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Building a local AI agent in Workshop on Ubuntu

Build a fully local AI coding agent on Ubuntu using Canonical Workshop and the Qwen3-Coder Inference Snap. In this step-by-step demo, you'll set up an isolated development environment, connect your coding agent to a locally hosted LLM, and run coding tasks without an internet connection. Follow along to recreate the setup on your own machine, using CPU or GPU inference. Chapters.

Your AI Agent Can Act Across Enterprise Systems. Now You Need Delegation of Authority

In 2025, attackers reached the Salesforce data of hundreds of companies without breaking into Salesforce. They used access tokens belonging to an AI chat agent. Google's threat intelligence team counted more than 700 potentially affected organizations, and because the tokens belonged to a trusted app, they carried the attackers past safeguards such as MFA. Enterprises already know how to manage this kind of risk for people.

Shipped: AI dimensions, organized around how your team works

AI Signals adds a standard set of AI dimensions to your dimension picker, plus custom dimensions built from your own data. Not every team needs all of them. The ones you don’t use make the ones you want harder to find. Cleaning that up used to require an engineer to run a script every time an account team needed a change. We identified the bottleneck and automated it, so account teams can manage dimensions themselves and engineering stays focused on product work.

AI cost per query: how to calculate and optimize it

AI cost per query is your total AI serving spend divided by the number of queries served over the same period. It combines model API charges (input and output tokens), retrieval and orchestration overhead, and shared infrastructure. Most production text queries cost between $0.0005 and $0.02. It's the base unit of AI unit economics, and the number finance needs before any ROI claim holds up.

How a beagle-shaped AI agent ships code to madewithvuejs.com

This is a guest post from Armin Ulrich, a fullstack developer, and founder of placid.app. He also created the MadeWith* network where he shares his projects and allows other developers to share theirs. Two people and one beagle run the project showcase madewithvuejs.com. The beagle is actually an AI agent that we have added to our workflows, but we’re very fond of her by now. Milly is part of the team, scouting for content, building features and sniffing for bugs.

Beyond Drag-and-Drop: What Sets AI Workflow Tools Apart

“AI workflow tool” now describes products with very different build models. One may generate an executable workflow from a plain-language request. Another may add a chat panel or autocomplete to the same drag-and-drop experience it has offered for years. That difference matters to buyers because teams choose workflow tools to reduce automation backlogs, eliminate repetitive work, and extend orchestration across ITSM, cloud, network, identity, and infrastructure systems.

10 Best AI ITSM Tools in 2026 (Compared)

Artificial intelligence (AI) is changing how IT teams manage service requests, troubleshoot technical issues, and respond to incidents. As IT environments become increasingly complex, traditional service management processes can struggle to keep up with growing ticket volumes, recurring incidents, and rising expectations for faster resolutions.

Bizally Review 2026: Accounting Packages, Bookkeeping Add-Ons and 3 Melbourne Alternatives

Bizally earns its fee if you want a long-running Blackburn South firm that bundles your annual returns, tax planning and a Xero subscription into one published monthly fee, with SMSF accounting under the same roof. It suits established owners who are happy in Xero; it suits you less if you need BAS and payroll included in the base price. Bizally's add-ons, not its headline package prices, set the real monthly cost for most employers, and the three Melbourne alternatives below structure their fees in quite different ways.

How Teams Can Pilot Text to STL for Rapid Prototyping

A request for a quick physical mockup often arrives before anyone has a measured drawing. N4D can turn a written description into a candidate STL model, giving a team something concrete to review early. The operational challenge is deciding which requests belong in that fast lane and which need a controlled CAD process from the start.

Why Cross System Workflows Fail and How to Build More Reliable Business Automation

A workflow can fail even when every application reports that it is healthy. A customer record reaches the CRM, an order reaches the ERP, and the provisioning request disappears between them. Reliable business automation requires an explicit record of what happened across those systems, who owns incomplete work, and how processing resumes after interruption.