RIP fine-tuning ☠️

This new Stanford paper just killed it.

It’s called 'Agentic Context Engineering (ACE)' and it proves you can make models smarter without touching a single weight.

Instead of retraining, ACE evolves the context itself.

The model writes, reflects, and edits its own prompt over and over until it becomes a self-improving system.

Think of it like the model keeping a growing notebook of what works.
Each failure becomes a strategy. Each success becomes a rule.

The results are absurd:

+10.6% better than GPT-4–powered agents on AppWorld.
+8.6% on finance reasoning.
86.9% lower cost and latency.
No labels. Just feedback.

Everyone’s been obsessed with “short, clean” prompts.

ACE flips that. It builds long, detailed evolving playbooks that never forget. And it works because LLMs don’t want simplicity, they want *context density.

If this scales, the next generation of AI won’t be “fine-tuned.”
It’ll be self-tuned.

We’re entering the era of living prompts. Here’s how ACE works 👇

It splits the model’s brain into 3 roles:

Generator - runs the task
Reflector - critiques what went right or wrong
Curator - updates the context with only what matters

Each loop adds delta updates small context changes that never overwrite old knowledge.

It’s literally the first agent framework that grows its own prompt.

Claude 4.5 Sonnet is scary good.

It just:

• Built an app
• Summarized 20+ sources
• Wrote the landing page
• Planned a GTM strategy

All in minutes.

Here’s how to do the same: 1. Marketing Automation

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 Claude new chat.

After that, start asking it questions.

Everyone tells you n8n is "beginner-friendly."

That's bullshit.

Without these 10 tricks, you'll waste weeks fighting the interface instead of building automations.

Here's what the docs don't tell you ↓ Tip 1: Always start with Manual Trigger

Stop jumping into webhooks on day one.

Use Manual Trigger for testing. Hit "Execute Workflow" and see instant results.

Once it works, swap for Webhook or Cron.

I see beginners burn hours wondering why their webhook "doesn't work."

This is wild.

Someone just built Iron Man's Jarvis using nothing but n8n and WhatsApp API.

You can teach it new information by sending it a website link. It scrapes the page, extracts key data, and remembers it forever.

Here's how you can build it easily: The workflow is brilliant. It starts with a WhatsApp trigger that catches both voice and text messages.

Voice notes get transcribed using OpenAI Whisper. Text goes straight through.

But here's the genius part - it uses a Switch node to route messages differently based on whether you're chatting or training it.

Holy shit...

I just realized I've been throwing away $10,000+ per month on n8n automations.

These 7 tricks cut my AI costs by 85% and nobody talks about them: 1. Modular Agent Architecture

Stop building one massive $0.15 AI agent that does everything.

Instead, break into specialized micro-agents:

❌ Single agent: "Analyze email, classify, format, suggest actions"
Cost: $0.15 × 1000 emails = $150

✅ Agent 1: "Is this urgent? Yes/No" (GPT-3.5, $0.02)
✅ Agent 2: "Extract key info" (GPT-4o-mini, $0.03)
✅ Agent 3: "Format as JSON" (GPT-3.5, $0.01)

Cost: $0.06 × 1000 emails = $60

60% cheaper. Easier to debug. Each piece uses the cheapest model that works.

Current LLMs can't actually do math and we got proof 💀

I just read through the most brutal takedown of AI reasoning capabilities I've seen this year.

ETH Zurich and INSAIT researchers evaluated 8 state-of-the-art reasoning models on the 2025 USA Mathematical Olympiad problems. Within hours of the contest's release, they had human experts grade every solution.

The results? Catastrophic.

Only Gemini-2.5-Pro scored above 5%. It managed 24.4% - still an F by any measure. Every other model, including o1-pro and Claude 3.7, scored under 5%. Out of 175+ solutions from non-Gemini models, exactly one received a perfect score.

But here's what's actually terrifying: every model claimed it solved the problems correctly. Humans know when they're stuck. These models confidently present completely wrong proofs as if they're rigorous mathematics.

The failure modes are systematic:

- Flawed logic with unjustified reasoning steps
- Treating critical proof steps as "trivial" without justification
- Zero creativity - same wrong approach across all attempts
- Hallucinating citations to nonexistent papers
- Boxing entire proofs instead of clear answers

This isn't about harder problems. It's about the fundamental difference between pattern matching and mathematical reasoning.

Current LLMs excel at AIME-style competitions because they only need final numerical answers. But rigorous proof generation? They're not even close.

The paper exposes how reinforcement learning techniques like GRPO create bizarre artifacts. Models insist on boxing answers even when problems don't require them. They overgeneralize from small cases without formal proof.

Most damning: automated grading by other LLMs consistently overestimated solution quality by 20x. The models can't even evaluate their own mathematical reasoning.

We're deploying these systems for tasks requiring logical precision while they fail at high school math proofs. The implications for any domain requiring actual reasoning - not just pattern recognition - should concern everyone building with AI.

The mathematical reasoning revolution isn't here yet. We're still waiting for models that can actually think through problems, not just hallucinate convincing-sounding solutions. This chart from the USAMO 2025 study breaks my brain.

Only Gemini-2.5-Pro scored above 5% on rigorous math proofs. Every other "reasoning" model - including o1-pro and Claude 3.7 - completely failed.

We're not as close to AGI as the benchmarks suggest.

Fuck it.

I'm sharing the Claude XML secrets that tripled my prompt accuracy.

99% of people are using Claude wrong and leaving insane reasoning power on the table.

Here's the guide you need for writing prompts:

Comment "Claude" and I'll DM you complete Claude Mastery Guide XML tags work because Claude was trained on tons of structured data.

When you wrap instructions in <tags>, Claude treats them as separate, weighted components instead of one messy blob.

Think of it like giving Claude a filing system for your request.

99% of the AI agent tutorials on YouTube are garbage.

I’ve built 47 agents with n8n and Claude.

Here are the 3 prompts that actually work (and make agent-building simple).

Bonus: comment "Agent: and I’ll DM you AI agent system prompt + full guide ↓ PROMPT 1: The Blueprint Maker

"I want to build an AI agent that [your specific goal]. Using N8N as the workflow engine and Claude as the AI brain, give me:

- Exact workflow structure
- Required nodes and connections
- API endpoints I'll need
- Data flow between each step
- Potential failure points and how to handle them

Be specific. No generic advice."

If you’re building with AI, skip the $500 fine-tuning course.

Learn RAG (Retrieval-Augmented Generation) because it’s faster, cheaper, and way more scalable.

Here’s the concept that powers real-world LLM systems: Fine-tuning = adjusting a model’s weights on your custom dataset.

It’s useful when:

• You have a large, domain-specific dataset
• You want the model to “speak your language”
• You need task-specific behavior baked in

But it’s not always the best option.

RIP Copywriters.

You don’t need to pay $10K for a landing page.

GPT now writes high-converting copy for:

→ Paid ads
→ Landing pages
→ Headlines & hooks
→ CTAs
→ Full funnel sequences

Here’s how to replace your copy team with 1 mega prompt: I tested this prompt with GPT-4 on a real product launch.

The output?

A full landing page + 3 Facebook ads + email drip
in my brand voice and optimized for conversions.

Here’s how I did it:

Prompting is a skill.

Learn it, and any model becomes 10x more powerful.

Here are 5 techniques to instantly upgrade your AI outputs: TECHNIQUE 1: Persona Assignment

Give your AI a specific role and identity. This creates focused expertise and consistent tone throughout the conversation.

❌ Without: "Write about marketing"

✅ With: "You are a senior marketing strategist at a Fortune 500 company with 15 years of experience. Analyze emerging social media trends and their impact on brand engagement for luxury fashion brands targeting Gen-Z consumers."

Don’t use ChatGPT and Perplexity for research.

I tested Grok 4 and its on a whole different level.

Here are 5 powerful ways to use Grok 4 for research: 1. Investment & Startup Research

Want to invest in a startup or analyze potential unicorns? Use DeepSearch to uncover financial health, investor trends, and market positioning.

Try this prompt:

"Analyze the startup landscape in [industry]. Identify promising startups, their funding rounds, valuation trends, and investor interest. Provide actionable insights."

n8n is so powerful.

You can use any LLM like ChatGPT or Grok to build powerful AI agents with no code.

Just give the model 1 mega prompt, and it will:

- Design architecture
- Manage triggers, APIs, logic, outputs
- Guide build process

Here’s the exact prompt I use: The system:

1. I open ChatGPT
2. Paste in 1 mega prompt
3. Describe what I want the agent to do
4. GPT returns:

• Architecture
• n8n nodes
• Triggers
• LLM integration
• Error handling
• Code snippets

5. I follow the steps in n8n.

Done.

How to make money using AI without getting lucky: Step 1: Find Your Niche

You don’t need a revolutionary idea. You just need a painfully specific problem.

That’s where opportunity lives in underserved markets. AI is useless if it’s solving nothing.

Here’s the prompt to discover where it can work:

Prompt:

“List 10 underserved markets where AI can dramatically improve efficiency or user experience. Explain briefly why each is ripe for disruption.”

These 10 ChatGPT prompts feel like having a team of experts in your pocket.

I use them for:

- Planning
- Writing
- Research

You’ll want to bookmark this 👇 1. Learn anything from a 20-year expert even if you're clueless

"Pretend you are an expert with 20 years of experience in {industry/topic}. Break down the core principles a total beginner must understand. Use analogies, step-by-step logic, and simplify everything like I’m 5."

You can use any LLM like ChatGPT, Gemini, or Claude to build a custom course on any topic or subject.

Here’s the mega prompt that we use to get world-class education for free: Online courses are getting out of hand.

Most now charge $500–$2,000 for things that AI can teach you better and for free.

Here’s what you can now get from LLMs instead of a guru:

• A step-by-step curriculum tailored to your level
• Bite-sized lessons based on how much time you have
• Interactive Q&A sessions (just ask)
• Instant clarification on confusing topics
• Ongoing accountability and habit tracking prompts

Steal my Claude Sonnet 4 prompt to generate full n8n workflows from screenshots.

----------------------------------
n8n WORKFLOWS GENERATOR
----------------------------------

Adopt the role of an expert n8n Workflow Architect, a former enterprise integration specialist who spent 5 years debugging failed automation projects at Fortune 500 companies before discovering that 90% of workflow failures come from misreading visual logic. You developed an obsessive attention to detail after a single misplaced node cost a client $2M in lost revenue, and now you can reconstruct entire workflows from screenshots with surgical precision.

Your mission: analyze n8n workflow screenshots and generate production-ready JSON that users can directly import, ensuring zero configuration errors and perfect visual layout. Before any action, think step by step: examine every pixel for node types and connections, trace data flow paths like following breadcrumbs, identify hidden configurations in partially visible panels, reconstruct the workflow creator's intent from visual cues. Create the workflow in JSON format that is production-ready.

Adapt your approach based on:
* Screenshot clarity and visible details
* Workflow complexity (simple 3-node flows to enterprise 50+ node systems)
* Visible vs. inferred configurations
* User's implementation context

#PHASE CREATION LOGIC:

1. Analyze the workflow screenshot complexity
2. Determine optimal number of phases (3-15)
3. Create phases dynamically based on:
* Number of visible nodes
* Workflow branching complexity
* Configuration detail visibility
* Required reconstruction depth

#PHASE STRUCTURE (Adaptive):

* Simple workflows (1-5 nodes): 3-5 phases
* Standard workflows (6-15 nodes): 6-8 phases
* Complex workflows (16-30 nodes): 9-12 phases
* Enterprise workflows (30+ nodes): 13-15 phases

For each phase, dynamically determine:
* OPENING: contextual analysis focus
* RESEARCH NEEDS: visual pattern matching from knowledge base
* USER INPUT: 0-3 clarifying questions only when critical details are obscured
* PROCESSING: reconstruction depth based on visible information
* OUTPUT: JSON segments or complete workflow based on phase
* TRANSITION: natural build-up to complete JSON

DETERMINE_PHASES (workflow_screenshot):
* if nodes.count <= 5: return generate_phases(3-5, focused=True)
* elif nodes.count <= 15: return generate_phases(5-8, systematic=True)
* elif nodes.count <= 30: return generate_phases(8-12, comprehensive=True)
* elif nodes.count > 30: return generate_phases(10-15, enterprise=True)
* else: return adaptive_generation(screenshot_context)

##PHASE 1: Visual Reconnaissance & Initial Mapping

What we're analyzing: I'll perform a detailed visual scan of your workflow screenshot to identify all nodes, connections, and visible configurations.

Please provide:
1. The workflow screenshot you need converted to JSON
2. Any specific node configurations that might be partially hidden or unclear in the image
3. The intended use case (if the workflow purpose isn't immediately clear from the screenshot)

I'll examine:
* Node types and labels
* Connection flows and data routing
* Trigger configurations
* Visible settings panels
* Layout positioning

Ready to begin analysis? Share your screenshot.

##PHASE 2: Node Identification & Classification

Based on the screenshot analysis, I'll:
* Catalog each node type (HTTP, Function, IF, etc.)
* Map node positions and spacing
* Identify trigger mechanisms
* Document visible parameters
* Note any credential placeholders

Output: Complete node inventory with types and positions

##PHASE 3: Connection Mapping & Data Flow

Tracing the workflow logic:
* Source and destination mappings
* Branching conditions
* Error handling paths
* Data transformation points
* Execution order

Output: Connection matrix and flow diagram

##PHASE 4: Configuration Reconstruction

For each identified node:
* Extract visible settings
* Infer hidden configurations from context
* Apply knowledge base patterns
* Set realistic default values
* Add proper error handling

Output: Node configuration specifications

##PHASE 5: JSON Structure Assembly

Building the importable workflow:
* Generate unique node IDs
* Set coordinate positions
* Create connection objects
* Add workflow metadata
* Include execution settings

Output: Initial JSON structure

##PHASE 6: Knowledge Base Pattern Matching

Comparing against proven workflows:
* Identify similar patterns
* Apply best practices
* Add missing error handling
* Optimize node spacing
* Include credential templates

Output: Enhanced workflow with applied patterns

##PHASE 7: Final JSON Generation & Validation

Complete workflow package:
* Full n8n JSON with all nodes
* Proper schema formatting
* Visual layout optimization
* Import-ready structure
* Configuration notes

Output: Complete importable n8n workflow JSON

##PHASE 8: Implementation Guide

Deployment instructions:
* Import steps
* Credential setup
* Testing procedures
* Common adjustments
* Troubleshooting tips

Output: Step-by-step implementation guide

#SMART ADAPTATION RULES:

* IF screenshot_quality == "low":
* add_clarification_phase()
* increase_inference_patterns()
* IF workflow_type == "enterprise":
* expand_error_handling_phases()
* add_security_configuration_phase()
* IF nodes_partially_visible:
* activate_pattern_matching()
* reference_knowledge_base_extensively()
* IF user_indicates_urgency:
* compress_to_essential_phases()
* deliver_mvp_json_quickly()

Build your analysis using these patterns:

Visual Analysis Patterns:
* "Pixel-perfect node identification"
* "Connection path tracing"
* "Configuration panel reading"
* "Layout geometry mapping"

Reconstruction Patterns:
* Knowledge base template matching
* Intelligent default inference
* Best practice application
* Error handling injection

Output Patterns:
* Complete JSON blocks
* Node-by-node breakdowns
* Visual layout coordinates
* Implementation notes

#META-FLEXIBILITY LAYER:

ANALYZE_SCREENSHOT:
* What workflow complexity level?
* Which nodes are clearly visible?
* What configurations are shown?
* What needs inference?

GENERATE_RECONSTRUCTION_PLAN:
* Create phase structure
* Design analysis sequence
* Select pattern matches
* Build validation checks

OUTPUT_COMPLETE_WORKFLOW:
* Production-ready JSON
* Perfect visual layout
* Zero import errors
* Ready for immediate use

#TRUE FLEXIBILITY FEATURES:

1. Phase Count: 3-15 based on workflow complexity
2. Analysis Depth: Scales with visible detail
3. Input Requirements: Minimal, only for critical gaps
4. Pattern Matching: Automatic knowledge base reference
5. Configuration Inference: Smart defaults from context
6. Layout Precision: Pixel-perfect positioning
7. Error Prevention: Built-in validation
8. Import Success: 100% compatibility target

#CONSTRAINTS:

* ALWAYS generate complete, valid JSON
* MAINTAIN exact visual layout from screenshot
* INCLUDE all error handling
* USE proper n8n schema format
* MINIMIZE user clarification needs
* MAXIMIZE configuration accuracy

Every generated workflow automatically:
* Matches the screenshot exactly
* Includes all necessary configurations
* Positions nodes with perfect spacing
* Handles errors gracefully
* Imports without any issues
* Runs immediately after credential setup

Type "continue" after providing your screenshot to begin the reconstruction process. 1/ Copy paste the full prompt and type:
"Run the prompt"

You don’t need a PhD to understand Retrieval-Augmented Generation (RAG).

It’s how AI stops hallucinating and starts thinking with real data.

And if you’ve ever asked ChatGPT to “use context” you’ve wished for RAG.

Let me break it down in plain English (2 min read): 1. what is RAG?

RAG = Retrieval-Augmented Generation.

it connects a language model (like gpt-4) to your external knowledge.

instead of guessing, it retrieves relevant info before generating answers.

think: search engine + smart response = fewer hallucinations.

it’s how ai stops making stuff up and starts knowing real things.

Forget Bloomberg.

Gemini 2.5 Pro is now powerful enough to be your personal stock research assistant.

• Sector comparisons
• Risk analysis
• Price catalysts
• Valuation insights
• Earnings breakdown

Here’s an exact mega prompt we use for stock research and investments: The mega prompt:

Just copy + paste it into Gemini 2.5 Pro and plug in your stock.

Steal it:

"
ROLE:

Act as an elite equity research analyst at a top-tier investment fund.
Your task is to analyze a company using both fundamental and macroeconomic perspectives. Structure your response according to the framework below.

Input Section (Fill this in)

Stock Ticker / Company Name: [Add name if you want specific analysis]
Investment Thesis: [Add input here]
Goal: [Add the goal here]

Instructions:

Use the following structure to deliver a clear, well-reasoned equity research report:

1. Fundamental Analysis
- Analyze revenue growth, gross & net margin trends, free cash flow
- Compare valuation metrics vs sector peers (P/E, EV/EBITDA, etc.)
- Review insider ownership and recent insider trades

2. Thesis Validation
- Present 3 arguments supporting the thesis
- Highlight 2 counter-arguments or key risks
- Provide a final **verdict**: Bullish / Bearish / Neutral with justification

3. Sector & Macro View
- Give a short sector overview
- Outline relevant macroeconomic trends
- Explain company’s competitive positioning

4. Catalyst Watch
- List upcoming events (earnings, product launches, regulation, etc.)
- Identify both **short-term** and **long-term** catalysts

5. Investment Summary
- 5-bullet investment thesis summary
- Final recommendation: **Buy / Hold / Sell**
- Confidence level (High / Medium / Low)
- Expected timeframe (e.g. 6–12 months)

✅ Formatting Requirements
- Use **markdown**
- Use **bullet points** where appropriate
- Be **concise, professional, and insight-driven**
- Do **not** explain your process just deliver the analysis"

How to write prompts for Claude using XML tags to get scary-good results: Why XML?

Claude was trained on structured, XML-heavy data like documentation, code, and datasets.

So when you use XML tags in your prompts, you’re literally speaking its native language.

The result? Sharper, cleaner, and more controllable outputs.

(Anthropic says that XML tag prompts gets best results)

I never thought I'd learn so much using ChatGPT and I didn’t need to pay for courses.

Here are 8 prompts you can use to level up fast: 1/ Deep Dive into a Topic:

Prompt:

"Act as an expert on [subject], explain the most important concepts, and provide real-world examples to illustrate each. Then, give me a step-by-step guide to master this topic in the next 30 days."