It is a huge privilege to build the Valdir universe together with a community of talented creators. Every new piece of work makes us stronger, louder, and more popular! And that is why we keep building ZarGates until it is impossible to ignore us. Huge respect to @challenger_ND for such amazing work! Bravo, maestro!

Rocket Lab CEO @Peter_J_Beck explains what Iridium’s L-band can do that Starlink broadband can’t, including provide a GPS-like signal 1,000X louder when GPS is jammed: "Think about L-band spectrum as like a really loud voice. It can shout through the rain and it can shout through buildings. Broadband is a quieter voice, so if it's raining, you start to lose your signal." "That means it becomes really important for defense and safety critical things, where the signal just must get through." "If you look at any of the trouble zones right now, GPS is basically almost useless because it's completely jammed and spoofed. L-band can also be used for providing a GPS position, but it's a thousand times louder." "If you're sitting on the ground and you're trying to jam GPS, it has to be a thousand times more powerful than it currently is, and that's a challenging thing to do." @RocketLab

Build and train an LLM "from scratch" yourself and you truly understand what's happening inside 🛠️ A complete educational implementation that runs on a single GPU. Title: FareedKhan-dev/train-llm-from-scratch URL: 🛠️ Overview An educational repository that implements a Transformer from scratch in PyTorch, based on "Attention is All You Need." It promises you can train your own million- to billion-parameter LLM on a single GPU. ❓ Challenges Solved LLMs are ubiquitous, but hands-on chances to train one from scratch and understand its internals are rare. ・Just using off-the-shelf frameworks leaves the Transformer's mechanics opaque ・Learners needed an end-to-end resource spanning pretraining through post-training alignment 💡 Content & Structure It covers the entire LLM lifecycle. ・Data acquisition and preprocessing (from The Pile) ・Core Transformer architecture (embeddings, attention, feed-forward networks) ・Model training (with DDP for distributed processing) ・Post-training alignment: SFT, reward modeling, PPO, DPO, GRPO ・Text generation and inference Code is organized into src/models, scripts, data_loader, configs, and a Streamlit ui. The stack is PyTorch, tiktoken, HDF5, and NumPy. 🌍 Use Cases / Audience For developers and researchers who want hands-on understanding of LLM training — from those with limited GPUs (starting at 13M parameters) to those targeting multi-billion-parameter models on enterprise hardware. #LLM# #MachineLearning#