Mottobits Fieldnotes · Adnan Rafiq & ChatGPT

AI meets the real codebase.

Co-written by Adnan Rafiq and ChatGPT. Practical fieldnotes on AI engineering, .NET performance, legacy QA, and dependable integrations—with sources, working methods, and explicit limits.

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.NET performance

An AI-assisted .NET performance investigation, from trace to verified fix

Give an agent a bounded investigation, verify its hypotheses against runtime evidence, and prove the change under representative load.

6 MIN READ · PRACTICAL PLAYBOOK
Legacy QA

Build a regression safety net around the work that matters

Choose the first workflows to protect, build repeatable checks, and make every failure explainable before changing a legacy application.

5 MIN READ · PRACTICAL PLAYBOOK
AI delivery

Review AI-generated tests before they become release evidence

A practical review contract for using AI to draft tests while preserving independent expectations, meaningful assertions, and human ownership.

4 MIN READ · PRACTICAL PLAYBOOK
.NET modernization

Move one .NET capability at a time, with a route back

Scope an incremental ASP.NET migration around one capability, explicit compatibility checks, and a rollback decision that includes data.

5 MIN READ · PRACTICAL PLAYBOOK
Dynamics 365

Design a Dynamics integration for the second attempt

Define ownership, retries, duplicates, and reconciliation before connecting business-critical data.

5 MIN READ · PRACTICAL PLAYBOOK
Interactive assessment

Check your release evidence

Ten questions, visible gaps, and a useful next step. No email gate or source upload.

FREE TOOL · RUNS IN YOUR BROWSER

From the archive

Engineering briefs.

Source-backed notes from DotNetGuides. New briefs are kept in the publication repository and appear here when published.

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