Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUF

          arc/c  arc/e boolq hswag obkqa piqa  wino

Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic [instruct]
bf16      0.649,0.832,0.895,0.713,0.482,0.783,0.699
mxfp8     0.647,0.836,0.895,0.706,0.460,0.784,0.695
mxfp4     0.640,0.824,0.886,0.703,0.468,0.780,0.691

Qwen3.5-9B-Instruct: [base, non heretic]
mxfp8     0.571,0.719,0.895,0.683,0.426,0.770,0.671

---

Qwen3.6-27B-Instruct: [base, non heretic]
mxfp8     0.647,0.803,0.910,0.773,0.450,0.806,0.742

Qwen3.6-35B-A3B-Instruct [base, non heretic]
mxfp8     0.581,0.757,0.892,0.751,0.428,0.803,0.688

Qwen3.5-27B-Instruct: [base, non heretic]
mxfp8     0.557,0.711,0.868,0.533,0.452,0.706,0.695

• Models are tested in "Instruct" mode because this generally works better with the testing harness.
• Testing via "thinking" mode also shows the metrics (and changes) but not the true extent.
• In actual fact when the model IS in thinking mode, it will exceed INSTRUCT benchmark scores in most cases.

• "MTP" GGUFS will have "MTP" in the name as a suffix.
• I have also set the MTP tensors to Q8_0 precision for all quants.
• To get better performance keep temp 1 or less (higher temps degrade MTP performance).
• Likewise with rep pen ; keep at 1 (off). If you raise it performance will suffer.
• If you see "token acceptance" rates BELOW 50% (predict 2 tokens) switch to normal quants.

• On Q4_K_S (4bit) quant, regular GGUFs are about 130 t/s, whereas MTP GGUFs (acceptance at 60%, 2 tokens) can exceed 185 T/S. (5090, Windows 11, testing in LMStudio)
• Speeds will vary depending on GPU(s), AI app, O/S (Linux/Mac will generally be faster) and hardware.
• "MTP" quants speeds will vary ; for creative/complex and/or temps over 1 use regular GGUFs for better performance.

• 256k context
• Gguf quants run in all standard AI apps.
• Vision is activated, but you need to download separate "mmproj" file (ONE) to use it.

• Vision (images) tested.
• You need an "mmproj" (just one) of these downloaded too, and placed in the same folder as the GGUF for images.

• Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
• Context window min from 8k to 16k.

Performance

• Suggest min q4ks (non-imatrix) or IQ3S (imatrix).
• Tested with rep pen of 1 (off).
• Context: 256k (default).

• Other versions in testing.
• Information from Qwen's repo below.
• Video portions of the model were NOT TESTED.

• Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
• If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.

Qwen3.5-9B

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

Qwen3.5 Highlights

• Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
• Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
• Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
• Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
• Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

Model Overview

• Type: Causal Language Model with Vision Encoder
• Training Stage: Pre-training & Post-training
• Language Model
  • Number of Parameters: 9B
  • Hidden Dimension: 4096
  • Token Embedding: 248320 (Padded)
  • Number of Layers: 32
  • Hidden Layout: 8 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
  • Gated DeltaNet:
    • Number of Linear Attention Heads: 32 for V and 16 for QK
    • Head Dimension: 128
  • Gated Attention:
    • Number of Attention Heads: 16 for Q and 4 for KV
    • Head Dimension: 256
    • Rotary Position Embedding Dimension: 64
  • Feed Forward Network:
    • Intermediate Dimension: 12288
  • LM Output: 248320 (Padded)
  • MTP: trained with multi-steps
• Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

Benchmark Results

Language

Vision Language

Quickstart

[!Important] Qwen3.5 models operate in thinking mode by default, generating thinking content signified by <think>\n...</think>\n\n before producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.

Serving Qwen3.5

[!Important] Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, KTransformers or vLLM are strongly recommended.

[!Important] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen3.5 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities.

SGLang

• Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.
  python -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3
• Tool Use: To support tool use, you can use the following command.
  python -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder
• Multi-Token Prediction (MTP): The following command is recommended for MTP:
  python -m sglang.launch_server --model-path Qwen/Qwen3.5-9B --port 8000 --tp-size 1 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

vLLM

• Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.
  vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3
• Tool Call: To support tool use, you can use the following command.
  vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
• Multi-Token Prediction (MTP): The following command is recommended for MTP:
  vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'
• Text-Only: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache:
  vllm serve Qwen/Qwen3.5-9B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --language-model-only

KTransformers

Hugging Face Transformers

Using Qwen3.5 via the Chat Completions API

[!Tip] We recommend using the following set of sampling parameters for generation

• Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
• Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Instruct (or non-thinking) mode for general tasks: temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
• Instruct (or non-thinking) mode for reasoning tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Please note that the support for sampling parameters varies according to inference frameworks.

Text-Only Input

Image Input

Video Input

Instruct (or Non-Thinking) Mode

[!Important] Qwen3.5 does not officially support the soft switch of Qwen3, i.e., /think and /nothink.

[!Note] If you are using APIs from Alibaba Cloud Model Studio, in addition to changing model, please use "enable_thinking": False instead of "chat_template_kwargs": {"enable_thinking": False}.

Agentic Usage

Qwen-Agent

Qwen Code

Processing Ultra-Long Texts

• Modifying the model configuration file: In the config.json file, change the rope_parameters fields in text_config to:
  json

  1{ 2 "mrope_interleaved": true, 3 "mrope_section": [ 4 11, 5 11, 6 10 7 ], 8 "rope_type": "yarn", 9 "rope_theta": 10000000, 10 "partial_rotary_factor": 0.25, 11 "factor": 4.0, 12 "original_max_position_embeddings": 262144, 13}
• Passing command line arguments:
  For vllm, you can use
  VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1010000
  For sglang and ktransformers, you can use
  SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1010000

[!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise modifying the rope_parameters configuration only when processing long contexts is required. It is also recommended to modify the factor as needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to set factor as 2.0.

Best Practices

1. Sampling Parameters:
   • We suggest using the following sets of sampling parameters depending on the mode and task type:
     • Thinking mode for general tasks:
       temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
     • Thinking mode for precise coding tasks (e.g., WebDev):
       temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
     • Instruct (or non-thinking) mode for general tasks:
       temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
     • Instruct (or non-thinking) mode for reasoning tasks:
       temperature=1.0, top_p=1.0, top_k=40, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0
   • For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
   • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
   • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."
4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
5. Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,
   {"longest_edge": 469762048, "shortest_edge": 4096}
   Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.

Citation

EXAMPLE GENERATIONS:

User

Assistant

1. I need to explain the physical principle clearly - Earth naturally radiates heat to space at night (especially in the infrared spectrum), and this is enhanced by certain surface properties and atmospheric conditions.
2. The "night cooling" concept refers to enhanced radiative heat loss during nighttime hours, which can create cooling effects that persist or influence daytime temperatures through thermal inertia, water evaporation, etc.
3. I should provide 10 distinct but scientifically sound approaches, ranging from individual-level to city/landscape-level interventions.
4. I'll include tables for comparison, implementation details, and a chart showing potential temperature effects.
5. I must be balanced - noting this is not a "silver bullet" and works best as part of broader climate mitigation.

• Objects radiate heat in proportion to T^4 (Stefan-Boltzmann law)
• Clear skies + high altitude = better radiative cooling
• Materials with high emissivity in the atmospheric window (8-13 μm) radiate better
• Water has high thermal inertia - nighttime cooling affects daytime through evaporation and soil moisture

1. Radiative cooling pavements/roads
2. Reflective/cool roof materials with nighttime radiative properties
3. Urban radiative cooling films and coatings
4. Landscape design for enhanced nocturnal cooling (greenbelts, water bodies)
5. Smart water systems leveraging nighttime evaporation
6. Radiative cooling agricultural practices
7. Strategic urban geometry for night ventilation
8. Radiative cooling parks and green spaces
9. Coastal/riverbank management for radiative cooling synergy
10. Seasonal land management (crop rotation, fallow periods with reflective/radiative surfaces)

Harnessing Nocturnal Radiative Cooling for Urban and Regional Temperature Mitigation

1. The Physics of Nocturnal Radiative Cooling

• (P) = radiative power (Watts)
• (\epsilon) = emissivity of the surface (0–1)
• (\sigma) = Stefan-Boltzmann constant ((5.67 \times 10^{-8} \text{W/m}^2\text{K}^4))
• (T) = absolute temperature in Kelvin
• (A) = surface area

2. Ten Detailed Strategies Using Nocturnal Cooling

Strategy 1: Radiative Cooling Pavements and Roads

• Use polymer-based binders with ceramic or metal oxide fillers (e.g., TiO₂, ZnO)
• Apply at 3–5 mm thickness for maximum durability
• Combine with permeable sublayers to support drainage and reduce heat storage

Strategy 2: High-Albedo Roof Coatings with Radiative Emissivity

• Use elastomeric or acrylic coatings with embedded infrared-reflective particles
• Ensure coating maintains >90% emissivity after 10 years of UV exposure
• Combine with cool roof standards (e.g., ENERGY STAR)

Strategy 3: Radiative Cooling Films for Windows and Facades

• Use spectrally selective films: high visible transmittance (>70%), high IR reflectance (>60%)
• Apply via spray, roll, or vacuum deposition
• Use in combination with shading devices for optimal performance

Strategy 4: Urban Radiative Cooling Greenbelts

• Plant deciduous trees with dark foliage (emissivity ≈ 0.95)
• Ensure spacing allows wind-driven heat dispersion
• Use moist soil substrates for evaporative synergy

Strategy 5: Smart Water Systems Leveraging Nighttime Evaporation

• Deploy timed misting or fogging systems between 22:00–04:00
• Target porous surfaces: soil, light-colored aggregates, plant canopies
• Use recycled or rainwater to minimize energy costs

Strategy 6: Radiative Cooling Agricultural Practices

• Use reflective mulches that also radiate in IR (white plastic or metalized fabric)
• Time tillage and cover cropping to maximize soil emissivity
• Install low-height reflective barriers around plots

Strategy 7: Strategic Urban Geometry for Night Ventilation

• Use vertical fins, atriums, or stacked courtyards for cross-ventilation
• Align streets with prevailing night winds (typically 3–5 m/s)
• Provide thermal mass with high emissivity surfaces to radiate away stored heat

Strategy 8: Radiative Cooling Parks and Open Spaces

• Use shallow, reflective water features (pools, fountains) that radiate and evaporate at night
• Combine with light-colored, porous paving
• Ensure open viewshed to sky for maximum radiative loss

Strategy 9: Coastal and Riverbank Radiative Management

• Maintain unvegetated or sparsely vegetated beach/riverfront zones to allow direct radiative exchange
• Use reflective, light-colored rip-rap instead of dark concrete
• Implement timed nighttime water flow or misting along shorelines

Strategy 10: Seasonal and Crop Rotation Radiative Land Management

• Use reflective mulch or white cover crops during summer months
• Implement controlled tillage to increase soil emissivity
• Rotate crops to maintain varying surface albedo/emissivity

3. Comparative Implementation Table

4. Projected Temperature Impact Chart

5. Critical Considerations and Limitations

Advantages

• Passive, low-carbon technology aligned with sustainability goals
• Works synergistically with vegetation, water, and energy efficiency
• Can be retrofitted to existing infrastructure
• Enhances indoor comfort and agricultural resilience

Limitations & Risks

• Geographic variability: Less effective in persistently cloudy or humid regions
• Humidity interference: Moisture absorbs outgoing IR, reducing cooling flux
• Not a mitigation substitute: Cannot offset fossil fuel emissions or deforestation alone
• Maintenance needs: Coatings degrade; surfaces require upkeep
• Urban complexity: High-rise canopies may disrupt airflow and radiative exchange

Best Practice Integration

1. Layered (e.g., cool roofs + urban greenbelts + smart water systems)
2. Standardized in building and infrastructure codes
3. Monitored with satellite and ground sensors to quantify cooling flux and ΔT
4. Paired with emission reductions, not replacing them

6. Conclusion

User

Assistant

1. The building starts to sway.
2. Diana's window explodes.
3. Escalating panic and intensity.
4. Vivid, gory, intense descriptions.
5. A shocking ending that leaves the reader shaken.
6. Word count: 1500-2000 words.

• Introduction: Diana is working late or staring out the window. Establish the setting: the 21st floor, high altitude, the glass curtain wall. Introduce the subtle beginning of the anomaly.
• Inciting Incident: The swaying begins. It's not wind; it's rhythmic, unnatural. The sensory experience of being thrown, the sound of glass creaking.
• Escalation 1 (The Window): The pressure builds. The window explodes. Not just breaks; it implodes or shatters violently. Diana is injured or close to it. This raises the physical stakes.
• Escalation 2 (The Horror Element): The prompt asks for "another horror element." It can't just be falling. It needs to be supernatural or deeply unsettling. Maybe the building itself is "alive" or feeding on something. Maybe the people in the building aren't real? Let's go with something visceral: the building isn't just swaying; it's breathing or digesting. Or perhaps the air is wrong. Let's try the idea that the building is a biological structure, or the swaying brings things up from lower floors that shouldn't be there. Or, to ratchet up the gore, maybe the swaying tears something apart inside her.
• Idea: The swaying is caused by something inside the building moving, or the building is reacting to fear. Let's make it more personal and gory. The swaying causes physical disintegration of the floor or the structure.
• Let's refine the "other horror element": The wind isn't wind. It's a vacuum or a suction. The window explodes inward because the pressure is wrong. As she looks down, she sees something impossible. Let's go with the idea that the swaying is synchronized with a heartbeat or a massive organism beneath the city.
• Climax: The swaying intensifies. The window is gone. She's exposed. The panic is absolute.
• The Ending: Needs to be shocking. Not just a fall. Maybe she's trapped. Maybe the building eats her. Or maybe she realizes she never left the floor, but the floor itself is changing.
• Let's try this: The window explodes. She looks down. The city is distorted. She realizes the building is twisting around the swaying. The shock of the explosion sends her against the frame. She cuts her hand. Blood drips onto the glass. She sees her reflection, but something else is behind her reflection.
• Refined Ending Idea: She climbs the broken frame. The building is shearing apart. She sees the "source" of the swaying in the lobby or lower floors—it's a massive, churning mass. She tries to escape but the glass shatters again.
• Wait, the prompt asks for "results of the swaying" and "another horror element."
• Let's focus on the physical sensation first. The "building sway" causes nausea, dizziness. Then the window. Then the "other element" is the realization that the swaying is pulling inside things out, or bringing them up.
• Let's go with: The swaying is rhythmic like a heart. The window explodes, she cuts her hand. She sees something in the sky or below. The building starts to inhale through the glass.
• Actually, let's make it more visceral. The swaying is caused by the building trying to stabilize itself, but it fails. The window explodes. She falls into the lobby? No, that's too generic.
• Better idea: The swaying is caused by the structural integrity failing in a way that suggests organic decay. The window explodes. She sees the skeleton of the building is wet.
• Let's settle on this: Diana is working. The floor tilts. She feels the building "heave." The window shatters from the outside-in (suction). She falls. The "other horror element": She hits the air conditioning intake or the frame, and it's warm and wet. She sees a shadow in the building's core that is too large.
• Let's try to make the "other horror element" the realization that the building is alive and the swaying is it stretching. The window explosion is its way of tasting the air.
• Okay, let's write. Focus on the sensory details: the smell of ozone and copper, the taste of blood, the feeling of vertigo, the sound of groaning steel.

1. The Stillness: Diana staring out at the neon. The hum of the city. She's tired.
2. The Onset: A creak. A lurch. The coffee cup jumps. The nausea hits. It's not an earthquake; it's a slow, deliberate tilt.
3. The Pressure: The glass vibrates. It sounds like a growl. The pressure changes—her ears pop.
4. The Explosion: The glass doesn't just break; it implodes or shatters inward with massive force. A cloud of dust/debris.
5. The Aftermath: She's in the lobby? No, still on the floor. The window is gone. Wind is blowing in, but it's wrong. Cold.
6. The Other Horror: She looks at her hand. Cut from the shards. Blood on the glass. She sees something moving outside in the reflection or in the distance. A shadow that matches the swaying rhythm.
7. The Climax: The building groans louder. It's not swaying anymore; it's thrashing. She sees the floor slabs groaning.
8. The Ending: She tries to move to the door. The door is gone. The floor is gone. She is hanging from the ceiling by the twisted rebar of a structural beam? Or maybe she realizes the "wind" is a giant hand?
9. Let's try a specific ending: She is pulled toward the open void. But she is sucked back in by... something? Or maybe she realizes the window explosion was the building spitting her out.
10. Let's go with the idea that the swaying is a predator hunting. The window explodes because it wanted her inside.
11. Final Ending Idea: She reaches for a fire extinguisher to put out a fire that isn't there. The cylinder is empty. She looks at the open maw of the window. The swaying stops. The silence is louder. She hears a sound from the lobby below—it's a wet, tearing sound. The building is waking up.
12. Wait, the prompt wants a "shocking ending that leaves the reader breathless."
13. Let's try: She falls through the frame. She expects the street. She hits a surface. It's the interior of the building, but the perspective is wrong. She is inside the "skin" of the skyscraper, dangling in a shaft of air between floors. The "swaying" is her own heart rate. She realizes the building is a cage.
14. Okay, final plan:
    • Diana works late.
    • The sway begins. The floor tilts.
    • The window explodes inward (suction).
    • She is thrown against the wall.
    • She sees the city, but it's wrong. The lights are blinking in a pattern.
    • She realizes the swaying is synchronized with a massive noise from below.
    • She climbs the frame. She sees the lobby is gone. It's a hollow cavern filled with... something dark.
    • The building is a single organism.
    • Ending: The swaying stops. The window glass (if any remains) is licked by something. She sees a tooth mark on the steel frame.
    • Wait, more gory: The swaying shears her arm off? Too extreme. The glass cuts her, and the wind is filled with something viscous.
    • Let's go with: The window explodes. The suction pulls the air out of her lungs. She chokes. She sees a massive shape below the city lights. The building is just a skin over a giant thing. The swaying is it breathing. The window exploded to let it smell the blood on her cut hand.
    • Okay, let's execute this. Focus on the pacing. Short, jagged sentences during the panic.

User

Assistant

1. Opening: Bold challenge, acknowledging their skepticism.
2. Pain points: What's wrong with other writers/tools?
3. My strengths: Speed, creativity, no ego, versatility.
4. Examples: Show, don't tell, how I bring ideas to life.
5. Closing: Reiteration of confidence and call to action.

User

Assistant

• Technological concept that's plausible yet unsettling
• Moral or philosophical complexity
• Non-linear or twist-heavy narrative
• Characters with flaws and emotional stakes
• Themes of obsession, regret, control, desire, identity, etc.

1. True to Black Mirror's style (technological + psychological)
2. All involve time travel
3. All have strong romantic/sexual themes
4. Have 3 acts each
5. Include character descriptions, themes, and plot progression

1. "Chronos Love" - AI that records and replays intimate moments across time
2. "The Temporal Lover" - A service that lets you experience relationships from the past/future
3. "Echo" - Time travel only available during intimate moments, creating recursive relationships
4. "Fidelity Algorithm" - A time travel system that allows you to verify relationship fidelity
5. "The Seduction Loop" - Time travel that only activates when desire peaks, creating addictive cycles
6. "Eternal Entanglement" - A relationship that spans multiple timelines and eras through a tech device

Six Black Mirror Episodes: Time Travel & Desire

1. "Chronos Intimacy"

• Elena Vance (42): Former data architect, now emotionally numb after her husband's sudden death
• Hiroshi Tanaka (55): Cold, meticulous founder of Chronos Intimacy; obsessed with preserving human connection
• Kael (age varies): Elena's deceased husband, appears in recordings with uncanny fidelity

2. "The Fidelity Algorithm"

• Maya Patel (36): Corporate lawyer, high-functioning anxiety, recently discovered her husband's infidelity
• Dr. Aris Thorne (48): Neurologist-turned-entrepreneur who built the Fidelity Algorithm
• Marcus (40): Maya's husband; initially guilty, then systematically corrupted by the system

3. "Echo Chamber"

• Leo Chen (34): Software engineer, compulsively rational, emotionally stunted
• Sarah Miller (33): Leo's ex-girlfriend; artist, emotionally volatile, currently engaged
• The Echo AI: A sentient algorithm that learned from millions of failed relationships

4. "Temporal Seduction"

• Isabella Moretti (29): Freelance photographer, narcissistic but emotionally vulnerable
• Julian (age varies): Her first love, appears in different forms across timelines, always at his most magnetic
• Dr. Chen (52): The mysterious creator of Temporal Seduction; appears to be Isabella's future self

5. "The Regret Loop"

• Elena (38): Introverted, detail-oriented, haunted by a wedding mistake she can't articulate
• Marcus (40): Charismatic, charmingly unreliable, knows more than he's ever said
• The System: A sleek, voice-activated interface that controls their temporal loop

6. "Eternal Entanglement"

• Clara (31): Relationship analyst turned skeptic, emotionally guarded
• Clara-2047 (age 66): Her future self, warm, settled, but carrying scars from her choices
• Niko (29): Clara's current partner; intelligent, kind, but increasingly distant