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Antares-1B is an open-weight, 1-billion parameter language model built on IBM Granite 4.0 for autonomous vulnerability localization in codebases. It operates as a terminal agent, outperforms much larger models on the VLoc benchmark, and is released under Apache 2.0 by Cisco.
Simon Willison deeply analyzed the open-source code of Grok Build, revealing the inner workings of a terminal programming agent, including system prompts, tool implementations, and privacy issues.
Ai2 and the University of Washington released a paper titled Tmax, proposing the strongest open-source terminal agent RL training recipe to date. A 9B parameter model outperforms larger models on Terminal-Bench 2.0, with the key being low-cost generation of vast amounts of verifiable training data, not model size or algorithm.
Ai2 released Tmax-27B, a terminal-agent LLM trained with DPPO (RL) on Qwen3.6-27B, and the author provides importance-matrix-calibrated GGUF quantizations that achieve competitive performance on agentic benchmarks even at very low bit-widths, with a grafted MTP draft head for speculative decoding.
Ai2 released TMax 27B, a 27B terminal agent that achieves 42.7% on Terminal Bench 2.0, rivaling models 40 times its size.
Introducing Tmax, open reinforcement learning terminal agent models that outperform prior open work on terminal use. All data, weights, and rollouts are being released publicly.
Ante is a lightweight, self-contained terminal agent harness written in Rust, designed to be fast and dependency-free. It topped Terminal Bench 2.0 and remains highly responsive to user feedback despite being in preview and not yet open-sourced.
Reasonix is a native backend terminal programming Agent designed for DeepSeek, using Cache-First loop and Flash optimization strategies to significantly reduce API call costs and provide real-time account balance viewing, making it a practical companion tool in the DeepSeek ecosystem.