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 ›
今年4月，Google reportedly 组建了这支AI编码突击队，由此前长期从事模型预训练工作的Google DeepMind研究工程师Sebastian Borgeaud负责牵头，重点针对复杂、耗时较长的大型编程任务场景。 Google联合创始人谢尔盖·布林以及Google DeepMind首席技术官Koray Kavukcuoglu也被曝参与其中，显示出公司在编码领域追赶竞争对手的高度重视。 DeepMind内部研究人员曾普遍认为，Anthropic在代码工具方面的表现已经领先于Google的Gemini系列，这也成为Google高层加码该项目的重要背景。Anthropic则把“写代码”视作自身AI战略的核心之一，通过Claude Code以及Claude模型家族持续发力这一方向。 最新的Claude Opus 4.8在代码与智能代理任务方面都进行了升级，同时，Anthropic还推出并又下架了Mythos和Fable等模型，继续在产品层面探索差异化路径。 从目前公开信息来看，在不少开发者和企业用户眼中，Anthropic的编码体验正成为衡量大模型竞争力的重要参考点之一... 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 ›
Discover Mistral AI technologies capabilities from basic tutorials to advanced use cases 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 ›
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 ›
# 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 ›
The Knowledge Augmentation Spectrum: CAG vs RAG vs CRAG For the past year, the industry has been obsessed with RAG \(Retrieval-Augmented Generation\) \. It was the “gold standard” for giving LLMs access to enterprise data\. But as our production requirements shift toward lower latency, higher accuracy, and better reliability, we are seeing the emergence of new paradigms\. If you are building AI applications today, you need to understand the architectural trade-offs between RAG , CAG \(Cache-A... 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 ›
Here's what I found: **The big picture:** The Reddit thread you originally linked and the broader community agree on one thing — system prompts are the *missing link* for local LLMs. We throw local models a couple of paragraphs and wonder why they don't perform like Claude or GPT. The frontier models get extremely detailed system prompts — essentially an entire "operating system" of instructions. **The best resource so far:** There's a GitHub project called **System-Prompt-Open** — an open da... Read more ›
In the past year, the enterprise AI ecosystem has gained enormous capability and zero consensus. Developers now have a remarkable set of tools for building AI agents: OpenAI’s frameworks, Anthropic’s Claude tooling, LangChain, LangGraph, CrewAI, Microsoft AutoGen, and a growing list of alternatives. Each promises to coordinate reasoning loops, manage multi-step task execution, and connect […] The post appeared first on <a href=" Read more ›
From pretraining to RLHF/GRPO — every algorithm hand-written in pure PyTorch. Read more ›
Introduction: The CVE Request Process and Its Challenges The Common Vulnerabilities and Exposures (CVE) ID request process serves as a cornerstone for identifying and tracking cybersecurity vulnerabilities. Administered by the MITRE Corporation, this system’s efficacy hinges on seamless communication and robust technical infrastructure. However, a recent user experience exposes critical flaws in this process. A cybersecurity researcher submitted a CVE ID request for a zero-day vulnerability v... 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 ›