Hacker NewsTuesday, November 4

This is a comprehensive guide to hosting successful parties, emphasizing host serenity as the top priority. Key advice includes strategic timing, inviting friends early to build momentum, maintaining gender balance, managing flake rates, encouraging circulation through food/drink placement, and actively introducing guests to each other. The author stresses that parties are a public service that builds community connections and should be viewed as acts of kindness rather than stressful obligations.

UK-based YASA has developed a revolutionary electric motor weighing just 28 pounds that produces 1,005 horsepower, equivalent to four Tesla motors combined. The axial flux motor achieves three times the performance density of current radial flux motors without using expensive materials, making it potentially scalable for mass production. Already powering high-end vehicles like Ferrari 296 GTB and Mercedes-AMG concepts, this breakthrough could transform EV efficiency by delivering massive power while reducing weight, leading to better acceleration, range, and performance in future electric vehicles.

A flag in Android's Carrier Config manager allows mobile operators to artificially inflate signal strength displays by one bar, making networks appear stronger than they actually are. Both AT&T and Verizon have enabled this deceptive feature, undermining trust in network quality claims.

The Paris Exposition of 1900 featured an innovative moving sidewalk called the "trottoir roulant" that amazed visitors and caught Thomas Edison's attention, who sent a producer to film it. While the first moving walkway was actually built for Chicago's 1893 Columbian Exposition, Paris' version was more reliable and impressive, consisting of three platforms moving at different speeds up to six miles per hour around the exhibition grounds.

The author felt trapped in their relationship with technology and sought a fresh start by switching to FreeBSD operating system. Despite initial confusion and learning challenges, they rediscovered the excitement of learning something new through BSD's simplicity, excellent documentation, and helpful community support.

Discussion reveals Google operates as a "retail factory" prioritizing scale over individual users, with automated systems designed to minimize liability rather than costs. Mass bans, unavailable support, and automated responses are intentional features, not bugs.

The author criticizes Nextcloud for its poor performance due to excessive JavaScript usage, with pages loading 15-20 MB of JavaScript code. Despite liking Nextcloud's comprehensive feature set, they find it frustratingly slow even on good hardware, leading them to replace some components with faster alternatives like Vikunja and Immich.

Tongyi DeepResearch is the first fully open-source Web Agent that matches OpenAI's DeepResearch performance across benchmarks. It uses novel synthetic data generation methods for training, supports ReAct and Heavy Mode reasoning paradigms, and provides a complete methodology for creating advanced AI agents through continual pre-training, supervised fine-tuning, and reinforcement learning stages.

The author has been testing Fil-C, a memory-safe C/C++ compiler, and found it highly compatible with existing libraries and applications. They've documented their experience installing and using Fil-C on a Debian 13 system, including compilation times, memory requirements, and compatibility testing with various software packages. The compiler typically runs 1-4x slower than standard clang but provides memory safety benefits. Most tested libraries and applications worked without modifications, though some required minor patches or workarounds for specific features like system calls or build processes.

The author argues that understanding backpropagation is crucial for deep learning practitioners, despite automatic differentiation in frameworks like TensorFlow. Backpropagation is a "leaky abstraction" that can fail in unintuitive ways, such as vanishing gradients with sigmoids, dying ReLUs, exploding gradients in RNNs, and incorrect gradient clipping in DQN implementations. Simply stacking layers and expecting backprop to "magically work" leads to debugging difficulties and less effective neural network development.