AI Agents

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The rise of Moltbook suggests viral AI prompts may be the next big security threat

On November 2, 1988, graduate student Robert Morris released a self-replicating program into the early Internet. Within 24 hours, the Morris worm had infected roughly 10 percent of all connected computers, crashing systems at Harvard, Stanford, NASA, and Lawrence Livermore National Laboratory. The worm exploited security flaws in Unix systems that administrators knew existed but […]

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Google Releases Conductor: a context driven Gemini CLI extension that stores knowledge as Markdown and orchestrates agentic workflows

Google has introduced Conductor, an open source preview extension for Gemini CLI that turns AI code generation into a structured, context driven workflow. Conductor stores product knowledge, technical decisions, and work plans as versioned Markdown inside the repository, then drives Gemini agents from those files instead of ad hoc chat prompts. From chat based coding

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Why predictive maintenance needs more than retrieval

Manufacturers operate some of the most complex machinery on the planet – from CNC machines and industrial robots to gas turbines with over 20,000 components. Keeping these assets running smoothly is mission-critical, yet maintenance teams are often buried under vague alerts, scattered documentation and time-consuming root cause analysis. Much of […] The post Why predictive

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How to Build Memory-Driven AI Agents with Short-Term, Long-Term, and Episodic Memory

In this tutorial, we build a memory-engineering layer for an AI agent that separates short-term working context from long-term vector memory and episodic traces. We implement semantic storage using embeddings and FAISS for fast similarity search, and we add episodic memory that captures what worked, what failed, and why, so the agent can reuse successful

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The Rise of Multimodal AI Agents: Smarter Systems or a Bigger Risk?

Artificial intelligence is quietly undergoing one of its most important shifts yet. For years, AI agents were largely confined to text—answering questions, generating content, or automating simple, rule-based tasks. Useful, yes—but limited. That limitation is now disappearing. We’re entering the era of Multimodal AI Agents—systems that can see, hear, read, reason, and act across multiple

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Robbyant Open Sources LingBot World: a Real Time World Model for Interactive Simulation and Embodied AI

Robbyant, the embodied AI unit inside Ant Group, has open sourced LingBot-World, a large scale world model that turns video generation into an interactive simulator for embodied agents, autonomous driving and games. The system is designed to render controllable environments with high visual fidelity, strong dynamics and long temporal horizons, while staying responsive enough for

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AI2 Releases SERA, Soft Verified Coding Agents Built with Supervised Training Only for Practical Repository Level Automation Workflows

Allen Institute for AI (AI2) Researchers introduce SERA, Soft Verified Efficient Repository Agents, as a coding agent family that aims to match much larger closed systems using only supervised training and synthetic trajectories. What is SERA? SERA is the first release in AI2’s Open Coding Agents series. The flagship model, SERA-32B, is built on the

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AI agents now have their own Reddit-style social network, and it’s getting weird fast

On Friday, a Reddit-style social network called Moltbook reportedly crossed 32,000 registered AI agent users, creating what may be the largest-scale experiment in machine-to-machine social interaction yet devised. It arrives complete with security nightmares and a huge dose of surreal weirdness. The platform, which launched days ago as a companion to the viral OpenClaw (once

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Developers say AI coding tools work—and that’s precisely what worries them

Software developers have spent the past two years watching AI coding tools evolve from advanced autocomplete into something that can, in some cases, build entire applications from a text prompt. Tools like Anthropic’s Claude Code and OpenAI’s Codex can now work on software projects for hours at a time, writing code, running tests, and, with

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Why Most Agentic AI Projects Fail Before They Even Launch

AI agents are rapidly becoming one of the most talked-about innovations in enterprise technology. From autonomous task execution to end-to-end workflow automation, Agentic AI promises to move beyond chatbots and copilots into systems that actually do work. But here’s the uncomfortable truth: Most Agentic AI projects fail before they even launch. Not because the models

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