CONTENT ROADMAP / ONE EXPERIMENT

Turn the calculator test into four useful artifacts.

The goal is not to explain an old feature to everyone. It is to show what you measured, where the obvious intuition broke, and how that changes the way tool-backed systems should be evaluated.

POSITIONING

Tool calling is old. Measuring the tradeoff is the story.

I measured what a calculator tool buys in accuracy, tokens, cost, and latency—and found that a deterministic tool can still fail when the model constructs the wrong arguments.

Primary audience: Technical generalists and working AI builders
Career signal: Evaluation, instrumentation, automation, and AI security
Primary channel: LinkedIn, supported by the blog, dashboard, and repository

PRODUCTION PLAN

Finish, publish, then reuse.

Check off tasks as you complete them. The first two items reflect work already finished in this project.

EVIDENCE

Finish the experiment

Close the remaining data gap before turning provisional observations into final claims.

Complete the linear benchmark1,000 shared calculations in both evaluation modes.

Build the comparison dashboardAccuracy, operators, tokens, cost, runtime, and failure rows.

Complete the Gaussian benchmarkWait for both modes to reach the same final sample size.

Freeze the final findingsRecord the exact claims, limitations, and most useful failure.

CORE RELEASE

Publish the useful version

Make one durable explanation and two short pieces that lead people back to the evidence.

Finish the build-log articleMethod, implementation problems, results, failure autopsy, and limitations.

Record the results video60–75 seconds: 77.2% versus 99.4% and the cost of that gain.

Record the failure autopsy45–60 seconds: the calculator was correct but received the wrong sign.

DISTRIBUTION

Reuse without multiplying the work

Publish once for the highest-value audience, then reuse the same assets with minimal editing.

Publish and cross-postLinkedIn first; reuse the same cut on YouTube Shorts and X.

CONTENT QUEUE

Four artifacts, each with a different job.

PRIMARY

Technical build log

Canonical article

For: Developers, technical leads, hiring managers
Promise: What a calculator tool changed—and what remained capable of failing.
Next action: Finish after the Gaussian results are stable.

PRIMARY

Headline results

60–75 second video

For: Technical generalists and AI builders
Promise: A 22.2-point accuracy gain in exchange for 7.4× the tokens.
Next action: Use the dashboard as the complete visual track.

STRONGEST ANGLE

Failure autopsy

45–60 second video

For: AI security and automation practitioners
Promise: A perfect calculator still failed because the model changed an argument.
Next action: Bridge the lesson into validation, permissions, and audit logs.

SEQUEL

Distribution comparison

Short post or chart walkthrough

For: Evaluation-minded practitioners
Promise: Whether a tighter Gaussian value distribution changes model accuracy.
Next action: Publish only after both Gaussian modes reach the final sample size.

SCOPE CONTROL

Do not turn this into a content treadmill.

Do not make a generic “What is tool calling?” explainer.
Do not record separate edits for every social platform.
Do not publish Gaussian conclusions before both runs are complete.
Do not make a long tutorial unless the short release creates demand.

Completion means one durable article, two short videos, one optional data sequel, and a clean path back to the evidence.