OpenJet v0.4: Zero-Config Local Coding Agent with llama.cpp Backend
OpenJet v0.4 is now available — an open-source terminal coding agent that runs local LLMs via llama.cpp with zero manual configuration. It gives you a Claude Code-style workflow without any API keys, running entirely on your own hardware.
What It Does
- Auto-detects your hardware (RTX, Mac, Jetson, CPU-only, etc.) and configures the llama.cpp backend accordingly.
- Reads files, searches your repository, edits code, and runs shell commands.
- Asks for permission before executing shell or write actions.
- Stores persistent memory across sessions, both per-project and system-wide.
Installation
curl -fsSL https://www.openjet.dev/install.sh | bash
openjet setup
openjetNew in v0.4
- Full new user interface (TUI).
- Improved model support for smaller GPUs.
- Multi-step coding agent capabilities.
Who It's For
Developers who want a local, private coding agent without the hassle of manually configuring models, backends, or permissions.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Mind Protocol: Open-source system gives Claude persistent memory and real-time biometric integration
Mind Protocol is an open-source system where Claude runs continuously as an autonomous companion with persistent memory across all sessions and real-time Garmin watch data injected into every conversation. The architecture uses Claude Code as the core engine with an orchestrator that spawns parallel sessions and manages lifecycle.
Using /probe to catch AI hallucinations before writing code
A developer shares a technique called /probe that forces AI-generated plans to make numbered claims with expected values, then probes the real system to catch discrepancies. The method caught four factual errors in Claude's description of its own JSONL format that would have caused code bugs.
Hollow AgentOS Reduces Claude Code Token Usage by 68.5% with JSON-Native OS Approach
Hollow AgentOS, a JSON-native operating system layer for AI agents, reduces Claude Code token usage by 68.5% by eliminating wasteful shell command overhead. The tool plugs into Claude Code via MCP and runs local inference through Ollama.
Why a Single run() Tool with Unix Commands Beats Function Calling for AI Agents
A backend lead with two years of agent-building experience argues that a single run(command="...") tool with Unix-style CLI commands outperforms traditional function calling catalogs. The approach leverages LLMs' existing familiarity with shell commands from training data.