What if an AI agent could look at a piece of content and think, “Huh, I don’t know much about this”—and then do something about it? Not just flag it for a human. Actually go out, find relevant data, validate it, verify it, and eventually use it to improve itself. This isn’t science fiction. It’s a practical architecture I’ve been thinking about, and I want to walk you through it. ...
Everyone talks about RAG, but few have actually built one. Let’s break the spell and implement a semantic search system step by step using Python and Qdrant.
Key Highlights Evalite provides a purpose-built test harness for AI-powered applications It offers a web UI for local iteration and a robust scoring system Evalite supports pluggable storage and scorer integrations for flexibility The increasing adoption of AI-powered applications has created a need for more efficient and reproducible testing methods. This is where Evalite comes in, a TypeScript-native eval runner that enables developers to write reproducible evals, capture traces, and iterate locally with a web UI. As the AI landscape continues to evolve, tools like Evalite are crucial for ensuring the reliability and performance of AI-driven features. ...
Key Highlights Semantic caching reduces LLM costs and latency by storing frequent queries and their responses. ScyllaDB’s Vector Search enables efficient semantic caching for large-scale LLM applications. Combining LLM APIs with ScyllaDB’s low-latency database optimizes performance and cost. The increasing adoption of Large Language Models (LLMs) in various applications has led to significant concerns about costs and latency. As LLMs continue to grow in complexity and size, the need for efficient and cost-effective solutions becomes more pressing. This move reflects broader industry trends towards optimizing AI workloads and reducing operational overhead. ScyllaDB’s semantic caching offers a promising solution to these challenges, allowing developers to reduce the number of LLM calls and improve response times. ...
Key Highlights Hugging Face CEO Clem Delangue believes we’re in an LLM bubble, which may burst next year Delangue argues that LLMs are not the solution for every problem and smaller, specialized models will gain traction The AI industry is diversifying, with Hugging Face taking a capital-efficient approach to spending The recent surge in Large Language Models (LLMs) has led to concerns about a potential bubble burst. Hugging Face CEO Clem Delangue shares this concern, stating, “I think we’re in an LLM bubble, and I think the LLM bubble might be bursting next year.” This sentiment reflects broader industry trends, where the focus on LLMs has led to overinvestment in the technology. As Delangue notes, “all the attention, all the focus, all the money, is concentrated into this idea that you can build one model through a bunch of compute and that is going to solve all problems for all companies and all people.” ...
Key Highlights Granite 4.0 offers up to 70% reduction in RAM requirements for long inputs and concurrent batches The new hybrid architecture combines Mamba-2 layers with conventional transformer blocks for improved efficiency ISO 42001 certification ensures the model’s safety, security, and transparency The launch of IBM Granite 4.0 marks a significant milestone in the development of large language models, as it introduces a new era of hyper-efficient and high-performance hybrid models designed specifically for enterprise applications. This move reflects broader industry trends towards more efficient and cost-effective AI solutions. By leveraging novel architectural advancements, Granite 4.0 achieves competitive performance at reduced costs and latency, making it an attractive option for businesses looking to deploy AI models at scale. ...
Key Highlights Kimi K2 is a large language model with 32 billion activated parameters and 1.04 trillion total parameters. The model achieves state-of-the-art results on benchmarks testing reasoning, coding, and agent capabilities. Kimi K2 is released as an open-source model, positioning it as a contender in the open-source model space. The release of Kimi K2 reflects broader industry trends towards developing more advanced and accessible AI models. As the demand for AI-powered solutions continues to grow, the need for open-source models that can be easily integrated into various applications becomes increasingly important. Kimi K2’s Mixture-of-Experts architecture and large parameter count make it an attractive option for developers looking to leverage AI in their projects. ...
The field of artificial intelligence (AI) is undergoing a significant transformation, shifting from single-model implementations to multiagent systems. This move reflects broader industry trends towards more collaborative and dynamic AI architectures. Microsoft’s AutoGen is at the forefront of this change, enabling developers to build complex workflows involving multiple AI agents. By leveraging AutoGen, organizations can create more effective and autonomous AI teams, capable of tackling real-world problems with greater accuracy and nuance. ...
Qwen has unveiled Qwen3-Max, its largest and most capable model to date—and the headline numbers are eye-catching: ~1 trillion parameters trained on 36 trillion tokens, delivered in a Mixture-of-Experts (MoE) architecture that emphasizes both training stability and throughput. The team says the preview of Qwen3-Max-Instruct hit the top three on the Text Arena leaderboard, and the official release improves coding and agent performance further. You can try Qwen3-Max-Instruct via Alibaba Cloud API or in Qwen Chat, with a Thinking variant under active training. ...