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Peihao Yuan outlines a disaster-recovery analytics workflow where an AI agent generates constrained SQL or Python, DuckDB computes reproducible facts from Parquet data, and the agent interprets the results. The article explains the determinism split, how to make analyses replayable, and four signals for choosing between an embedded engine and a managed lakehouse.
At QCon AI, Susan Chang (Principal Data Scientist at Elastic) described how Elastic moved from siloed, ad-hoc agent evaluations to a unified production-grade evaluation framework that combines LLM-as-judge and deterministic rules, connects Python data-science evals with TypeScript production code, and uses deep tracing to detect regressions across RAG and cybersecurity workloads while preserving domain context.
The arXiv preprint introduces Rules to Tools (R2T), a workflow that supplies prepared executable checks of public scientific requirements for LLM-based scientific-coding agents. In matched SciCode repair-group experiments, teams sharing written checks, starting programs, model and budgets gave a callable implementation to a tool group; across two task-ID cohorts prepared checks achieved 29/30 complete repairs versus 26/30 for detailed text, with mixed task-level outcomes (three task IDs favor tools, one favors text, eleven tie) and a reported task-cluster bootstrap 95% interval of [-12.5, 43.75] percentage points for the difference; other cohorts and PDE comparisons show small, task-dependent differences in success rates and changes in agent-side output and public CPU use.
The paper introduces the Epydemix Agent Framework, an additive layer over the open-source Epydemix Python library for stochastic compartmental epidemic modeling that lets AI agents based on large language models discover available models and parameters, validate declarative scenario specifications, execute models through tested library code, and inspect results. The authors demonstrate an end-to-end workflow comparing vaccination strategies for a novel respiratory virus and evaluate the framework across 50 agent sessions and five modeling tasks, finding it reduced interaction turns, output tokens, and cost on most tasks while noting tradeoffs when allocating resources for per-point reproducibility.
OpenAI describes how it transformed Habitat from a Python library into a globally distributed online storage platform that now serves over 1 billion ChatGPT users and handles about 22 million requests per second.
NVIDIA has released CUDA Python 1.0, presenting stable APIs and a unified foundation intended to give Python developers fuller access to CUDA and GPU capabilities without requiring C++ extension toolchains. The release aims to simplify building GPU-accelerated Python applications across the NVIDIA platform.
Google Research published DP-Auditorium, an open-source Python library that audits differential privacy guarantees using only black-box access to mechanisms. The library provides property testers and dataset finders (including novel HockeyStick, Rényi, and MMD testers) and demonstrates the ability to detect DP guarantee violations across multiple DP definitions (pure, approximate, Rényi).
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