World Wire

Google Research team has introduced a new agentic RAG framework. It is built into the Gemini Enterprise Agent Platform. It powers a feature called Cross-Corpus Retrieval, now in public preview. The target is a known failure mode in enterprise search. Standard single-step RAG was not built for multi-source, multi-hop queries. Ask “What are the specs of the server used in Project X?” The system may find a document naming a server ID. It will not know to take that ID and search a second database for specs. The answer comes back partial, or as “not found.” What is Google’s New Agentic RAG Agentic RAG plans, reasons, and iteratively interacts with data sources. It handles complex queries to increase dependability and accuracy. Google’s version is the Gemini Enterprise Agent Platform-hosted Cross-Corpus Retrieval powered by Agentic RAG. Like other multi-agent RAG frameworks, it uses agents that work together. Unlike them, it adds a sufficient c...

Low-code and no-code platforms have moved from simple drag-and-drop builders to AI-native development environments. In 2026, most of them ship a built-in assistant that turns a text prompt into a working app, agent, or automation. This list covers 21 tools that AI practitioners use today, grouped by what they do best. Each tool name links to its official site so you can verify pricing and features directly. App and UI builders These tools let non-developers ship functional applications, often from a single prompt. 1. Atoms * (10% discount with code MARKTECHPOST10 ) is a no-code AI platform that lets anyone build and launch a fully functional product without writing a single line of code. It moves beyond drag-and-drop interfaces by deploying a team of AI agents that handle every stage of the process, from validating your idea with deep market research to building the backend, deploying the app, and optimizing it for search. Built-in support for user authentication, databas...

Most search agents are trained as policies over a growing transcript. The model decides how to search. It must also remember what it saw, which evidence matters, and which claims it checked. A team of researchers from University of Illinois Urbana-Champaign, UC Berkeley, and Chroma argues this asks too much. Reinforcement learning ends up optimizing both search decisions and routine bookkeeping at once. Their answer is Harness-1 , a 20B retrieval subagent built on gpt-oss-20b. It was trained with reinforcement learning inside a stateful search harness. The harness holds the bookkeeping. The policy keeps the semantic decisions. The weights and harness code are publicly released. https://ift.tt/f34nbos What is Harness-1 Actually Harness-1 produces a ranked set of documents for a downstream answering model. It does not answer questions itself. It runs inside a state-machine harness centered on a per-episode WORKINGMEMORY. Each turn works as a loop. The harness renders...

In this tutorial, we analyze NVIDIA garak as a practical framework for defensive LLM red-teaming. We start by setting up Garak, then move through plugin discovery, dry runs, real-model scans, multi-probe evaluations, report analysis, custom probe creation, custom detector creation, and AVID export. Instead of running only a single scan, we use Garak end-to-end to understand how probes, detectors, generators, reports, and vulnerability scores work together in a complete LLM security testing workflow. Check out the  FULL CODES Here . Setting Up NVIDIA garak and Defining Helper Functions Copy Code Copied Use a different Browser import os, sys, json, glob, subprocess, importlib def sh(cmd, capture=False): print(f"\n$ {cmd}") return subprocess.run(cmd, shell=True, text=True, capture_output=capture) sh(f"{sys.executable} -m pip install -q -U garak") os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") os.en...

This week, Google AI team released the Colab CLI . The tool connects your local terminal to remote Colab runtimes. It lets developers and AI agents run code on cloud GPUs and TPUs. You stay in your terminal the entire time. The CLI is open source under the Apache 2.0 license. What is Google Colab CLI The Colab CLI is a command-line interface for Google Colab. You can create sessions, run code, and manage files from the terminal. Any agent with terminal access can call the tool. That includes Claude Code, Codex, and Google’s Antigravity. Google ships a prepackaged skill file named COLAB_SKILL.md . It gives agents built-in context on how to use the CLI. Installation uses a single uv tool install command from the GitHub repository. Copy Code Copied Use a different Browser uv tool install git+https://github.com/googlecolab/google-colab-cli A minimal session looks like this: Copy Code Copied Use a different Browser colab new # ...

Moonshot AI has released Kimi Code CLI, an open-source coding agent that runs in the terminal. The tool reads and edits code, runs shell commands, searches files, and fetches web pages. It then chooses its next step based on the feedback it receives. The project is MIT-licensed and lives on GitHub.. Kimi Code CLI is the successor to the older kimi-cli. The new agent is written in TypeScript and distributed via npm. It works out of the box with Moonshot AI’s Kimi models. It can also be configured to use other compatible providers. What is Kimi Code CLI Kimi Code CLI is an AI agent for software development and terminal operations. It can implement new features, fix bugs, and complete refactors. It can also explore an unfamiliar codebase and answer architecture questions. Batch file processing, builds, and chained test runs are supported too. The execution model is feedback-driven. The agent plans steps, modifies code, runs tests, and reports its actions. Read-only ...