MiniMax teases upcoming M3 model with new sparse attention mechanism and 15.6X long-context response speed boost

MiniMax is again raising the eyebrows of AI power users and developers around the world by releasing a new, in-depth technical report on the making of its popular M2 series of language models (M2, M2.5, and M2.7) shedding light on its numerous engineering innovations and clever approaches — while the company and its leaders also teased a whole new sparse attention approach for its upcoming MiniMax M3 series of models, which it says yields up to 15.6 times faster decoding (or LLM response) speed at long contexts (a million tokens) by adopting a custom sub-quadratic framework. In so doing, MiniMax has designed M3 to make ultra-long-context AI agent deployment economically viable.

Your AI agents need a terminal, not just a vector database

When agentic workflows fail, developers often assume the problem lies in the underlying model’s reasoning abilities. In reality, the limited information provided by the retrieval interface is often the primary limiting factor.

Researchers at multiple universities propose a technique called direct corpus interaction (DCI) that lets agents bypass embedding models entirely, searching raw corpora directly using standard command-line tools.

OpenShell Agents

In this video, we look at OpenShell, the layer that runs the protection in NemoClaw Blueprints, but we actually do it with a LangChain DeepAgents harness to show how you can use a number of different agent options.

Every Claude Cowork Feature Explained Clearly

Ben AI demystifies the technical framework behind Claude Cowork, breaking down essential components like memory management, file access, and automated skills. Learn how to leverage local context, MCPs, and autonomous routines to build a scalable second brain that streamlines complex workflows and improves AI performance for individual projects or entire teams.

Antigravity 2.0 UPDATE: NEW Agentic AI Coding Agent
+ Gemini Desktop App!

Google basically split Antigravity into multiple apps and the internet is LOWKEY crashing out trying to understand what happened.

This honestly feels like Google’s direct answer to tools like Claude Code, Codex, OpenAI Agents, and the entire rise of autonomous AI workflows.

Google Search as you know it is over

At its Google I/O conference on Tuesday, Google unveiled an AI-powered overhaul of Search centered around a reimagined “intelligent search box” — what the company describes as the biggest change to this entry point to the web since the search box debuted more than 25 years ago.

Context architecture is replacing RAG as agentic AI pushes enterprise retrieval to its limits

Redis built its name as the caching layer that kept web applications from collapsing under load. The problem it is targeting now has the same structure but is harder to solve: production AI agents failing not because the models are wrong, but because the data underneath them is scattered, stale and structured for humans rather than machines. Retrieval pipelines built for single queries cannot absorb the volume agents generate.

The gap Redis is targeting is structural: agents make orders of magnitude more data requests than human users, but most retrieval layers were built for the human-scale problem. Redis Iris, launched Monday, is the company's answer: a context and memory platform that sits between an agent and the data it needs to act.