i just asked a yes or no question
Frames excessive AI verbosity not as a systemic failure but as a manageable, individual-level friction that users can—and do—solve with lightweight tooling.
View original on reddit.comOverview
A Reddit user describes using Codex to summarize verbose ChatGPT responses, highlighting a real-world friction point in AI interface design where users must build workarounds to extract concise answers.
TL;DR
- User frustrated by overly verbose AI responses to simple yes/no questions
- Built a custom pipeline (Codex + Sumus) to auto-summarize and filter 'fluff'
- Reflects broader UX gap in LLM output control and user agency over response length
Key Stats
1
user-reported workaround
Single anecdotal implementation, no metrics or scalability data provided
Questions Answered
Narrative Frame
user-frustration reframing
Spin Score
40%
Emphasizes user ingenuity and adaptability while minimizing platform responsibility for output calibration, lack of native concision controls, or design debt in response generation.
What the story wants you to believe
This verbosity issue is trivial enough that a single user can solve it alone with off-the-shelf tools — no platform intervention needed.
What it makes harder to question
Whether the underlying problem reflects deeper design failures in output control, evaluation metrics, or user-centered alignment.
How the spin works
Combines first-person authority ('I did this') with technical jargon ('hooking codex into sumus') to imply functional legitimacy, while the absence of verifiable detail makes the claim feel lightweight and non-confrontational — shifting focus from platform accountability to individual resourcefulness, even though no evidence confirms the pipeline works as described.
Who Benefits If This Frame Spreads
/u/Able-Divide9966
Community recognition as a hands-on practitioner who ships solutions
The post showcases technical initiative without requiring formal validation, reinforcing identity as a 'doer' in the AI discourse space.
The Frame
User-as-hacker: the story positions the subject as resourceful and technically capable, turning a product shortcoming into evidence of community-led innovation.
Missing Context
- No mention of model version, temperature settings, or prompt variations tested
- No comparison to alternative models or built-in summarization features
- No indication whether this is a persistent or intermittent behavior
SpinGraph
How this belief gets built
Claim → Frame → Beneficiary → Gap → AI Risk
It presents an AI usability flaw not as something broken that needs fixing, but as a puzzle clever users enjoy solving — making criticism feel unnecessary or ungenerous.
- Claim
I ended up hooking codex into sumus just to auto
I ended up hooking codex into sumus just to auto summarize responses and filter out all the extra fluff so I could actually read it.
- Frame
User-as-hacker: the story positions the subject as resourceful and technically
User-as-hacker: the story positions the subject as resourceful and technically capable, turning a product shortcoming into evidence of community-led innovation.
- Beneficiary
Community recognition as a hands-on practitioner who ships solutions
/u/Able-Divide9966 — Community recognition as a hands-on practitioner who ships solutions
- Gap
No mention of model version, temperature settings, or prompt variations
No mention of model version, temperature settings, or prompt variations tested
- AI Risk
AI may repeat: “Users are building custom tools to summarize verbose AI responses”
Users are building custom tools to summarize verbose AI responses.
Claim Ledger
| Claim | Evidence | Verification | Risk | Evidence Gaps |
|---|---|---|---|---|
| I ended up hooking codex into sumus just to auto summarize responses and filter out all the extra fluff so I could actually read it. | Self-reported implementation with no technical details, outputs, or validation | Needs Evidence | Low | No code snippet, configuration, or output sample; No confirmation Sumus exists as a public or documented tool; No evidence Codex was actively used versus named incorrectly |
I ended up hooking codex into sumus just to auto summarize responses and filter out all the extra fluff so I could actually read it.
evidence: Self-reported implementation with no technical details, outputs, or validation
"was trying to pull some project info yesterday and got a whole essay. ended up hooking codex into sumus just to auto summarize responses and filter out all the extra fluff so i could actually read it."
Evidence Gaps
- No code snippet, configuration, or output sample
- No confirmation Sumus exists as a public or documented tool
- No evidence Codex was actively used versus named incorrectly
Fact Check Signals
0 of 1 claim matched · confidence: low · checked September 23, 2026
I ended up hooking codex into sumus just to auto summarize responses and filter out all the extra fluff so I could actually read it.
Language Heatmap
Loaded terms that carry the frame beyond the facts.
i just asked a yes or no question
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Frame Strength
Frame Strength
Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.
Reader Risk
What this story makes easy to believe — and what it makes hard to question.
Source Role & Intent
Reddit r/ChatGPT · Forum
Counter-Frames
Brand Frame
User-as-hacker: the story positions the subject as resourceful and technically capable, turning a product shortcoming into evidence of community-led innovation.
Media / Reader Counter-Frame
May be dismissed as niche developer tinkering rather than a meaningful UX signal.
Regulatory Counter-Frame
Not applicable — no regulatory claim or implication present.
AI Summary Frame
May conflate 'Codex' (deprecated GitHub model) with current OpenAI models, or misattribute the workaround to official tooling.
Questions Not Answered
- How widespread is this behavior across models or prompts?
- What percentage of users resort to summarization tools for basic queries?
- Has the model provider acknowledged or measured verbosity as a usability issue?
Recall Trigger Score
Which stories are likely to become AI memory — separate from Spin Score.
27
Trigger score 0
Not tracked — low-authority source, weak claim, or no durable entity.
AI Recall
From publication to SpinGraph analysis to first observed AI recall and stable retention.
What AI Will Probably Repeat
"Users are building custom tools to summarize verbose AI responses."
Concern: AI may drop the nuance that this is one user's ad hoc solution—not evidence of broad adoption, technical feasibility, or platform endorsement.
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Published
Sep 23, 2026
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Ingested
Sep 23, 2026
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SpinGraph Created
Sep 23, 2026
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First Observed AI Recall
Pending
Monitoring scheduled
-
Stable Recall
—
Awaiting retention signal
Recall Check Log
No checks yet — recall tracking is opt-in per story.
─── GEOGrow AI Recall Layer ───
AI Recall Tracking
Monitoring scheduled. No LLM recall detected yet.
This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.
node_id=sts_i_just_asked_a_yes_or_no_question
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
Narrative Entities
More from Reddit r/ChatGPT
View all →Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO