AI Agents

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Build an Agent with Nanobot, Lighter Replacement for OpenClaw

Virtual assistants in business are changing fast. Massive enterprise systems like OpenClaw pack hundreds of thousands of lines of code, but nanobot challenges the idea that bigger automatically means better. With just 4000 lines of Python, it delivers core AI assistant capabilities in a lightweight, focused package while cutting codebase size by about 99% without […]

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Sixteen Claude AI agents working together created a new C compiler

Amid a push toward AI agents, with both Anthropic and OpenAI shipping multi-agent tools this week, Anthropic is more than ready to show off some of its more daring AI coding experiments. But as usual with claims of AI-related achievement, you’ll find some key caveats ahead. On Thursday, Anthropic researcher Nicholas Carlini published a blog

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AI companies want you to stop chatting with bots and start managing them

On Thursday, Anthropic and OpenAI shipped products built around the same idea: instead of chatting with a single AI assistant, users should be managing teams of AI agents that divide up work and run in parallel. The simultaneous releases are part of a gradual shift across the industry, from AI as a conversation partner to

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Anthropic Releases Claude Opus 4.6 With 1M Context, Agentic Coding, Adaptive Reasoning Controls, and Expanded Safety Tooling Capabilities

Anthropic has launched Claude Opus 4.6, its most capable model to date, focused on long-context reasoning, agentic coding, and high-value knowledge work. The model builds on Claude Opus 4.5 and is now available on claude.ai, the Claude API, and major cloud providers under the ID claude-opus-4-6. Model focus: agentic work, not single answers Opus 4.6

Anthropic Releases Claude Opus 4.6 With 1M Context, Agentic Coding, Adaptive Reasoning Controls, and Expanded Safety Tooling Capabilities Read More »

OpenAI Just Launched GPT-5.3-Codex: A Faster Agentic Coding Model Unifying Frontier Code Performance And Professional Reasoning Into One System

OpenAI has just introduced GPT-5.3-Codex, a new agentic coding model that extends Codex from writing and reviewing code to handling a broad range of work on a computer. The model combines the frontier coding performance of GPT-5.2-Codex with the reasoning and professional knowledge capabilities of GPT-5.2 into a single system, and it runs 25% faster

OpenAI Just Launched GPT-5.3-Codex: A Faster Agentic Coding Model Unifying Frontier Code Performance And Professional Reasoning Into One System Read More »

Increase of AI bots on the Internet sparks arms race

The viral virtual assistant OpenClaw—formerly known as Moltbot, and before that Clawdbot—is a symbol of a broader revolution underway that could fundamentally alter how the Internet functions. Instead of a place primarily inhabited by humans, the web may very soon be dominated by autonomous AI bots. A new report measuring bot activity on the web,

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Google Introduces Agentic Vision in Gemini 3 Flash for Active Image Understanding

Frontier multimodal models usually process an image in a single pass. If they miss a serial number on a chip or a small symbol on a building plan, they often guess. Google’s new Agentic Vision capability in Gemini 3 Flash changes this by turning image understanding into an active, tool using loop grounded in visual

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A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

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

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Qwen Team Releases Qwen3-Coder-Next: An Open-Weight Language Model Designed Specifically for Coding Agents and Local Development

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

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