LLMs

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SLUUG Talk: Demystifying Large Language Models on Linux

 💡AI Reasoning  Content type: Code
github.com··DEV

Ollama 0.30 GPU Boost: Faster local Qwen inference on NVIDIA

 🔓Open-source Models
everylocalai.com··DEV

The Neutral Mask: How RLHF Provides Shallow Alignment while Leaving Partisan Structure Intact in a Large Language Model

 🎮RL  Content type: Academic
arxiv.org·

What Ollama Reveals About Local AI, Agents, and Open Models

 🕵️AI Agents  Content type: Blog
odsc.medium.com·

Comprehensive evaluation of LLM capabilities for interpretation and analysis of genome-scale metabolic models in metabolic engineering

 💡AI Reasoning  Content type: Academic
biorxiv.org·

MCP Architecture Explained for Beginners: Why AI Needs a Structured Communication System

 🕵️AI Agents  Content type: Blog
medium.com
·

Claude vs GPT-4: Which AI API Is Better for Developers? (2026)

 🎭Multimodal AI
kalyna.pro··DEV

Intelligent inference scheduling with llm-d on Red Hat AI

 🔓Open-source Models
developers.redhat.com·

MTP Isn't Always a Win: 1.95x on My 3090, but Speculative Decoding Is Hardware-Dependent

 Quantization  Content type: Blog
bric.pe.kr··DEV

Why Your LLM Gets Dumber With More Context

 👁️VLMs
siliconopera.com·

AMD's Lemonade SDK For Local AI Adds NVIDIA CUDA Support

 🔓Open-source Models
phoronix.com··r/artificial

Improved performance and model support with GGUF

 Quantization  Content type: Blog
ollama.com·

Why LLMs (still) lack taste

 🎮RL

New comment by alroma90 in "Ask HN: Who wants to be hired? (June 2026)"

 🔧Tool Use  Content type: Discussion

Fixing a stuck Ollama runner and building a GPU watchdog

 🔓Open-source Models

Agentic AI vs Generative AI: Why one without the other hits a ceiling

 🕵️AI Agents  Content type: Blog
udacity.com·

CommBench: Can LLMs Write Correct and Efficient GPU Communication Code?

 Quantization

A free diagnostic for the Claude Certified Architect exam

 🔧Tool Use  Content type: Discussion  Content type: Tutorial

AI 101: From Prompt Engineering to Skill Engineering

 🕵️AI Agents
turingpost.com·

Inferoa AI harness claimed 90% cache savings. We ran it and measured 97.8%

 🖥️Inference Compute

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