Systems | Development | Analytics | API | Testing

Distributed rate limiting is essential for managing traffic across multiple servers, ensuring fairness, preventing abuse, and maintaining system reliability. Unlike local rate limiting, which works on a single server, distributed rate limiting uses a centralized datastore to enforce limits globally, making it ideal for large-scale applications and multi-node setups.

AI has become the talk of the town nowadays, right? There are tons of AI tools available for different tasks, and new advancements are coming up daily like vibe coding. But how do you actually do vibe coding? Or how do you try out these models? You could use tools like ChatGPT or Claude, but they come with restrictions, and you often need to pay to access full features. What if you don’t want your data to become part of their training models? That’s where open source coding models come in.

In today’s data-driven world, organizations face the challenge of data processing and integrating vast amounts of information from diverse sources. Open source ETL (Extract, Transform, Load) frameworks have emerged as powerful tools to streamline data workflows, offering cost-effective, scalable, and customizable solutions. This blog delves into the benefits, features, and top ETL solutions in the open source ETL landscape.

AI models are not just about LLMs and generating text. Multimodal vision models—which understand and generate images, videos, and even audio alongside text—are enabling new AI applications. At their core, multimodal vision models combine: There are several different types of multimodal vision models: vision-language models (VLMs) that generate text based on images, vision-reasoning models that answer complex questions based on images, and more.

Open source LLMs continue to compete with proprietary models on performance benchmarks for natural language tasks like text generation, code completion, and reasoning. Despite having fewer resources than closed models, these open LLMs offer cutting-edge AI without the high costs and restrictions of proprietary models. However, running these open-source models in production and at scale remains a challenge.

The market for artificial intelligence (AI) stood at $184 billion in 2024 and is expected to more than quadruple in the next six years. While these expectations are astonishing, AI experts think they’re conservative, to say the least, and the actual market value would be considerably bigger. Large language models (LLMs) like GPT 3 have ushered in the age of AI. They’re finding applications as varied as complex scientific research and writing lyrics for rap battles.