Results for “autograd”

[OMX] Strict autonomous loop: $deep-interview -> $ralplan -> $ultragoal (+ $team if needed) -> $code-review -> $ultraqa

Upgrade gstack to the latest version. Detects global vs vendored install, runs the upgrade, and shows what's new. Use when asked to "upgrade gstack", "update gstack", or "get latest version". Voice triggers (speech-to-text aliases): "upgrade the tools", "update the tools", "gee stack upgrade", "g stack upgrade".

Iteratively improves a compiled LaTeX paper through two rounds of external LLM review, fix implementation, and recompilation.

Autonomous experiment loop — iteratively mutate code, measure a metric, keep only improvements (hill-climbing ratchet)

AutoAugment: Learning Augmentation Strategies from Data

Scaffolds a program.md research program for autoresearch by auto-detecting codebase signals and interviewing for missing details.

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:review or asks what has been learned and what should be promoted or pruned.

当设定个人或工作目标，希望目标本身能带来持续的内在动力和满足感时

ADR Fleet

Generates unit tests for multiple languages and frameworks, including coverage reports and boundary/exception cases.

Graduate a proven pattern from auto-memory (MEMORY.md) to CLAUDE.md or .claude/rules/ for permanent enforcement.

Pre-commit/ship code review: Codex default; optional Claude, Pi, Droid, Copilot, Cursor, or OpenCode.

Mental models and decision frameworks for building autonomous agents that continuously learn, explore, and accumulate skills in open-ended environments without human supervision

DriveLM: Driving with Graph Visual Question Answering

Full autonomous execution from idea to working code

Blocks Edit/Write/Bash actions until the agent presents concrete facts about imports, data schemas, and the user's instruction, improving output quality.

Check post-launch traction for a shipped VARD app and recommend iterate, graduate, or archive

Runs a structured code review as a pre-commit or pre-ship gate, supporting multiple review engines and scope governance.

Meta-skill — defines when to apply other skills automatically without being asked

Provides guidance on using JAX for machine learning and mathematical analysis, covering core concepts, transformations, ML specifics, control flow, and parallelism.