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All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.

5339
Stories
Knowledge base1 мин

Ground to Cloud to Fabric: The State of Microsoft SQL

Hi, thanks Ricardo. I am super excited to be here to talk about SQL from ground to cloud to fabric and the state of Microsoft SQL. So, let's get right into it. Now, Satya Nadella, everyone knows Satya, recently said that every software company is becoming a software company. And…

YouTube — Microsoft Developer
Infrastructure1 мин

Building an AI Data Center – Grid Power, Liquid Cooling, & Cloud Economics

This course takes you deep inside the physical infrastructure powering the artificial intelligence revolution. You will learn about the massive power distribution networks, advanced computing racks, and complex cooling systems that make modern AI data centers possible. The…

YouTube — freeCodeCamp AI
Knowledge base1 мин

The Problem with Jev: Can You Trust AI Decisions?

Will it rain tomorrow? It is decidedly so. Oh, really? How can you be so sure? Reply hazy. Try again. Okay, I'm trying again. How do you know it's going to rain? You seem pretty confident about that. Um. Better not tell you now. Okay. That's not helping. This is clearly not…

YouTube — IBM Technology AI
Knowledge base1 мин

What Is Loop Engineering? Building Better AI Agents

Loop engineering is a term that's related to working with AI agents, and it's kind of like the the newer, cooler version of prompt engineering. But with prompt engineering, that's all about writing the one, perfect, ultimate prompt and then sending that prompt to a large…

YouTube — IBM Technology AI
Knowledge base1 мин

Multiplayer Gaming & Flight Simulation - Computerphile

I was thinking of looking at flight simulation or all general simulation, but but really the sharing of the data. I want to know where you are, you want to know where I am so that we can project something onto a screen and and we can see each other. And there's lots and lots of…

YouTube — Computerphile AI
Research1 мин

Did Google just dethrone OpenAI and Anthropic?

Last week, gas changed the world forever. Twice a Tesla finally unveiled the Roadster and its ability to fly by farting cold gas. But while Elon was busy pulling off his greatest magic trick, Google was cooking up a noble gas of its own, Gemini 4 Argon, which is the most…

YouTube — Fireship AI
Research1 мин

Build a Better Shopping Experience with OpenAI | Shopping Agent Demo

I've logged into Vasper and Alder, a fictitious homegoods app. I'm going to use their shopping agent to restyle my room. Let's give this a shot. I'm hosting some people for the holidays. And could you give me some ideas to make this corner of my room warmer? I'm thinking a…

YouTube — OpenAI Official
General1 мин

Spotify has removed more than 50 million AI tracks from its recommendations since the beginning of the year.

🎵 Spotify has removed more than 50 million AI tracks from recommendations since the beginning of the year. The streaming service has stepped up the fight against music spam and low-quality compositions created by neural networks. Another 75 million AI tracks were completely removed for violating platform rules. To identify...

Telegram — GPT Time | Нейросети | ИИ
Research1 мин

LightCRS: Lightweight Multi-Domain Conversational Recommendation System

arXiv:2610.26948v1 Announce Type: new Abstract: Conversational recommender systems have recently adopted vision-language models to match user dialogue intents with multimodal product information. However, extending these methods to many product domains faces a dilemma: deploying…

arXiv — cs.AI
Research1 мин

DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution

arXiv:2610.26924v1 Announce Type: new Abstract: General-purpose agents are increasingly expected to perform professional analytics work, which requires sustained reasoning, iterative investigation, and reliable evidence. Training these capabilities requires diverse and…

arXiv — cs.AI
Research1 мин

Fix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 4

arXiv:2610.26942v1 Announce Type: new Abstract: Automated theorem provers often rely on sampling language models, but proof assistants like Lean reject each failed step with a precise error message --- information that pure sampling discards. To overcome this, we introduce a…

arXiv — cs.AI
Research1 мин

SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents

arXiv:2610.26995v1 Announce Type: new Abstract: LLM-based agents increasingly rely on reusable skills. However, scaling skill libraries makes efficient and accurate skill use difficult. Full skill loading incurs high token and runtime overhead. In contrast, flat semantic…

arXiv — cs.AI
Research1 мин

Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?

arXiv:2610.27027v1 Announce Type: new Abstract: Multi-agent LLM systems improve performance on clinical tasks, and that improvement is often read as evidence of a more trustworthy system. However, the two are not the same claim. A clinician acts on the stated reasoning and not…

arXiv — cs.AI
Research1 мин

Teaching AI Agent to Play Poker Strategically Through Preference Optimization

arXiv:2610.27028v1 Announce Type: new Abstract: A poker agent can make a legal move and still make a strategically poor one. We ask whether compact AI agents can exhibit strategic behavior under hidden information and whether preference optimization can improve beyond static and…

arXiv — cs.AI
Research1 мин

M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement

arXiv:2610.27032v1 Announce Type: new Abstract: Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn tasks, yet they continue to struggle with long-horizon tasks, particularly in environments without prior training. A key…

arXiv — cs.AI
Research1 мин

PORA: Learning When More Reasoning Pays Off for LLM Agents

arXiv:2610.27119v1 Announce Type: new Abstract: LLM agents perform tasks over multiple steps and revise their actions based on feedback. As the total reasoning budget is often limited and must support future steps, the agent must choose between short and long reasoning before…

arXiv — cs.AI
Research1 мин

PROVISO: Provenance-Informed Action Selection for Secure LLM Agents

arXiv:2610.27115v1 Announce Type: new Abstract: LLM agents act on the external world through tools and third-party skills, so a single executed action can cause irreversible harm. Existing defenses judge a proposed action without tracking whether its content comes from the user…

arXiv — cs.AI
Research1 мин

EDMA: Entropy-Driven Multimodal Answering

arXiv:2610.27120v1 Announce Type: new Abstract: Multimodal question answering (MMQA) is based on integrating heterogeneous data sources, selectively leveraging relevant modalities, and ignoring distractors. Existing approaches based on Multimodal Large Language Models (MLLMs)…

arXiv — cs.AI
Research1 мин

MoSAIK: Motion Search with Artificial Intelligence in K-space

arXiv:2610.27156v1 Announce Type: new Abstract: Object: Motion artefacts remain a major limitation of routine two-dimensional T2-weighted turbo spin-echo (TSE) brain MRI, reducing diagnostic confidence and often necessitating repeated acquisitions. MoSAIK (Motion Search with AI…

arXiv — cs.AI