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While we were all worried about AI taking over human jobs, artificial intelligence leaped across the length of our imagination and took a seat at the helm. Till now, the main concern for employees across the globe was human workforce being replaced by AI systems. It seems the tables have turned now, and it is […]
AI Agents Can Now Hire Real Humans Read More »
In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a behavior dataset from a constrained policy, and then train both a Behavior Cloning baseline and a Conservative Q-Learning agent using d3rlpy. By structuring the workflow around offline
Qwen team has just released Qwen3-Coder-Next, an open-weight language model designed for coding agents and local development. It sits on top of the Qwen3-Next-80B-A3B backbone. The model uses a sparse Mixture-of-Experts (MoE) architecture with hybrid attention. It has 80B total parameters, but only 3B parameters are activated per token. The goal is to match the
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
The rise of Moltbook suggests viral AI prompts may be the next big security threat Read More »
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
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
Why predictive maintenance needs more than retrieval Read More »
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
How to Build Memory-Driven AI Agents with Short-Term, Long-Term, and Episodic Memory Read More »
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
The Rise of Multimodal AI Agents: Smarter Systems or a Bigger Risk? Read More »
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
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
