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Your AI isn't underperforming. Your data foundation is.

New research reveals why Australian businesses are entering the new financial year with bigger AI budgets and the same unsolved problem. One in three Australian businesses exceeded their AI budget last year. Yet, half of them plan to increase AI spending again this year. Yet the behaviour that caused those budget overruns remains largely unaddressed.

Instrumenting AI Agents for the Agent Timeline: A Practical OpenTelemetry Guide

AI agents are nondeterministic, multi-step, and opaque. When one fails in production, "the model said something weird" is the cheapest, most useless line in your incident postmortem. To debug agents the way they actually run, you need telemetry that captures all of it, in order, with enough context to reconstruct what happened. The OpenTelemetry GenAI Semantic Conventions give you a vendor-neutral way to do exactly that.

Why Observability Isn't Enough for AI Coding Agents

Observability platforms collect pre-instrumented logs, metrics, and distributed traces to monitor production systems and surface failures to human engineers. The adoption of AI into engineering has led observability providers to offer those same signals to agents. This is often packaged as AI observability, but the signals themselves were designed around a human investigation loop. AI coding agents work faster, consume data differently, and need feedback as they work rather than after deployment.

Bus Lanes and Loading Zones in US Cities: A Continuity Problem, Not Just an Enforcement One

Most bus lanes and loading zones stay blocked not because enforcement is too lenient, but because it only exists in the moments someone happens to be watching. Councils tend to read this as a compliance problem that more citations will eventually fix. Recent enforcement data points to something else. The curb is not ignored. It is watched intermittently, and violations cluster in exactly the hours nobody is looking.

Best AI Store Builders for Shopify in 2026

Most entrepreneurs underestimate how fast AI Store Builders have changed what's possible for first-time store owners. You no longer need a developer, a designer, or weeks of setup time to launch something that looks professional. The challenge now is picking the right platform, because not all of them handle product descriptions, niche targeting, or Shopify-specific setup with the same depth. After reviewing the top options across features, merchant feedback, and real-world results, this guide breaks down the five best picks for 2026.

Teach Your AI Coding Agent to Answer Production Questions | Lightrun Ask Prod AI Skill

Lightrun's Gidi Freud demonstrates Ask Prod, the latest Lightrun AI Skill that teaches AI coding agents how to use Lightrun to answer production questions with live runtime evidence. Watch Codex use the skill to discover runtime sources, collect focused runtime data, adapt its investigation, and return an evidence-backed answer. Compatible with Claude Code, Cursor, GitHub Copilot, and other AI coding agents through the Lightrun MCP.

Language AI to physical AI explained

What is physical AI? Physical AI embeds machine learning directly into hardware, enabling algorithms to interact, move, and perform autonomous tasks in the physical world. Traditionally, robots relied on precise, hardcoded coordinates; if an object shifted by a single millimeter, the entire system failed. Today, robotics is moving past rigid automation toward truly adaptive architecture. Neural networks help machines process raw sensor data in real time. Consequently, machines can dynamically reason through the unpredictable physical world.

Ship Reliable AI Faster: How to Operate AI Agents with Control and Confidence

Replace "AI shipped on hope" with an operating model that holds up once real users depend on it. AI quality is multi-dimensional, covering accuracy, tone, safety, and faithfulness to user data, and can't be debugged from outputs alone. Without visibility into what their AI actually did in production, teams miss regressions, reverse-engineer chains by hand, and watch a single bad answer erode trust built over hundreds of right ones.

The AI vendors just started watching the meter. CFOs need to watch the return.

On June 18, OpenAI gave ChatGPT Enterprise admins new credit usage analytics and spend controls. It’s a single view of credit consumption broken down by user, product, and model, default workspace budgets, per-group limits, and a Cost API for pulling the data into their own systems. Two days earlier, Microsoft shipped Copilot Cowork with spending limits, budget allocation, usage alerts, and user-level caps. This is a step in the right direction.