Results for “miles”

Start a new milestone cycle — update PROJECT.md and route to requirements

Train large-scale MoE models with FP8/INT4 low-precision RL, speculative decoding, and train-inference alignment using the miles framework.

Create, name, scope, assign, and close GitHub milestones. Use when adding a milestone, deciding whether work belongs in one, or naming the next one. One axis per project (release / date / theme); never as labels.

Manages a milestone from a single terminal, showing a phase dashboard and dispatching discuss, plan, and execute actions.

Archives phase directories from completed milestones into versioned archive folders, showing a dry-run summary and moving directories only after confirmation.

Archive accumulated phase directories from completed milestones

Turns complex business goals into navigable strategic maps, identifying milestones, resource nodes, risk points, bottlenecks, and route logic to clarify the path before execution.

Manage Scope entities — bounded containers grouping related work (sprint, milestone, project, quarter). Use when creating or updating a grouping boundary — sprint, milestone, quarter, project, release — that other entities will reference.

Use after design approval for milestone plans with executable acceptance.

Track elapsed time from a set epoch with tamper-evident locking, supporting count-up, count-down, and count-between modes, milestones, and email notifications.

Add phase to end of current milestone in roadmap

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

Use only for video game projects; sequence prototype, vertical slice, demo, store page, festival, launch, and post-launch milestones

Produce a ready-to-run onboarding plan — pre-boarding checklist, day-1 agenda, first-week plan, and 30/60/90 ramp milestones with owners.

Plan tailored engagement for a specific stakeholder before critical conversations, milestones, or relationship repairs.

Roadmap parsing, analysis, and mutation operations for ROADMAP.md. Handles phase and milestone lifecycle including add, insert (decimal), remove, complete, and requirements coverage analysis.

Design a project-based learning brief with a driving question, milestones, and assessment criteria. Use when planning PBL units, inquiry projects, or extended investigations.

Takes an approved plan from docs/plans/ and drives it to an open PR. Implements it by milestones, commits per milestone in the repo's style, and opens the PR against the repo's base branch. Use whenever the user approves a plan, says "ship it", "go", "start", "implement it", "do it", or asks to open a PR for already-planned work — or in Spanish "dale", "arrancá", "implementalo", "hacelo".

Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.

Consulte métricas de uso de recursos para serviços Railway. Use quando o usuário perguntar sobre uso de recursos, CPU, memória, rede, disco ou desempenho do serviço, como "quanto de memória meu serviço está usando" ou "meu serviço está lento".

Track and visualize ML training experiments with Trackio, including logging metrics, firing alerts, and retrieving data via CLI. Supports real-time dashboards, webhook alerts, and HF Space syncing.

Configure fault tolerance, straggler detection, preemption, in-process restart, and re-run state machine for Megatron Bridge training jobs.

Lint and format Python code for Megatron-LM using ruff, black, isort, pylint, and mypy, with commands for autoformatting and import ordering.

Run evaluation tasks against a NeMo Platform server using the Evaluator plugin CLI and Python SDK.

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much memory is my service using" or "is my service slow".

Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much memory is my service using" or "is my service slow".

ML pipeline design — data versioning, experiment tracking, deployment patterns, drift monitoring. Use when building an ML pipeline from data to deployment, setting up MLOps tooling (DVC, MLflow, model registry), choosing deployment patterns (shadow, canary, A/B), or designing monitoring for drift and degradation.

Use when a carrier asks about tire program design, when to retread vs replace, position management (steer vs drive vs trailer), inflation pressure standards, casing management, scrub rates, road-call prevention, or vendor program selection (Bridgestone, Michelin, Continental, Goodyear). Covers 49 CFR 393.75 and DOT roadside out-of-service criteria.