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marcme

We expected openai's announcement of the multi-modal models and built our platform to complement it. In modern LLM/agents, even openAI and Google have trouble defining their moat. So, as a bootstrapped startup, we are in no position to postulate a moat.

Instead, we aim to achieve a growing advantage by having considerable user adoption that exposes the agents and platform to the ever-growing long tail of unexpected edge cases. And boy, do we have weird edge cases in web design worldwide. But this is good and helps build up robustness. We have one agent per critical workflow to accommodate that. Despite this, our sign-in agent struggles with icon depictions. We purposefully didn't debug it or invest in computer vision, as we knew multi-modal was around the corner.

Instead, we invested in traditional engineering and ensured the table stakes and workflows were robust and scaled well. We use it ourselves multiple times per day on every pull request to ensure it feels natural.

Imho, that's as close to an advantage as possible, but what's your opinion on it?

Show HN: Automated, hosted UI tests with AI test discovery (octomind.dev)

https://www.octomind.dev/

I am Marc, the co-founder of octomind.dev. I am a former chess player turned machine learner turned entrepreneur. I always wanted to develop products based on the latest AI research. I did it at my previous startup with computer vision and now at octomind.dev with LLMs/agents.Octomind is an AI-powered end-to-end testing platform for web apps. You enter your URL, and we will auto-generate, run,...

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