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How to Use KaneAI — Best Gen AI Native (FULL GUIDE) 🔥” QA Engineers & DevOps: Stop wasting 80% of your time on manual tests. KaneAI converts natural language into executable End to End tests instantly: ✅ Upload JIRA issues, PDFs, or videos → Auto-generate test cases ✅ Self-healing...

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Stop writing tests. Start describing them. KaneAI converts natural language into end-to-end tests in seconds. ✔ JIRA / PDF / video uploads ✔ Self-healing UI tests ✔ GitHub PR-based test creation ✔ API & backend validation Modern testing, powered by GenAI Give it a try : Follow #AITesting #KaneAI #DevOps #QualityEngineering
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Stop writing tests. Start describing them. KaneAI converts natural language into end-to-end tests in seconds. ✔ JIRA / PDF / video uploads ✔ Self-healing UI tests ✔ GitHub PR-based test creation ✔ API & backend validation Modern testing, powered by GenAI Give it a try : Follow #AITesting #KaneAI #DevOps #QualityEngineering

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🚀 Meet KaneAI TestMu AI the next-generation AI-powered testing assistant for modern QA teams. KaneAI helps teams plan, author, and evolve end-to-end tests using natural language, turning requirements directly into executable code. Built for enterprise-scale complexity, it handles sophisticated workflows across all major programming languages and frameworks. QA teams often face hurdles like rapid development cycles, test case debt, and complex framework management. KaneAI addresses these challenges by: ✅ Generating structured test cases from text, JIRA, PDFs, images, audio, video, or spreadsheets ✅ Enabling complex testing workflows with natural language ✅ Supporting multi-language code exports and deep integrations with JIRA and GitHub ✅ Automating API and database testing, smart versioning, and auto bug detection ✅ Self-healing tests for UI changes and flexible manual interaction KaneAI augments QA teams, boosting productivity, reducing errors, and helping teams focus on outcomes rather than repetitive tasks. Learn more about KaneAI here: Connect with TestMu AI : LinkedIn: Twitter: #QA #TestAutomation #AI #GenAI #KaneAI #TestMu
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🚀 Meet KaneAI TestMu AI the next-generation AI-powered testing assistant for modern QA teams. KaneAI helps teams plan, author, and evolve end-to-end tests using natural language, turning requirements directly into executable code. Built for enterprise-scale complexity, it handles sophisticated workflows across all major programming languages and frameworks. QA teams often face hurdles like rapid development cycles, test case debt, and complex framework management. KaneAI addresses these challenges by: ✅ Generating structured test cases from text, JIRA, PDFs, images, audio, video, or spreadsheets ✅ Enabling complex testing workflows with natural language ✅ Supporting multi-language code exports and deep integrations with JIRA and GitHub ✅ Automating API and database testing, smart versioning, and auto bug detection ✅ Self-healing tests for UI changes and flexible manual interaction KaneAI augments QA teams, boosting productivity, reducing errors, and helping teams focus on outcomes rather than repetitive tasks. Learn more about KaneAI here: Connect with TestMu AI : LinkedIn: Twitter: #QA #TestAutomation #AI #GenAI #KaneAI #TestMu

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Anyone who’s worked in QA knows the gap. Requirements live in one place and tests live somewhere else. Every change creates another round of rewriting. Kane AI by TestMu AI (formerly LambdaTest) approaches that gap differently. Instead of forcing teams to rewrite tests in a new abstraction, it lets you describe intent in natural language and turns that into structured, executable tests that can be exported into your existing frameworks. During exploration, the most practical part wasn’t just test generation, it was how expressive the language layer is. Complex assertions, branching conditions, and validations can be described without switching mental modes into code, but the resulting output still integrates cleanly with real pipelines. There’s also a strong emphasis on API coverage and auto-bug detection, which matters when UI tests aren’t enough. Kane AI by TestMu AI (formerly LambdaTest) can detect failures during execution and attempt self-healing based on original intent, while still requiring review and validation from the team. It doesn’t pretend QA disappears. It acknowledges QA is hard, contextual, and constantly changing. That honesty makes it useful. If you’re dealing with test maintenance and curious how natural-language testing fits into real QA work, Kane AI by TestMu AI (formerly LambdaTest) is available to try. See it in action: TestMu AI
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Anyone who’s worked in QA knows the gap. Requirements live in one place and tests live somewhere else. Every change creates another round of rewriting. Kane AI by TestMu AI (formerly LambdaTest) approaches that gap differently. Instead of forcing teams to rewrite tests in a new abstraction, it lets you describe intent in natural language and turns that into structured, executable tests that can be exported into your existing frameworks. During exploration, the most practical part wasn’t just test generation, it was how expressive the language layer is. Complex assertions, branching conditions, and validations can be described without switching mental modes into code, but the resulting output still integrates cleanly with real pipelines. There’s also a strong emphasis on API coverage and auto-bug detection, which matters when UI tests aren’t enough. Kane AI by TestMu AI (formerly LambdaTest) can detect failures during execution and attempt self-healing based on original intent, while still requiring review and validation from the team. It doesn’t pretend QA disappears. It acknowledges QA is hard, contextual, and constantly changing. That honesty makes it useful. If you’re dealing with test maintenance and curious how natural-language testing fits into real QA work, Kane AI by TestMu AI (formerly LambdaTest) is available to try. See it in action: TestMu AI

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What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

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New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

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