Does a lexical retriever suffice as large language models (LLMs) become more capable within an agentic search loop? We revisit this question by pairing BM25 with increasingly capable LLMs and introduce Piika, a search agent equipped with three tools for retrieving, browsing, and reading documents. On the BrowseComp-Plus benchmark, under the same GPT-5 + BM25 setup, Piika improves the accuracy from 58.3% to 74.6% while reducing cost from $400 to $95. Despite relying only on lexical retrieval, it also outperforms the previously reported GPT-5 + dense-retriever setup, which achieves 73.0% accuracy at $361. Piika further scales with stronger LLMs, reaching 83.1% answer accuracy and 94.7% surfaced-evidence recall with GPT-5.5. Across model families, our results show that simple lexical retrieval, coupled with capable LLMs, a lightweight harness, and an effective tool interface, can remain highly competitive with more complex retrieval systems. Source code and evaluation artifacts are available at https://piika.ai.

We present rosaOS, an open-source agentic operating system for embodied LLMs: interactive, LLM-driven agents coordinate various software tools and physical devices through a desktop companion, the Reachy Mini robot. Existing LLM–robotic systems are generally built as a tight, intertwined stack, making it difficult to switch hardware, add extra capabilities, or expand to multiple devices without bespoke integration. Our system aims to provide a classic OS-inspired architecture where an agentic kernel manages all task execution and mediates device access, while process agents invoke tools to perform actions. We adopt industry-standard interfaces with MCP for agentic tool-calling and ROS for robot interactions, and demonstrate rosaOS on a multi-device setup including a quadruped robot, a wheeled mobile robot, and a smart lamp, all controlled through interactions with the Reachy Mini. By incorporating MCP extensibility with ROS hardware interoperability, rosaOS enables a plug-and-play ecosystem for building embodied agentic systems. Our OS is available at rosaos.ai.

Enterprises today are increasingly compelled to adopt dedicated vector databases for retrieval-augmented generation (RAG) in applications based on large language models (LLMs).As a potential alternative for these vector databases, we propose that organizations leverage existing relational databases for retrieval, which many have already deployed in their enterprise data lakes, thus minimizing additional complexity in their software stacks.To demonstrate the simplicity and feasibility of this approach, we present QuackIR, an information retrieval (IR) toolkit built on relational database management systems (RDBMSes), with integrations in DuckDB, SQLite, and PostgreSQL. Using QuackIR, we benchmark the sparse and dense retrieval capabilities of these popular RDBMSes and demonstrate that their effectiveness is comparable to baselines from established IR toolkits. Our results highlight the potential of relational databases as a simple option for RAG scenarios due to their established widespread usage and the easy integration of retrieval abilities. Our implementation is available at quackir.io.