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A Hackers' guide to language models. #video#llm#ml#ai @thedevs https://thedevs.link/nWHcWR

I Built a Safer #OpenClaw Alternative Using #Claude#AI Code OpenClaw is the fastest-growing open-source AI project in recent memory - 185,000 GitHub stars already! A legitimately impressive personal AI assistant that can manage your life and you can talk to it anywhere. But it has serious security issues - the docs literally say "there is no perfectly secure setup." So I took the core genius ideas from OpenClaw - the memory system, the proactive heartbeat, the multi-platform adapters, the extensibility through skills - and I built my own version using just Claude Code. It took me two days. The result is simpler, more secure, and tailored exactly to what I need. I'll introduce you to how I did this now and more content on this coming soon! https://youtu.be/XmweZ4fLkcI #assistant#agent

🌟 Greetings from Ashish Sharma! 🌟 🚀 Exciting News! Join us for an enlightening session with Ashish Sharma, a Solution Architect at AI Rudder and a Microsoft AI MVP, as he shares his expertise and insights into the transformative world of AI. 🎙 Session Title: "The Evolution and Applications of Large Language Models: Advancements, Challenges, and Future Directions" 🔍 What You'll Learn: ✅ The evolution of Large Language Models (LLMs) from inception to state-of-the-art innovations, including the transformative Transformer architecture and GPT series. ✅ Understanding the challenges of hallucinations in LLMs and strategies to mitigate them. ✅ An introduction to Retrieval-Augmented Generation (RAG) and how it enhances model accuracy. ✅ Hands-on workshop: Practical implementation of RAG using open-source tools and Azure services. ✅ Insights into the future potential and groundbreaking applications of LLMs. 📅 Date: November 30, 2024 📍 Location: Al-Khorazmi School, Tashkent 👉 Register now: https://mdcuzbekistan.com/register #MDCConf2024#LLM#Speaker @mdcuzbekistan

UCSD and Together AI Research Introduces Parcae: A Stable Architecture for Looped Language Models That Achieves the Quality of a Transformer Twice the Size The core idea is to recast the looped forward pass as a nonlinear time-variant dynamical system over the residual stream. By analyzing the linearized form of this system, the research team shows that prior injection methods — addition and concatenation-with-projection — produce marginally stable or unconstrained parameterizations of the state transition matrix Ā. Parcae fixes this by constraining Ā via discretization of a negative diagonal parameterization, guaranteeing ρ(Ā) < 1 at all times. Two additional training fixes accompany the architectural change: a normalization layer on the prelude output to prevent late-stage loss spikes, and a per-sequence depth sampling algorithm that corrects a distributional mismatch bug in prior recurrence sampling methods. On results: → Parcae reduces validation perplexity by up to 6.3% over parameter- and data-matched RDMs at 350M scale → A 770M Parcae model matches the Core benchmark quality of a 1.3B standard Transformer → At 1.3B parameters, Parcae outperforms the parameter-matched Transformer by 2.99 points on Core and 1.18 points on Core-Extended On scaling laws: → Compute-optimal training scales mean recurrence µ_rec and tokens D in tandem following power laws (µ_rec ∝ C^0.40, D ∝ C^0.78) → Test-time looping follows a saturating exponential decay — gains plateau near the training recurrence depth µ_rec, setting a hard ceiling on inference-time scaling → A unified law predicts held-out model loss within 0.85–1.31% average error Pretrained models from 140M to 1.3B are available on Hugging Face. Full analysis: https://www.marktechpost.com/2026/04/16/ucsd-and-together-ai-research-introduces-parcae-a-stable-architecture-for-looped-language-models-that-achieves-the-quality-of-a-transformer-twice-the-size/ Paper: https://arxiv.org/pdf/2604.12946 Technical details: https://www.together.ai/blog/parcae Models: https://huggingface.co/collections/SandyResearch/parcae #MachineLearning#NLP#LLM#DeepLearning#AIResearch