Alexadar

The easiest way is to build it locally (ask your AI agent to do it) and run it in "fantastic canvas" mode. That starts a local web app where you can add prebundled agents, and your CLI coder should be in the double-click menu. Start it, ask it to look around, then ask it to build something for you.

Most AI coders produce source code and a chat log. This one builds an interactive UI for you, which feels more like a personal garage where you build your artwork than the usual task list and chat log we've seen in Lovable or the agentic VS Code view.

You can also ask your AI agent how to use the kernel to build your own agentic system. It may blow your mind: all actors live on one flat surface and exchange calls, with permissions on every call between them. It's harder to understand at first, but once you get it, you'll forget about agent loops and agent graphs and use it as the base runtime for your AI software.

I packed a lot in here, so it's not easy to explain; I hope this makes sense. In one sentence: a flat runtime that replaces agent loops and agent graphs, plus a canvas web app for your state-of-the-art AI workflow in the browser.

use console AI coders with some free/base subscriptions, as your teachers+practitioners - its amazing how they answer questions now and speedup learning.

Thats unfortunate. And how to fix it - not a trivial one.

Besides units and e2e tests i prompt machine to create a 20-30 line files in a critical locations like web ingress classes, to verify by eyes fast, knowing everything after it (hopefully) have less critical issues i.e. unauthorized access

AI is a system of equations, used as a statistical automata. This should be able to be controlled by definition.

No worries, it will change again multiple times in our lifetimes. Internet state derives on how people want to exchange information in a given moment of time.

Interesting to test. I set up all scientific subroutines with claude code generated automation and visualization. Honestly, i think that this product would not be a fit for all given diversity of scientific tasks.

Statistical models (What we call AI) will not kill it