The Docker MCP Toolkit is the Docker Desktop feature that removes the operational friction of running Model Context Protocol (MCP)) servers locally. You configure an MCP server once and share it across multiple AI clients via named profiles (collections of servers), instead of repeating per-client c Read more ›
A step-by-step build of Andrej Karpathy’s LLM Wiki pattern — Obsidian as the window, Claude Code as the programmer, and a markdown wiki as… Read more ›
Route prompts to the cheapest model that handles them. Claude + GPT-4o + Groq. Live cost tracking. Built with pydantic-ai + litellm. - Reactance0083/pydantic-ai-multi-llm-cost-optimizer Read more ›
In this article, you will learn how to distinguish agentic workflows from autonomous agents by focusing on who owns control flow — a human writing code in advance, or a model reasoning at runtime. Read more ›
# System Prompts See updates to the core system prompts on claude.ai and the Claude iOS app and Claude Android app. --- Claude's web interface (claude.ai) and mobile apps use a system prompt to provide up-to-date information, such as the current date, to Claude at the start of every conversation. The system prompt also encourages certain behaviors, such as always providing code snippets in Markdown. This prompt is periodically updated to improve Claude's responses. These system prompt upda... Read more ›
An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights ... Read more ›
Seekstone is a filesystem-direct Obsidian MCP server for Claude. Search and edit your vault in milliseconds, with ~575× smaller payloads than the REST plugin. No plugins, no Obsidian app required. Read more ›
Discover Mistral AI technologies capabilities from basic tutorials to advanced use cases Read more ›
Demis Hassabis says AI is entering a “species-level transition.” Read more ›
A new LTS-144 version 144\.0\.7559\.256 \(Platform Version: 16503\.88\.0\), is being rolled out for most ChromeOS devices\. This version includes selected security fixes including: 519258799 High CVE-2026-12034 Insufficient validation of untrusted input 499449324 High CVE-2026-7922 Use after free in ServiceWorker 523677844 Critical CVE-2026-13033 Out of bounds read in Blink\>InterestGroups 506653647 High CVE-2026-9970 Use after free in WebGL 511765713 High CVE-2026-10969 Insufficient validati... Read more ›
URL Source: Markdown Content: Guan Wang 1,∗,†, Changling Liu 1,∗, Chenyu Wang 2, Cai Zhou 2, Yuhao Sun 1, Yifei Wu 1, Shuai Zhen 1, Luca Scimeca 1, Yasin Abbasi Yadkori 1,† 1 Sapient Intelligence 2 MIT ###### Abstract The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contrast, biological systems demonstrate highly sample-efficient learning through multi-timescale p... Read more ›
Series — Fine-Tuning, Smallest to Largest: LoRA (1.5B) ← you are here In I fully fine-tuned a 270M model — updating every weight. That's fine for a tiny model. It gets painful as models grow, because full fine-tuning needs gradients and optimizer state for every parameter (~4× the model size in memory). So: what do you do when the model is too big to comfortably fine-tune all of? The idea behind LoRA LoRA (Low-Rank Adaptation) rests on one observation: the change fine-tuning makes to a weight... Read more ›
Sub-20 ms enforcement, 7 compliance frameworks, and immutable audit trails. Govern every AI agent your company runs with Execlave. Read more ›
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞 - fix(ci): hydrate full testbox live auth · openclaw/openclaw@a632300 Read more ›
Hallucination is the wrong word for what I keep running into. It implies the model is confused or malfunctioning. The more accurate description is confident incorrectness: the model produces plausible-sounding output, in well-formed prose, citing nothing, with no hedging, and the claim is simply false. I have been building and operating an AI system that … The post appeared first on . Read more ›
Anthropic claimed that a campaign by operators linked to Alibaba's Qwen AI lab targeted Claude's most prized capabilities, including software engineering and agentic reasoning. Read more ›
A beginner-friendly breakdown of how Policy Gradient methods differ from value-based approaches and how they power modern reinforcement… Read more ›
Enterprises can now control which plugins their users can install in GitHub Copilot CLI and VS Code. This setting is now available in public preview. Add strictKnownMarketplaces to your enterprise-managed… The post appeared first on . Read more ›