Best AI Coding Assistants for Linux

A state-of-the-art AI that builds better software, cheaper and faster. K.Explorer is an AI powered Code Assistant trained on many millions of private corporate lines of code, for specific domains, and on billions of public and open-source lines of code for general purposes. Its code auto-completion features suggest code completions and entire function bodies as you type or as you search the engine for help. To make development faster and more agile it even supports Natural Language for programmers to get guidance while telling a text story about the code they want to write.

Datalore is a collaborative data science and analytics platform aimed at boosting the whole analytics workflow and making work with data enjoyable for both data scientists and data savvy business teams across the enterprise. Keeping a major focus on data teams workflow, Datalore offers technical-savvy business users the ability to work together with data teams, using no-code or low-code together with the power of Jupyter notebooks. Datalore enables analytical self-service for business users, enabling them to work with data using SQL and no-code cells, build reports and deep dive into data. It offloads the core data team with simple tasks. Datalore enables analysts and data scientists to share results with ML Engineers. You can run your code on powerful CPUs or GPUs and collaborate with your colleagues in real-time.

Mistral Vibe CLI is a command-line interface built for “vibe-coding,” enabling developers to interact with their codebases through natural-language commands rather than manual edits or rigid IDE workflows. It hooks into version control (e.g., Git repositories), inspects project files, directory structure, and Git status to build context, and uses that context along with backend AI coding models (such as Devstral 2/Devstral Small) to execute operations like multi-file edits, refactoring, code generation, search, and file manipulation, all triggered via plain-English instructions. Because it maintains project awareness (dependencies, file structure, history), it can perform coordinated, cross-file changes (e.g., renaming a function and updating all references across the repo), generate boilerplate across modules, or even scaffold new features from a high-level prompt.