KevinSimper

You want to put a AI agent online so that people can try it out and benefit from it. The easiest way would be to require everyone to sign up as a user. We know that is not a good user experience, it takes time and may be too much effort if it is just for a search on a website.

The solution is often a captcha, a small widget that tries to detect if you are a real human. It often relies on huristics and metrics. In the GDPR ages, they are forced to not save personal data, but it then makes it harder to disguinsish who is the good and bad if you can't have history on a user.

Why do we need captcha? Two reasons:

1. to protect the valuable data we have
2. prevent abuse which cost money

What are our options to prove that we are human that we can use, that is also cheap and fit our usage case for search:

It is funny how every AI company has copied ChatGPT interface (Claude, Deepseek, Mistral, all history on the left, a small text box in the middle and a conversation interface once you begin writing. It is boring.

Here is what I would suggest doing better:

Favorite conversation

Make it possible to favorit certain conversations, some conversations you want to come back to.

Cursor and Windsurf has shown that AI can be an incredible enabler. You can quickly ask the AI and add context to your prompts.

Windsurf is new in that it is the most agentic, in that a agent can call functions and follow its own lead.

Ideas to make it better

1. Multi Agent

   I want and should be able to create multiple agents inside my repo. I want to create an expert agent that knows how to edit my prisma schema. I want an agent that is an expert in creating visual components. I don't want to instruct the agent to do that, it should be based on the folder it is trying to edit in, then it should delegate responsibility to that agent and wait for it to finish.

I quit my job recently because I wanted to start something new, jump into the unknown.

It can feel scary to start something new, how is it going to end, what are the next steps. I have been comfortable with not always knowing the way, I have multiple times had times where I worked on my own thing or did something else.

Compute is everywhere and companies like AWS and Google Cloud are some of the most profitable companies in the world and they keep growing. They make it really easy and affordable to get compute.

But getting GPU compute is still new and unsolved, GPU compute is expensive and we are very much tied to a single provider called Nvidia if you want to do something.

The easiest way to get started is definitely your own computer, but most times if not all the time your computer is simply not powerful enough, so many things are not even possible to run locally in relation to machine learning.

What is also easy is using Google Colab, it is a easy web interface that allows you to run Python through what is called a notebook, concretely a Jupyter notebook. It is really good for proving a point and visualising the output of your machine learning or data processing.