GRAML: Graph-Grounded Reasoning and Multi-Task Learning for LLM-Based Software Vulnerability Detection by Ruohan Li, Miaoqing Tian, Haipeng Qu, and Ruobing Jiang
PDF Document by Ruohan Li, Miaoqing Tian, Haipeng Qu, and Ruobing Jiang
Research involving 1,222 participants found that relying on AI for as little as 10 minutes can weaken people’s ability to focus, solve problems independently, and persist when tasks become difficult. When AI assistance was removed, participants were more likely to make mistakes or give up, suggesting that overreliance on quick answers can undermine learning and critical thinking. AI is most beneficial when it strengthens human understanding and skills rather than replacing the effort needed to develop them.
https://vcresearch.berkeley.edu/news/using-ai-just-10-minutes-erodes-your-ability-persist-hard-things
A UC Berkeley researcher raises profound questions about how we interact with artificial intelligence in daily life.
Generative AI can help creative platforms offer personalized content, improve customer experiences, and increase profits, potentially allowing them to charge more for AI-generated work. However, these systems rely on human-created content for training, raising concerns about fair compensation, artists’ livelihoods, and platforms benefiting from creators’ work without paying them. Balancing AI innovation with fair rewards for human creators is essential to building a sustainable creative marketplace.
https://giesbusiness.illinois.edu/news/2026/10/10/artificial-intelligence-and-its-impact-on-creative-content
Generative AI can help creative marketplaces deliver more personalized content and increase profits, potentially allowing platforms to charge a premium. These benefits rely on work contributed by human creators, who may receive little or no compensation when their content is used to train AI.
Public concern over AI’s risks remains high, with strong support for independent safety standards and a slower pace of frontier development even if it delays progress, while experts outline how a verifiable global pause could work through specialized inference-only chips and mutual monitoring. Others caution that deliberate slowdowns risk missing the innovations needed to solve safety problems themselves or that current dangers arise more from poorly defined human instructions, inadequate containment, and misuse than from any autonomous “doomsday” scenario. Meanwhile, rising everyday suspicion of AI-generated content and competing worldviews—from utopian optimism to existential alarm to focus on present-day harms—highlight the need for clearer guardrails, better verification tools, and international coordination amid geopolitical competition.
https://news.berkeley.edu/2026/10/09/a-global-hardwired-pause-of-frontier-ai-training-is-feasible-says-new-report/
Scholars from top institutions, including UC Berkeley, have released a study that maps out — and confirms the viability of — such a pause.
HawDB is an embedded graph database built in Rust that enables applications to efficiently store, query, and explore connected data using native Cypher and #PostgreSQL-compatible #SQL. It combines query optimization, reliable ACID transactions with crash recovery, optional full-text and vector search, and graph analytics in a lightweight, configurable package that runs directly within an application. Developers can experiment with the online playground
https://hawdb.ai/playground/
A real terminal for HawDB: type Cypher or SQL, press Enter, see the result. Runs entirely in your browser via an experimental wasm32 build.
