JetBrains AI Assistant
Deeply integrated AI powered by the IntelliJ platform's semantic code understanding.
State-of-the-art open-source generative AI for enterprise-grade code synthesis and local-first development.
Code Llama Assistant is a specialized implementation of Meta's Code Llama model family, designed to accelerate software development workflows through advanced code completion, infilling, and instruction following. Built on the Llama architecture, the assistant is fine-tuned specifically for programming tasks, supporting popular languages including Python, C++, Java, PHP, C#, TypeScript, and Bash. In the 2026 landscape, Code Llama remains a dominant force in the 'Local-First' AI movement, allowing enterprises to maintain total data sovereignty by running inference on private infrastructure. Technically, it utilizes a Fill-in-the-Middle (FIM) capability that allows it to insert code into existing files with high contextual awareness. The 2026 iterations support context windows of up to 100k tokens, enabling the processing of entire repositories for complex refactoring. Its positioning is uniquely centered on being the high-performance alternative to closed-source assistants like GitHub Copilot, particularly for organizations with strict SOC2 and GDPR compliance requirements that forbid sending source code to third-party cloud providers.
Uses a specific training objective to predict missing code blocks given the preceding and following code context.
Deeply integrated AI powered by the IntelliJ platform's semantic code understanding.
Build secure, high-performance applications with an AI coding companion integrated directly into the AWS ecosystem.
The open-source autopilot for software development that puts you in control of your models and context.
State-of-the-art bimodal code intelligence for high-fidelity code generation and refinement.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A dedicated model branch fine-tuned on an additional 100B tokens of Python-specific code.
Modified RoPE (Rotary Positional Embeddings) allows the model to process extremely long sequences.
RLHF and supervised fine-tuning on natural language instructions for coding tasks.
Native support for GGUF, EXL2, and AWQ formats for 4-bit and 8-bit precision.
Integration with vector databases to retrieve relevant code snippets from the local workspace.
Trained on a diverse corpus of 50+ programming languages.
Converting old Python 2 codebases to Python 3 with modernized syntax and typing.
Registry Updated:2/7/2026
Developers skipping testing due to time constraints.
Manually writing repetitive CRUD operations or API wrappers.