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The article introduces DataGovBench, a benchmark derived from governmental open data, designed to evaluate LLMs on real-world data analysis tasks including table question answering and insight discovery. Experiments show current LLMs still underperform in complex data analytics scenarios.
This book teaches Python users how to automate Excel workflows using pandas, from reading messy spreadsheets to generating reports and emailing results, all with a single script.
An analysis of OpenRouter data shows that US and Chinese companies train nearly all of the world's most-used AI models, with Chinese models rapidly increasing to 20 out of the top 50 by May 2026.
Hobbes is an open-source language, embedded JIT compiler, and runtime from Morgan Stanley for efficient dynamic expression evaluation, data storage, and analysis in C++ applications.
GP_ELITE is a pure-Python library for genetic-programming based symbolic regression, enabling discovery of interpretable mathematical formulas from small experimental datasets. Version 0.2.0 introduces Levenberg–Marquardt constant fitting, multi-restart reliability, Pareto front output, and extrapolation mode.
Tobi recommends using Ducklake with DuckDB for efficient data import, such as health data from Fitbit, and suggests telling agents to learn it.
A guide to open source or free AI agents that can analyze data from Excel files, covering available tools and their capabilities.
DWN.Bridge is an open-source desktop client that enables zero-knowledge AI analysis of local Excel files and databases by only sending schema to the LLM while executing queries locally.
the-stats-duck v0.6.0 is an open-source DuckDB extension that brings statistical analysis and plotting directly into SQL, including regression, bootstrapping, and ggplot-like visualization.
A discussion thread asking about real-world ROI from AI agent workflows in areas like software development, research, customer support, operations, sales, and data analysis, seeking architecture details, metrics, and lessons learned.
This paper argues that language model agents should assist causal discovery workflows by providing contextual support and explanations rather than generating causal conclusions, and introduces causal-learn+ platform to demonstrate this principle.
After analyzing 3,978 primary school exam papers, the author points out that exams mainly test basic textbook knowledge, and the effect of tutoring is limited. They argue that by 2026, AI can replace tutoring, and promote their gamified learning app, advocating that children should master knowledge through play.
This article analyzes and projects forward Metr's time horizon data, likely related to AI development timelines and forecasting.
TwinBI is a framework that couples an LLM-based agent with an executable BI dashboard state to maintain consistency during multi-step analytical interactions, improving accuracy and reducing timeout rates in benchmarks.
Amplitude introduces Wave, a proactive product agent that automates the build-ship-use-learn loop by analyzing data, surfacing opportunities, and tracking outcomes to help teams build self-improving products.
TabClaw is an open-source interactive AI agent for spreadsheet manipulation and table reasoning that uses LLMs to automate data analysis, support multi-table reasoning, and adapt to user preferences through memory and skill extraction.
Lium AI is an AI tool designed to handle complex data, as featured on ProductHunt.
An API for viewing, monitoring, and analyzing over 1.8 million US job postings.
DataCOPE is an unsupervised verifier-guided skill discovery framework for data-analytic agents that derives verifier signals from exploration trajectories without labeled supervision. It improves performance by 9.71% and 32.30% on report-style and reasoning-style data analysis tasks respectively.