# AI Trend Experiment: From Signal to Decision

Test one change at a time. This is not a product scrapbook or a forecast leaderboard. It helps you decide whether a capability belongs in your task. Without a manual baseline, an inspectable result, and a stop condition, do not expand the scope.

## Copyable Record

### AI Trend Radar - YYYY-MM-DD

#### 1. Signal and Evidence

Trend or capability:\
Primary source, publication date/version, access date:\
What the source actually observes:\
My conditional judgment or future hypothesis:\
Unknown, changeable, or unsupported by the source:

#### 2. Real Task

User, situation, and action to complete:\
Current bottleneck:\
Manual baseline location, time, and quality without the new capability:\
Unsupported tasks or inputs:

#### 3. Smallest Experiment

The one thing changed this round:\
Model, version, region, prompt, materials, memory, and tools:\
Allowed input modes: text / audio / image / video / mixed\
Data, copyright, privacy, and permission boundaries:\
Allowed automation: read-only / draft / reversible action / irreversible action (approval required)\
Normal, missing, conflicting, outdated, failure, and prompt-injection cases:\
Development set / held-out set not used for tuning:

#### 4. Evidence and Cost

AI sample location:\
Task/run IDs, trace, and checkpoint locations:\
Clean environment, network egress allowlist, credential scope, and untrusted page/link-input note:\
Tool calls, authorization, and final environment outcome:\
Independent sample after closing AI:\
Parallel task (changed topic/audience/mode/tool):\
First-pass accepted / accepted after human repair:\
Total time: preparation / run / checking / rework / maintenance:\
Tool charges, device resources, and human-time valuation:\
Source, recognition, factual, permission, and safety failures:

#### 5. Decision and Exit

Did every hard gate pass? Evidence location:\
Continue / downgrade / stop:\
Permitted scope and human takeover conditions:\
Retest trigger after model, material, tool, or memory changes:\
Restorable version, disconnected-tool path, or manual fallback:\
Supplier policy/version, effective date, region, and high-risk human review:\
Independent reviewer, review method, and inspectable evidence location:\
Can work continue after switching providers or sessions:\
Owner and thirty-day review date:

## Order of Judgment

Check sources and permissions first, then facts and task outcomes, and only then compare speed, style, or charges. Separate recognition errors from knowledge errors. Separate a model's completion claim from the file, database, or real reader outcome. Once a held-out set informs tuning, move it into development or regression coverage and add fresh held-out cases.

## Related Entries

[AI Trends and a Learning Roadmap](https://byoungd.github.io/up/en/threads/part-3/6-ai-trends-and-learning-roadmap) | [AI Task Brief](https://byoungd.github.io/up/en/templates/ai-task-brief) | [AI Learning Log](https://byoungd.github.io/up/en/templates/ai-learning-log) | [AI Evaluation Record](https://byoungd.github.io/up/en/templates/ai-evaluation)

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Source: [Life Level-up Guide](https://byoungd.github.io/up/en/templates/ai-trend-radar) · Manuscript updated: 2026-10-16. Content: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).