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Flowtest.ai – AI agent that performs E2E testing in your website

Hacker News

Flowtest.ai – AI agent that performs E2E testing in your website

Hey, we are 3 co-founders working on Flowtest.ai, an AI agent that can use websites as a real human and perform there regular end-to-end testing without coding and complex setups. You simply provide a prompt in natural language, watch a live video to ensure it behaves as expected, schedule it to run regularly, and you have an automated test built and launched. Agent will always run in a new Chrome window acting as a real visitor so if you change elements - it will adapt and you won't need to heal test. Our goal is to simplify automated websites testing, so you don't need extensive coding, scripting, and ongoing maintenance to heal tests. While we don't aim to replace traditional tools like Selenium, Playwright or Cypress, we want to enable teams to start automated testing sooner without hiring QA engineers or enable existing QA team reach broader test coverage with minimum effort. The tech behind Flowtest.ai: we convert visible screen elements into text for the LLM (currently GPT-4o), including visible text, element type, and sometimes HTML attributes like alt text or class names. This helps the LLM understand elements that don't have text (e.g. icons). LLM will interact with the page in a similar way as a real user would by simulating mouse click events, using keyboard to enter text, scrolling the page, etc. You can describe tests as you would to a human, like "use the search box to find a t-shirt and add the first product to the cart" or be more specific with instructions. We offer a free plan so you can experiment and launch your initial tests without any cost. Sign up here: https://app.flowtest.ai/register?plan=free For a quick overview, today I recorded this 4 minute demo video: https://youtu.be/OJIvaXnnAlI?si=jxZvYorbrtb575tf We'd love to hear your feedback and opinions.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, new · Missing: mac, agents, macos
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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