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AI 101AI 101
Topic 33: Slim Attention, KArAt, and XAttention Explained – What’s Really Changing in Transformers?

Topic 33: Slim Attention, KArAt, and XAttention Explained – What’s Really Changing in Transformers?

We explore three advanced attention mechanisms which improve how models handle long sequences, cut memory use and make attention learnable

Alyona Vert.
Alyona Vert.
"Froth on the Daydream""Froth on the Daydream"
FOD#93: When AI meant Ambient Intelligence

FOD#93: When AI meant Ambient Intelligence

and other stories from the past future, including assistants lived in desks, and the future ran on buttons – a look back at the digital dreams that shaped today

Ksenia Se
Ksenia Se
Interviews with InnovatorsInterviews with Innovators
🎙️🧩 TP/Inference: Sharon Zhou on AI Hallucinations, Agents Hype, and Giving Developers the Keys to GenAI

🎙️🧩 TP/Inference: Sharon Zhou on AI Hallucinations, Agents Hype, and Giving Developers the Keys to GenAI

How they do a surgery on a model, rethink AI education and unlock the real magic of generative AI

Ksenia Se
Ksenia Se
AI 101AI 101
Topic 32: What is Qwen-Agent framework? Inside the Qwen family

Topic 32: What is Qwen-Agent framework? Inside the Qwen family

we discuss the timeline of Qwen models, focusing on their agentic capabilities and how they compete with other models, and also explore what is Qwen-Agent framework and how you can use it

Alyona Vert.
Alyona Vert.
"Froth on the Daydream""Froth on the Daydream"
🌁#92: Fight for Developers and the Year of Orchestration

🌁#92: Fight for Developers and the Year of Orchestration

we discuss OpenAI's and Anthropic’s diverging strategies for agentic AI, highlighting new models, relevant papers and news

Ksenia Se
Ksenia Se
🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?

🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?

everything you need to know about Model Context Protocol

Ksenia Se
Ksenia Se
AI 101AI 101
Topic 31: How to Reduce Memory Use in Reasoning Models

Topic 31: How to Reduce Memory Use in Reasoning Models

we explore how combining LightThinker and Multi-Head Latent Attention cuts memory and boosts performance

Alyona Vert.
Alyona Vert.
"Froth on the Daydream""Froth on the Daydream"
🌁#91: AI Literacy – How Much Do We Know About AI?

🌁#91: AI Literacy – How Much Do We Know About AI?

As AI models graduate to more sophisticated reasoning, humans – especially policymakers – remain stuck in the basics

Ksenia Se
Ksenia Se
AI AgentsAI Agents
🦸🏻#13: Action! How AI Agents Execute Tasks with UI and API Tools

🦸🏻#13: Action! How AI Agents Execute Tasks with UI and API Tools

we explore UI-driven versus API-driven interactions, demystify function calling in LLMs, and compare leading open-source frameworks powering autonomous AI actions

Ksenia Se
Ksenia Se
AI 101AI 101
Topic 30: Everything You Need to Know about Knowledge Distillation

Topic 30: Everything You Need to Know about Knowledge Distillation

This is one of the hottest topics thanks to DeepSeek. Learn with us: the core idea, its types, scaling laws, real-world cases and useful resources to dive deeper

Alyona Vert.
Alyona Vert.
"Froth on the Daydream""Froth on the Daydream"
🌁#90: Why AI’s Reasoning Tests Keep Failing Us

🌁#90: Why AI’s Reasoning Tests Keep Failing Us

we discuss benchmark problems, such as benchmark saturation, and explore potential solutions. And as always, we offer a curated list of relevant news and important papers to keep you informed

Ksenia Se
Ksenia Se
AI AgentsAI Agents
🦸🏻#12: How Do Agents Learn from Their Own Mistakes? The Role of Reflection in AI

🦸🏻#12: How Do Agents Learn from Their Own Mistakes? The Role of Reflection in AI

we explore how reflection enables AI to self-correct without always needing external feedback, making systems more reliable and autonomous

Ksenia Se
Ksenia Se
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