I used DeepSeek and TestZeus to do agentic testing

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Hello, Robin here. I am one of the cofounders at Testzeus, and we had built an open source testing agent. Using AG2 framework, our agent uses tools like Playwright for browser interaction, Nuclei for security scanning or AXE for accessibility. So, with the release of Deepseek R1, and earlier launch of OpenAI Operator, I was curious if we could generate UI tests using Deepseek R1, and feed them directly to the open source testing agent for autonomous execution. And similar case with API testing, where we could feed an OpenAPI spec to Deepseek, ask it to generate tests and then directly run these tests with the testing agent. To my surprise, this pipeline worked pretty well for both UI and API scenarios. Here is a video around my observations: https://youtu.be/-gbNDZ1eqnY . We feel that engineering teams still struggle with velocity of delivery versus its quality, so agentic testing with open source solutions like AG2, Deepseek and TestZeus will change the world for better.

Visit product View on Hacker NewsJan 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic, openai · Missing: mac, agents, macos

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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TrustMRRFits verified-revenue profile · Strong signals: ios, video · Missing: mobile apps, personal, entrepreneurs

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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