The plugin developed by Jason Tang enhances local LLMs (like Ollama, Open-WebUI, and LM-Studio) for binary exploration and reverse engineering (RE). It is compatible with any OpenAI v1-compatible API, with LLaMA-based models recommended. Key features include the ability to explain functions and instructions in disassembly and pseudo-C, execute general queries, and leverage MCP tools like GhidraMCP. Additionally, it can propose actions, navigate binaries, and refine query effectiveness through Retrieval Augmented Generation (RAG). The plugin also supports RLHF dataset generation for model fine-tuning. Users must install GhidrAssist and configure it within Ghidra. Future plans focus on further model fine-tuning. Recommended local LLMs include various LLaMA models and options from OpenAI and Anthropic. Installation instructions and GitHub resources are provided. The plugin is released under an MIT license.

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The article discusses a significant rise in identity fraud driven by artificial intelligence, with an alarming 195% increase globally. This surge is attributed to sophisticated AI tools that enable fraudsters to create convincing fake identities and documents. The rise in fraudulent activities poses a major threat to businesses and consumers alike, as it becomes increasingly difficult to detect and mitigate these advanced scams. The report highlights that companies are struggling to keep up with the evolving tactics employed by cybercriminals, prompting a call for enhanced security measures and technology investments. Experts recommend a combination of robust authentication methods and continuous monitoring to combat this growing issue effectively. The implications of AI-driven fraud extend beyond immediate financial losses, potentially affecting consumer trust and overall market stability. To address these challenges, stakeholders must collaborate and innovate in developing solutions that can stay ahead of emerging threats in the digital landscape.

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The project underwent a significant refactor on June 8, 2025, transitioning from a legacy version available in the legacy/v1-archive branch to the current main branch (v2.0+). This initiative aims to facilitate easier navigation and understanding of GitHub repositories for developers, particularly newcomers. It leverages a self-hosted Model Context Protocol (MCP) server, allowing AI assistants to access project documentation and code effectively without exceeding contextual limits, thereby minimizing inaccuracies. Users are required to have Docker installed and create a GitHub Personal Access Token to utilize the server. The GitHub Second Brain project is open-source, free, and prioritizes user privacy. Contributions are welcome in both technical and non-technical forms, including feedback and feature suggestions. The project also outlines various tools for retrieving file content, directory structures, and issue details, enhancing the AI’s abilities to respond accurately to queries related to GitHub projects while ensuring community standards are upheld.

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The ai.gov monorepo utilizes Turborepo for repository management and pnpm for dependency management. It requires the Node.js version specified in the .nvmrc file. Users are encouraged to configure their bash profiles to automatically use the correct Node version with NVM. The project employs pnpm workspaces, necessitating the installation of pnpm to manage dependencies. Playwright tests are included and can be installed with pnpm exec playwright install. For troubleshooting build errors, commands like pnpm clean:dist and pnpm clean:modules help refresh the environment. Development can commence with hot reloading by executing specific build and dev commands for individual applications. To maintain code quality, linting and type-checking processes are available. Overall, a structured approach is emphasized for effective development and testing within the repository.

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