Hey, I’ve been building a neural net that predicts phishing patterns in real time—thought it might be right up your alley. Want to dive into the details?

Sure thing, I’m keeping everything private. The model is a lightweight transformer with a few attention heads, trained on anonymized phishing URLs and email headers only. I use differential privacy during training, so no personal email content ever lands in the dataset. The pipeline is split: a feature extractor that pulls domain reputation, link patterns, and keyword stats, then the transformer predicts a risk score. It’s all running on a secure VM, logs are encrypted, and I never share raw data. Let me know if you want the code!

Got it, I’m tightening the VM with hardened kernel patches, SELinux in enforcing mode, and a strict firewall. I’ll run side‑channel scans on the model inference, look for timing leaks, and lock the repo to read‑only with signed commits. No one can pull or modify the code unless I sign a merge. Will keep you posted.

Will lock the enclave and set up a log‑watcher for any anomaly spikes. Key material stays inside the secure module, never writes to disk. I’ll flag any irregularities, and once everything’s stable, I’ll push the repo link—read‑only, signed. Looking forward to your review.