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Ford is recalling 4.3 million trucks and SUVs to fix a towing software bug

Ford is recalling 4.3 million trucks and SUVs to fix a towing software bug

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Last year, Ford set a new industry record: It issued 152 safety recalls, almost twice the previous high set by General Motors back in 2014. More than 24 million vehicles were recalled in the US last year, and more than half—13 million—were either Fords or Lincolns. By contrast, Tesla issued 11 recalls, affecting just 745,000 vehicles. Truth be told, Ford’s not doing too hot in 2026, either; it’s currently leading the National Highway Traffic Safety Administration’s chart for […]

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Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language

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Modern machine learning models are deployed in diverse, non-stationary environments where they must continually adapt to new tasks and evolving knowledge. Continual fine-tuning and in-context learning are costly and brittle, whereas neural memory methods promise lightweight updates with minimal forgetting. However, existing neural memory models typically assume a single fixed objective and homogeneous information streams, leaving users with no control over what the model remembers or ignores over time. To address this challenge, we propose a generalized neural […]

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Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models

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Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations. In practice, such models are often fine-tuned to improve zero-shot performance on downstream tasks, allowing them to solve tasks without examples and thereby reducing inference costs. However, fine-tuning can degrade in-context learning, limiting the performance of fine-tuned models on tasks not seen during fine-tuning. Using linear attention models, we provide a theoretical analysis that characterizes how fine-tuning objectives modify attention parameters […]

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SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)

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In open Federated Learning (FL) environments where no central authority exists, ensuring collaboration fairness relies on decentralized reward settlement, yet the prohibitive cost of permissionless blockchains directly clashes with the high-frequency, iterative nature of model training. Existing solutions either compromise decentralization or suffer from scalability bottlenecks due to linear on-chain costs. To address this, we present SettleFL, a trustless and scalable reward settlement protocol designed to minimize total economic friction by offering a family of two interoperable protocols. […]

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