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173 results for "component"
Why Your Frontend Is Actually a State Machine (And AI Makes It More Complicated)
<p>When most developers think about frontend development, they imagine components, UI elements, and responsive layouts. </p> <p>What we rarely acknowledge is that <strong>every modern frontend is fundamentally a state machine</strong> — a system where the state drives the UI, and events drive state changes. </p> <p>Add AI-driven features, predictive models, or automated agents, and your “simple” frontend suddenly becomes a complex web of interacting states, transitions, and events.</p> <h2> Frontends Are State Machines </h2> <p>Consider what a state machine is:</p> <ul> <li> <strong>States</strong> represent the current status of your system.</li> <li> <strong>Transitions</strong> are triggered by events (user clicks, API responses, timers, etc.).</li> <li> <strong>Actions</strong> happen
Your AI Writes Code. Who Fixes the Build?
<p>Every AI coding tool in 2026 can write code. Some of them write great code. But here's the question nobody asks during the demo: <strong>what happens when the build fails?</strong></p> <p>Because the build will fail. It always does.</p> <h2> The Invisible 40% </h2> <p>When you watch a demo of an AI coding tool, you see the impressive part: the AI generates a full component, a complete function, an entire page. It looks magical.</p> <p>What you don't see is what happens next:</p> <ul> <li>The import path is wrong because the AI didn't read the project's module structure</li> <li>There's a type mismatch because the API response shape changed last week</li> <li>A dependency is missing because the AI assumed it was already installed</li> <li>A CSS class doesn't exist because the AI used Tai
Create a workspace scheduler using Bryntum Scheduler Pro and MongoDB
<p><em>This Tutorial was written by <a href="https://www.linkedin.com/in/khattakdev/" rel="noopener noreferrer">Arsalan Khattak</a>.</em></p> <p><a href="https://bryntum.com/products/schedulerpro/" rel="noopener noreferrer">Bryntum Scheduler Pro</a> is a scheduling UI component for the web. With features such as a scheduling engine, constraints, and a <a href="https://bryntum.com/products/schedulerpro/docs/api/SchedulerPro/view/ResourceUtilization" rel="noopener noreferrer">resource utilization view</a>, it simplifies managing complex schedules.</p> <p>In this tutorial, we'll use Bryntum Scheduler Pro and <a href="https://www.mongodb.com/?utm_campaign=devrel&utm_source=third-party-content&utm_medium=cta&utm_content=workplace-mongodb&utm_term=tony.kim" rel="noopener noreferr
Your project has .gitignore — where's your .rules/?
<p>Every developer in 2026 is using AI to write code.</p> <p>Almost none of them have a system for governing the output.</p> <p>I built one.</p> <h2> The Problem Nobody Talks About </h2> <p>AI writes code. But it also <em>breaks</em> code. It removes imports you need. It truncates files to save tokens. It changes function signatures that three other modules depend on. It ignores your naming conventions, your architecture decisions, your project's entire history — because it doesn't know any of it.</p> <p>Every new AI session starts from zero. No memory of the time it broke your auth middleware. No memory that you use <code>camelCase</code> for services and <code>PascalCase</code> for components. No memory that you spent four hours last Tuesday fixing the code it "improved."</p> <p>We solve
How We Finally Solved Test Discovery
<h1> How We Finally Solved Test Discovery </h1> <p>Yesterday I wrote about <a href="https://gitauto.ai/blog/why-our-test-writing-agent-wasted-12-iterations-reading-files?utm_source=devto&utm_medium=referral" rel="noopener noreferrer">why test file discovery is still unsolved</a>. Three approaches (stem matching, content grepping, hybrid), each failing differently. The hybrid worked best but had a broken ranking function - flat scoring that gave <code>src/</code> the same weight as <code>src/pages/checkout/</code>. Today it's solved.</p> <h2> The Problem With Flat Scoring </h2> <p>The March 30 post ended with this bug: <code>+30</code> points for any shared parent directory. One shared path component got the same bonus as three. With 3 synthetic inputs, other factors dominated. With 29
Towards Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models
arXiv:2409.04270v2 Announce Type: replace Abstract: Evolutionary multi-task optimization (EMTO) is an advanced optimization paradigm that improves search efficiency by enabling knowledge transfer across multiple tasks solved in parallel. Accordingly, a broad range of knowledge transfer methods (KTMs) have been developed as integral components of EMTO algorithms, most of which are tailored to specific problem settings. However, the design of effective KTMs typically relies on substantial domain expertise and careful manual customization, as different EMTO scenarios require distinct transfer strategies to achieve performance gains. Meanwhile, recent advances in large language models (LLMs) have demonstrated strong capabilities in autonomous programming and algorithm synthesis, opening up new
LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression
arXiv:2505.18602v3 Announce Type: replace Abstract: Large language models (LLMs) have revolutionized algorithm development, yet their application in symbolic regression, where algorithms automatically discover symbolic expressions from data, remains limited. In this paper, we propose a meta-learning framework that enables LLMs to automatically design selection operators for evolutionary symbolic regression algorithms. We first identify two key limitations in existing LLM-based algorithm evolution techniques: lack of semantic guidance and code bloat. The absence of semantic awareness can lead to ineffective exchange of useful code components, while bloat results in unnecessarily complex components; both can hinder evolutionary learning progress or reduce the interpretability of the designed
GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification
arXiv:2603.29112v1 Announce Type: new Abstract: We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike traditional RecSys benchmarks that focus on item prediction accuracy, our benchmark evaluates how well LLMs can extract and verify user interests from engagement data. We propose two novel metric families: Interest Groundedness (IG), decomposed into precision and recall components to separately penalize hallucinated interest categories and reward coverage, and Interest Specificity (IS), which assesses the distinctiveness of verified LLM-predicted user profiles. We release a synthetic dataset constructed on real user interactions on a global short-form video platform. Ou
An Explicit Surrogate for Gaussian Mixture Flow Matching with Wasserstein Gap Bounds
arXiv:2603.28992v1 Announce Type: new Abstract: We study training-free flow matching between two Gaussian mixture models (GMMs) using explicit velocity fields that transport one mixture into the other over time. Our baseline approach constructs component-wise Gaussian paths with affine velocity fields satisfying the continuity equation, which yields to a closed-form surrogate for the pairwise kinetic transport cost. In contrast to the exact Gaussian Wasserstein cost, which relies on matrix square-root computations, the surrogate admits a simple analytic expression derived from the kinetic energy of the induced flow. We then analyze how closely this surrogate approximates the exact cost. We prove second-order agreement in a local commuting regime and derive an explicit cubic error bound in
Spontaneous Functional Differentiation in Large Language Models: A Brain-Like Intelligence Economy
arXiv:2603.29735v1 Announce Type: new Abstract: The evolution of intelligence in artificial systems provides a unique opportunity to identify universal computational principles. Here we show that large language models spontaneously develop synergistic cores where information integration exceeds individual parts remarkably similar to the human brain. Using Integrated Information Decomposition across multiple architectures we find that middle layers exhibit synergistic processing while early and late layers rely on redundancy. This organization is dynamic and emerges as a physical phase transition as task difficulty increases. Crucially ablating synergistic components causes catastrophic performance loss confirming their role as the physical entity of abstract reasoning and bridging artifici
Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data
arXiv:2603.29715v1 Announce Type: cross Abstract: Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, $X$, by the product of two nonnegative factors, $WH$, where $W$ has $r$ columns and $H$ has $r$ rows. In this paper, we consider NMF using the component-wise L1 norm as the error measure (L1-NMF), which is suited for data corrupted by heavy-tailed noise, such as Laplace noise or salt and pepper noise, or in the presence of outliers. Our first contribution is an NP-hardness proof for L1-NMF, even when $r=1$, in contrast to the standard NMF that uses least squares. Our second contribution is to show that L1-NMF strongly enforces sparsity in the factors for sparse input matrices, thereby favoring interpretability. However, if the data is affected by false zeros, too spar
Foundations of Polar Linear Algebra
arXiv:2603.28939v1 Announce Type: new Abstract: This work revisits operator learning from a spectral perspective by introducing Polar Linear Algebra, a structured framework based on polar geometry that combines a linear radial component with a periodic angular component. Starting from this formulation, we define the associated operators and analyze their spectral properties. As a proof of feasibility, the framework is evaluated on a canonical benchmark (MNIST). Despite the simplicity of the task, the results demonstrate that polar and fully spectral operators can be trained reliably, and that imposing self-adjoint-inspired spectral constraints improves stability and convergence. Beyond accuracy, the proposed formulation leads to a reduction in parameter count and computational complexity,
Structural Compactness as a Complementary Criterion for Explanation Quality
arXiv:2603.29491v1 Announce Type: new Abstract: In the evaluation of attribution quality, the quantitative assessment of explanation legibility is particularly difficult, as it is influenced by varying shapes and internal organization of attributions not captured by simple statistics. To address this issue, we introduce Minimum Spanning Tree Compactness (MST-C), a graph-based structural metric that captures higher-order geometric properties of attributions, such as spread and cohesion. These components are combined into a single score that evaluates compactness, favoring attributions with salient points spread across a small area and spatially organized into few but cohesive clusters. We show that MST-C reliably distinguishes between explanation methods, exposes fundamental structural diff
Wherefore Art Thou? Provenance-Guided Automatic Online Debugging with Lumos
arXiv:2603.29013v1 Announce Type: new Abstract: Debugging distributed systems in-production is inevitable and hard. Myriad interactions between concurrent components in modern, complex and large-scale systems cause non-deterministic bugs that offline testing and verification fail to capture. When bugs surface at runtime, their root causes may be far removed from their symptoms. To identify a root cause, developers often need evidence scattered across multiple components and traces. Unfortunately, existing tools fail to quickly and automatically record useful provenance information at low overheads, leaving developers to manually perform the onerous evidence collection task. Lumos is an online debugging framework that exposes application-level bug provenances--the computational history link
A Machine Learning Based Explainability Framework for Interpreting Swarm Intelligence
arXiv:2509.06272v4 Announce Type: replace Abstract: Swarm based optimization algorithms have demonstrated remarkable success in solving complex optimization problems. However, their widespread adoption remains sceptical due to limited transparency in how different algorithmic components influence the overall performance of the algorithm. This work presents a multi-faceted interpretability related investigations of Particle Swarm Optimization (PSO). Through this work, we provide a framework that makes the PSO interpretable and explainable using novel machine learning approach. We first developed a comprehensive landscape characterization framework using Exploratory Landscape Analysis to quantify problem difficulty and identify critical features in the problem that affects the optimization p
ASI-Evolve: AI Accelerates AI
arXiv:2603.29640v1 Announce Type: new Abstract: Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, long-horizon, and weakly supervised research loops that drive real AI progress. We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle. ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI-Evolve is the first unified fram