Microsoft Previews Cloud-Hosted Foundry MCP Server for AI Agent Development
Microsoft Foundry has introduced a preview cloud-hosted Foundry MCP Server that lets AI agents securely access Foundry tools for model, agent, deployment, and evaluation workflows from VS Code, Visual Studio, or Foundry itself
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Basic PSA. PocketPal got updated, so runs Gemma 4.
Just because I've seen a couple of "I want this on Android" questions, PocketPal got updated a few hours ago, and runs Gemma 4 2B and 4B fine. At least on my hardware (crappy little moto g84 workhorse phone). Love an app that gets regular updates. I'm going to try and squeak 26B a4 iq2 quantization into 12gigs of ram, on a fresh boot, but I'm almost certain it can't be done due to Android bloat. But yeah, 2B and 4B work fine and quickly under PocketPal. Hopefully their next one is 7-8B (not 9B), because the new Qwen 3.5 models just skip over memory caps, but the old ones didn't. Super numbers are great, running them with OS overhead and context size needs a bit smaller, to be functional on a 12gig RAM phone. Bring on the GemmaSutra 4 4B though, as another gold standard of thinking's and qu
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Unnoticed Gemma-4 Feature - it admits that it does not now...
Edit: "it admits that it does not know" (sorry for the TYPO!) Although Qwen3.5 is a great series of models, it is prone to make very broad assumptions/hallucinate stuff and it does it with a great confidence, so you may believe what it says. In contrast, Gemma-4 (specifically I tested E4b Q8 version) admits that it does not know right at the start of conversation: Therefore, I cannot confirm familiarity with a single, specific research study by that name. However, I am generally familiar with the factors that researchers and military trainers study regarding attrition in elite training programs... That is very important feature and it may hint to changing model training routine, where admitting to not know stuff is penalized less than trying to guess and then fail. submitted by /u/mtomas7
Exploring RAG Embedding Techniques in Depth
Exploring RAG Embedding Techniques in Depth Introduction and Problem Framing Traditional embedding methods in NLP, such as Word2Vec or GloVe, often face limitations when handling complex NLP tasks. These methods struggle to capture the nuances of language, particularly in tasks that require understanding contextual information. To address these limitations, researchers have introduced RAG embeddings. RAG embeddings, short for Retrieve And Generate embeddings, combine the benefits of both retrieval-based and generation-based approaches. By incorporating contextual information from a pre-trained language model, RAG embeddings can enhance the performance of NLP models in tasks like question-answering. import torch from transformers import RagTokenizer , RagRetriever , RagModel tokenizer = Rag
How I Built a Multi-Agent Geopolitical Simulator with FastAPI + LiteLLM
What happens when you give four LLM agents their own strategic doctrines, red lines, and constraints — then throw them into a geopolitical crisis? I built Strait of Hormuz Simulator to find out. It's a multi-agent sandbox where four nations (Iran, US, Israel, Gulf States) are each controlled by an independent LLM, and the results are surprisingly realistic. The Architecture Each country is a markdown file (a "soul") that defines its strategic doctrine and default parameters: backend/souls/ ├── iran.md # Asymmetric warfare, Strait control ├── us.md # Three bad options, escalation management ├── israel.md # Nuclear red line, preemptive calculus └── gulf_states.md # Oil leverage, diplomatic survival The backend runs each agent sequentially — every round, each nation receives: A shared situati
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