ROI

Agents don’t build Rome from scratch—they retrofit the city. GitTaskBench (arXiv:2508.18993) is the first benchmark that grades code agents on how well they exploit existing GitHub repositories to deliver real-world outcomes, not just pass algorithm puzzles. It also puts a price tag on success via an Alpha value that blends accuracy with cost, bringing long-missing business realism to agent evals. TL;DR What’s new: 54 tasks across 7 modalities (image, video, speech, office docs, web scraping, security/privacy, biosignals), each paired to a real repo and a practical, automated test harness. Why it matters: The hard part isn’t just writing code—it’s environment setup, dependency wrangling, repo comprehension, and workflow orchestration. Headline result: Even the best stack—OpenHands + Claude 3.7—passes only ~48% of tasks; environment/setup issues cause ~65% of all failures. Business twist: The Alpha value estimates net economic benefit per task by combining success, quality, and token costs. Expensive tasks become clear wins; cheap tasks require ruthless cost control. The Benchmark, de-jargoned Problem framed: In real shops, devs search, fork, and adapt. GitTaskBench simulates that reality. Each task gives an agent a specific repo (e.g., DeOldify, Scrapy, NeuroKit, SpeechBrain) and a concrete user goal (e.g., “colorize this photo” or “extract author/quote pairs into CSV”). Success is determined by a task-specific metric (e.g., NIQE for image quality; SNR/SDR for speech separation; field-level F1 for scraping; column/row fidelity for office docs) and an execution check (the thing actually runs and outputs in the right format). ...

Most “AI for analytics” pitches still orbit model metrics. The more interesting question for executives is: What should we do next, and why? A recent paper proposes an AI Data Scientist—a team of six LLM “subagents” that march from raw tables to clear, time‑boxed recommendations. The twist isn’t just automation; it’s hypothesis‑first reasoning. Instead of blindly optimizing AUC, the system forms crisp, testable claims (e.g., “active members are less likely to churn”), statistically validates them, and only then engineers features and trains models. The output is not merely predictions—it’s an action plan with KPIs, timelines, and rationale. ...