The Mathematics Search Engine

6+ min ago (582+ words) Been building AI applications lately? You've probably felt the pain of vector embedding storage. Vector databases are powerful, yeah but expensive and a hassle to work with. Surprise. AWS just launched something that can save you up to 90% on vector storage. Let me introduce you to Amazon S3 Vectors. Let's get our feet wet in the fundamentals before jumping into S3 Vectors. In case you already store vectors in Pinecone, Weaviate, or have your own vector database, you're already aware that vectors are numeric encodings of your data text, images, audio, etc. They encode semantic meaning so you can retrieve similar things, drive RAG applications, or grant your AI agents some memory. The problem? Traditional vector databases are: Say hello to S3 Vectors the first cloud object store with native vector support. It's similar to Amazon S3, but optimized for vectors. You get the…...

18+ min ago (480+ words) Cuando hablamos de ejecutar modelos de lenguaje como Llama, Mistral, Phi-3 u otros LLMs, es com'n pensar que el modelo "se ejecuta solo. Sin embargo, en la pr'ctica necesitamos una pieza clave del ecosistema: el runner (o ejecutor). Un runner es un programa encargado de cargar, ejecutar y gestionar un modelo de IA en tu hardware. Act'a como el puente entre los pesos del modelo y tu aplicaci'n. En t'rminos simples, un runner se encarga de: Sin un runner, el modelo es solo un archivo de datos inerte. Un runner es el software responsable de interpretar y ejecutar un modelo de lenguaje. Existen muchos, pero estos son algunos de los m's utilizados hoy en d'a: llama.cpp es un runner escrito en C++ que permite ejecutar modelos en formato GGUF, como por ejemplo: Este proyecto es la base de muchos…...

45+ min ago (211+ words) I'm still learning, and I'm okay with that. Writing regularly is slowly teaching me how to be more clear and confident. Some days feel easy, some don't " but showing up matters more than doing things perfectly. This journey is helping me grow at my own pace, and that feels good. I'm still learning, and I'm okay with that. Writing regularly is slowly teaching me how to be more clear and confident. This journey is helping me grow at my own pace, and that feels good. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well I'm still learning, and I'm okay with that. Writing regularly is slowly teaching me…...

46+ min ago (982+ words) Computer-aided design (CAD) systems are tried-and-true tools used to design many of the physical objects we use each day. But CAD software requires extensive expertise to master, and many tools incorporate such a high level of detail they don't lend themselves to brainstorming or rapid prototyping. In an effort to make design faster and more accessible for non-experts, researchers from MIT and elsewhere developed an AI-driven robotic assembly system that allows people to build physical objects by simply describing them in words. Their system uses a generative AI model to build a 3D representation of an object's geometry based on the user's prompt. Then, a second generative AI model reasons about the desired object and figures out where different components should go, according to the object's function and geometry. The system can automatically build the object from a set of prefabricated…...

1+ hour, 2+ min ago (234+ words) In modern systems, we often treat nondeterminism as a feature. Especially in AI-heavy architectures. If something behaves unpredictably, we call it emergent. If it's hard to debug, we say it's complex. If it breaks, we blame the model. That framing is wrong. A deterministic system doesn't mean "everything is static." It means that given the same inputs, structure, and constraints, the system behaves the same way " or fails in a way you can explain, replay, and fix. That distinction matters more as systems grow. In traditional engineering, we learned this the hard way: That's why methodologies like IOSM exist: to turn improvement into an algorithm " gated, measurable, and automatable " instead of a vibe-driven process. The same lesson applies to AI systems. When LLMs are embedded without contracts, schemas, or deterministic execution paths, we don't get intelligence " we get plausible chaos....

1+ hour, 12+ min ago (491+ words) Las dimensiones temporales m'ltiples, es una propuesta cuyo pionero es el doctor en fisica Itzhak Bars, que plantea la hipotetica idea de que nuestro Universo y en especial el tiempo que percibimos sea el resultado de un universo con dos tiempos fundamentales, un espacio de 4 dimensiones espaciales y 2 temporales. Pero, un dia, mientras debia estar programando, le" un art"culo y me pregunte: Y por qu" tratan al tiempo como espacio? All" estaba el Bug, estaban tratando al tiempo como si fuera una dimension espacial m's, aun cuando claramente no lo era, se trataba de un error de tipado. No soy genial por esta propuesta, solo hice lo que como programador estoy acostumbrado a hacer: mire el problema desde otra optica. Y mirar desde otra optica es facil cuando literalmente estas en otro campo. Adem's un skill que debi usar…...

1+ hour, 13+ min ago (1664+ words) I've been coding for some years. I've seen frameworks rise and fall, languages evolve, and entire paradigms shift. But nothing'nothing'has made me question the future of my profession quite like the past three years of AI development. That same week, OpenAI CEO Sam Altman admitted he's worried about "the rate of change that's happening in the world right now", calling it the fastest technology adoption in history, with over 800 million people using ChatGPT weekly. And then Anthropic CEO Dario Amodei predicted AI could eliminate half of entry-level white-collar jobs within one to five years, while maintaining a 25% chance AI development goes "really badly." So here we are. Standing at the edge of the 2030s, watching the most powerful minds in tech issue both promises and warnings. What does the next decade actually look like for developers, for knowledge workers, for humans?...

1+ hour, 49+ min ago (224+ words) Hyderabad: A six-day Faculty Development Programme (FDP) on Data Science and Analytics for Decision Making: Tools and Techniques, sponsored by the All India Council for Technical Education (AICTE),... Hyderabad: A six-day Faculty Development Programme (FDP) on Data Science and Analytics for Decision Making: Tools and Techniques, sponsored by the All India Council for Technical Education (AICTE), commenced at Bhaskar Engineering College (BEC), Moinabad, on Monday. Around 60 faculty members from various institutions, including JBIT, JBREC, CBIT, KG Reddy College, GITAM University and BEC are participating in the programmeThe inaugural session began with the ceremonial lighting of the lamp by Gouriprasad M., Programme Director " Data Analytics, YASH Technologies, and Dr. Sumagna Patnaik, Principal of Bhaskar Engineering College, in the presence of Prof Ch Sanjay, Director, JB Education Society, and Dr N Vijay Kumar, Coordinator of the FDP The programme aims to equip faculty…...

1+ hour, 55+ min ago (350+ words) A new technical paper titled "Kitsune: Enabling Dataflow Execution on GPUs with Spatial Pipelines" was published by researchers at NVIDIA and the University of Wisconsin-Madison. Abstract "State-of-the-art DL models are growing in size and complexity, with many modern models also increasing in heterogeneity of behavior. GPUs are still the dominant platform for DL applications, relying... " read more The post Efficient Synchronous Dataflow Execution For GPUs (NVIDIA, UW-Madison) appeared first on Semiconductor Engineering. A new technical paper titled "Kitsune: Enabling Dataflow Execution on GPUs with Spatial Pipelines" was published by researchers at NVIDIA and the University of Wisconsin-Madison. "State-of-the-art DL models are growing in size and complexity, with many modern models also increasing in heterogeneity of behavior. GPUs are still the dominant platform for DL applications, relying on a bulk-synchronous execution model which has many drawbacks and is ill-suited for the…...

1+ hour, 55+ min ago (840+ words) I want to show you how to build pictures from your data. Not just static charts, but living, interactive stories that respond to a user's touch. When I started with JavaScript visualizations, it felt overwhelming. The tools seemed complex, the performance issues daunting. But I learned that by mastering a handful of key techniques, you can create almost anything. This is a practical guide. I'll walk you through the core methods I use every day, with code you can adapt. We'll start from the ground up. First, let's talk about drawing directly. Sometimes, you need total control over every pixel on the screen. That's where the HTML Canvas element comes in. Think of it as a digital sketchpad. You use JavaScript commands to draw lines, shapes, and text. The trick is setting it up properly. You must account for high-resolution…...