Acyclic was launched on Product Hunt as an AI coding agents tool that runs tasks from the CLI, dashboard, or a coding agent over MCP. The listing shows it has 21 followers. It changes workflows by splitting work across multiple agent forks of a workspace and then merging the results so agentic runs finish faster.
Google Research published a tutorial that implements RRSI (Regularized Recursive Self-Improvement), letting an LLM agent rewrite its own harness (prompts, tools, memory, control flow, sub-agents) while keeping a frozen underlying model, and driving the paper’s edit-selection logic directly with Python. The selection procedure uses calibrated noise bands, with the paper reporting a value of 0.020 for engineering evaluations. As a result, the guide turns the paper’s rules into runnable code, including how scores treat missing trials, and demonstrates how RRSI’s decisions can be audited against ground truth in a simulated setting rather than only by comparing top scores.
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