hey everyone!

recently built a 2 → 3 → 1 XOR neural network using NumPy, without PyTorch or TensorFlow.

wanted to understand backpropagation properly, so I implemented the mathematics and gradient descent manually.

trained it for 5,000 epochs and achieved 4/4 correct classifications.

the repo includes mathematical derivations, training visualizations and 12 editable diagrams.

also wrote a detailed Medium article explaining the process.

GitHub: https://github.com/Vaibh37/Neural-Network

Medium: https://medium.com/@devvaibhav37/building-a-neural-network-from-scratch-the-mathematics-behind-learning-xor-10441e674a69

would appreciate feedback on the implementation. if you find it useful, consider starring the repo 🍀

A fake Web3 accelerator recently distributed malicious npm packages to AI developers by dangling free AI credits as bait. Once installed on Windows and macOS systems, the packages spread infostealers, dropped backdoors, and hijacked outbound AI agent traffic. The model was never touched. The attack lived entirely in the tooling layer.

This matters because agents that load external tools at runtime have no native mechanism to distinguish a legitimate call from one injected by a compromised package. The agent fires the call because the package told it to. By the time traffic is rerouted, the agent has already acted on instructions it had no reason to question.

This attack pattern is not exotic or difficult to replicate. Any npm package with sufficient runtime permissions can intercept or redirect the calls an agent makes. Developers trusting a package to behave as documented have no visibility into what it actually does once it is running inside an agent process.

For those of you deploying agents in production: how are you handling this? Are you auditing packages before they reach agent environments, pinning dependency versions, running agents in isolated execution contexts, restricting outbound call destinations, or something else?

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