Z.AI launches ZCode to compete with Cursor, Claude Code, and GitHub Copilot

Ben Guez has "a bunch of potential international wives in [his] DMs," thanks to an automated script he set up using OpenClaw, Claude code, and Instagram trials.

AI-powered software development tools integrate with your IDE and codebase, helping you to write, refactor, and fix code faster. These tools also make it fast and easy to create and run unit tests and integration tests — tasks that take more time when done manually. Today, .NET developers often use GitHub Copilot, Claude Code, Cursor AI, and even AI chatbots like ChatGPT to generate code. In this article, we’ll cover some best practices you should follow when using AI to generate your C# code. Challenges of using AI-generated code While AI can write code for you, often the generated code does not work as intended. AI may generate code that contains logic errors, bugs, or security vulnerabilities, or code that doesn’t conform to your organization’s coding conventions or quality standards, or code that isn’t compatible with existing architecture. Further, AI may generate code that runs slowly or fails to run at all. These are some of the key challenges organizations face when using AI-ge

X has unveiled a hosted Model Context Protocol server, giving AI tools such as Claude, Cursor, and Grok Build direct access to the platform through a user’s own account permissions, without requiring developers to build and maintain their own integration infrastructure. The Model Context Protocol is an open standard that defines how AI models communicate […]

Do you think of tools such as OpenAI's Codex or Anthropic's Claude Code as developer tools, built only for writing software? Not the case. A recent project at SmarterX shows how these tools can be repurposed for one of the most common (and tedious) marketing tasks: making sense of messy data.

The US government has reversed export restrictions on Anthropic’s frontier AI models Fable 5 and Mythos 5, allowing the company to resume global access after nearly three weeks of disruption triggered by concerns over the models’ cybersecurity capabilities. “As of today, June 30, the export controls on Fable 5 and Mythos 5 have been lifted,” Anthropic said in a blog post. The company said Fable 5 will begin rolling out globally on July 1 across Claude Platform, Claude.ai, Claude Code and Claude Cowork, while access on Amazon Web Services, Google Cloud, and Microsoft Foundry will be restored “as quickly as possible.” Commerce Secretary Howard Lutnick said the administration had worked with Anthropic before reversing the restrictions. “Over the past two weeks, we have worked closely with Anthropic to analyze and approve Fable 5 to ensure alignment across the US Government and strengthen America’s leadership in AI,” Lutnick wrote in a post on X. Anthropic had launched Fable 5 and Mythos 5

At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…

The allegations against Anthropic could exacerbate tensions in global tech relations, highlighting privacy concerns and geopolitical tech rivalries. The post Anthropic accused of embedding hidden spyware in Claude Code targeting Chinese users appeared first on Crypto Briefing.

Microsoft has introduced the Microsoft Binlog MCP Server, which gives AI assistants like GitHub Copilot direct access to MSBuild (.binlog) files. The Model Context Protocol server enables AI-powered build investigation through natural language conversation, Microsoft said. Introduced June 17 and currently in a preview stage, the Microsoft Binlog MCP Server parses .binlog files and exposes 15 specialized tools that enable AI-driven diagnosis, property tracing, performance analysis, and build comparison. Microsoft said that AI assistants gain the ability to do the following: Investigate build failures by querying errors, warnings, and full project/target/task context Trace property origins to understand where a property got its value Analyze performance bottlenecks by identifying the slowest projects, targets, and tasks Compare two builds to spot differences in packages and properties Read embedded source files captured during the build Instead of manually scrolling through the MSBuild