Why developers are spinning up AI behind your back — and how to detect it. The article discusses the rise of 'Shadow AI' - developers integrating LLMs into production without approval, the risks involved, and strategies for organizations to manage it effectively.
>We’ve seen LLMs used to auto-tag infrastructure, classify alerts, generate compliance doc stubs, and spin up internal search tools on top of knowledge bases. We’ve also seen them quietly embedded into CI/CD workflows...
>We’ve seen LLMs used to auto-tag infrastructure, classify alerts, generate compliance doc stubs, and spin up internal search tools on top of knowledge bases. We’ve also seen them quietly embedded into CI/CD workflows...
Running GenAI models is easy. Scaling them to thousands of users, not so much. This guide details avenues for scaling AI workloads from proofs of concept to production-ready deployments, covering API integration, on-prem deployment considerations, hardware requirements, and tools like vLLM and Nvidia NIMs.
PaperCoder is a multi-agent LLM system that transforms scientific papers into code repositories through a three-stage pipeline: planning, analysis, and code generation. It aims to create faithful, high-quality implementations.
A new study reveals that while current AI models excel at solving math *problems*, they struggle with the *reasoning* required for mathematical *proofs*, demonstrating a gap between pattern recognition and genuine mathematical understanding.
This article provides a hands-on guide to Anthropic’s Model Context Protocol (MCP), an open protocol designed to standardize connections between AI systems and data sources. It covers how to set up and use MCP with Claude Desktop and Open WebUI, along with potential challenges and future developments.
A Reddit thread discussing preferred local Large Language Model (LLM) setups for tasks like summarizing text, coding, and general use. Users share their model choices (Gemma, Qwen, Phi, etc.) and frameworks (llama.cpp, Ollama, EXUI) along with potential issues and configurations.
| **Model** | **Use Cases** | **Size (Parameters)** | **Approx. VRAM (Q4 Quantization)** | **Approx. RAM (Q4)** | **Notes/Requirements** |
|----------------|---------------------------------------------------|------------------------|-----------------------------------|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Gemma 3 (Meta)** | Summarization, conversational tasks, image recognition, translation, simple writing | 3B, 4B, 7B, 8B, 12B, 27B+ | 2-4GB (3B), 4-6GB (7B), 8-12GB (12B) | 4-8GB (3B), 8-12GB (7B), 16-24GB (12B) | Excellent performance for its size. Recent versions have had memory leak issues (see Reddit post – use Ollama 0.6.6 or later, but even that may not be fully fixed). QAT versions are highly recommended. |
| **Qwen 2.5 (Alibaba)** | Summarization, coding, reasoning, decision-making, technical material processing | 3.5B, 7B, 72B | 2-3GB (3.5B), 4-6GB (7B), 26-30GB (72B) | 4-6GB (3.5B), 8-12GB (7B), 50-60GB (72B) | Qwen models are known for strong performance. Coder versions specifically tuned for code generation. |
| **Qwen3 (Alibaba - upcoming)**| General purpose, likely similar to Qwen 2.5 with improvements | 70B | Estimated 25-30GB (Q4) | 50-60GB | Expected to be a strong competitor. |
| **Llama 3 (Meta)**| General purpose, conversation, writing, coding, reasoning | 8B, 13B, 70B+ | 4-6GB (8B), 7-9GB (13B), 25-30GB (70B) | 8-12GB (8B), 14-18GB (13B), 50-60GB (70B) | Current state-of-the-art open-source model. Excellent balance of performance and size. |
| **YiXin (01.AI)** | Reasoning, brainstorming | 72B | ~26-30GB (Q4) | ~50-60GB | A powerful model focused on reasoning and understanding. Similar VRAM requirements to Qwen 72B. |
| **Phi-4 (Microsoft)** | General purpose, writing, coding | 14B | ~7-9GB (Q4) | 14-18GB | Smaller model, good for resource-constrained environments, but may not match larger models in complexity. |
| **Ling-Lite** | RAG (Retrieval-Augmented Generation), fast processing, text extraction | Variable | Varies with size | Varies with size | MoE (Mixture of Experts) model known for speed. Good for RAG applications where quick responses are important. |
**Key Considerations:**
* **Quantization:** The VRAM and RAM estimates above are based on 4-bit quantization (Q4). Lower quantization (e.g., Q2) will reduce memory usage further, but *may* impact quality. Higher quantization (e.g., Q8, FP16) will increase quality but require significantly more memory.
* **Frameworks:** Popular frameworks for running these models locally include:
* **llama.cpp:** Highly optimized for CPU and GPU, especially on Apple Silicon.
* **Ollama:** Simplified setup and management of LLMs. (Be aware of the Gemma 3 memory leak issue!)
* **Text Generation WebUI (oobabooga):** Web-based interface with many features and customization options.
* **Hardware:** A dedicated GPU with sufficient VRAM is highly recommended for decent performance. CPU-only inference is possible but can be slow. More RAM is generally better, even if the model fits in VRAM.
* **Context Length:** The "40k" context mentioned in the Reddit post refers to the maximum number of tokens (words or sub-words) the model can process at once. Longer context lengths require more memory.
| **Model** | **Use Cases** | **Size (Parameters)** | **Approx. VRAM (Q4 Quantization)** | **Approx. RAM (Q4)** | **Notes/Requirements** |
|----------------|---------------------------------------------------|------------------------|-----------------------------------|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Gemma 3 (Meta)** | Summarization, conversational tasks, image recognition, translation, simple writing | 3B, 4B, 7B, 8B, 12B, 27B+ | 2-4GB (3B), 4-6GB (7B), 8-12GB (12B) | 4-8GB (3B), 8-12GB (7B), 16-24GB (12B) | Excellent performance for its size. Recent versions have had memory leak issues (see Reddit post – use Ollama 0.6.6 or later, but even that may not be fully fixed). QAT versions are highly recommended. |
| **Qwen 2.5 (Alibaba)** | Summarization, coding, reasoning, decision-making, technical material processing | 3.5B, 7B, 72B | 2-3GB (3.5B), 4-6GB (7B), 26-30GB (72B) | 4-6GB (3.5B), 8-12GB (7B), 50-60GB (72B) | Qwen models are known for strong performance. Coder versions specifically tuned for code generation. |
| **Qwen3 (Alibaba - upcoming)**| General purpose, likely similar to Qwen 2.5 with improvements | 70B | Estimated 25-30GB (Q4) | 50-60GB | Expected to be a strong competitor. |
| **Llama 3 (Meta)**| General purpose, conversation, writing, coding, reasoning | 8B, 13B, 70B+ | 4-6GB (8B), 7-9GB (13B), 25-30GB (70B) | 8-12GB (8B), 14-18GB (13B), 50-60GB (70B) | Current state-of-the-art open-source model. Excellent balance of performance and size. |
| **YiXin (01.AI)** | Reasoning, brainstorming | 72B | ~26-30GB (Q4) | ~50-60GB | A powerful model focused on reasoning and understanding. Similar VRAM requirements to Qwen 72B. |
| **Phi-4 (Microsoft)** | General purpose, writing, coding | 14B | ~7-9GB (Q4) | 14-18GB | Smaller model, good for resource-constrained environments, but may not match larger models in complexity. |
| **Ling-Lite** | RAG (Retrieval-Augmented Generation), fast processing, text extraction | Variable | Varies with size | Varies with size | MoE (Mixture of Experts) model known for speed. Good for RAG applications where quick responses are important. |
**Key Considerations:**
* **Quantization:** The VRAM and RAM estimates above are based on 4-bit quantization (Q4). Lower quantization (e.g., Q2) will reduce memory usage further, but *may* impact quality. Higher quantization (e.g., Q8, FP16) will increase quality but require significantly more memory.
* **Frameworks:** Popular frameworks for running these models locally include:
* **llama.cpp:** Highly optimized for CPU and GPU, especially on Apple Silicon.
* **Ollama:** Simplified setup and management of LLMs. (Be aware of the Gemma 3 memory leak issue!)
* **Text Generation WebUI (oobabooga):** Web-based interface with many features and customization options.
* **Hardware:** A dedicated GPU with sufficient VRAM is highly recommended for decent performance. CPU-only inference is possible but can be slow. More RAM is generally better, even if the model fits in VRAM.
* **Context Length:** The "40k" context mentioned in the Reddit post refers to the maximum number of tokens (words or sub-words) the model can process at once. Longer context lengths require more memory.
Google’s John Mueller downplayed the usefulness of LLMs.txt, comparing it to the keywords meta tag, as AI bots aren’t currently checking for the file and it opens potential for cloaking.
DeepMind researchers propose a new 'streams' approach to AI development, focusing on experiential learning and autonomous interaction with the world, moving beyond the limitations of current large language models and potentially surpassing human intelligence.
Notte is an open-source browser using an agent, designed to improve speed, cost, and reliability in web agent tasks through a perception layer that structures webpages for LLM consumption. It offers a full stack framework with customizable browser infrastructure, web scripting, and scraping endpoints.
This article details an iterative process of using ChatGPT to explore the parallels between Marvin Minsky's "Society of Mind" and Anthropic's research on Large Language Models, specifically Claude Haiku. The user experimented with different prompts to refine the AI's output, navigating issues like model confusion (GPT-2 vs. Claude) and overly conversational tone. Ultimately, prompting the AI with direct source materials (Minsky’s books and Anthropic's paper) yielded the most insightful analysis, highlighting potential connections like the concept of "A and B brains" within both frameworks.
