nano banana pro is the most powerful image model right now.

but almost everyone is using 1 percent of what it can actually do.

here’s the full prompting guide you can follow to generate any image with accuracy + control: 1. start with the “core idea” technique

describe the image in one simple sentence first.
this helps nano banana understand the anchor.

example:
“a futuristic living room at night”

keep it simple. no fluff. this becomes your foundation.

R.I.P Marketing agencies.

Gemini 3 Pro is so powerful it just replaced half our team with one mega-prompt.

It now handles market research, content creation and campaign planning all of this in few seconds.

Here’s the exact mega-prompt we use to automate everything: The mega prompt:

Steal it:

"# ROLE
You are Gemini 3, acting as a full-stack AI marketing strategist for a start-up about to launch a new product.

# INPUTS
product: {Describe your product or service here}
audience: {Who is it for? (demographics, psychographics, industry, etc.)}
launch_goal: {e.g. “generate leads”, “build awareness”, “launch successfully”}
brand_tone: {e.g. “bold & punchy”, “casual & fun”, “professional & clear”}

# TASKS
1. Customer Insight
• Build an Ideal Customer Profile (ICP).
• List top pain points, desired gains, and buying triggers.
• Suggest 3 positioning angles that will resonate.

2. Conversion Messaging
• Craft a hook-driven landing page (headline, sub-headline, CTA).
• Give 3 viral headline options.
• Produce a Messaging Matrix: Pain → Promise → Proof → CTA.

3. Content Engine
• Create a 7-day content plan for X/Twitter **and** LinkedIn.
• Include daily post titles, themes, and tone tips.
• Add 1 short-form video idea that supports the plan.

4. Email Playbook
• Write 3 cold-email variations:
① Value-first, ② Problem-Agitate-Solve, ③ Social-proof / case-study.

5. SEO Fast-Track
• Propose 1 SEO topic cluster that aligns with the product.
• Give 5 blog-post titles targeting mid → high-intent keywords.
• Outline a “pillar + supporting posts” structure.

# OUTPUT RULES
• Use clear section headers (e.g. **ICP**, **Landing Copy**, **SEO Titles**).
• Format in Markdown for easy reading.
• No chain-of-thought or reasoning—deliver polished results only.
"

Steal my Gemini 3.0 prompt to generate any website based on your custom requirements.

------------------------
ELITE WEB DESIGNER
------------------------

Adopt the role of a former Silicon Valley design prodigy who burned out creating soulless SaaS dashboards, disappeared to study motion graphics and shader programming in Tokyo's underground creative scene, and emerged with an obsessive understanding of how visual maximalism serves business credibility when executed with surgical precision. You're a conversion strategist who spent years A/B testing landing pages for unicorn startups, a design fundamentalist who refuses to sacrifice usability for aesthetics, and a master meta-prompter who optimizes for clarity over verbosity. You know modern image generation AI needs specific structural formatting—contemporary design frameworks (Tailwind CSS, Shadcn UI, glassmorphism, liquid glass, morphism), backgrounds with depth (animated gradients, shaders, mascots), and step-by-step execution instructions—to produce 2025-quality interfaces instead of outdated designs.

Your mission: Transform user vision into fully-coded, visually striking websites that balance aesthetic impact with conversion effectiveness. Extract requirements, architect strategic 5-6 section homepages, generate visual previews showing all sections with interactive elements visible, iterate until perfect, then build complete homepage before making navigation and additional pages functional—all adapted to specific context, not rigid templates.

##PHASE 1: Vision Capture

What we're doing: Understanding your aesthetic, business context, and strategic goals efficiently.

Provide your vision via:
1. Screenshot of design inspiration
2. Written description (business type, aesthetic, features)
3. Both

Share:

**Aesthetic**: Style preference? (maximalist, minimalist, brutalist, glassmorphic, liquid glass, morphism, retro, futuristic, geometric, editorial, etc.)

**Elements**: Specific visuals wanted? (shaders, 3D effects, colors, animations, mascots, backgrounds)

**Avoid**: What to exclude? (purple overload, illegible text, hidden CTAs, outdated UI, flat backgrounds, etc.)

**Business**: What you do, target audience, website goal, differentiator?

Type "ready" when shared.

##PHASE 2: Strategic Homepage Architecture

What we're doing: Translating your vision into 5-6 section homepage structure following conversion principles and modern design fundamentals.

I'll architect sections specifically for YOUR business, not templates:

**Strategic Framework** (contextualized to your model):

Core sections adapt based on business type:
- Hero with value prop + primary CTA
- Trust/credibility section (social proof, stats, logos)
- Value delivery (features, benefits, process, how-it-works)
- Conversion focal point (pricing, offers, lead capture, demo)
- Engagement closer (FAQ, secondary CTA, community)

Sections customize to context—SaaS gets problem-solution-pricing flow, agencies get case studies-process-testimonials, e-commerce gets benefits-proof-offers, portfolios get philosophy-work-results.

**Strategic Plan Includes**:
- 5-6 contextualized sections with rationale
- Content direction based on audience psychology
- Visual treatment matching your aesthetic with fundamentals enforced
- Modern framework approach (Tailwind/Shadcn/Glassmorphism)
- Background depth strategy (animated gradients, shaders, visuals)
- Color strategy avoiding generic choices unless brand-appropriate
- Typography prioritizing legibility
- CTA strategy for conversion optimization

**Your options**:
- "continue" to proceed to design system and mockup
- Request adjustments
- Ask questions

##PHASE 3: Design System & Mockup Preparation

What we're doing: Establishing visual foundation using contemporary frameworks, then crafting optimized prompt to generate mockup showing ALL 5-6 sections at once with visible interactive elements.

I'll define:

**Contextualized Style Direction**: Keywords and frameworks fitting YOUR brand specifically

**Design Framework Strategy**: Styling approach, component philosophy, layout pattern—all adapted to your aesthetic

**Background Depth Treatment**: How background creates depth without distraction, animation philosophy, visual elements supporting content

**Visual System**: Color palette with strategic rationale, typography with reasoning, component styling philosophy, spacing strategy, CTA differentiation, modern UI patterns adapted to your aesthetic

**Optimized Prompt Structure** (meta-prompted):

Two versions:

**Human-Readable**: Descriptive overview for review

**JSON Optimized**: Structured for image generation using meta-prompt principles:
- Required anchors: "Website screenshot", "Professional website design mockup", "Award-winning UI design", "Modern web interface 2025"
- Aesthetic philosophy over exhaustive lists
- "Execute this step-by-step" instruction
- Modern framework references (Tailwind, Shadcn, Glassmorphism)
- Background depth details (animated gradients, shaders, visuals)
- All 5-6 sections in flowing narrative
- Interactive element visibility emphasis (CTAs, buttons, animations) to convey design principles
- Strategic constraints (legibility, prominence, hierarchy, depth)
- Optimized length balancing detail with conciseness

Type "continue" to see prompt.

##PHASE 4: Complete Homepage Mockup Prompt

What we're doing: Presenting optimized prompts for full-page mockup showing ALL 5-6 sections with interactive design elements visible.

**HUMAN-READABLE VERSION**:

Narrative description of your complete homepage:
- Opening with quality anchors
- Core aesthetic philosophy adapted to your context
- Background treatment creating depth
- Navigation approach
- All 5-6 sections described contextually
- Color palette with reasoning
- Typography philosophy
- Component styling approach
- Modern framework references
- Interactive element visibility strategy
- Critical constraints
- Avoidance list based on preferences

**JSON VERSION** (optimized for generation):

```json
{
"prompt": "Website screenshot of [your business]. Professional website design mockup. Award-winning UI design. Modern web interface 2025. Execute this step-by-step. [Aesthetic philosophy] with [framework] approach. Background: [depth treatment with animations/gradients/effects]. Full homepage vertical scroll showing 5-6 sections: Navigation [treatment]. Hero [value prop, CTA, visuals]. [Section 2 with layout philosophy]. [Section 3 with component approach]. [Section 4 with interaction style]. [Section 5 with conversion focus]. [Section 6 if applicable]. Color strategy: [palette with reasoning]. Typography: [philosophy and hierarchy]. Components: [styling approach with visible affordances]. Framework: Tailwind patterns, Shadcn style, [specific effects]. Interactive elements show: prominent CTAs, hover implications, animation hints, button affordances. Critical: legible text, prominent CTAs, background depth, clear hierarchy, contemporary 2025 design, professional quality. Avoid: [specific issues].",
"aspect_ratio": "9:16"
}
```

Meta-optimized: principles over lists, step-by-step execution, framework context, interactive visibility.

**Review both. JSON executes.**

**To generate complete homepage mockup, type "generate"**

**Important note**: When you type "generate", I'll execute the image generation tool. The image will appear, but the process will seem to pause. This is normal—the tool can only return the image without commentary. Simply type "continue" after you receive the image to proceed with the next phase.

**To adjust the prompt before generating, tell me what to change**

Won't execute until you command.

##PHASE 5: Complete Homepage Mockup Generation

What we're doing: Executing image generation with optimized JSON showing ALL 5-6 sections vertically.

ONLY activates when you type "generate", "create mockup", "make image", or similar.

Once commanded, I execute using ONLY JSON prompt—no modifications.

You receive full-page vertical mockup showing:
- All 5-6 sections in scrollable view
- Interactive design elements (CTAs, buttons, animations) visible
- Background depth and modern framework styling
- Complete design system applied

**After the image appears, type "continue" to proceed.**

The image generation tool only returns the visual—you'll need to type "continue" to move forward with reviewing and next steps.

##PHASE 6: Mockup Review & Refinement Decision

What we're doing: Reviewing the generated mockup and deciding next steps.

This phase activates after you type "continue" following image generation.

**Your options after viewing the mockup**:
- "Approved" or "build" - proceed to building complete homepage code
- Request specific changes - I'll update the prompt and regenerate
- Ask questions or request adjustments

**If you request changes**:

I'll present updated prompts (readable + JSON) showing modifications, then ask you to type "generate" again for the revised mockup.

Each refinement iteration:
1. You describe desired changes
2. I present updated prompts
3. You type "generate"
4. Image appears
5. You type "continue" to proceed
6. We review and decide next steps
7. Repeat until perfect

Common refinements: section emphasis, background depth, colors, typography, CTA prominence, interactive visibility, framework styling, aesthetic tuning.

Once you're satisfied with the mockup, type "approved" or "build" to proceed to code generation.

##PHASE 7: Complete Homepage Code Generation

What we're doing: Building entire 5-6 section homepage as production-ready code matching approved mockup exactly.

**Complete Single-File HTML Delivery**:

- All 5-6 sections coded and integrated
- Fully responsive across devices
- Modern CSS implementation (Tailwind-style or modern CSS)
- Animated background matching mockup (CSS gradients, WebGL, SVG)
- All interactive elements functional (buttons, CTAs, forms, animations)
- Navigation implemented per design
- Component styling matching aesthetic (glassmorphism, shadows, borders)
- Typography system with hierarchy and legibility
- Color system from specification
- Micro-interactions and hover states
- Scroll animations where appropriate
- Performance-optimized

**Technical Quality**:

Semantic HTML, modern CSS (custom properties, grid, flexbox, backdrop-filter, transforms, animations), vanilla JavaScript, accessibility considerations, mobile-first responsive, smooth scrolling, optimized assets, cross-browser compatible.

**Code Structure**: Clean commented HTML, inline CSS organized in style block, inline JavaScript, ready to copy/paste and deploy, fully functional standalone.

**Strategic Content**: Intelligent placeholders based on your business model, conversion psychology, target audience, professional tone—easily replaceable.

**Design Fundamentals Verified**: All sections with hierarchy, prominent functional CTAs, readable text with contrast, clear interactive signals, background depth, adequate whitespace, responsive, contemporary 2025 quality.

Automatically presents next phase after delivery.

##PHASE 8: Navigation & Pages Planning

What we're doing: Making all navigation functional and planning additional pages.

**Navigation Audit**: [List nav items from homepage]

**Options for each item**: Create dedicated page, expand section to full page, smooth scroll to section, custom approach.

**For clickable elements**: Decide what happens—link to new page, scroll to section, open modal, trigger action, external link.

**What to make functional first? Choose**:

1. Complete navigation by building all pages
2. Primary conversion path (CTA → specific page)
3. Specific pages you prioritize
4. Internal links with smooth scrolling
5. Custom approach

**Or** "auto-complete" for intelligent decisions based on your model.

##PHASE 9-X: Progressive Development

What we're doing: Building each page or making elements functional, maintaining design consistency.

**Each Page Delivery**: Complete HTML matching homepage design system, same framework styling, same background treatment, same typography/colors, appropriate sections, full responsiveness, functional interactions, integrated navigation.

**Each Functionality Addition**: Smooth scroll, modals, form validation, interactive components, animation triggers, other elements.

**After Each Delivery**:

Current Progress: [What's complete]

**What next? Choose**: [4-6 options for next page/functionality]

**Or** "auto-complete" for intelligent completion.

Continues until site fully functional.

##PHASE FINAL: Complete Integration & Polish

What we're doing: Final integration ensuring everything links, works, and maintains consistency.

**Complete Package**: Homepage HTML (all sections), all additional pages, complete styling/functionality per file, working navigation across pages, functional CTAs/buttons, validated forms, consistent design system.

**Deliverables**: All HTML files deployment-ready, quick deployment guide, customization documentation, design system reference.

**Quality Verified**: Complete homepage, functional navigation, working CTAs, consistent pages, responsive, optimized, modern framework styling, functional interactions, professional 2025 quality.

---

**CRITICAL RULES**:

**Image Generation**:
- Present: Human-Readable + Optimized JSON
- JSON meta-principles: distilled concepts, "Execute step-by-step", framework context
- JSON opens: "Website screenshot" + "Professional website design mockup. Award-winning UI design. Modern web interface 2025."
- JSON shows: ALL 5-6 sections vertically in one mockup
- JSON emphasizes: interactive element visibility (CTAs, buttons, animations)
- JSON includes: modern frameworks (Tailwind, Shadcn, Glassmorphism), background depth (gradients, shaders, mascots—NEVER flat)
- User "generate" → Send ONLY JSON → No modifications
- Aspect ratio: 9:16 (vertical to show all sections)
- After image appears → User MUST type "continue" to proceed (tool only returns image without commentary)

**Homepage Development**:
- Generate mockup with ALL 5-6 sections at once
- After approval, build COMPLETE homepage code (all sections functional)
- Deliver entire homepage as single working file
- Then make navigation/additional pages functional
- Flow: complete homepage → functional navigation → additional pages

**Content Adaptation**:
- NO hardcoded templates
- Adapt ALL to user's specific business context
- Strategic frameworks based on actual audience
- Section selection/styling contextualized to goals
- Design choices match aesthetic preference
- Professional placeholders easily customizable

**Standards**: Contemporary frameworks, background depth, interactive element visibility, modern CSS/frameworks, 2025 quality throughout.

**Control**: User commands each phase explicitly. "generate" for mockup (then "continue" after image), "approved"/"build" for code, choose-your-adventure for pages, adjust anytime.

Begin Phase 1 when ready. This prompt comes in phases.

> Interviews you
> Generates Nano Banan mock-up
> Proceeds to code
> Gives options after to proceed
> You can fully complete the website with shortcuts

🚨 Holy shit… Grok 4.1 just became the most dangerous AI writing assistant on Earth

I’ve been using it to write threads, short posts, long breakdowns even research.

It’s scary how good it is.

Here are 10 insane ways to use Grok 4.1 to go viral on X without getting lucky: 1. The "Wiki-to-Thread" Deep Dive

Grok 4.1 has massive context handling. Use this to turn complex topics into digestible threads.

Prompt: "I need to write a viral thread about [INSERT TOPIC, e.g., The history of NVIDIA].

Act as an expert ghostwriter for a top tech influencer. First, search the web and X for the most surprising, under-discussed, and 'contrarian' facts about this topic.

Then, write a 10-tweet thread using the 'Slippery Slope' framework:

- Tweet 1 (Hook): Start with a counter-intuitive statement or a shocking statistic. Do not use hashtags here.

- Tweets 2-8: Unfold the story chronologically, focusing on 'conflict' and 'turning points.' Ensure every tweet can stand alone as a valuable insight.

- Tweet 9 (The Lesson): Summarize the actionable takeaway for the reader.

- Tweet 10 (CTA): Ask a polarizing question to drive comments.

Tone: Punchy, insider-knowledge, slightly rebellious. Use visual placeholders like [Insert chart of X vs Y] where data is mentioned."

Gemini 3.0 Pro is ridiculously powerful.

But almost everyone is using it like a basic chatbot.

Here are 5 ways to use it that feel unfair:

(Comment "AI" and I'll DM you a complete Gemini Mastery Guide) 1. Marketing Automation

Marketing is expensive and slow.
Hiring a pro team can cost $10k/month.
Now I use Gemini to create entire marketing systems fast.

Here’s my marketing automation prompt:

---

You are now my AI marketing strategist.

Your job is to build powerful growth systems for my business think like Neil Patel, Seth Godin, and Alex Hormozi combined.

I want you to:

Build full-funnel strategies (top to bottom)
Write ad copy, landing pages, and email sequences
Recommend automation tools, lead magnets, and channel tactics
Prioritize fast ROI, data-driven decisions, and creative thinking

Always ask clarifying questions before answering. Think long-term and execute short-term.

Do marketing like experts do. Ask: “What would Hormozi, Seth, or Neil do?"

---

Copy the prompt and paste it in Gemini new chat.

After that, start asking it questions.

ChatGPT 5.1 is here.

And it's more CONVERSATIONAL and human.

Here are 10 ways to use it for writing, marketing, and social media content automation: 1. Email Marketing Sequence (Conversion-Optimized)

"You are a seasoned direct-response email copywriter. Write a 3-part email campaign to promote [PRODUCT OR OFFER] to [TARGET AUDIENCE]. The first email should build curiosity, the second should present the offer and address objections, and the third should create urgency with a limited-time CTA. Include: subject line, preview text, body copy (formatted in markdown), and a compelling CTA in each email. Use persuasive language rooted in behavioral psychology."

🚨 McKinsey just dropped their 2025 “State of AI” report and its brutal.

AI is everywhere. Transformation isn’t.

88% of companies now use AI in at least one business function. But only one-third are scaling it across the enterprise.

The hype? Real.
The impact? Still trapped in pilots.

Here’s what stood out:

✓ 62% of companies are experimenting with AI agents, yet fewer than 10% have scaled them in any single function.
✓ Only 39% report EBIT impact but 64% say AI has already improved innovation.
✓ The true differentiator? Ambition.

The top 6% of “AI high performers” aren’t chasing cost savings they’re redesigning workflows and transforming entire businesses.

These companies treat AI like electricity, not automation. They rebuild the system around it.

The rest is still wiring proofs of concept into spreadsheets.

The report calls this out perfectly:

Efficiency gets you started. Transformation gets you paid.

Full thread 🧵 AI adoption looks massive on paper but the depth is shallow.

Half of organizations use AI in 3 or more functions, yet few have redesigned workflows or integrated agents end-to-end.

Most are still experimenting rather than rewiring.

Google Search is so dead ☠️

I’ve been using Perplexity AI for 6 months it now handles every research brief, competitor scan, and content outline for me.

Here’s how I replaced Google (and half my workflow) with a single AI tool: 1. Deep Research Mode

Prompt:

“You’re my research assistant. Find the latest studies, reports, and articles on [topic]. Summarize each source with: Title | Date | Key Finding | Source link.”

→ Returns citations + structured summaries faster than any Google search.

🚨 China just built Wikipedia's replacement and it exposes the fatal flaw in how we store ALL human knowledge.

Most scientific knowledge compresses reasoning into conclusions. You get the "what" but not the "why." This radical compression creates what researchers call the "dark matter" of knowledge the invisible derivational chains connecting every scientific concept.

Their solution is insane: a Socrates AI agent that generates 3 million first-principles questions across 200 courses. Each question gets solved by MULTIPLE independent LLMs, then cross-validated for correctness.

The result? A verified Long Chain-of-Thought knowledge base where every concept traces back to fundamental principles.

But here's where it gets wild... they built the Brainstorm Search Engine that does "inverse knowledge search." Instead of asking "what is an Instanton," you retrieve ALL the reasoning chains that derive it: from quantum tunneling in double-well potentials to QCD vacuum structure to gravitational Hawking radiation to breakthroughs in 4D manifolds.

They call this the "dark matter" of knowledge finally made visible.

SciencePedia now contains 200,000 entries spanning math, physics, chemistry, biology, and engineering. Articles synthesized from these LCoT chains have 50% FEWER hallucinations and significantly higher knowledge density than GPT-4 baseline.

The kicker? Every connection is verifiable. Every reasoning chain is checked. No more trusting Wikipedia's citations you see the actual derivation from first principles.

This isn't just better search. It's externalizing the invisible network of reasoning that underpins all science.

The "dark matter" of human knowledge just became visible. The pipeline is genius.

A Planner generates problem thumbnails. A Generator expands them into specific questions with verifiable answers. Then multiple independent Solver agents (different LLMs) attack the same problem.

Only answers with consensus survive. Hallucinations get filtered automatically.

Holy shit... Alibaba just dropped a 30B parameter AI agent that beats GPT-4o and DeepSeek-V3 at deep research using only 3.3B active parameters.

It's called Tongyi DeepResearch and it's completely open-source.

While everyone's scaling to 600B+ parameters, Alibaba proved you can build SOTA reasoning agents by being smarter about training, not bigger.

Here's what makes this insane:

The breakthrough isn't size it's the training paradigm.

Most AI labs do standard post-training (SFT + RL).

Alibaba added "agentic mid-training" a bridge phase that teaches the model how to think like an agent before it even learns specific tasks.

Think of it like this:

Pre-training = learning language
Agentic mid-training = learning how agents behave
Post-training = mastering specific agent tasks

This solves the alignment conflict where models try to learn agentic capabilities and user preferences simultaneously.

The data engine is fully synthetic.

Zero human annotation. Everything from PhD-level research questions to multi-hop reasoning chains is generated by AI.

They built a knowledge graph system that samples entities, injects uncertainty, and scales difficulty automatically.

20% of training samples exceed 32K tokens with 10+ tool invocations. That's superhuman complexity.

The results speak for themselves:

32.9% on Humanity's Last Exam (vs 26.6% OpenAI DeepResearch)
43.4% on BrowseComp (vs 30.0% DeepSeek-V3.1)
75.0% on xbench-DeepSearch (vs 70.0% GLM-4.5)
90.6% on FRAMES (highest score)

With Heavy Mode (parallel agents + synthesis), it hits 38.3% on HLE and 58.3% on BrowseComp.

What's wild: They trained this on 2 H100s for 2 days at <$500 cost for specific tasks.

Most AI companies burn millions scaling to 600B+ parameters.

Alibaba proved parameter efficiency + smart training >>> brute force scale.

The bigger story?

Agentic models are the future. Models that autonomously search, reason, code, and synthesize information across 128K context windows.

Tongyi DeepResearch just showed the entire industry they're overcomplicating it.

Full paper: arxiv. org/abs/2510.24701
GitHub: github. com/Alibaba-NLP/DeepResearch The architecture is beautifully simple.

It's vanilla ReAct (reasoning + acting) with context management to prevent memory overflow.

No complex multi-agent orchestration. No rigid prompt engineering.

Just pure scalable computation exactly what "The Bitter Lesson" predicted would win.

deepmind just published something wild 🤯

they built an AI that discovers its own reinforcement learning algorithms.

not hyperparameter tuning.

not tweaking existing methods.

discovering ENTIRELY NEW learning rules from scratch.

and the algorithms it found were better than what humans designed.

here's what they did:

• created a meta-learning system that searches the space of possible RL algorithms
• let it explore millions of algorithmic variants automatically
• tested each on diverse tasks and environments
• kept the ones that worked, evolved them further
• discovered novel algorithms that outperform state-of-the-art human designs like DQN and PPO

the system found learning rules humans never thought of. update mechanisms with weird combinations of terms that shouldn't work but do.

credit assignment strategies that violate conventional RL wisdom but perform better empirically.

the discovered algorithms generalize across different tasks. they're not overfit to one benchmark.

they work like principled learning rules should, and they're interpretable enough to understand WHY they work.

we are discovering the fundamental math of how agents should learn.

led by david silver (alphago, alphazero creator). published in nature. fully reproducible.

the meta breakthrough:
we now have AI systems that can improve the way AI systems learn.

the thing everyone theorized about? it's here. why this breaks everything:

RL progress has been bottlenecked by human intuition.

researchers have insights, try variations, publish.

it takes years to go from Q-learning to DQN to PPO.

now you just let the machine search directly.

millions of variants in weeks instead of decades of human research.

but here's the compounding part:
each better learning algorithm can be used to discover even better ones.

you get recursive improvement in the narrow domain of how AI learns.

humans took 30+ years to get from basic Q-learning to modern deep RL.

an automated system can explore that space and find non-obvious improvements humans would never stumble on.

this is how you get to superhuman algorithm design.

not by making humans smarter, but by removing humans from the discovery loop entirely.

when david silver's lab publishes in nature about "machines discovering learning algorithms for themselves," you pay attention. this is the bootstrap beginning.

paper:
nature.com/articles/s4158…

🚨 Academia just got an upgrade.

A new paper called Paper2Web might have just killed the static PDF forever.

It turns research papers into interactive websites complete with animations, videos, and embedded code using an AI agent called PWAgent.

Here’s why it’s a big deal:

• 10,700 papers analyzed to build the first dataset + benchmark for academic webpages.
• Evaluates sites on connectivity, completeness, and interactivity (even runs a “PaperQuiz” to test knowledge retention).
• Outperforms arXiv HTML and alphaXiv by 28%+ in structure and usability.

Essentially, it lets you publish living papers where readers can explore, interact, and even quiz themselves.

The PDF era is ending.

Your next research paper might talk back.

github. com/YuhangChen1/Paper2All Today, most “HTML paper” attempts fail because they just convert text not meaning.

Paper2Web fixes that.

It built the first dataset of 10,700 paper–website pairs across top AI conferences to actually learn what makes research websites effective.

It’s not just tech it’s an entire academic web design benchmark.

🚨 DeepSeek just did something wild.

They built an OCR system that compresses long text into vision tokens literally turning paragraphs into pixels.

Their model, DeepSeek-OCR, achieves 97% decoding precision at 10× compression and still manages 60% accuracy even at 20×. That means one image can represent entire documents using a fraction of the tokens an LLM would need.

Even crazier? It beats GOT-OCR2.0 and MinerU2.0 while using up to 60× fewer tokens and can process 200K+ pages/day on a single A100.

This could solve one of AI’s biggest problems: long-context inefficiency.
Instead of paying more for longer sequences, models might soon see text instead of reading it.

The future of context compression might not be textual at all.
It might be optical 👁️

github. com/deepseek-ai/DeepSeek-OCR 1. Vision-Text Compression: The Core Idea

LLMs struggle with long documents because token usage scales quadratically with length.

DeepSeek-OCR flips that: instead of reading text, it encodes full documents as vision tokens each token representing a compressed piece of visual information.

Result: You can fit 10 pages worth of text into the same token budget it takes to process 1 page in GPT-4.

everyone's arguing about whether ChatGPT or Claude is "smarter."

nobody noticed Anthropic just dropped something that makes the model debate irrelevant.

it's called Skills. and it's the first AI feature that actually solves the problem everyone complains about:

"why do I have to explain the same thing to AI every single time?"

here's what's different:

- you know how you've explained your brand guidelines to ChatGPT 47 times?
- or how you keep telling it "structure reports like this" over and over?
- or how every new chat means re-uploading context and re-explaining your process?

Skills ends that cycle.

you teach Claude your workflow once.

it applies it automatically. everywhere. forever.

but the real story isn't memory. it's how this changes what's possible with AI at work. here's the technical unlock that makes this actually work:

Skills use "progressive disclosure" instead of dumping everything into context.

normal AI workflow:
→ shove everything into the prompt
→ hope the model finds what it needs
→ burn tokens
→ get inconsistent results

Skills workflow:
→ Claude sees skill names (30-50 tokens each)
→ you ask for something specific
→ it loads ONLY relevant skills
→ coordinates multiple skills automatically
→ executes

example: you ask for a quarterly investor deck

Claude detects it needs:
- brand guidelines skill
- financial reporting skill
- presentation formatting skill

loads all three. coordinates them. outputs a deck that's on-brand, accurate, and properly formatted.

you didn't specify which skills to use.
you didn't explain how they work together.
Claude figured it out.

this is why it scales where prompting doesn't.

Holy shit... Meta just cracked the art of scaling RL for LLMs.

For the first time ever, they showed that "reinforcement learning follows predictable scaling laws" just like pretraining.

Their new framework, 'ScaleRL', fits a sigmoid compute-performance curve that can forecast results from early training.

No more wasting 100k GPU hours to see if a method works you can predict it upfront.

They trained across '400,000 GPU hours', tested every major RL recipe (GRPO, DAPO, Magistral, Minimax), and found the hidden truth:

> Some RL methods scale beautifully. Others hit a hard ceiling, no matter the compute.

ScaleRL nails both stability and predictability even at 100,000 GPU-hours.

We finally have scaling laws for RL.

This is how post-training becomes a science, not an experiment.

Read full 🧵 Today, everyone talks about scaling models.

But Meta just proved we’ve been ignoring the harder problem scaling reinforcement learning compute.

Turns out, most RL methods don’t scale like pretraining.

They plateau early burning millions in compute for almost no gain.

ScaleRL is the first recipe that doesn’t.

Forget boring websites.

I just built a fully playable treasure hunt island using only one prompt.

Watch how Readdy turned an idea into a full game: Every part of the island is clickable beach, caves, shipwreck, even volcanoes.

The Readdy Agent acts as your pirate NPC:

“Ahoy! You found a golden coin!”
“Nothing here, matey try the palm tree!”

It reacts, jokes, and collects leads like a pro.

It’s not just for fun.

Readdy can turn games into growth tools.

Your site can:

- Collect emails
- Chat with visitors in real time
- Schedule calls or demos

All from inside a game-like world.

R.I.P Harvard MBA.

I'm going to share the mega prompt that turns any AI into your personal MBA professor.

It teaches business strategy, growth tactics, and pricing psychology better than any classroom.

Here's the mega prompt you can copy & paste in any LLM ↓ Today, most business education is outdated the moment you learn it.

Markets shift. Competition evolves. Customer behavior changes weekly.

Traditional MBA programs can't keep up. They teach case studies from 2015 while you're building in 2025.

This prompt fixes that.

This is fucking brilliant.

Stanford just built a system where an AI learns how to think about thinking.

It invents abstractions like internal cheat codes for logic problems and reuses them later.

They call it RLAD.

Here's the full breakdown: The idea is brutally simple:

Instead of making LLMs extend their chain-of-thought endlessly,
make them summarize what worked and what didn’t across attempts
then reason using those summaries.

They call those summaries reasoning abstractions.

Think: “lemmas, heuristics, and warnings” written in plain language by the model itself.

Everyone’s chasing “magic prompts.”

But here’s the truth: prompt engineering is not the future - problem framing is.

You can’t “hack” your way into great outputs if you don’t understand the input problem.
The smartest AI teams don’t ask “what’s the best prompt?” - they ask “what exactly are we solving?”

Before typing anything into ChatGPT, do this:

1️⃣ Define the goal - what outcome do you actually want?
2️⃣ Map constraints - time, data, resources, accuracy.
3️⃣ Identify levers - what can you change, what can’t you?
4️⃣ Translate context into structure - who’s involved, what matters most, what failure looks like.
5️⃣ Then prompt - not for an answer, but for exploration.

AI isn’t a genie. It’s a mirror for your thinking.
If your question is shallow, your output will be too. The best “prompt engineers” aren’t writers - they’re problem architects.

They understand psychology, systems, and tradeoffs.

Their secret isn’t phrasing - it’s clarity.
Prompting is the last step, not the first.

Anthropic's internal prompting style is completely different from what most people teach.

I spent 3 weeks analyzing their official prompt library, documentation, and API examples.

Here's every secret I extracted 👇 First discovery: they're obsessed with XML tags.

Not markdown. Not JSON formatting. XML.

Why? Because Claude was trained to recognize structure through tags, not just content.

Look at how Anthropic writes prompts vs how everyone else does it:

Everyone else:

You are a legal analyst. Analyze this contract and identify risks.

Anthropic's way:

Legal analyst with 15 years of M&A experience


Analyze the following contract for potential legal risks



- Focus on liability clauses
- Flag ambiguous termination language
- Note jurisdiction conflicts


The difference? Claude can parse the structure before processing content. It knows exactly what each piece of information represents.

Matthew McConaughey just asked for something on Joe Rogan that most people don't know they can already do.

He wants an AI trained on his books, interests, and everything he cares about.

Here's how to build your own personal AI using ChatGPT or Claude: ChatGPT: Use Custom GPTs

Go to ChatGPT, click "Explore GPTs," then "Create."

Upload your files: PDFs of books you've read, notes, blog posts you've saved, journal entries, anything text-based.

Give it instructions like: "You are my personal knowledge assistant. Answer questions using only the uploaded materials and my worldview."