Enabling Physical AI and Robotics: Platform for the Intelligent Edge - semiengineering.com
Enabling Physical AI and Robotics: Platform for the Intelligent Edge semiengineering.com
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Built a Lightweight GitHub Action for Deploying to Azure Static Web Apps
TL;DR I created shibayan/swa-deploy — a lightweight GitHub Action that only deploys to Azure Static Web Apps, without the Docker-based build overhead of the official action. It wraps the same StaticSitesClient that SWA CLI uses internally, includes automatic caching, and supports both Deployment Token and azure/login authentication. The Problem with the Official Action When deploying static sites (built with Astro, Vite, etc.) to Azure Static Web Apps, the standard approach is to use the official Azure/static-web-apps-deploy action that gets auto-generated when you link a GitHub repo to your SWA resource. Unlike other Azure deployment actions (e.g., for App Service or Azure Functions), this action uses Oryx — the build engine used across Azure App Service — to build your application intern
A Reasoning Log: What Happens When Integration Fails Honestly
This is a log of a language model running through a structured reasoning cycle on a deliberately difficult question. The structure has eleven levels. The interesting part is not the final answer — it is what happens at the integration point. The question chosen for this run: "Why, in the modern world, despite unprecedented access to information, knowledge, and technology, do depth of understanding and wisdom not grow on average — and in many respects actually decline?" This question was selected because it carries genuine tension between two parallel streams: the facts (information abundance, attention economy, algorithmic amplification) and the values (what it actually means for understanding to deepen). That tension is what makes it a useful test. The structure The reasoning cycle separa
Efficient3D: A Unified Framework for Adaptive and Debiased Token Reduction in 3D MLLMs
arXiv:2604.02689v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have expanded reasoning capabilities into 3D domains, enabling fine-grained spatial understanding. However, the substantial size of 3D MLLMs and the high dimensionality of input features introduce considerable inference overhead, which limits practical deployment on resource constrained platforms. To overcome this limitation, this paper presents Efficient3D, a unified framework for visual token pruning that accelerates 3D MLLMs while maintaining competitive accuracy. The proposed framework introduces a Debiased Visual Token Importance Estimator (DVTIE) module, which considers the influence of shallow initial layers during attention aggregation, thereby producing more reliable importa
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Built a Lightweight GitHub Action for Deploying to Azure Static Web Apps
TL;DR I created shibayan/swa-deploy — a lightweight GitHub Action that only deploys to Azure Static Web Apps, without the Docker-based build overhead of the official action. It wraps the same StaticSitesClient that SWA CLI uses internally, includes automatic caching, and supports both Deployment Token and azure/login authentication. The Problem with the Official Action When deploying static sites (built with Astro, Vite, etc.) to Azure Static Web Apps, the standard approach is to use the official Azure/static-web-apps-deploy action that gets auto-generated when you link a GitHub repo to your SWA resource. Unlike other Azure deployment actions (e.g., for App Service or Azure Functions), this action uses Oryx — the build engine used across Azure App Service — to build your application intern
Defining and creating a basic Design System based on any website (in Figma and React) using Claude
It's 2026, and MCP and design tooling are constantly changing with various improvements in the AI space. Arguably, one of the most long-awaited features was Figma MCP supporting agents writing to its canvas. The funny thing is, I created a particular workflow 2 weeks ago, the lack of the write to canvas was a bit of a bottleneck but could be worked around since Claude Code was pretty good at generating plugins to create and clean up tokens (variables), as well as components and their props. Thankfully, that gap didn't hang around for long enough, and now there's native support for writing directly to canvas. Firstly, we'll look at how we can set up the correct tooling for the following use case: You've joined a startup, they have a website or a web page, but lack any reusable design system
Why Some AI Feels “Process-Obsessed” While Others Just Ship Code
I ran a simple experiment. Same codebase. One AI rated it 9/10 production-ready . Another rated it 5/10 . At first, it looks like one of them is wrong. But the difference is not accuracy — it’s philosophy. Two Types of AI Behavior 1. Process-Driven (Audit Mindset) Focus: edge cases, failure modes, scalability Conservative scoring Assumes production = survives real-world stress 2. Outcome-Driven (Delivery Mindset) Focus: working solution, completeness Generous scoring Assumes production = can be shipped What’s Actually Happening Both are correct — under different assumptions. One asks: “Will this break in production?” The other asks: “Does this solve the problem?” You’re not comparing quality. You’re comparing evaluation lenses . Failure Modes Process-driven systems Over-analysis Slower shi
The convergence of FinTech and artificial intelligence: Driving efficiency and trust in financial services - cio.economictimes.indiatimes.com
The convergence of FinTech and artificial intelligence: Driving efficiency and trust in financial services cio.economictimes.indiatimes.com
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