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.
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.
The company formerly known as Intercom just did something that no major customer service platform has attempted at scale: it built an AI agent whose sole job is to manage another AI agent.
Fin Operator is a new AI-powered system designed specifically for the back-office teams that configure, monitor, and improve Fin, the company's customer-facing AI agent. Rather than replacing human support agents — which is what Fin itself does on the front lines — Operator targets the growing army of support operations professionals who spend their days updating knowledge bases, debugging conversation failures, and combing through performance dashboards.
The /goal is incredible in both Codex and Claude Code, and really this is the beginning of being able to work with AI coding agents in a completely different way - for longer running and more complex tasks, with less human oversight.
In this video I break down how to use /goal, how to structure your goals before running them, and how to build a full application from one /goal just using a prd and product-roadmap markdown file.
Claude Code use cases go way beyond coding. In this video we break down how to use Claude Code as a second brain, video editor, research engine, and design tool. From AI automation to claude code skills, see how anthropic claude can run your day.
Google announced a number of new Gemini Intelligence-branded AI features at its Android Show: I/O Edition event on Tuesday. These include the ability for AI to complete tasks across apps, browse the web, fill out forms, dictate speech, and even allow you to vibe-code your own Android widgets.
Researchers at Sakana AI have introduced the "RL Conductor," a small language model trained via reinforcement learning to automatically orchestrate a diverse pool of worker LLMs. Conductor dynamically analyzes inputs, distributes labor among workers, and coordinates among agents.
This automated coordination achieves state-of-the-art results on difficult reasoning and coding benchmarks, outperforming individual frontier models like GPT-5 and Claude Sonnet 4 as well as expensive human-designed multi-agent pipelines. It achieves this performance at a fraction of the cost and with fewer API calls than competitors. RL Conductor is the backbone of Fugu, Sakana AI’s commercial multi-agent orchestration service.