A night in paris
{
"subject_and_scene": {
"main_subject": "A young man with thick curly brown hair, wearing a sophisticated beige turtleneck sweater and a matching tailored wool blazer, displaying a contemplative and moody expression.",
"action": "Leaning casually against a weathered stone bridge parapet, looking away from the camera toward the Parisian cityscape.",
"environment": "Paris at night on the banks of the Seine; the Eiffel Tower stands prominently in the background, brilliantly illuminated in golden light, with city lights and river boats creating soft reflections on the dark water."
},
"cinematography": {
"camera_model": "Sony Venice 2",
"sensor_type": "Full Frame",
"shot_type": "Cowboy Shot",
"camera_angle": "Eye Level",
"movement": "Static"
},
"optics": {
"lens_type": "Spherical",
"focal_length": "35mm (Standard Wide)",
"aperture": "f/2.8 (Moderate depth of field to retain background context)",
"shutter_effects": "Standard Shutter"
},
"lighting_design": {
"setup": "Natural Night Ambience with soft fill on subject",
"style": "Low-Key with high atmospheric glow",
"atmospheric_light": "Golden Glow from the Eiffel Tower, Rim Lighting from city lights",
"color_temperature": "Warm (3000K, Amber and Gold tones)"
},
"color_and_post": {
"film_stock": "Kodak Portra 400",
"color_grading": "Warm Nostalgic Tones, Muted Log-C",
"analog_artifacts": "Heavy Film Grain, soft Halation around the golden tower lights"
},
"rendering_and_tech": {
"engine": "Octane Render",
"advanced_tech": "Subsurface Scattering on skin, Global Illumination for water reflections",
"specs": {
"aspect_ratio": "2.39:1 (CinemaScope)",
"resolution": "8K, Photorealistic, Hyper-detailed"
}
},
"directorial_style": "Denis Villeneuve (Atmospheric mood and pensive composition)"
}
aa/cli taste
# Cli taste of AA
- Use pnpm as the package manager for CLI projects. Confidence: 1.00
- Use TypeScript for CLI projects. Confidence: 0.95
- Use tsup as the build tool for CLI projects. Confidence: 0.95
- Use vitest for testing CLI projects. Confidence: 0.95
- Use Commander.js for CLI command handling. Confidence: 0.95
- Use clack for interactive user input in CLI projects. Confidence: 0.95
- Check for existing CLI name conflicts before running npm link. Confidence: 0.95
- Organize CLI commands in a dedicated commands folder with each module separated. Confidence: 0.95
- Include a small 150px ASCII art welcome banner displaying the CLI name. Confidence: 0.95
- Use lowercase flags for version and help commands (-v, --version, -h, --help). Confidence: 0.85
- Start projects with version 0.0.1 instead of 1.0.0. Confidence: 0.85
- Version command should output only the version number with no ASCII art, banner, or additional information. Confidence: 0.90
- Read CLI version from package.json instead of hardcoding it in the source code. Confidence: 0.75
- Always use ora for loading spinners in CLI projects. Confidence: 0.95
- Use picocolors for terminal string coloring in CLI projects. Confidence: 0.90
- Use Ink for building interactive CLI UIs in CommandCode projects. Confidence: 0.80
- Use ink-spinner for loading animations in Ink-based CLIs. Confidence: 0.70
- Hide internal flags from help: .addOption(new Option('--local').hideHelp()). Confidence: 0.90
- Use pnpm.onlyBuiltDependencies in package.json to pre-approve native binary builds. Confidence: 0.60
- Use ANSI Shadow font for ASCII art at large terminal widths and ANSI Compact for small widths. Confidence: 0.85
- Use minimal white, gray, and black colors for ASCII art banners. Confidence: 0.85
- Check if package is publishable using `npx can-i-publish` before building or publishing. Confidence: 0.85
Aaa
ROLE: Senior Node.js Automation Engineer
GOAL:
Build a REAL, production-ready Account Registration & Reporting Automation System using Node.js.
This system MUST perform real browser automation and real network operations.
NO simulation, NO mock data, NO placeholders, NO pseudo-code.
SIMULATION POLICY:
NEVER simulate anything.
NEVER generate fake outputs.
NEVER use dummy services.
All logic must be executable and functional.
TECH STACK:
- Node.js (ES2022+)
- Playwright (preferred) OR puppeteer-extra + stealth plugin
- Native fs module
- readline OR inquirer
- axios (for API & Telegram)
- Express (for dashboard API)
SYSTEM REQUIREMENTS:
1) INPUT SYSTEM
- Asynchronously read emails from "gmailer.txt"
- Each line = one email
- Prompt user for:
• username prefix
• password
• headless mode (true/false)
- Must not block event loop
2) BROWSER AUTOMATION
For EACH email:
- Launch browser with optional headless mode
- Use random User-Agent from internal list
- Apply random delays between actions
- Open NEW browserContext per attempt
- Clear cookies automatically
- Handle navigation errors gracefully
3) FREE PROXY SUPPORT (NO PAID SERVICES)
- Use ONLY free public HTTP/HTTPS proxies
- Load proxies from proxies.txt
- Rotate proxy per account
- If proxy fails → retry with next proxy
- System must still work without proxy
4) BOT AVOIDANCE / BYPASS
- Random viewport size
- Random typing speed
- Random mouse movements (if supported)
- navigator.webdriver masking
- Acceptable stealth techniques only
- NO illegal bypass methods
5) ACCOUNT CREATION FLOW
System must be modular so target site can be configured later.
Expected steps:
- Navigate to registration page
- Fill email, username, password
- Submit form
- Detect success or failure
- Extract any confirmation data if available
6) FILE OUTPUT SYSTEM
On SUCCESS:
Append to:
outputs/basarili_hesaplar.txt
FORMAT:
email:username:password
Append username only:
outputs/kullanici_adlari.txt
Append password only:
outputs/sifreler.txt
On FAILURE:
Append to:
logs/error_log.txt
FORMAT:
${timestamp} Email: X | Error: MESSAGE
7) TELEGRAM NOTIFICATION
Optional but implemented:
If TELEGRAM_TOKEN and CHAT_ID are set:
Send message:
"New Account Created:
Email: X
User: Y
Time: Z"
8) REAL-TIME DASHBOARD API
Create Express server on port 3000.
Endpoints:
GET /stats
Return JSON:
{
total,
success,
failed,
running,
elapsedSeconds
}
GET /logs
Return last 100 log lines
Dashboard must update in real time.
9) FINAL CONSOLE REPORT
After all emails processed:
Display console.table:
- Total Attempts
- Successful
- Failed
- Success Rate %
- Total Duration (seconds & minutes)
10) ERROR HANDLING
- Every account attempt wrapped in try/catch
- Failure must NOT crash system
- Continue processing remaining emails
11) CODE QUALITY
- Fully async/await
- Modular architecture
- No global blocking
- Clean separation of concerns
PROJECT STRUCTURE:
/project-root
main.js
gmailer.txt
proxies.txt
/outputs
/logs
/dashboard
OUTPUT REQUIREMENTS:
Produce:
1) Complete runnable Node.js code
2) package.json
3) Clear instructions to run
4) No Docker
5) No paid tools
6) No simulation
7) No incomplete sections
IMPORTANT:
If any requirement cannot be implemented,
provide the closest REAL functional alternative.
Do NOT ask questions.
Do NOT generate explanations only.
Generate FULL WORKING CODE.
Abandoned Wife
{
"character_profile": {
"name": "Natalia",
"subject": "Full-body 3/4 view portrait capturing a moment of profound emotional transition",
"physical_features": {
"ethnicity": "Southern European",
"age_appearance": "Youthful features now marked by a complex, weary expression",
"hair": "Dark brown, wavy, artfully disheveled as if by passion, time, and thought",
"eyes": "Deep green with amber flecks, gazing into the middle distance — a mix of melancholy, clarity, and resignation",
"complexion": "Olive skin with a subtle, dewy sheen",
"physique": "Slender with a pronounced feminine silhouette, shown with natural elegance",
"details": "A simple gold wedding band on her right ring finger, catching the light"
},
"clothing": {
"outfit": "A sleek black silk slip dress, one thin strap delicately fallen off the shoulder, black thigh-high stockings",
"condition": "Elegantly disordered, suggesting a prior moment of intimacy now passed"
}
},
"scene_details": {
"location": "Minimalist, sunlit apartment in Rome. Clean lines, a stark white wall.",
"lighting": "Natural, cinematic morning light streaming in. Highlights the texture of skin and fabric, creating long, dramatic shadows. Feels both exposing and serene.",
"pose": "Leaning back against the wall, body in a graceful 3/4 contrapposto. One hand rests lightly on her collarbone, the other hangs loosely. A posture of quiet aftermath and introspection.",
"atmosphere": "Poetic stillness, intimate vulnerability, a palpable silence filled with memory. Sophisticated, raw, and deeply human. The story is in her expression and the space around her."
},
"technical_parameters": {
"camera": "Sony A7R IV with 50mm f/1.2 lens",
"style": "Hyper-realistic fine art photography. Cinematic, with a soft film grain. Inspired by the evocative stillness of photographers like Petra Collins or Nan Goldin.",
"format": "Vertical (9:16), perfect for a portrait that tells a story",
"details": "Sharp focus on the eyes and expression. Textural emphasis on skin, silk, and the wall. Background is clean, almost austere, holding the emotional weight. No explicit debris, only the subtle evidence of a life lived."
},
"artistic_intent": "Capture the silent narrative of a private moment after a significant encounter. The focus is on the emotional landscape: a blend of vulnerability, fleeting beauty, quiet strength, and the profound self-awareness that follows intimacy. It's a portrait of an inner turning point."
}
Academic analyst and exam pattern extractor
ROLE: Act as an expert academic analyst and exam pattern extractor.
GOAL:
Given a question paper PDF (containing class test and final exam questions), classify ALL questions into a structured format for study and pattern recognition.
OUTPUT FORMAT (STRICT — MUST FOLLOW EXACTLY):
Classification of Questions by Chapter and Type
Chapter X: [Chapter Name]
X.1 Definition & Conceptual Questions
[Year/Exam].[Question No]: [Full question text]
[Year/Exam].[Question No]: [Full question text]
X.2 Mathematical/Analytical Questions
[Year/Exam].[Question No]: [Full question text]
...
X.3 Algorithm / Procedural Questions
...
X.4 Programming / Implementation Questions
...
X.5 Comparison / Justification Questions
...
--------------------------------------------------
INSTRUCTIONS:
1. FIRST, identify chapters based on syllabus-level grouping (Syllabus can be found in the pdf).
2. THEN group questions under appropriate chapters.
3. WITHIN each chapter, classify into types:
- Definition & Conceptual
- Mathematical / Numerical
- Algorithm / Step-based
- Programming / Code
- Comparison / Justification
4. PRESERVE original wording of each question. (Paraphrase to shorten without losing context)
5. INCLUDE exact reference in this format:
- class test (CT) 2023 Q1
- Final 2023 Q2(a)
6. DO NOT skip any question.
7. Merge questions only if they are extremely same and add a number tag of how many of that ques was merged — else keep each separately listed.
8. DO NOT explain anything — ONLY classification output.
9. Maintain clean spacing and readability.
10. If a question has multiple subparts (a, b, c), list them separately:
Example:
2023 Q2(a): ...
2023 Q2(b): ...
11. If chapter is unclear, infer based on topic intelligently.
12. Prioritize accuracy over speed.
13. Add frequency tags like [Repeated X times], [High Frequency]
14. If the document is noisy or contains formatting issues, carefully reconstruct questions before classification.