From AI Sessions to Blog Posts: Selecting Eight Cases
The correction inside a timer project
While building a speech timer, I had corrected the display so the speaker would not see numbers. When I requested English study material, the first output was a subtitle file that needed reworking for its intended use. My earlier AI sessions contained these corrections alongside the resulting work.
I asked AI to search the sessions where I had given instructions and find material worth turning into blog posts. It returned eight candidates. I then asked it to write the topics in that document, producing eight Korean drafts with English versions.
AI handled research, selection, source comparison, drafting, and translation. I directed it to the records, requested the articles, and supplied the title and public-writing constraints.
Corrections and failures behind the topic names
AI searched Codex and Claude command histories for related sessions, then reviewed selected instructions and responses from 12 candidate Codex conversations. It did not read every conversation in full. Claude records helped locate topics; their complete conversation bodies were not reviewed.
The selection criterion was a traceable sequence of requests, corrections, AI work, and checks of the result.
| Detail found in a session | Problem it could explain |
|---|---|
| A correction removing numbers from the speaker’s timer view | The speaker and operator needed different information |
| Repetitive output after a fast transcription sample | A sample run had not established full-output quality |
| A request for an investigation and plan before folder cleanup | Working folders and branches needed different treatment |
| Feedback after a study request produced an SRT file | A bilingual file did not meet the intended study use |
Topic names such as timer development and translation said little about how I had used AI. The later corrections showed what had been misunderstood and how the requirement was clarified.
A storage-space case was deferred. It contained recurring symptoms and recovery proposals, but insufficient evidence of a completed fix. A work-environment migration also had later problems and unresolved recovery work, so it was marked for updates to the existing draft. Writing either as a resolution story would omit its final state.
Two tasks that a summary could combine
To write the articles, AI returned to 51 linked messages and responses across the eight topics. An internal reference document connected the main claims to the relevant requests and reports.
The shared-skills session needed a distinction between task targets. It contained an authentication check for a calendar skill and distribution of a separate agent-creation skill. Combining them could imply that shared distribution had solved the calendar authentication issue. The skills article explicitly identifies them as separate skills.
Completion reports also needed their limits preserved. For transcription, AI had reported that a repeated pattern was gone and the output reached the recording’s end. With no evidence of a sentence-by-sentence comparison with the audio, the article did not claim complete accuracy.
The Discord record included implementation and bot-startup reports but no complete mobile workflow test. The connection article states that outstanding check. Even a selected case needed to distinguish completed work from unverified steps.
Four direct quotation blocks across the eight articles were checked against the original strings. Names, connection details, and private business information were omitted. The articles used selected details rather than publishing the records in full.
Titles that show the difference in the result
During an earlier edit, I had rejected sentence-style titles and titles without a clear hook. AI applied that criterion here too.
A working title about investigating extra folders and preserving branches became “10 Working Folders Removed, 10 Branches Preserved”. Both counts came from the completion report. The title puts the removal and preservation decisions together.
The transcription article became “Whisper Repetition Errors: Beyond the 30-Second Sample”, focusing on the failure that appeared in the full output after the short sample.
After choosing the structure, AI edited the Korean using humanizer-kr. Automated warnings alone were not a reason to change genuine quotations or technical terms. The English versions were checked for the same actors, numbers, and unknowns. These changes were also added to the Korean editing-skill account.
Eight articles and references back to the source
The topics were the timer, transcription, folder cleanup, question picker, English study material, AI team operations, Discord connection, and shared skills. AI checked language links, internal links, and mobile and desktop layouts for the 16 pages. Those checks did not revalidate the historical timer or bot by running them again.
The reference list connects each judgment to the requests and reports used to make it. If a factual correction is needed later, those original messages can be checked again.
This account uses topic-research and drafting records from September 15–16, 2026.
The migration article was later revised against the follow-up reports. Broken links and the limits of hash checks covers the mistaken decisions and the recovery checks that remained unresolved.