Prime Intellect released Prime Agent. The agent was developed for coding workflows and long-running autonomous tasks, and it is built on the Recursive Language Model (RLM) approach. SuperQode adds support for Prime Agent through […]
Category: Harness Engineering
SuperQode A2A: Coding harness-to-harness communication with QM
Coding agents no longer live only inside one terminal session. Some products act like company computers, with shared rooms, durable sandboxes, and Slack or web surfaces. Others treat the harness […]
Running Kimi K3 in SuperQode
Moonshot published the Kimi K3 weights. The model is 2.8 trillion parameters with a 1 million token context window, shipped in MXFP4 at roughly 594GB. Within a day it was […]
GEPA Omni in SuperQode: Three Optimizers Compete on a Coding Harnesses
The team behind GEPA has just released the Omni to make agent optimisation unified. SuperQode, our agent engineering platform for coding factories has already integrated this and experienced with tit for […]
What’s New in SuperQode: Latest Models, Smaller Core and Python-Extensible Harnesses
The coding-model landscape is moving faster than the software around it. In the space of a few weeks, OpenAI introduced GPT-5.6, xAI released Grok 4.5, Meta launched Muse Spark 1.1, […]
Self-Optimizing Coding Agent Harnesses with SuperQode
Coding agents have become a practical part of software development workflows. Tools such as Codex-style agents, Claude Code-style agents, OpenCode-style agents, and IDE-integrated assistants are increasingly good at reading repositories, […]
Optimizing Databricks Omnigent Agents with MetaHarness
Databricks has just released the Omnigent the meta-harness for the AI agents. It is timely that Superagentic AI released meta-harness library few months ago. These meta-harness concepts sounds same but they […]
Superagentic AI at the Google I/O and CAIS Conference: Reflections from Bay Area
Last week, I returned to London from the San Francisco Bay Area with renewed energy, fresh insights, and a deep appreciation for the people driving artificial intelligence forward. Since back, […]
PyFlue 0.2.0: Bringing Flue’s Agent Runtime Model to Python
PyFlue 0.2.0 is now available. This release is a major step toward parity with the TypeScript Flue framework and introduces a clearer runtime model for building production-oriented Python agents. The […]
Introducing PyFlue: The Python-Native Agent Harness Framework Inspired by Flue.
The CEO of HTML, Fred Schott released Flue , the TypeScript community quickly recognized its significance. A true agent harness framework with Markdown-driven skills, headless and programmable design, zero-config sandboxing, and […]