Bug0

Shoutout to that one test that only fails when the CEO is watching. #qa #testing #ai

We’ve been building forward-deployed QA teams powered by AI agents that test like real engineers Bug0. They write, heal, and verify browser tests with a human-in-loop for extra reliability. Cool to see our work with Gemini mentioned in Google’s latest case study on AI-native QA testing. The space is evolving fast, and it’s exciting to see what’s possible when humans and AI work together. 👉 https://lnkd.in/gCpkqrFH

Bug0 uses Gemini 2.5 Pro’s multimodal reasoning capabilities to automate testing and cut manual debugging by 60%. Integrating the model for two primary functions, AI assertion and agentic failure summarization, vastly improved the platform’s debugging workflows and reliability. Explore how they did it: https://goo.gle/42PrUVO

One of the underrated benefits of truly agentic testing is that tests continue to work even with active A/B testing, region-specific layouts and so on. At Bug0 we benefit from this immensely and it cuts down so many if else conditions, and try/catch blocks. The future of testing is natural language, not scripts.

AI summaries ftw! Almost all of ours customers at Bug0 now get AI-summaries on their test execution reports. Makes it so easy to pin point the failure. Another hidden benefit is that it helps us get an insight into how our AI agent thinks and its reasoning.

No AI QA tool talks about caching the underlying tool calls for complex apps - so tests can run deterministically and cheaply. For the past few weeks, my main goal was to make it happen at Bug0. Glad to say it’s finally working. We're trying it out for some of our customers and are already seeing massive gains in both speed and cost efficiency. Best part? When cached code fails due to changes in user flows, AI automatically takes over and regenerates the right steps. This minimizes human intervention significantly and is the best of both the worlds, IMO.

OpenAI killed a wave of startups today with Agent Builder. But no one's talking about how Playwright quietly did the same to "AI QA" startups with Test Agents. 👀 This is a massive leap forward, and it’s not just another tool; it’s a system of AI helpers designed to automate the grunt work of QA. Here's the breakdown: 🔹 The Planner: Explores your app and writes test plans in Markdown. It thinks through the user journey first. 🔹 The Generator: Turns those plans into actual, runnable Playwright code, complete with selectors and assertions. 🔹 The Healer: Watches for failures, identifies UI changes, and attempts to patch broken tests automatically. This entire system is powered by a secure bridge powered by official Playwright MCP server, which lets an AI model safely interact with your browser without direct code execution. It's a well-architected, powerful approach. But it’s just the beginning. The core limitation is that these agents still think in terms of structure: DOM elements, selectors, and locators. They generate code that can break when your UI fundamentally changes. The Healer is reactive, fixing things after they fail. The real future of testing isn't about getting better at generating code. It's about moving beyond it. The next phase is intent-based testing. Imagine describing a goal in plain English: "A new user signs up, verifies their email, and lands on the dashboard." The AI understands the intent and executes the flow, regardless of whether a button is renamed or the layout is redesigned. No selectors, no generated scripts to maintain. Just goals and outcomes. That’s the future we’re building at Bug0. We're focused on automating the entire QA lifecycle, from planning and execution to maintenance, by building an AI that understands your product's intent -- your forward deployed QA team that combines AI agents with human expertise. Playwright Test Agents are a critical step in the right direction, proving that AI-driven orchestration is viable. The next step is to free our engineering teams from the code itself. I have written a detailed post on the new Playwright Test Agents on our blog. Link in the comments. 👇 #Playwright #AITesting #TestAutomation #OpenAI #EngineeringLeadership #TestAgents

Microsoft's official Playwright MCP is here for browser-based automation, and it's getting a lot of buzz from indie hackers and engineers. So it's no surprise when your best engineers show you a mind-blowing demo. An AI is navigating your app and running QA checks from a simple text prompt. It feels like the future. Your first instinct is to greenlight it. This is a trap. As a founder, I've learned that the gap between a cool tech demo and a system that actually accelerates your business is a minefield of hidden costs. Before you let your team spend a quarter building an internal AI QA framework, you have to ask the hard questions. I just wrote a deep dive on this, but here are the highlights: 1️⃣ Who owns the AI's mistakes? Playwright MCP is powerful, but the underlying AI will hallucinate. It will produce flaky tests and false positives. So when a test fails at 2 AM, is your on-call engineer debugging a real bug, or are they trying to figure out why the AI got confused? You're not eliminating test maintenance; you're trading test code for ghost-hunting. 2️⃣ Who maintains the AI's brain? Your team spends a month perfecting test prompts. Then the product team ships a UI redesign. Your entire test suite explodes because the AI's 'map' of your app is now useless. Who is on the hook for retraining the AI and rewriting every single prompt? It's a recurring, high-cost engineering nightmare. 3️⃣ What is the real opportunity cost? The "prompt engineering" needed to make an AI 99.9% reliable isn't a side project; it's a full-time job. You're about to turn your best, most expensive product engineer into an internal QA tool developer. The real cost isn't their salary; it's the feature your competitor ships while your engineer is debugging a prompt. We're building Bug0 to solve this exact problem. We believe you shouldn't have to choose between debugging an AI's mistakes or hiring a slow, manual QA team. We aren't selling you a tool like Playwright MCP. We aren't selling you an agency's hours. We sell the outcome your team was trying to build in the first place: guaranteed, accurate QA on every single commit. Our AI agents, guided by human experts, create and maintain your tests, run them on our infra, and deliver clear reports inside your PRs. Your team does nothing but build. Don't let your company's focus drift from your product to an internal QA tool. Link to the full blog post in the comments. #PlaywrightMCP #AITesting #EngineeringLeadership #BuildVsBuy #QualityAssurance

We're wasting our most talented engineers on a fundamentally broken process. We ask them to build innovative products, then saddle them with writing and maintaining brittle E2E tests. Every hour an engineer spends fixing a cy.get() selector or a flaky Playwright test is an hour they're not building your core product. We made that mistake in my previous startup. Tests were outdated the very next day as the UI/UX and flows were changing rapidly. We finally ditched regression tests, and our engineers hated maintaining them. It wasted a lot of money – testing tools + engineering hours. Many teams, feeling this pain, have turned to new "AI QA" tools only to get burned. They're tired of expensive pilots that end with AI-generated test code their own engineers have to debug and maintain. It's the same problem, just with a new layer of abstraction. This is why our early adopters – all Series A+ companies – are working with us at Bug0. We're not another tool that generates code. And we're not an agency selling man-hours. We sell one thing: an outcome. Fast, deterministic regression testing on every single commit. We deliver this by pairing tireless AI agents with expert human QA strategists. The AI does the grunt work; our experts handle the strategy. The result is instant feedback in your PRs and a QA process that just works. So your developers can finally just... build. For too long, QA has been an incompetent bottleneck; we believe serious companies deserve an outcome: instant, reliable quality on every single commit.