'experiments' 검색 결과

Operator-side analysis of historical ML experiment PRs in Senpai research tracks. Use this skill whenever the user asks to: analyze experiments, categorize PRs, bucket experiments, summarize what's been tried, understand experiment history, review merged vs closed results, or asks "what experiments have we run / worked / failed". Also triggers for: "pull the latest experiments", "what's been tried so far", "category breakdown of PRs", "which experiments succeeded", "noam track analysis". When a branch name is mentioned (e.g. "noam branch", "on the noam branch"), pass it as the base branch to scope the fetch to just those PRs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Focused Signals scout for PostHog projects running A/B experiments. Watches running experiments for validity threats (sample ratio mismatch, multi-variant contamination, exposure stalls, mid-run flag mutations) and lifecycle drift (zombie experiments running long past their useful life, decided-but-still-running experiments, ended experiments whose flags still serve multiple variants). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate whether completed experiments support a research claim using a separate Codex reviewer. Identify unsupported conclusions and missing evidence, then determine whether to revise the claim, run additional experiments, or move toward paper writing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check research claims before drafting a paper
• Plan additional experiments for incomplete evidence
• Reconsider an approach after unsupported results

Evaluate whether completed experiments support a research claim by checking cited evidence and requesting a Codex review. Identify unsupported conclusions, missing comparisons, and needed revisions, then determine whether to add experiments, change direction, or prepare for writing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking claims before writing a paper
• Interpreting ambiguous experiment results
• Planning experiments to fill evidence gaps

Launch machine learning experiments on local GPUs, remote servers, Vast.ai, or Modal. It checks the configured environment, deploys code, saves logs, and verifies startup, with optional W&B metric logging and automatic Vast.ai result collection and instance cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run training jobs on a local GPU
• Deploy experiments to an SSH server
• Run experiments on Vast.ai and collect results

Run batches of machine learning experiments on remote GPUs over SSH, varying settings and random seeds. The skill schedules jobs, tracks progress, retries GPU memory failures, and cleans up leftover sessions before starting subsequent batches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run experiments across multiple random seeds
• Test combinations of model settings
• Chain teacher training and student distillation

Design controlled failure experiments for distributed systems to examine how services recover. Produce failure injection scripts, monitoring plans, rollback procedures, and exercise guides, then turn findings into documented improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning controlled failure experiments
• Testing recovery after Kubernetes Pod failures
• Checking service behavior under network delays

Autonomous growth experimentation framework: creates experiments with hypotheses, logs data points, runs statistical analysis (bootstrap CI + Mann-Whitney U), auto-promotes winners to a living playbook, and suggests next experiments. Use when asked to run growth experiments, analyze A/B tests, build experiment scorecards, or generate pacing alerts. For SEO-specific experiments see seo-ops; for outbound experiments see outbound-engine.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Organize repeated experiments to improve models, prompts, or agent systems against measurable goals. The skill records hypotheses and lessons in a persistent tree, isolates trials, and compares promising candidates using evaluation data kept separate from development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare repeated experiments to improve model training
• Improve an agent system's measured success rate
• Test prompt changes against a fixed evaluator

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Guides experiments that inspect and change internal model values using nnsight. Covers local PyTorch models and remote experiments through NDIF, including replacing intermediate values to test how particular layers affect a model’s predictions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting layer outputs and attention patterns
• Replacing internal values to compare predictions
• Running large-model experiments through NDIF

Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Logging training metrics and viewing progress in real time
• Comparing experiments with different training settings
• Searching automatically for better training settings

Plan low-effort experiments to check assumptions about an existing product before building a feature. Compare prototypes, A/B tests, and other methods, with a clear assumption, procedure, metric, and success threshold for each experiment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking feature assumptions before implementation
• Planning prototype usability tests
• Defining metrics and success thresholds for A/B tests

Turn a new product idea into a testable hypothesis and two or three simple experiments. Define target customers, expected actions, measurement methods, and success thresholds to assess demand through behavior such as sign-ups, pre-orders, or paid commitments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing demand before building a new product
• Planning landing page or waitlist experiments
• Checking willingness to pay through pre-orders

Plan ablation studies, experiments that remove or replace parts of a machine learning method to test their contribution. Define reviewer questions, expected outcomes, priorities, execution order, and compute estimates, then implement experiments and record findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing the contribution of individual method components
• Planning additional experiments before paper submission
• Prioritizing experiments within a GPU budget

Turn a research experiment plan into training and evaluation code, run a small setup check, and launch GPU experiments. Collect initial metrics and update progress records so the results are ready for further review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implementing experiments from a written plan
• Checking a small run before full GPU experiments
• Collecting baseline and main-method results

Manage machine learning experiments with MLflow by recording settings, metrics, files, and trained models. Covers experiment comparison, model version management, reproducible project configurations, and deployment workflows across local and cloud environments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording experiment settings and evaluation results
• Comparing model performance across experiments
• Managing model versions for production use

Guides experiments that inspect and modify internal values in PyTorch models to study their effects on predictions. Covers local analysis and remote experiments through NDIF for models too large for local GPUs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect internal values and attention patterns across layers
• Replace internal values to compare changes in predictions
• Run large-model experiments without local GPUs

Turn A/B tests and product experiment records into decision reports. Organize hypotheses, setup, and metrics, explain findings and uncertainty, then recommend shipping, iterating, extending, or stopping, with follow-up experiments based only on supplied data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether to launch a tested product change
• Reviewing growth or pricing experiments
• Assessing onboarding changes or email experiments

Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Track ENCODE experiments locally with publications, citations, and provenance. Use when the user wants to build a collection of experiments, manage citations, compare experiments, or track data provenance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design and improve SaaS upgrade screens, paywalls that restrict access to paid features, and upsell dialogs. Use it to shape screens that encourage paid subscriptions and support experiments with pricing pages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing screens that encourage upgrades to paid plans
• Improving paywalls for paid features
• Designing dialogs that offer additional purchases

Plan A/B tests to compare page changes using clear hypotheses and meaningful measures. The skill helps define sample size, duration, traffic allocation, and result interpretation, and organize a continuing program of prioritized experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare headlines or signup buttons
• Estimate test sample size and duration
• Interpret results and possible adverse effects

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate metabolic fluxes from carbon-13 labeling
• Validate atom mappings and measurement inputs
• Simulate labeling for proposed reaction rates