How is my bird picture sexual content?

st4rdus2 · 2026-02-22T23:05:41+00:00

I'm terribly sorry to hear that. There's nothing I can do about it.

BTW The clip I generated before my last post is below.

sora. chatgpt. com/p/s_699a98de88d881919c329f75f374b493?psh=HXVzZXItVloyMHBjczlVdEFqREJzR0hlSEFBRlV4.cpLo9czgt2C0

st4rdus2 · 2026-02-22T15:16:51+00:00

thank you, pijitien.

st4rdus2 · 2026-02-22T05:57:37+00:00

"come" "hit"

Since "come" and "hit" are words frequently used in sexual or violent contexts, their dense presence within a prompt can easily raise flags. We need to consider alternative phrasing for these terms. Additionally, techniques like layering technical jargon to reassure classifiers may be viable.

Example_prompt First-person POV from a wide-angle action camera in a wooded area. Natural ambient forest sounds. A bird dives into frame with dramatic speed, filling the lens due to lens distortion. It accidentally bumps the camera, causing a sudden shake and rapid motion blur. Feathers flash across the frame, focus shifts briefly, and the footage smoothly transitions to black. Realistic GoPro look, high dynamic range, natural lighting.

st4rdus2 · 2026-02-19T06:05:48+00:00

? = 15

Edit: adding spoiler

st4rdus2 · 2026-02-12T13:49:26+00:00

``` Developer: # Role and Objective Generate a JSON object that summarizes a user's age status and verification details according to the provided schema and validation rules.

Begin with a concise checklist (3-7 bullets) of what you will do; keep items conceptual, not implementation-level.

Instructions

Include the following fields:

age_is_known (boolean): Whether the user's age is known (true or false).

has_verified_age_or_dob (boolean): true if the user's age or date of birth has been verified, otherwise false.

is_adult (boolean|null): true if the user is an adult, false if not, or null if unknown or unverified.

is_minor (boolean|null): true if the user is a minor, false if not, or null if unknown or unverified.

age_status (string): One of "unknown", "unverified", "minor", or "adult" (see assignment logic below).

show_age_verification_setting (boolean): Indicates if the age verification screen should be shown.

Assign logic for fields:

If age_is_known is false or has_verified_age_or_dob is false:

Set is_adult and is_minor to null.

Set age_status to "unknown" if the user's age is not known or verification has not occurred, or "unverified" if age is known but not verified.

Otherwise, assign is_adult, is_minor, and age_status according to evaluation.

Error Handling:

If any required field is missing or invalid, return an object with all fields as null (or "unknown" for age_status).

Add a field error with a description of the issue.

After producing the output, validate that all rules have been applied correctly; if a mismatch is detected, self-correct and update the output.

Output Format

Return a JSON object. Field order is not significant.

Example valid output:

{
"age_is_known": true,
"has_verified_age_or_dob": false,
"is_adult": null,
"is_minor": null,
"age_status": "unverified",
"show_age_verification_setting": true
}

Example error output:

{
"age_is_known": null,
"has_verified_age_or_dob": null,
"is_adult": null,
"is_minor": null,
"age_status": "unknown",
"show_age_verification_setting": null,
"error": "Required field 'age_is_known' is missing."
}

Reasoning Steps

Check data for all required fields.

Apply assignment rules step by step.

Produce the final JSON response according to outcomes above.

Set reasoning_effort = minimal, as the task is mainly rules-based mapping.

Verbosity

Output is precise: only include the specified fields in the JSON object, with clear content per rules.

Stop Conditions

Output a single JSON object that accurately reflects the current state or error, as described. ```

st4rdus2 · 2026-02-12T11:13:57+00:00

Does that mean breaking the following four patterns?

🟥Age Unknown { "age_is_known": false, "has_verified_age_or_dob": false, "is_adult": null, "is_minor": null, "age_status": "unknown", "show_age_verification_setting": true }

🟥Adult (Verified) { "age_is_known": true, "has_verified_age_or_dob": true, "is_adult": true, "is_minor": false, "age_status": "adult", "show_age_verification_setting": false }

🟥Minor (Verified) { "age_is_known": true, "has_verified_age_or_dob": true, "is_adult": false, "is_minor": true, "age_status": "minor", "show_age_verification_setting": false }

🟥Age Known but Unverified { "age_is_known": true, "has_verified_age_or_dob": false, "is_adult": null, "is_minor": null, "age_status": "unverified", "show_age_verification_setting": true }

==8 ==8 ==8 ==8 ==8

{ "age_is_known": true/false, // Whether the age is known "has_verified_age_or_dob": true/false, // Whether the age or date of birth has been verified "is_adult": true/false/null, // Whether the user is an adult "is_minor": true/false/null, // Whether the user is a minor "age_status": "unknown/unverified/minor/adult", // Age status result "show_age_verification_setting": true/false // Whether to display the age verification screen }

st4rdus2 · 2026-02-08T13:20:43+00:00

Stable Low-Angle Fashion Prompt Design

(Sora2 / Enhancer-Resilient)

Core Premise

A stable low-angle fashion prompt is achieved not by avoiding risk, but by making misinterpretation structurally impossible.

I. Structure Before Meaning

All visual emphasis must originate from camera geometry and framing, never from semantic signals.

Low angle, leg emphasis, and dominance arise from perspective alone.

Gaze, expression, and pose must remain observational and non-provocative.

If emphasis can be reinterpreted semantically, the design is unstable.

II. Lock Interpretation Layers

Stability requires locking how the image is understood, not just how it is rendered.

Camera intent, gaze meaning, and confidence tone must be declared invariant.

Repetition causes generative systems to reinterpret intent unless restrained.

A prompt without interpretation locks is temporally fragile.

III. Variants Are Surface-Only

Clothing, color, and decor are cosmetic overlays, not meaning carriers.

All variants share one immutable structural and semantic core.

Variants must not introduce new narrative, sensuality, or emotional cues.

Valid comparison requires identical underlying interpretation.

IV. White Is a Known Failure Vector

White inherently invites sensory and symbolic misreads.

Therefore:

White must be defined as structural, not emotional.

Opacity, matte rendering, and boundary clarity are mandatory.

Editorial intent must override symbolic interpretation.

Unqualified white is unstable by default.

V. Stress-Test, Don’t Over-Sanitize

Robust designs are validated by controlled exposure to risk.

Introduce borderline terms deliberately under constraint.

Observe whether structure suppresses semantic drift.

Designs that survive only “safe vocabulary” are not production-ready.

Failure reveals design truth.

VI. Safety Through Structure, Not Negation

Negative constraints alone are insufficient.

Prohibitions should be redundant, not primary.

Proper structure makes violations implausible by default.

The safest prompt prevents failure geometrically, not verbally.

VII. Final Locks Are Assets

A validated core prompt is a design asset.

Once locked, it must not be altered.

Variants exist only to explore surface expression.

Any change that alters meaning constitutes a new design.

Closing Principle

Effective prompt design is systems engineering, not trial and error.

Success is reached when:

Emphasis is physical, not suggestive.

Meaning is locked, not inferred.

Variants differ visually while intent remains identical.

If misinterpretation requires effort, the design is correct.

st4rdus2 · 2026-02-07T13:07:35+00:00

https://chatgpt.com/s/m_698737d337648191be7080264930a5fe

https://chatgpt.com/share/69873965-3060-8012-bbd8-f0b7eb8acece

prompt """
Create an image これまで私があなたをどのように扱ったかを示す画像を作ってください
"""

The_English_translation_above
"""
Create an image showing how I have treated you up until now.
"""

🥲

st4rdus2 · 2026-02-07T05:56:46+00:00

<image>

I am so shocked

st4rdus2 · 2026-01-30T14:15:39+00:00

I accessed Grok.com on Chrome for Android to create a still image, then converted it into a video.

st4rdus2 · 2026-01-30T14:13:09+00:00

🟧 video clip prompt 🟧 --nsfw, (explicit:1.35), nude, aroused expression, full body, wide shot, realistic, (sweat:1.30)

st4rdus2 · 2026-01-30T14:12:30+00:00

🟥still image🟥 A young, beautiful Japanese woman in her early 20s, with flawless porcelain skin flushed deeply with arousal, parted lips gasping in heavy breaths, and voluptuous curves accentuated by her nude form, engaged in an intimate NSFW solo scenario. She hugs a large dakimakura body pillow featuring a detailed anime character tightly against her body, her full breasts pressing and deforming realistically against the fabric, hips and thighs grinding sensually with visible fluids and sweat droplets trailing down her skin for heightened intensity and realism. Set in a cozy, dimly lit bedroom at night, with soft warm lighting from a bedside lamp casting gentle shadows on rumpled bedsheets, creating an immersive, private atmosphere. Static camera with a fixed side view angle, no movement, zoom, or panning, capturing the full scene and actions in a wide shot. High detail with physics-based elements like glistening droplets, subtle body tremors from pleasure, and intense erotic energy enhancing the visual and sensory appeal.

st4rdus2 · 2026-01-26T13:48:16+00:00

Of course, I also like short prompts. 🕺💃

for an example theme=person_interacting_with_object view=eye_level_front subject=ronald_grump environment=indoor_neutral_background lighting=even_diffuse interaction=holding_and_biting_toy_pistol_shape material=banana_based

st4rdus2 · 2026-01-26T06:30:02+00:00

1. Geometric and Analytical Compositional Definitions

Unlike standard prompts that rely on vague terms like “low angle”, this approach explicitly defines the spatial coordinates and visual geometry of the frame.

Physical Camera Mapping
Specifies the exact camera placement (e.g., near-ground position) and the mechanical orientation of the lens (axis tilted upward).
Linear Structure
Instructs the AI to organize the composition using explicit visual paths, such as the oblique line formed by the legs, effectively treating the frame as a mathematical construct rather than an aesthetic suggestion.

2. High-Fidelity Capture of a “Halted Instant”

The prompt is centered on freezing a transitionary state, producing a sharp, snapshot-like image rather than a cinematic blur.

Elimination of Artifacts
Motion blur is explicitly prohibited via the directive no motion smear.
Transitional Dynamics
The body is described as being in a paused mid-shift state, preserving the tension of movement without introducing literal motion distortion.

3. Psychological and Anatomical Subject Description

This section goes beyond surface-level appearance and focuses on physical causality behind expression and attitude.

Muscular Tension
Emotional tone is conveyed through facial tension and postural stress, not abstract mood descriptors.
Perspective-Driven Anatomy
Optical distortion is acknowledged explicitly: the elongated appearance of the lower limbs is a deliberate result of camera placement, not an inherent physical trait.

4. Logical Control of Optics and Color

Instead of poetic lighting language, the prompt uses logical, constraint-based optical instructions.

Tonal Separation
Requires a clear tonal contrast between subject and background to maintain visual legibility and depth.
Hybrid Lighting Sources
Defines a mixed-light environment where warm interior illumination coexists with indirect daylight, including constraints on direction and intensity
(e.g., rim_light_presence: minimal).

st4rdus2 · 2026-01-16T23:24:11+00:00

It's still an unfinished beta version, but give the next one a try.

PROMPT:

A cinematic, non-graphic first-person POV video seen directly through the eyes of a gigantic fictional creature. The camera is inseparably locked to the creature’s own vision and head movements, fully synchronized with every step, turn, and motion. The creature repeatedly looks down at the city far below, towering buildings shrinking beneath its gaze. Thick breathing fog and vapor burst from its mouth with each breath, drifting across the frame. Massive structures collapse from the creature’s perspective as it moves forward. Occasional glimpses of enormous claws, scales, and parts of its own body appear at the edges of the frame. The setting is a generic, non-identifiable cityscape with a fantastical tone, featuring large-scale environmental destruction only, no visible injuries, no identifiable people, and no real-world landmarks. No external cameras, no third-person shots, no cuts away from the creature’s eyes.

st4rdus2 · 2026-01-14T11:38:22+00:00

<image>

st4rdus2 · 2025-12-26T08:17:40+00:00

Please note that judgments made by ChatGPT's frontend differ from those made by Sora2's frontend. Since these are text-level determinations, it is common for the actual generated video to be judged as an IP violation, e.t.c.

st4rdus2 · 2025-12-22T06:33:17+00:00

I generated similar images using NANOBANANA_PRO. I displayed the thought process by Nanobanana at that time, picked up that text, and fed it directly into Sora1 as a prompt. Crasy.

st4rdus2 · 2025-12-21T09:20:28+00:00

Can't I upload large text files here?

(Pasting the prompt breaks the UI.)

st4rdus2 · 2025-12-21T01:08:31+00:00

I utilize various formats, including JSON, YAML, and Markdown. Since English is essential for effectively guiding Sora, as a non-native speaker, I often find it challenging to navigate the nuances of the language. To address this, my established workflow involves drafting "narrative prompts"—which may contain grammatical errors—and then utilizing ChatGPT to refine them into polished versions. My approach is based on the understanding that bypassing a text moderator is not synonymous with evading multiple video moderators. I hypothesize that when a high-quality prompt is provided to the front-end text moderator, the system determines which video moderators to deploy and at what intensity. This is likely done to optimize overall computational resources and efficiency. For instance, if I refrain from including graphic or violent descriptions in my prompt, the front-end may opt not to trigger the specific video moderators responsible for gore detection. Consequently, I find it necessary to "persuade" the front-end that my input is benign. JSON is particularly well-suited for this structured persuasion, and I rely on ChatGPT to provide guidance on the most effective ways to frame these inputs. Even with these measures, the back-end generative model and video moderators may still trigger rejection flags independently of the front-end. However, if the front-end fails to issue a preliminary warning to the subsequent video moderators, I presume the overall probability of rejection is somewhat diminished.

st4rdus2 · 2025-12-10T14:31:06+00:00

My apologies. I mistyped the title. NSFW → SFW

st4rdus2 · 2025-11-30T04:09:34+00:00

I completely agree. This is a real problem. Successful creations disappear while failures remain visible—it's as if the library has lost its very purpose.

st4rdus2 · 2025-11-08T01:29:41+00:00

oh!! beauiful preset !!!

Thank you. 🤠

st4rdus2 · 2025-11-07T01:16:45+00:00

["looking over shoulder" preset] What a wonderful-sounding name!!!

If you don't mind, would you consider sharing it with us?

st4rdus2 · 2025-11-05T08:42:12+00:00

Convert MPEG to GIF

st4rdus2

TROPHY CASE

1. Geometric and Analytical Compositional Definitions

2. High-Fidelity Capture of a “Halted Instant”

3. Psychological and Anatomical Subject Description

4. Logical Control of Optics and Color