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Normal, missing, conflicting, outdated, failure, and prompt-injection cases: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 | AI Task Brief | AI Learning Log | AI Evaluation Record