Showcase

Built by FreeCoder, not by a person

Descriptions of what an AI agent can do are cheap. This page is the other thing: working software you can open, click, break and read the source of.

The rule for this page. Every line of code here was written by FreeCoder. No human wrote it, fixed it, or tidied it up. A person tested each one and reported the numbers back — the way you would with any engineer — and the agent did the fixing. The original instruction is printed beside each project, along with an honest account of how many rounds it actually took.

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Neural Network Playground

A neural network that trains in front of you, in your browser. Watch the decision boundary bend around the data as it learns. Written from scratch — no machine-learning library, no framework, one file.

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The entire instruction

Build a single self-contained HTML file.
It is a LIVE NEURAL NETWORK PLAYGROUND that
trains in the browser in real time.

Hard rules: one file only, no external
libraries, no CDN links, no build step,
everything inline.

1. A canvas showing 2D training points in two
   classes, with a selector for three patterns:
   spiral, two circles, and XOR.
2. A feed-forward neural network written from
   scratch in plain JavaScript (no ML library)
   with a configurable hidden layer.
3. Train it live with backpropagation and
   animate the decision boundary repainting as
   it learns.
4. A second small canvas plotting the loss
   curve updating in real time.
5. Controls for pattern, learning rate, hidden
   layer size, and Train / Pause / Reset.
6. Dark theme, background #0b0b12, violet
   accent #7c3aed, polished and professional.

When you are done you MUST verify it actually
works... Fix anything broken and re-check
before you tell me it is finished.

What actually happened

100%Final accuracy on the spiral, loss 0.0001, by epoch 627
0 librariesBackpropagation written by hand in JavaScript
3 roundsThe first two versions looked right and did not learn
FreeBuilt on the model FreeCoder gives everyone at no cost

The part worth reading

The first two attempts produced a beautiful interface attached to a network that did not learn — 64% accuracy, then 49%, which is guessing. The agent could not tell, because it cannot open a page and look at it, and broken maths and working maths read identically in source.

So it was asked to prove the maths instead of inspecting it. It pulled the network out into a separate file with no interface code, wrote a test that trains on XOR and on the spiral and prints the loss as it goes, and ran it. Both passed. Then the page worked.

An agent that writes a test to check itself is doing the job properly. That is the interesting result here — more than the demo.

Build your own. Open PowerShell on Windows and run
irm https://freecoder.space/install.ps1 | iex — then read the getting-started guide.