https://archive.ph/20251109191103/https://www.bloomberg.com/opinion/articles/2025-11-09/how-much-of-silicon-valley-is-built-on-chinese-ai

Microsoft has launched a new rewards program offering Chrome users "real cash value" points to switch to Edge browser[^1]. When users search for "Chrome" on Bing, they receive a prompt offering 1,300 Microsoft Rewards points that can be exchanged for gift cards, including on Amazon[^1]. The Browser Choice Alliance, representing Chrome, Opera and Vivaldi, criticizes this as Microsoft's latest tactic to manipulate browser choice, following earlier practices like "forced resets, misleading prompts, and hidden settings"[^1]. The market context shows why Microsoft is pursuing this strategy - Edge holds less than 9% market share compared to Chrome's 78%[^1]. The rewards program appears targeted specifically at Chrome users, with Windows Latest noting "we're not seeing ads for other browsers, such as Opera, Firefox or Brave"[^1]. [^1]: [Forbes - Microsoft Offers Chrome Users 'Real Cash' Rewards To Change Browser](https://www.forbes.com/sites/zakdoffman/2025/11/11/real-cash-value-how-windows-users-get-microsofts-free-new-offer/)

cross-posted from: https://lemmy.ml/post/38941578 > The second company to manage that after SpaceX.

cross-posted from: https://lemmy.ml/post/38836048

What if you used a late 80s Unix system for your job or university, but still wanted a nice and pretty GUI to use? Well then, let’s discover a nice selection of window managers and graphical user interfaces that will make your boring installation look awesome!

link to model https://huggingface.co/WeiboAI/VibeThinker-1.5B

cross-posted from: https://feditown.com/post/2129456

A new paper argues that current LLMs are fundamentally broken because they're completely static. They call it "anterograde amnesia", which is honestly spot on. A model gets pre-trained, and from that moment on, its weights are frozen. It can't actually learn anything new. Sure, it has a context window, but that's just short-term memory. The model can't take new information from its context and permanently update its own parameters. The knowledge in its MLP layers is stuck in the past, and the attention mechanism is the only part that's live, but it forgets everything instantly. The paper introduces what they term Nested Learning to fix this. The whole idea is to stop thinking of a model as one big, deep stack of layers that all update at the same time. Instead, they take inspiration from the brain, which has all kinds of different update cycles running at different speeds in form of brain waves. They represent the model as a set of nested optimization problems , where each level has its own update frequency. Instead of just deep layers, you have levels defined by how often they learn. The idea of levels was then used to extend the standard Transformer which has a fast attention level that updates every token and the slow MLP layers that update only during pre-training. There's no in-between. The paper presents a Hierarchical Optimizers and Parallel Extensible model with additional levels. You might have a mid-frequency level that updates its own weights every, say, 1,000 tokens it processes, and a slower-frequency level that updates every 100,000 tokens, and so on. The result is a model that can actually consolidate new information it sees after pre-training. It can learn new facts from a long document and bake them into that mid-level memory, all while the deep, core knowledge in the slowest level stays stable. It creates a proper gradient of memory from short-term to long-term, allowing the model to finally learn on the fly without just forgetting everything or suffering catastrophic forgetting.

https://archive.ph/macJp

https://web.archive.org/web/20251106084318/https://www.scmp.com/news/china/science/article/3331031/china-unveils-power-thorium-reactor-worlds-largest-cargo-ship

https://archive.ph/Jvmnn

The Internet faces an existential crisis as nearly 50% of all traffic is now non-human, with AI-generated content and bots threatening to overwhelm authentic human interaction[^1]. According to recent studies, this includes automated programs responsible for 49.6% of web traffic in 2023, a trend accelerated by AI models scraping content[^1]. The problems are stark: - Search engines flooded with AI-generated content optimized for algorithms rather than humans - Social media platforms filled with AI "slop" and automated responses - Genuine human content being drowned out by machine-generated noise - Erosion of trusted information sources and shared truth However, concrete solutions exist: 1. Technical Defenses: - Open-source spam filtering tools like [mosparo](https://mosparo.io/) for protecting website forms - AI scraper blocking through systems like [Anubis](https://xeiaso.net/blog/2025/anubis/) - Content authenticity verification via the CAI SDK[^1] 2. Community Building: - Supporting decentralized social networks (Mastodon, Lemmy) - Using open-source forum platforms that emphasize human moderation - Participating in curated communities with active fact-checking[^1] 3. Individual Actions: - Using privacy-focused browsers and search engines - Supporting trusted news sources and independent creators - Being conscious of data sharing and digital footprint[^1] "While exposure to AI-generated misinformation does make people more worried about the quality of information available online, it can also increase the value they attach to outlets with reputations for credibility," notes a 2025 study by Campante[^1]. [^1]: [It's FOSS - The Internet is Dying. We Can Still Stop It](https://news.itsfoss.com/internet-is-dying/)