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Real-Time Data Governance: Managing Data Quality as AI Decisions Happen
What if an AI system makes the right decision using the wrong data?Continue reading on Medium »
What The CCAS Certification Regime Actually Specifies
CCAS v1.0 maps governance requirement to governance consequence three grades, each a conformity claim calibrated to a distinct Continue reading on Medium »
Noise from Universal’s latest ride made rich locals furious, fast
Don’t have too much fun, you’ll upset the neighbors. | Photo: Ronaldo Bolaños / Los Angeles Times via Getty Images Universal Studios Hollywood has pledged to erect another sound barrier around its latest attraction after local residents complained about the bloodcurdling screams from riders. In a meeting attended by at least 350 people yesterday, park execs also promised that Fast Furious: Hollywood Drift won't operate between 8AM and 9AM for the rest of the year, Deadline reports. Several mitigations have already been implemented into the ride - which opened on September 16th this year - including an existing sound barrier, designing the 72 mph rollercoaster to barely make a sound during operation, and even making the rider cars turn away from the re … Read the full story at The Verge.
Claude Code Custom Commands & Skills: Automate Your Workflow
Every dev team has repeatable setup tasks: spinning up a new project, running a deployment checklist, generating a file structure, or typing the same instructions into Claude again and again. Claude Code helps reduce that friction with slash commands, including built-in commands and custom commands you can define for your own workflows. These commands turn [ ] The post Claude Code Custom Commands Skills: Automate Your Workflow appeared first on Analytics Vidhya.
ANYbotics launches Shift to streamline robot fleet operations, scale inspections
ANYbotics designed Shift to help maintenance teams run industrial inspections, detect failing equipment, and turn data into work orders. The post ANYbotics launches Shift to streamline robot fleet operations, scale inspections appeared first on The Robot Report.
A Second Model Pass Is Not a Contract Accuracy Guarantee
A Python reference workflow with Strands, type-specific schemas, SQLite and a test that compares extraction with verification.Continue reading on Recohut AI Labs »
Why an AI Coding Agent Needs a Design System
This week in my software engineering bootcamp, we focused on a part of working with AI coding agents that I had not thought much about Continue reading on Medium »
Current AI’s Recursive Capabilities are Very Limited
I recently read the Guardian article AI Godfathers warn of runaway intelligence explosion . Continue reading on Medium »
Building an AI Client for Our Internal Developer Platform (IDP)
How we put a conversational interface on top of our platform without handing a language model the keys to production.Continue reading on Medium »
Sage: Formalization with Semantic Correction
arXiv:2609.35790v2 Announce Type: new Abstract: While neural theorem provers have achieved impressive milestones in formal mathematics, they largely operate on the assumption that faithful Lean 4 formal statements are already provided. Translating informal natural language into a formal language is a critical data bottleneck plagued by an "illusion of rigor": standard type-checkers accept statements that compile but drop hypotheses, introduce vacuous truths, or subtly alter mathematical bounds. To resolve this, we introduce Sage (Semantic Agent-Guided Formalization Engine), an agentic framework that replaces monolithic translation with a four-stage decomposed generation pipeline coupled with a dual-signal semantic correction loop. By pairing Lean 4 compiler diagnostics with multi-dimensional semantic feedback, our correction loop enforces mathematical fidelity alongside syntactic validity. By explicitly accounting for the gap between open-ended queries and declarative formal targets, o
Travel Time Prediction in Supply Chain Management Using Machine Learning
arXiv:2609.38190v1 Announce Type: new Abstract: The purpose of this research is to find data and methods using machine learning and deep learning to correctly predict the estimated travel time for transportation and logistics in a supply chain system. The supply chain ecosystem is very complex and heavily relies on the transportation and logistics of raw materials and finished goods. Accurate travel time estimation is critical because it helps supply chain members to improve logistics consistency and performance. This helps in planning, demand forecasting, lead time management and assembly planning. The logistics on the delivery side of the customer also plays a crucial role in customer satisfaction and voice of customer. With the collection of huge historical data and using novel techniques, the research builds an accurate model to predict travel time of inventory.
EHR2Trace: Auditable EHR Data Infrastructure for Patient World Models and Clinical Agents
arXiv:2609.38193v1 Announce Type: new Abstract: Patient world models and clinical agents aim to predict changes in patients' health and support clinical work. Developing these systems requires reliable histories of patient conditions, treatments, and the information available at each decision. Electronic health records (EHRs) contain these histories, but differences in how events are recorded make them difficult to use consistently. We present EHR2Trace, a system that converts EHRs from different sources into traceable patient events for model training and evaluation. It links events to source records, separates event time from information availability, and distinguishes medication orders, dispensing, and administration. A shared event representation supports both OMOP and MEDS exports, with automated validation and reproducible builds. Across three clinical datasets, EHR2Trace converted 846.4 million events, with every applicable check passing except one unit-consistency check on MIMI