February 2026

The Marvel Cinematic Universe (MCU) has been ongoing since 2008. Dozens of tv shows and movies later, so many incredible characters have been introduced to the general public. Characters that were only known to comic book fans became worldwide icons. If you go anywhere in the world and say, “I am Groot,” there’s a good chance that somebody will get your reference. But even still, there are a ton of characters that have remained surprisingly unused. Here are […]

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The complexity of imperative coding is currently a major bottleneck in deploying AI agents. This article introduces Ackgent, a production-ready starter kit that leverages the new Agent Config feature in Google ADK (v1.12.0+) to shift development toward a declarative paradigm. Instead of writing intricate Python boilerplate for execution and state management, developers can now define agent behaviors, tools, and multi-agent orchestration using simple YAML files. By combining this approach with modern tooling like uv and just, Ackgent offers […]

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Quantum reservoir computing offers a promising route for time series learning by modelling sequential data via rich quantum dynamics while the only training required happens at the level of a lightweight classical readout. However, studies featuring efficient and implementable quantum reservoir architectures along with model learning guarantees remain scarce in the literature. To close this gap, we introduce QuaRK, an end-to-end framework that couples a hardware-realistic quantum reservoir featurizer with a kernel-based readout scheme. Given a sequence of […]

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Someone asked if there was an Anthropic equivalent to OpenAI’s IRS mission statements over time. Anthropic are a “public benefit corporation” but not a non-profit, so they don’t have the same requirements to file public documents with the IRS every year. But when I asked Claude it ran a search and dug up this Google Drive folder where Zach Stein-Perlman shared Certificate of Incorporation documents he obtained from the State of Delaware! Anthropic’s are much less interesting that […]

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In this work, we investigate the use of data-driven equation discovery for dynamical systems to model and forecast continuous-time dynamics of unconstrained optimization problems. To avoid expensive evaluations of the objective function and its gradient, we leverage trajectory data on the optimization variables to learn the continuous-time dynamics associated with gradient descent, Newton’s method, and ADAM optimization. The discovered gradient flows are then solved as a surrogate for the original optimization problem. To this end, we introduce the […]

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