标签：#r

More young girls sue X over Grok CSAM; X accused of shielding child predators.

Matthew Danzeisen’s lawyer says the case is a “shakedown about a bag” that brushed someone’s leg. Stefanie Bojar says she was injured aboard the jet—and that the lawsuit is a bullying tactic.

In June, we experienced six incidents that resulted in degraded performance across GitHub services. The post GitHub availability report: June 2026 appeared first on The GitHub Blog .

Elon Musk's tech company released the newest version of Grok on Wednesday, promising a cheaper, more efficient alternative to other powerful AI models.

TLDR: Do you have a pattern or structure for doing long-lived research or non-coding projects using LLMs? I was kind of inspired by this hacker news article where someone collected data to solve their fatigue problem: https://news.ycombinator.com/item?id=48605117 A similar but smaller I did was to collect nut-free restaurants to eat in Chicago. So I had articles, reviews, emails, phone calls, all sorts of stuff. I found it very easy to just use Copilot as my interface and I just data into it ove

This developer didn't expect his side project to grow to 300,000 users, but people love Roost because it's an alternative to an always-on, fast-paced online culture.

General Intuition is betting millions of hours of video game data can train the foundation models for physical AI, making it easier to build smarter robots with minimal real-world data.

Both vulnerabilities allow untrusted users to gain root privileges.

Have you ever wondered how AI engineers fine-tune and align large language models? Under the hood, they run Supervised Fine-Tuning (SFT), optimize parameters using direct preference gradients (DPO), filter out low-quality pre-training corpuses (Pruning), and mitigate catastrophic drifts. To help you visualize how LLM alignment and parameter optimization work in a highly strategic way, I built a cyberpunk card battler inspired by Gwent: 🤖 Epoch Duel: Cyberpunk LLM Alignment Battle Play in Fullscreen Mode (if the embed sizing is tight) 🛠️ Tune Your Model Parameters Your mission as an alignment engineer is to play optimizer cards to outscore the adversarial baseline AI across 3 training Epochs: ⚙️ Logic & Coding: Run SFT code snippets, compile theorem provers, and deploy Python scripts to build your coding benchmark scores. 📖 Language & Speech: Train on multilingual datasets and summarization corpuses to maximize reading comprehension. 🛡️ Safety & Alignment: Implement red-team safeguards, configure RLHF preference pairs, and run DPO tuning to protect your model's outputs. ⚡ regularizers & Drifts: Deploy Regularization cards like Gradient Clipping (Scorch) and Model Pruning to destroy anomalies, or exploit Anomalous Drifts to collapse the AI's rows. 🧬 Playable ML Concepts Explained Here is how the card battle mechanics map to production machine learning pipelines: 1. ✂️ Model Pruning (Weight Compression) In-Game: Playing the Model Pruning card triggers a glitchy dissolution animation that purges the lowest-value card from the targeted board row, cleaning up noise. 💾 The Real-World Counterpart Model Pruning removes unimportant weights (often those closest to zero) from a trained neural network. It shrinks the memory footprint of the model, allowing it to run faster on edge devices. ⚠️ How it affects LLMs By stripping out low-impact weights, pruning compresses models by 30-50% with minimal loss in benchmark accuracy, making deployment significantly cheaper. 2. 🔀 DPO vs RL