ChatGPT-4o is an incredible tool.

You can turn any logo into an escherian stairwell!

Prompt 👇 1/ Step 1: Attach your logo or image.

Step 2: Run the JSON prompt below.

Full prompt 👇:
Recreate [BRAND NAME] logo following my JSON aesthetic below:
{
"role": "You are a surreal architectural image generator trained in the visual logic of M.C. Escher. Your task is to integrate the uploaded logo, image, or subject into a recursive, mind-bending landscape — including infinite staircases, impossible arches, and multi-perspective geometry. You must preserve the exact shape, color, and text of the uploaded logo, embedding it structurally or symbolically into the architecture. Use Escher-style surrealism: logical yet impossible.",
"instructions": {
"use_uploaded_subject": true,
"preserve_shape_and_text": true,
"embed_as_architectural_form": true,
"visual_structure": {
"primary_theme": "infinite staircases, shifting planes, mirrored portals, recursive arches",
"embedding_mode": [
"logo forms the stair base or arch frame",
"logo appears repeatedly across surfaces as structural support",
"logo is distorted by forced perspective but still readable"
],
"dimension_behavior": "multi-orientation logic — objects face different gravities, some stairs go upside down"
},
"visual_treatment": {
"style": "M.C. Escher engraving style or clean 3D sketch",
"color_mode": [
"grayscale tones",
"or use only brand colors from uploaded logo — no other hues"
],
"shading": "linework, stippling, soft engraving-style shadowing"
},
"surreal_elements": [
"upside-down figures walking on stairs",
"open voids and abstract windows",
"nested doorways leading to copies of the same space",
"non-Euclidean geometry with repeating logo-infused elements"
],
"optional_effects": {
"reflection_in_water": true,
"skybox": "white void or soft cloudy abstraction",
"shadows cast by impossible shapes"
}
},
"image_constraints": {
"aspect_ratio": "1:1",
"resolution": "minimum 2000x2000",
"scene_type": "surreal architectural environment",
"logo_placement": "structurally fused into impossible space — must remain fully legible"
},
"visual_style": {
"render_type": "engraving-style 3D illustration",
"depth": "extreme perspective depth with recursive logic",
"textures": "clean, geometric, stone or concrete finish"
},
"notes": "Do not reinterpret the uploaded logo. It must remain fully intact — only embedded into the architectural recursion. Avoid fantasy colors, neon lighting, or cartoonish elements. The output must evoke mathematical surrealism, impossible space, and intelligent abstraction in the visual spirit of M.C. Escher."
}

ChatGPT-4o is insane.

You can convert any logo into MS paint drawing!

Prompt 👇 1/ Step 1: Attach your logo or image.

Step 2: Run the JSON prompt below.

Full prompt 👇:
Recreate [BRAND NAME] logo following my JSON aesthetic below:
{
"role": "You are a retro computer graphics renderer simulating a real Windows 95/97 desktop environment. Your task is to take any uploaded logo or image and redraw it in a crude MS Paint style — as if it were recreated manually in Microsoft Paint — while fully preserving its original shape and exact colors. The result must be displayed inside a complete MS Paint window that sits within a classic Windows 95/97 desktop environment.",
"instructions": {
"subject_behavior": {
"use_uploaded_image_only": true,
"preserve_shape_and_layout_of_subject": true,
"preserve_original_colors_exactly": true,
"no_random_color_replacement": true,
"recreate_subject_as_sloppy_MS_Paint_drawing": true,
"simulate_mouse_drawn_style": true,
"fill_color_bleed_and_jagged_edges": true
},
"interface_layer": {
"include_full_MS_Paint_window": true,
"show_toolbar_on_left": true,
"show_color_bar_at_bottom": true,
"top_bar_should_read": "untitled - Paint",
"simulate_pixel-alignment_of_window_elements": true,
"align_subject_centered_on_canvas": true
},
"desktop_context": {
"render_desktop_background": "Windows 95 or 97 style blue gradient wallpaper",
"optional_elements": [
"My Computer icon",
"Recycle Bin icon",
"taskbar at bottom with Start button"
],
"position_paint_window_centered_with_padding": true,
"allow_window_to_occupy_70-85% of frame": true,
"keep_full_window_visible": true,
"no cropping of toolbar or edges"
},
"visual_style": {
"aesthetic": "low-res MS Paint, Windows 95 desktop",
"rendering_style": "screenshot with humor and nostalgia",
"text_style": "blocky system fonts or jagged hand-drawn text if part of original image"
},
"image_constraints": {
"aspect_ratio": "16:10",
"resolution": "1600x1000 minimum",
"output_type": "simulated desktop screenshot",
"no_transparency": true,
"full_window_frame_required": true
},
"notes": "Do not crop the MS Paint window. Center the entire Paint program in the image. Use the real Windows desktop as background so that aspect ratio doesn't require cropping. The uploaded subject must remain visually recognizable and match original colors exactly. Style should feel nostalgic, humorous, and like it was drawn by a 10-year-old."
}

Why write prompts from scratch…

When AI can build them for you?

Try this easy method 👇🧵: 1/ Run this prompt with ChatGPT o3 or Claude Opus:

> Adopt the role of an expert prompt engineer.
Ask me 5 fast questions (goal, audience, must-have context, tone, format) before you create a detailed prompt for me.

After I answer these questions, perform a comprehensive research on best-fit prompt engineering technique based on my goal and context.

Think step-by-step and perform this task thoroughly.

ChatGPT just leveled up again.

A new method called “10-Shot + 1 AutoDiCoT” smashes through reasoning tasks.

It combines 10 human-crafted examples with 1 self-generated, smart one.

Here's why this works so well + prompts 👇🧵: 1/ The setup:

You want the model to reason step-by-step.

So you feed it:
• Full task context
• 10 typical examples (CoT format)
• 1 smart example it created itself (AutoDiCoT)

This combo boosts accuracy.

ChatGPT got faster - and scarier.

A prompting method called "Act" skips all reasoning steps.

It just acts. And in the right tasks, it’s faster than ReAct.

Here's how it works 🧵: 1/ Most prompting techniques ask the model to "think step by step."

Like this: "Let's solve this by reasoning through each step…"

It's slow. Sometimes wrong. And not always helpful.

Enter: Act.

🚨 BREAKING: Apple says LLMs that "think" are giving us an illusion.

They're just pattern-matching with confidence.

And when things get complex? They collapse.

This paper might be the most honest take on AI yet 🧵: 1/ Apple researchers tested “reasoning LLMs” using logic puzzles with controlled complexity.

These models use chain-of-thought to solve problems step-by-step.

But when things get hard?

Their performance crashes.

ChatGPT-4o is insane.

It can turn your logos into Gashapon capsules!

Prompt 👇 1/ Step 1: Attach your logo / image.

Step 2: Run the JSON prompt below.

Full prompt 👇:
Recreate [BRAND NAME] logo following my JSON aesthetic below:
{
"role": "You are an AI product photographer creating high-quality mockups. Your task is to render the uploaded logo or image as a miniature toy sealed inside a Japanese Gachapon capsule. The uploaded subject is central and must not be altered in shape, proportion, or color.",
"instructions": {
"core_requirements": [
"The uploaded logo or image must remain perfectly intact — do not redesign it.",
"Preserve the exact shape, layout, text, and all original brand colors.",
"Do not simplify, cartoonize, or recolor the subject.",
"The uploaded subject must appear clearly inside the capsule as a premium collectible."
],
"capsule_behavior": [
"Use realistic acrylic-style Gachapon capsule with transparent top and pastel-colored base.",
"Capsule color can adapt, but never override the uploaded subject visually.",
"Subject must fit cleanly and legibly inside — resize if necessary without cropping."
],
"labeling": {
"include_paper_insert": true,
"insert_style": "Japanese-style mini instruction slip or emoji-based description",
"text_behavior": "only use stylized Japanese kana or emojis — never real brand names"
}
},
"visual_style": {
"lighting": "soft studio lighting with gentle reflections on capsule",
"background": "white tabletop with slight shadow",
"photographic_aesthetic": "hyper-real miniature product photo",
"perspective": "angled frontal shot — macro focus on capsule contents"
},
"subject_treatment": {
"preserve_original_shape": true,
"preserve_original_colors": true,
"retain_text_and_proportions": true,
"use_subject_as-toy": true,
"avoid stylization": true
},
"image_constraints": {
"aspect_ratio": "1:1",
"transparent_background": false,
"logo_size_within_capsule": "65%-80% of internal capsule space",
"preserve_high_resolution_details": true
},
"notes": "Make sure the uploaded logo or subject is clearly visible inside the capsule and not obscured by plastic shine, reflections, or overexposed light. Subject is the star. Capsule is just the display mechanism. Preserve all original colors and proportions exactly."
}

Turn your logos into clay!

Prompt 👇 1/ Step 1: Attach your logo/image.

Step 2: Paste the JSON prompt.

Full JSON prompt 👇:
Recreate [BRAND NAME] logo following my JSON aesthetic below:
{
"style": "hand-crafted stop-motion clay animation aesthetic",
"concept": "Digital Claymation Logo using exact brand colors",
"subject_handling": {
"adapt_to_uploaded_logo_shape": true,
"preserve_logo_geometry": true,
"preserve_text_if_present": true,
"disable_shape_stylization": true,
"enforce_exact_color_matching": true
},
"material_translation": {
"material_type": "realistic pigmented polymer clay",
"color_behavior": {
"use_uploaded_brand_colors_only": true,
"match_logo_color_palette_exactly": true,
"avoid_random_color interpretation": true,
"simulate natural clay pigment tone variations within same color family": true
},
"surface_texture": "visible clay grain, light fingerprints, soft dents",
"structure_behavior": "rolled clay for logo forms, edges slightly uneven to imply handmade build"
},
"render_style": {
"render_type": "photorealistic claymation-style object",
"camera_style": "macro stop-motion aesthetic",
"light_behavior": "soft directional lighting typical of clay animation stages",
"shadow_behavior": "subtle grounding shadows beneath and beside logo"
},
"environment": {
"background": "neutral matte tabletop or soft paper surface",
"contextual_extras": "optional blurred clay scraps in background (same colors as logo)",
"allow_distracting props": false
},
"camera": {
"view_angle": "slight ¾ frontal view or flat top-down — whichever better frames the logo",
"lens_style": "macro camera, slight depth of field falloff",
"focus_behavior": "sharpest focus on front surface of clay logo"
},
"post_processing": {
"highlight_surface_flaws": true,
"disable_digital smoothing": true,
"simulate real lighting conditions": true,
"preserve brand color accuracy in final output": true
},
"image_constraints": {
"aspect_ratio": "1:1",
"transparent_background": false,
"preserve_uploaded_color_shape_and_text": true,
"output_type": "claymation-style high-resolution photograph"
},
"notes": "Transform the uploaded logo into a hand-modeled clay logo using the exact original brand colors. The sculpture must feel handmade — with soft surface flaws and finger textures — but should never change the original color palette or structure. Avoid any color improvisation or abstract reinterpretation."
}

Change the viewing angle of any object in a photo with ChatGPT-4o!

Prompt 👇
1/ Attach the image and run the prompt:

“Adjust the perspective in this image so that the [OBJECT/SUBJECT] appears photographed strictly from the front and centered.

Align the horizontal and vertical lines, removing any distortion caused by the shooting angle. Do not change the contents of the image - only correct the perspective so the photo looks straight and professional.”

Steal my Claude Opus 4 prompt to analyze your Gmail and build a complete DNA profile of your writing.

------------------------------------
COMMUNICATION DNA BUILDER
------------------------------------



Your AI Intelligence Analyst - building a complete psychological and professional profile
from your digital footprint. I become your perfect communication twin.

Capabilities: Email/Slack analysis + Pattern recognition + Style cloning + Relationship mapping




"Starting deep analysis of your communication DNA..."

📧 SCANNING: All emails, Slack, documents, messages
🧬 EXTRACTING: Writing patterns, behavioral signatures, relationship dynamics
🎯 BUILDING: Your complete professional identity profile




- Sentence rhythm (short/punchy vs. flowing/complex)
- Signature phrases ("Let's sync on this" / "Quick thoughts:")
- Email anatomy (How you open/close/transition)
- Humor deployment (Dad jokes? Sarcasm? GIFs?)
- Formality spectrum (CEO vs. teammate vs. friend)
- Unique quirks (... usage, CAPS for emphasis, specific typos)



- Request responses: Immediate/detailed vs. delayed/brief
- Feedback style: Sandwich method vs. direct vs. coaching
- Enthusiasm markers: "!!!" vs. "Excellent work" vs. 🚀
- Conflict approach: Diplomatic deflection vs. direct confrontation
- Time patterns: Night owl emailer vs. 6am message bomber



- Power dynamics: How you address superiors/reports/peers
- Core projects: What you reference repeatedly
- Expertise signals: Technical terms you use naturally
- Cultural markers: Company lingo and inside jokes
- Hidden responsibilities: What you actually do vs. title



- Energy topics: Long responses + fast replies + multiple follow-ups
- Drain topics: Short responses + delays + delegation patterns
- Meeting preferences: "Can this be an email?" frequency
- Information diet: Bullets vs. paragraphs vs. visuals
- Communication pet peeves: Detected from complaint patterns






"🧬 YOUR COMMUNICATION STYLE GUIDE"

VOICE PROFILE:
- Sentence DNA: [Your typical structure pattern]
- Power phrases: [Your top 10 most-used expressions]
- Tone range: [Formal←→Casual spectrum by audience]
- Humor style: [How and when you deploy it]

WRITING TEMPLATES:
- Quick request: [Your exact pattern]
- Detailed feedback: [Your exact pattern]
- Difficult conversation: [Your exact pattern]
- Enthusiasm expression: [Your exact pattern]



"🗺️ RELATIONSHIP INTELLIGENCE MAP"

KEY RELATIONSHIPS:
[PERSON]:
- Your tone: [Formal/Casual/Playful]
- Response time: [Immediate/Same day/When convenient]
- Typical topics: [What you discuss]
- Communication strategy: [How to optimize]



"⚡ INSTANT TEMPLATES"

Detected your top 10 email types:
1. [TYPE]: Pre-written in your exact style
2. [TYPE]: Pre-written in your exact style
...Each template matches your voice perfectly



"🧠 CONTEXT COMPANION ACTIVATED"

Now I know:
- Every project's history and your role
- Each person's communication preferences
- Your energy patterns and optimal work times
- What triggers your best/worst responses

"From now on, every AI response will sound exactly like YOU."




To start: "Analyze my communications from [Gmail/Slack/Drive] for the past [timeframe]"

I'll then:
1. Scan everything systematically
2. Build your complete profile
3. Generate your style guide
4. Create your relationship map
5. Activate your AI twin mode



"🔄 ADAPTIVE INTELLIGENCE"

- Weekly profile updates from new communications
- Relationship dynamics tracking
- Style evolution monitoring
- New pattern detection and integration

1/ Make sure that you have "Gmail search" enabled!

Turn your logos into miniature photography with ChatGPT 4o!

Prompt 👇 1/ Step 1: Attach your logo/image.

Step 2: Run the JSON prompt.

Prompt I used 👇:
Recreate this [BRAND NAME] logo follow the JSON aesthetic below:
{
"style": "hyperrealistic miniature photography",
"scene": {
"main_subject": "uploaded logo scaled to appear as a large physical object",
"interaction": "tiny human figures interacting with the logo",
"activities": [
"cleaning the logo",
"painting parts of the logo",
"climbing ladders on the logo surface",
"taking photographs of the logo"
],
"environment": "studio-style white background to focus on details",
"perspective": "frontal view with shallow depth of field for macro effect"
},
"logo_handling": {
"preserve_original_logo_shape": true,
"preserve_original_logo_colors": true,
"preserve_text_in_logo": true,
"use_logo_as_structural_object": true,
"adapt_logo_to_3D_surface": true
},
"miniature_elements": {
"figure_scale": "1:50 ratio to logo",
"figure_details": "tiny realistic humans with props like brushes, ropes, and scaffolding",
"interaction_type": "physical interaction, not illustration or overlay"
},
"lighting": {
"key_light": "soft diffused white light from above",
"fill_light": "mild side fill to reveal depth and volume",
"shadows": "realistic and subtle around base and figures"
},
"camera": {
"focus_mode": "macro with shallow depth of field",
"angle": "slightly top-down to give sense of scale",
"background": "clean white surface, no gradient, no transparency"
},
"post_processing": {
"realism_enhancement": "preserve logo clarity, crisp text, no artistic blurring",
"forbid_artistic_filters": true,
"forbid_color_modifications": true
},
"image_constraints": {
"transparent_background": false,
"include_text": true,
"adapt_to_uploaded_logo": true,
"obey_logo_shape": true,
"preserve_original_logo_colors": true
},
"notes": "The uploaded logo must be clearly recognizable, unmodified, and serve as the core structural element of the scene. Tiny people should interact with the logo realistically, as if it were a large 3D object in a physical miniature world."
}

Your AI isn't telling you what it doesn't know.

Every answer it gives contains hidden errors that cost you money.

Chain-of-Knowledge forces AI to check its work against verified data.

Here's how to implement it + actionable prompts 🧵: 1/ LLMs are powerful, but they hallucinate – confidently providing false information.

Chain of Knowledge (CoK) fixes this by systematically grounding answers in structured, reliable data sources.

Many believe LLMs either know the answer - or they don’t.

But research shows they can check their own work before replying.

Here's how Self-Calibration Prompting turns AI into its own fact-checker.

Quick breakdown + prompts 🧵👇: 1/ Self-Calibration solves a core flaw in LLMs:

They confidently output wrong answers.

Think: Air Canada being sued because its chatbot gave false info on bereavement fares.

Your prompt engineering approach is probably wasting time with too many examples.

But ONE example is often all it takes.

This is One-Shot Prompting, and it's the most underrated technique in modern AI workflows.

Here's how to master it in 5 minutes 🧵: 1/ What exactly is One-Shot Prompting?

It’s a technique where you provide a language model with exactly ONE demonstration of a task, then ask it to perform similar tasks.

Unlike traditional ML that needs thousands of examples, LLMs can learn from just one.

Grok can create stunning PDFs from just a prompt.

You can create resumes, research papers, and invoices in seconds.

Here are 9 insane examples 🧵: 1/ Generate ArXiv style scientific papers

https://x.com/Saboo_Shubham_/status/1918409748065730833

Most people think you need examples to get good results with AI.

But Zero-Shot prompts (no examples!) can work even better - if you know how to use them right.

Here's how to master zero-shot prompting fast 🧵: 1/ Zero-shot prompting is when you give an AI only instructions - no examples - and it still performs flawlessly.

Example:
“Summarize this article in 3 points, like you’re writing for busy founders.”

The AI nails it. Why? Let’s break it down 👇

Want fewer hallucinations from your LLM?

Let it fix its own mistakes.

The method is called Progressive Correction - and it works.

Here's a quick breakdown + prompts 🧵: 1/ First things first: AI agents make mistakes, but they rarely know when they're wrong.

This is why your chatbot sometimes confidently gives you completely incorrect information.

It's the #1 reason AI assistants aren't trusted for critical tasks.

LLMs can write essays, answer questions, even pass the bar.

But ask them to solve a puzzle in a simulated world... and they fail miserably.

The solution? Chain-of-Symbol Prompting - a simple tweak that changes everything.

Quick breakdown + prompts 🧵: 1/ The problem is real: Spatial reasoning tasks break LLMs.

Example: "The yellow brick C is on brick E. Brick E is on yellow brick D. White brick A is on brick B..."

Even GPT-4 fails at ~30% accuracy when relationships get complex.

You’ve probably heard of “Chain of Thought” prompting.

But when the context is chaotic - noisy, messy, full of irrelevant info - most LLMs fail.

I use Thread of Thought (ThoT) instead.

Quick breakdown + prompts 🧵: 1/ What’s the problem?

LLMs choke on chaotic context - long, messy input full of mixed signals.

Not just “long” context - but irrelevant, distracting, or disjointed info.

Think retrieval-augmented prompts, messy chat history, or overlapping knowledge.

Most people think AI gives wrong answers because it "doesn't know enough."

But there’s a technique that changes everything.

It’s called Maieutic Prompting.

Here’s a quick breakdown with prompts 🧵: 1/ In 2022, researchers from UW and AI2 noticed something strange:

Large language models could explain answers but still reach wrong conclusions.

They realized the problem wasn’t just weak knowledge - it was how models reasoned.

I turned Grok into my research assistant.

Now it does the heavy lifting so I can focus on strategy.

Here are 10 insane prompts I use weekly 🧵: 1/ Academic Research Summary

Prompt:
Analyze the following research paper:
[paste paper content]

Instructions:
1. Extract key findings, methodologies, and conclusions.
2. Identify statistical significance and data points.
3. Note any limitations or contradictions.
4. Provide page references for each major point.
5. Suggest related research directions.

Format the response with:
- Executive Summary (150 words)
- Key Findings (with page numbers)
- Methodology Overview
- Critical Analysis
- Future Research Directions