The Next Enterprise AI Challenge: AI Tool Sprawl and the Multi-Model Workplace
For the last two years, enterprise AI strategy has largely focused on one thing: AI adoption.
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Air-Gapped AI SRE Agent: Run AURA on a Local LLM with llama.cpp
The model, the serving layer, and the agent asking questions all sit on local hardware. The first thing it debugs is the AI stack it is running on. No external inference provider in the path.
Why Tracking AI Overviews Is a Data Pipeline Problem, Not a Marketing One
Something quietly moved onto the ops backlog over the past eighteen months. Executives began asking whether the company appears in AI-generated search answers, and the request landed with whoever owns data collection rather than with the people who own the question.
How To Draw AI Review: Testing a Step-by-Step Sketch Tool for Two Weeks
I found How To Draw by accident. I was searching for a faster way to practice figure drawing. A friend mentioned an AI that turns photos into step-by-step sketch guides, so I decided to test it for two weeks straight.
10 Tools To Build Visibility in AI-Driven Answers in 2026
People increasingly ask AI assistants for recommendations before they ever open a website, and Google now answers a large share of searches with an AI Overview. When the assistant names a few options, those are the brands that get considered. Most people never scroll to the sources behind the answer.
New SolarWinds research reveals the gap between AI potential and payoff in IT Service Management
Survey of 800+ IT professionals finds AI is meeting expectations on paper, but only 7 percent say the true cost matched what they planned for.
Are AI and art in conflict with each other?
I recently heard a song that was written by a girl but sung and composed by AI. It left me confused— I'm not sure if I liked it. I was a bit prejudiced and automatically hated it because it was composed by AI. Similar to many works of AI, it had a rhythm, but it was too repetitive and lacked emotions. This made me think about all the other forms of art that might be created by AI, and it left me with a bitter feeling.
AI Was Supposed to Mean Working Less. For Some Developers, It's Doing the Opposite.
AI coding tools were supposed to mean developers work less. On a recent webinar recorded with LeadDev, senior engineering manager Vernon put words to something a lot of teams are quietly noticing instead: “It’s concerning because it’s the opposite of what was promised. We were supposed to be working less.”
How we teach LLMs to write BadgerQL
We just added two new AI features to our app: natural-language translation for Error search and Insights queries. Honeybadger has two query languages: Error search speaks a simple token syntax in the spirit of Solr or a basic Elasticsearch query, while Insights runs on BadgerQL (BQL), our own language for digging into your event data, designed to feel familiar to CloudWatch Insights and Splunk users. Both are powerful, but sometimes you just want something that works without having to open up the docs.
AI Agent Builder: Create Agents That Fit Your IT Environment
AI agents are quickly becoming part of the enterprise automation conversation because, among other things, they help teams move faster. But there is a major difference between an AI agent that sounds useful in a demo and an AI agent that is ready for production. Production agents need scope. They need to know what they own, which systems they can touch, which workflows they can run, which teams they support, and where the boundaries are.