AI Agents

MiniMax, the AI analysis firm behind the MiniMax omni-modal mannequin stack, has launched MMX-CLI — Node.js-based command-line interface that exposes the MiniMax AI platform’s full suite of generative capabilities, each to human builders working in a terminal and to AI brokers operating in instruments like Cursor, Claude Code, and OpenCode. What Problem Is MMX-CLI Solving?…

MiniMax has formally open-sourced MiniMax M2.7, making the mannequin weights publicly out there on Hugging Face. Originally introduced on March 18, 2026, MiniMax M2.7 is the MiniMax’s most succesful open-source mannequin thus far — and its first mannequin to actively take part in its personal growth cycle, a significant shift in how massive language fashions…

In this tutorial, we construct and function a absolutely native, schema-valid OpenClaw runtime. We configure the OpenClaw gateway with strict loopback binding, arrange authenticated mannequin entry via surroundings variables, and outline a safe execution surroundings utilizing the built-in exec instrument. We then create a structured customized talent that the OpenClaw agent can uncover and invoke…

Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information video games — eventualities the place gamers act sequentially and can’t see one another’s non-public data, like poker — has traditionally relied on handbook iteration. Researchers determine weighting schemes, discounting guidelines, and equilibrium solvers by means of instinct and trial-and-error. Google DeepMind researchers proposes AlphaEvolve, an…

In this tutorial, we work immediately with the A-Evolve framework in Colab and construct a full evolutionary agent pipeline from the bottom up. We arrange the repository, configure an OpenAI-powered agent, outline a customized benchmark, and construct our personal evolution engine to see how A-Evolve truly improves an agent by way of iterative workspace mutations….