How Google Got Its Groove Back and Edged Ahead of OpenAI I picked up a few interesting tidbits from this Wall Street Journal piece on Google’s recent hard won success with Gemini. Here’s the origin of the name “Nano Banana”: Naina Raisinghani, known inside Google for working late into the night, needed a name for the new tool to complete the upload. It was 2:30 a.m., though, and nobody was around. So she just made one up, a […]

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Author(s): Hayanan Originally published on Towards AI. Unveiling the Magic of Gradient Descent, Feedforward Architectures, and Universal Function Approximation in AI Ne‌ural⁠ networks‌ form the backbone of mod‌ern artifici⁠al inte​lligence, powering b‍rea⁠kthroughs in computer v⁠ision, natur⁠al language pro‌cess‍ing,​ re‍commende‍r syst‌ems, and s⁠cientific disc​overy​.‍ Yet beneath today’s deep architectures lie simple mathematic⁠a‌l ideas developed deca‍de​s‌ ago. T‌his a‍rticle pr⁠esents a⁠ c‍omprehensive, e⁠nd-to-end jo​urney⁠ thro‌ugh the evolution of neur​al‍ net‍wo​rks fr‌om the foundational percep⁠tro‌n to sigmoid n​euro‌ns, g‌radient-based l‌earn‌ing, feedfo‍rwar⁠d​ […]

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Qwen3-Max-Thinking is Alibaba’s new flagship reasoning model. It does not only scale parameters, it also changes how inference is done, with explicit control over thinking depth and built in tools for search, memory, and code execution. https://qwen.ai/blog?id=qwen3-max-thinking Model scale, data, and deployment Qwen3-Max-Thinking is a trillion-parameter MoE flagship LLM pretrained on 36T tokens and built on the Qwen3 family as the top tier reasoning model. The model targets long horizon reasoning and code, not only casual chat. It […]

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arXiv:2510.09676v2 Announce Type: replace-cross Abstract: Inverse problems, where the goal is to recover an unknown signal from noisy or incomplete measurements, are central to applications in medical imaging, remote sensing, and computational biology. Diffusion models have recently emerged as powerful priors for solving such problems. However, existing methods either rely on projection-based techniques that enforce measurement consistency through heuristic updates, or they approximate the likelihood $p(boldsymbol{y} mid boldsymbol{x})$, often resulting in artifacts and instability under complex or high-noise […]

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[Updated on 2018-10-28: Add Pointer Network and the link to my implementation of Transformer.] [Updated on 2018-11-06: Add a link to the implementation of Transformer model.] [Updated on 2018-11-18: Add Neural Turing Machines.] [Updated on 2019-07-18: Correct the mistake on using the term “self-attention” when introducing the show-attention-tell paper; moved it to Self-Attention section.] [Updated on 2020-04-07: A follow-up post on improved Transformer models is here.]

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Processing video is one of the heaviest tasks a web application can handle. If you are still running shell_exec(‘ffmpeg …’) inside a Controller, you are likely blocking your PHP-FPM threads, frustrating your users, and risking timeouts. In 2026, we don’t do that. We treat video processing as an asynchronous, distributed pipeline. With the release of Symfony 7.4, we have new tools that make this robust and native. We now have a dedicated #[Video] validation constraint, native support for shared directories, and the mature Messenger component to […]

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arXiv:2602.04078v1 Announce Type: cross Abstract: Deep learning has achieved remarkable success across a wide range of domains, significantly expanding the frontiers of what is achievable in artificial intelligence. Yet, despite these advances, critical challenges remain — most notably, ensuring robustness to small input perturbations and generalization to out-of-distribution data. These critical challenges underscore the need to understand the underlying fundamental principles that govern robustness and generalization. Among the theoretical tools available, Lipschitz continuity plays a pivotal role in […]

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We present MeshGraphNet-Transformer (MGN-T), a novel architecture that combines the global modeling capabilities of Transformers with the geometric inductive bias of MeshGraphNets, while preserving a mesh-based graph representation. MGN-T overcomes a key limitation of standard MGN, the inefficient long-range information propagation caused by iterative message passing on large, high-resolution meshes. A physics-attention Transformer serves as a global processor, updating all nodal states simultaneously while explicitly retaining node and edge attributes. By directly capturing long-range physical interactions, MGN-T eliminates […]

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arXiv:2601.05273v1 Announce Type: new Abstract: Probabilistic Coalition Structure Generation (PCSG) is NP-hard and can be recast as an $l_0$-type sparse recovery problem by representing coalition structures as sparse coefficient vectors over a coalition-incidence design. A natural question is whether standard sparse methods, such as $l_1$ relaxations and greedy pursuits, can reliably recover the optimal coalition structure in this setting. We show that the answer is negative in a PCSG-inspired regime where overlapping coalitions generate highly coherent, near-duplicate columns: […]

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arXiv:2601.09741v1 Announce Type: new Abstract: The need and theoretical methods for measuring and reducing CO2 emitted by computing systems are well understood, but real-world examples are still limited. We describe a journey towards green software for a live product running on a public cloud. We discuss practical solutions found, in particular using the cost implications of serverless systems to drive efficiency. We end with some `green software’ principles that worked well in this project.

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