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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.

Inside Kraken: Regression Testing With Traffic Replay

A readiness check should make a release safer. During one release validation, ours kept Kraken’s traffic replays stuck in Initializing until they timed out. The operator was asking Kubernetes for EndpointSlices. Its installed permissions did not allow that request. The code needed a real cluster, the generated permissions, and a replay trying to start before that mistake became obvious. Kraken put those pieces together during release validation.

Test and Mock GraphQL APIs Without Trusting the 200

Replay a GraphQL recording at the server that produced it, and every request comes back 200 OK. Here is one of those 200s, from a Session query whose recorded token has expired: A GraphQL server reports most failures inside a successful HTTP response, so any check that stops at the status code passes this request. And that is the second way GraphQL hides from URL-shaped tooling.

gRPC Mocks, Tests and Contract Checks From a .proto

Change one field in a.proto from double to string, and nothing complains. The code compiles, the deploy goes green, and a service still built from the old contract keeps answering with a double. Here’s what Go’s protobuf runtime does when a client on the new contract reads that answer: No error. risk_score comes back empty, and the nine bytes of the double are tucked away as unknown fields.