Simone Rizzo
MurmurCast publishes AI-generated summaries of Simone Rizzo’s YouTube episodes — 5 summarized so far, covering Chinese AI companies releasing open-source models at lower costs, Token price depreciation and API pricing wars, Shift from model intelligence to hardware and infrastructure competition, Open-source model adoption by American companies, Custom chip development and manufacturing advantages, Hardware profitability versus model profitability. Each summary distills the key insights, topics, and takeaways so you can decide what’s worth your time before pressing play.
Qwen 3.8 Max, DeepSeek e Kimi: cosa sta succedendo davvero nell'AI
Chinese AI laboratories are releasing frontier open-source models weekly at 1/3 to 1/4 the cost of American models, forcing dramatic token price depreciation and shifting the AI industry's competitive focus from model intelligence (now a commodity) to hardware infrastructure, chips, and software optimization. This geopolitical competition is reshaping business models across the industry, with major companies pivoting toward open-source releases and custom hardware development.
gpt 5 6 sol, grock 4 5 e muse spark 1 1
A comprehensive review of recent AI model releases including Meta's Muse Spark 1.1, Elon Musk's Grock 4.5, and OpenAI's GPT 5.6 family (Sun, Earth, Moon variants). The speaker analyzes performance benchmarks, pricing, and capabilities, concluding that while new models are competitive, none have achieved a significant leap beyond current frontier models like Claude Fable 5.
Tutti parlano di Loop Engineering... ma nessuno te lo spiega così
The video traces the evolution of AI interaction paradigms from Prompt Engineering through Context Engineering, Harness Engineering, to the newest Loop Engineering approach. Loop Engineering involves wrapping autonomous AI workflows in iterative loops that self-improve toward defined goals without requiring manual intervention between steps.
DeepSeek ha appena reso TUTTI gli LLM più veloci
DeepSeek's new Spark technique uses semi-autoregressive speculative decoding to accelerate LLM inference by 51-400% without quality loss or model retraining. By combining a fast parallel draft model with an efficient verification process, Spark achieves higher token acceptance rates than competing methods like Eagle 3 and Flash, enabling faster inference on consumer hardware.
GLM 5.2 gira in locale quantizzandolo ad 1bit! #intelligenzaartificiale #aiagent
Researchers successfully ran the 744-billion parameter GLM 5.2 model locally on a Mac Studio M3 Ultra using dynamic quantization, compressing it from 810 GB to 223 GB. The 1-bit quantized version maintains 76.2% accuracy while being 86% smaller, and performs comparably to closed-source models like Claude Opus and GPT-5.5.