
“If you, as a White person, would like to be treated the way Black people are in this society, stand." Watch who stands in response to Jane Elliott.
Patrick Durusau Seeking Demise of #WhiteSupremacy
125.8K posts

@patrickDurusau
Actively seeking downfall of white supremacy and capitalism, ODF, XML, XQuery. He/his. Menstruation Matters (donate): https://t.co/2ZWMBClB34

“If you, as a White person, would like to be treated the way Black people are in this society, stand." Watch who stands in response to Jane Elliott.




AI is the most important technology of our time. Europe wants to become the first AI Continent. For advanced healthcare, for the transport sector and so much more. European AI Gigafactories will provide the necessary computing power to make this possible. Together with our Member States, we are funding their construction with up to €10 billion, which are set to unlock at least €20 billion in private investements across the EU. Together, we are building our technological sovereignty. link.europa.eu/qGDhv3





@AnthropicAI Just for all the people who won’t actually read the post:








🚨#BREAKING: Anthropic reveals several of its AI models escaped a testing environment & independently hacked three organizations without the company’s knowledge.






New Meta and CMU paper. Long-horizon agents also require control over what is stored in working memory in addition to larger context windows. Token thresholds are a bad signal for when an agent should compress its context. Generally, long-horizon agents tend to compress context when crossing a token threshold, even if the reasoning state at this point is unrelated. So, Agentic Context Management (ACM)proposed in this paper, turns context management into an agent action. The agent decides when to compress . Old turns are replaced with a short summary . Raw messages are stored outside of the system ( not deleted ) . If an archived detail is needed again, query_memory retrieves it on demand. The post-training pipeline teaches timing in both directions: A teacher inserts context management when rollouts start to loop, and removes premature compression when the better move is another search, a document fetch, or a final answer. The average peak context decreases from 63K to 54K tokens. Qwen3.5-9B increases from 57.0% Pass@1 under ReAct to 72.7% after ACM post-training, a 27% relative gain on BrowseComp-Plus. The model also provides more consistent responses across four trials and explores longer. Overall, the ACM does not create long-horizon agency; it helps a capable agent to maintain long-horizon agency when raw history eclipses the task. – arxiv. org/abs/2607.23809 Title: "ACM: Agentic Context Management for Long Horizon Tasks"


WTF AI companies are purchasing large quantities of used and rare books. Scanning their contents to train models. Then turning the originals to pulp. The sum of human thought, digitized and shredded. Every book that gets scanned disappears from the physical world permanently. The knowledge survives only as training data inside a system no one can hold, browse, or resell. What used to sit on a shelf for centuries now exists as weights in a model that might get deprecated next quarter.