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reacted to scthornton's post with ❤️ about 3 hours ago
SecureCode update: we went back and fact-checked our own security dataset and corrected what didn't hold up. The original claim was "complete incident grounding, every example ties to a documented CVE." An adversarial re-audit found that it was overstated: many CVEs were misattributed, and many "incidents" were representative scenarios carrying invented statistics. So we fixed it. - Grounding: re-verified every reference. Removed 802 misattributed CVEs on the web side, corrected or honestly relabeled the incident narratives, and confirmed the AI/ML conversation CVEs are real (EchoLeak CVE-2025-32711, EmailGPT CVE-2024-5184, and others). - Fix-correctness: reviewed whether each "secure" example actually eliminates the vulnerability. Removed 28 that did not (a "secure" secret scanner whose entropy check always returned zero, an Angular example still using bypassSecurityTrustHtml, and more). - Leakage: re-split so near-duplicates stay on one side. Test contamination went from 11.6% to zero. - Viewer, schema, and metadata: rebuilt as parquet under a shared schema. All three viewers are live. - Models: retrained the whole family on the corrected data so the fix reaches the weights, not just the cards. Now ten open models (3B to 26B), including two new Gemma 4 variants, refreshed locally on a DGX Spark GB10. The paper (arXiv:2512.18542) was revised to match. Counts moved from 2,185 to 2,372 unified (web 1,625 + AI/ML 747). A slightly smaller, fully-checked dataset beats a larger one you have to take on faith. Full writeup and links in the article. Datasets: scthornton/securecode, scthornton/securecode-web, scthornton/securecode-aiml
reacted to PsamSahil's post with 👍 about 3 hours ago
I dont know why dataset is not visible and download speed is terrible today #huggingfacebug
reacted to Quazim0t0's post with 👍 about 3 hours ago
Big update to 🕸️ DaisyChain-Web - the browser demo where your spare devices pretrain a language model together, peer-to-peer. 🌼 Since launch, the demo has grown from a proof-of-concept into something much more real: - Block-scaled INT8 quantization - Batched attention GEMM - Fused dequant+ReLU epilogue - Weight-tied unembedding - WebSocket relay fallback - Server keepalive ping/pong every 30s - disconnected-state redial - Visibility/network-change reconnect (Phones that lock the screen or hop wifi↔cellular reconnect on resume.) - DAISY_RTC_CONFIG - operators can supply their own TURN/ICE config via env var without touching client code. - Split-K f32 backward - Gather-fused attention Net effect of this push: compute step 821ms -> 420ms (1.95×); full 2-device run 177s -> 131s. 🌼 Try the demo: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web 📦 Full project: https://huggingface.co/DaisyChainAI/DaisyChain-Train _____ ⚡Also: 🌌 Wheeler–DeWitt‑62M - 2B Tokens Pretrain on a 3060 GPU. 📦 Model: https://huggingface.co/Quazim0t0/Wheeler-DeWitt-62M 🧠 Demo Chat: https://huggingface.co/spaces/Quazim0t0/Wheeler-Chat A 62.9M‑parameter research language model whose per‑layer channel mixer is the Wheeler–DeWitt equation of canonical quantum gravity, with a fractal (Cantor‑set) RoPE frequency spectrum. - Elo / Bradley-Terry key rating - keys accumulate a persistent "reputation" score that biases future attention logits, carried through the KV cache. - Channel mixer (the headline): WheelerDeWittBlock = replaces the MLP with a leapfrog integration of the Wheeler-DeWitt wave equation over 64 minisuperspace modes under a Lorentzian DeWitt supermetric (4 wave steps, learnable lapse), with a Hamiltonian-constraint aux loss ⟨H²⟩ pushing each layer toward HΨ=0. - Positional encoding: Fractal RoPE - RoPE frequencies placed on a Cantor set (γ=1.0) instead of the geometric ladder; baked in from scratch.
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