Ship with Kane CLI

API Testing just got a serious upgrade.

This is Kane CLI running a smoke test on an endpoint, one of the simplest but most critical checks in any API workflow.

No writing test scripts. No manually hitting endpoints and cross-checking responses. No waiting around for a QA cycle to catch something that should've been caught in seconds.

Just describe what you want tested, and Kane CLI handles the rest, instantly, reliably, every time.

This is what testing looks like when AI actually understands your application, not just your code.

The afternoon you used to spend on manual smoke tests? Now it's a coffee break.

Try now 🔗 https://www.testmuai.com/kane-cli/?utm_source=pmm&utm_medium=social&utm_campaign=kanecli_whop&utm_date=aug_05

@S.S. Rahman @Siddhant Sinha @Neha Tewari @Yatish Jhamb

Agent mode in Kane CLI spits NDJSON.

We made it readable. Watch the highlighting parse every step so debugging isn't painful.

This is what separates "I ran a test" from "I understand what happened."

Learn more about it → https://www.testmuai.com/kane-cli/?utm_source=pmm&utm_medium=social&utm_campaign=kanecli_whop&utm_date=jul_31

@Yatish Jhamb @S.S. Rahman @Siddhant Sinha @Bhawana

This is Kane CLI running as a Kiro Power, start to finish, in a single session.

AWS's agentic IDE just opened its Powers ecosystem, and Kane CLI is part of that first wave. What you're watching is the whole point of it: no context switch, no second tool, no leaving the agent loop to find out whether the build holds up.

One objective in natural language. Kiro takes it from there.

Real Chrome opens on screen, not headless.

Every step streams back as structured events.

34 seconds later: passed, page title extracted, assertion cleared.

The test case lands in Test Manager on its own.

Kiro brings the spec-driven workflow.

TestMu AI brings the browser.

The loop closes without either one leaving the session.

You'll find us in the Powers panel under Kane CLI: Browser Automation and AI Test Authoring, by TestMu AI. One click to install. 🔗 https://kiro.dev/launch/powers/add/?name=kane-cli

This is how Kane CLI works with chat interface AI agents.

You simply point your AI agent to the testmuai.com/kane-cli/agents.md file, and it instantly knows how to use Kane CLI for browser testing and verification.

In this demo, we're using Lovable. The agent builds the feature, Kane CLI verifies it in a real browser using plain English, and returns a structured pass or fail you can trust.

• No custom integration

• No manual test scripts.

Just AI building and Kane CLI verifying.

Try for free 🔗 https://www.testmuai.com/kane-cli/?utm_source=pmm&utm_medium=social&utm_campaign=kanecli_whop&utm_date=jul_28

#AIAgents #BrowserTesting #Lovable #KaneCLI #AITesting #QA #AutomationTesting #DeveloperTools

Gemini CLI can find a hardcoded price mismatch. If you ask it to.
That "if you ask" is the whole problem.
AI coding agents reason about source brilliantly. Then they say "done" the moment the code looks right. They never open the page the user actually lands on.

So we gave Gemini CLI eyes.
kane-cli run "go to localhost:3000, click Buy Pro, assert the total due today shows $19.00" --agent
Kane CLI spins up real Chrome, clicks through to checkout, makes the call. Advertised $19. Charged $29. Failed, with a shareable report.
Gemini CLI takes that report, opens page.js and checkout/page.js, finds the hardcoded $29, patches it. A second run checks the fix.

Pass.
The point isn't that Gemini couldn't find $29 on its own. It's that the loop closed itself. Build, verify, fix, reverify, nobody clicking through.
And $29 in the source is the easy case. A price pulled from an API. A coupon that applies on render. An overlay covering the total. None of that lives in the code. The browser is the only ground truth.
Write, test, fix, verify, all in one terminal: https://www.testmuai.com/kane-cli/?utm_source=pmm&utm_medium=social&utm_campaign=kanecli_whop&utm_date=jul_24

#KaneCLI #TestMuAI

Kane CLI is an agentic quality verifier. One loop, end-to-end:

→ Reads the requirement
→ Designs the tests
→ Executes them in a real Chrome browser
→ Measures coverage from what actually happened on screen
→ Returns a verdict you can trace back to the requirement

Coverage is measured from the run itself, so a pass means exactly what it proved and nothing more.

Every run ships as .evidence, an open proof pack in plain YAML and Markdown, plus a share link you can drop straight into a PR or a Slack thread. Diffable in git. Portable across tools. Readable by engineers, agents, and auditors.

The browser is the only ground truth. Everything else is inference.

Try it: https://www.testmuai.com/kane-cli/?utm_source=pmm&utm_medium=social&utm_campaign=kanecli_whop&utm_date=jul_23

You build with AI agents. Claude Code writes the code, CI goes green, everything looks done.

Then a user hits the refund flow and it breaks. The green lied.

That's why we built Kane CLI.

Green is a claim based on code. It means your tests ran, not that your app is proven. The evidence is real, but it's scattered across CI, screenshots, artifacts, and logs. Nobody holds it together.

Kane CLI pulls it into proof. It reads your PRD, extracts what the app is supposed to do, and measures coverage against those requirements. Your AI agent drives the loop until the proof gate is met. Where the proof runs thin, Kane CLI shows you, ranked by risk.

If you're shipping AI-built products, this is the difference between "it should work" and "here's the evidence it does."

Free to try 🔗 https://www.testmuai.com/kane-cli/?utm_source=pmm&utm_medium=social&utm_campaign=kanecli_whop&utm_date=jul_21