Grok Build Agent Dashboard

Terminal-based dashboard for orchestrating multiple concurrent AI coding agents

Description

A terminal UI for Grok Build that allows developers to manage and monitor multiple AI coding agents simultaneously. The dashboard integrates with git worktrees to run parallel tasks and prioritizes agents that require human intervention by moving them to the top of the queue.

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Details

Why this is interesting: Demonstrates a sophisticated UX for managing multi-agent AI coding workflows with deep git integration.
Attributed to: @tetsuoai
Built with: Grok 4.7 Redwood AI Hermes 3
Tags: #coding #workflow #terminal #automation

Discovered: X
First posted: X · Oct 4, 2026
Listed: Added 5h ago
Build: Strong
Models: Strong

Prompt

/goal Reproduce the results of the paper below and do not stop until the metrics match.

Paper: paste the arXiv link or attach the PDF.
Target: the specific result to reproduce, for example the headline accuracy on the benchmark and split the paper reports.

Plan first. Read the paper and extract the method, architecture, datasets, training and evaluation setup, hyperparameters, and the exact number you are aiming for. Write that target number at the top of report. md before you write any code.

Implement the method cleanly. Build an evaluation harness that computes the same metric on the same benchmark and split the paper uses. Wire that harness into a hook so it re-grades on every change and you always have a live number to chase.

Loop with memory. After each run, compare your number to the target. When there is a gap, write one specific hypothesis for the cause in report. md, whether data prep, tokenization, a hyperparameter, the eval protocol, or seeds, then test that single hypothesis and rerun. Never repeat a hypothesis that already failed. Every attempt builds on the last.

Generate your own sanity checks beyond the paper to catch silent bugs in data loading and metric computation. A number that looks right for the wrong reason is the failure mode to hunt.

Done means your metric lands within a stated tolerance of the target on a clean rerun. If a gap remains after real effort, do not fudge it. Document the most likely causes with the evidence you gathered.

Deliver a runnable repo, report. md comparing target versus achieved with every hyperparameter, and the full attempt log.

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