A small experiment in remote SQL execution The post Running SQL Concurrently Across Three Remote DuckDB Servers with Quack appeared first on Towards Data Science .
Starting with a local Parquet file, then joining it to data stored in the cloud The post Building a Data Lakehouse with DuckDB and DuckLake appeared first on Towards Data Science.
Zeta‑Lite is a WebAssembly‑based in‑browser SQL database that brings concurrent, snapshot‑isolated transactions and copy‑on‑write database branching to the client side. It is a compact 2.87 MB gzipped build of the Zeta engine, offering a full PostgreSQL‑compatible feature set—including joins, CTEs, window functions, JSONB, full‑text search, HNSW vector search, and graph queries—while maintaining high read/write throughput across major browsers. The engine’s log‑centric asynchronous MVCC core enables overlapping transactions on a single thread and unique branching capabilities rarely seen even in server‑side databases. whyItMatters":"Zeta‑Lite’s concurrent, branchable design provides a lightweight, privacy‑preserving, and offline‑ready memory layer for in‑browser AI agents, enabling them to explore, test, and commit speculative changes efficiently."
arXiv:2606. 01246v1 Announce Type: new Abstract: Text-to-SQL on complex schemas is unreliable on a single pass, so recent systems generate multiple SQL candidates and let voting filter out errors.
The paper proposes typed federated artifacts—schema‑validated objects with per‑field privacy and dispute resolution—to enable tool‑routing knowledge sharing among frozen, heterogeneous LLM agents. By replacing flat text prompts with typed fields, the authors achieve near‑centralized routing performance on StableToolBench while reducing data size to 20 MB JSON per client. The study also highlights that a simple TF‑IDF classifier can outperform LLM routing on labeled benchmarks, indicating limitations in current evaluation methods.
A practical walkthrough using text-to-SQL as the example The post Why I Stopped Using One Agent and Built a Multi-Agent Pipeline Instead appeared first on Towards Data Science .
arXiv:2607. 02630v1 Announce Type: cross Abstract: Hardware accelerators now sit on the critical path of online serving.
The paper introduces ModularSQL, a lightweight runtime guardrail designed to detect and correct multiplicity errors—such as missing DISTINCT clauses, inflated aggregates, and Cartesian join explosions—in Text-to-SQL systems. It highlights the Multiplicity Blind Spot (MBS), where standard set-based accuracy metrics fail to capture these errors, and proposes Multiset-EX as a more comprehensive evaluation criterion. Experiments on several models show that ModularSQL can improve multiplicity-aware accuracy while adding minimal computational overhead.
The article explains how to detect a payload that appears correct yet is not, by employing a watchdog pattern in Python. It discusses the challenges that cause many multi‑agent systems to fail even when their evaluations succeed. The post was originally published on Towards Data Science.
Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases.
The study examines the composition of a random sample from the Model Context Protocol (MCP) registry, revealing that only 48.8% of the 400 sampled npm/stdio servers successfully complete an initialization handshake, compared to 66.7% for a hand‑curated frame. Among the servers that run, there are no fatal JSON Schema violations across 2,766 advertised tools, but optional safety annotations vary widely, with a 58.8% omission rate in the random draw versus 41.5% in the curated set. The authors also compare MCP tool descriptions to two benchmark corpora, finding minimal near‑duplication in real MCP tools (2.8%) and significant repetition in synthetic datasets (up to 85.6%).
A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server The post The Protocol That Cleaned Up Our Agent Architecture appeared first on Towards Data Science .