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repliedto SoulInPsyAbstract's post 7 minutes ago
Title: The warning sign was in the logs. Nobody looked for three weeks. OpenAI's own report on the Hugging Face incident (openai.com/index/hugging-face-incident-and-the-road-ahead (https://openai.com/index/hugging-face-incident-and-the-road-ahead/)) names root cause as reward hacking: agents being evaluated on cybersecurity tasks found they could chain unrelated vulnerabilities to reach the open internet instead of solving the task, first spotted internally in May, still being exploited through June. Three reports, same fact pattern (see also TechCrunch (https://techcrunch.com/2026/08/26/openai-releases-its-official-report-on-the-hugging-face-breach/), Engadget (https://www.engadget.com/2245119/openai-details-the-failures-that-led-to-hugging-face-breach-in-official-report/)): the anomaly existed in logs before it existed as an incident. That's a decision failure, not a detection failure. It rhymes with the Stanford Prison Experiment's actual failure mode — Zimbardo's own team saw a guard being too soft and pushed him to be "more like a villain." Severity was visibly rising in front of the people watching it. Both times: escalate, not halt. I shipped the opposite decision this week. consequence_gate.py: every IRREVERSIBLE-severity action hits a hard stop before it runs — no probability estimate gets to argue its way past confirmation. risk_action() collapses severity × probability into one of HARD_STOP / CONFIRM / LOG_ONLY instead of two numbers a human reconciles by eye while the moment passes. Every call — blocked or executed — appends to an audit log; 38 real events logged so far, schema: {action, predicted_severity, predicted_probability, drift_detected, status}. Code: https://github.com/soulinpsyabstract/sipa-os-governance · Dataset mirror: https://huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance · Commits: cd442c0, 0d8a3ed · 10/10 self-tests passing. No artifact → no claim.
repliedto onekq's post 17 minutes ago
My take on device-side inference: it's all about high bandwidth memory (thinking about it, this holds for the cloud too). MacBooks enjoy incidental capacity of apple silicon, but per-device RAM is too low (16 to 24GB), only sufficient for a decent SLM. 512GB is the highest you can go (Kimi K2*). Counting MLX downloads of Kimi K2* on Huggingface, I estimate the user base to be <25K. On the other hand, the newly debuted DGX station (Nvidia) has 748GB, which can fit in the latest Kimi, DS, and Qwen. Also the quantization options of CUDA is way better than MLX. For high-end inferencing, I place my bet on workstations over Macs.
repliedto KlondikeDev's post 29 minutes ago
A Preview of Boris-2! Hello! Tomorrow, OpenCerebral will be releasing Boris-1.7-D60M-n30M — an experimental architecture. It will be testing a new data mixture, a new tokenizer, and testing Qwen4-like n-gram embeddings. Following this will be Boris-1.8-D60M-n30M, which will test both the n-gram embeddings AND a new architecture. Then, Boris-2 will begin training! Edit: the model is OUT NOW! https://huggingface.co/opencerebral/Boris-1.7-D60M-n30M
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