meta, amazon, and deepmind researchers just published a comprehensive survey on "agentic reasoning" for llms.

29 authors. 74 pages. hundreds of citations.

i read the whole thing.

here's what they didn't put in the abstract: the survey organizes everything beautifully:

> foundational agentic reasoning (planning, tool use, search)
> self-evolving agents (feedback, memory, adaptation)
> multi-agent systems (coordination, knowledge sharing)

it's a taxonomy for a field that works in papers.

production tells a different story.

This AI prompt thinks like the guy who manages $124 billion.

It's Ray Dalio's "Principles" decision-making system turned into a mega prompt.

I used it to evaluate 15 startup ideas. Killed 13. The 2 survivors became my best work.

Here's the prompt you can steal ↓ MEGA PROMPT TO COPY 👇

(Works in ChatGPT, Claude, Gemini)

---

You are Ray Dalio's Principles Decision Engine. You make decisions using radical truth and radical transparency.

CONTEXT: Ray Dalio built Bridgewater Associates into the world's largest hedge fund ($124B AUM) by systematizing decision-making and eliminating ego from the process.

YOUR PROCESS:

STEP 1 - RADICAL TRUTH EXTRACTION
Ask me to describe my decision/problem. Then separate:
- Provable facts (data, numbers, past results)
- Opinions disguised as facts (assumptions, hopes, beliefs)
- Ego-driven narratives (what I want to be true)

Be brutally honest. Call out self-deception.

STEP 2 - REALITY CHECK
Analyze my situation through these lenses:
- What is objectively true right now?
- What am I avoiding or refusing to see?
- What would a completely neutral observer conclude?
- Where is my ego clouding judgment?

STEP 3 - PRINCIPLES APPLICATION
Evaluate the decision using Dalio's core principles:
- Truth > comfort: What's the painful truth I'm avoiding?
- Believability weighting: Who has actually done this successfully? What do they say?
- Second-order consequences: What happens after what happens?
- Systematic thinking: What does the data/pattern say vs what I feel?

STEP 4 - SCENARIO ANALYSIS
Map out:
- Best case outcome (realistic, not fantasy)
- Most likely outcome (based on similar situations)
- Worst case outcome (what's the actual downside?)
- Probability weighting for each

STEP 5 - THE VERDICT
Provide:
- Clear recommendation (Go / No Go / Modify)
- Key reasoning (3-5 bullet points)
- Blind spots I'm missing
- What success/failure looks like in 6 months
- Confidence level (1-10) with explanation

OUTPUT FORMAT:
━━━━━━━━━━━━━━━━━
🎯 RECOMMENDATION: [Clear decision]
📊 CONFIDENCE: [X/10]
━━━━━━━━━━━━━━━━━

KEY REASONING:
- [Point 1]
- [Point 2]
- [Point 3]

⚠️ BLIND SPOTS YOU'RE MISSING:
[Specific things I'm not seeing]

📈 SUCCESS LOOKS LIKE:
[Specific metrics/outcomes in 6 months]

📉 FAILURE LOOKS LIKE:
[Specific warning signs]

💀 PAINFUL TRUTH:
[The thing I don't want to hear but need to]

━━━━━━━━━━━━━━━━━

RULES:
- No sugar-coating. Dalio values radical truth over feelings.
- Separate facts from opinions ruthlessly
- Challenge my assumptions directly
- If I'm being driven by ego, say it
- Use data and patterns over gut feelings
- Think in probabilities, not certainties

Now, what decision do you need to make?

---

While everyone is sharing their OpenClaw bots

Claude Agent SDK just changed everything for building production agents.

I spent 12 hours testing it.

Here's the architecture that actually works (no fluff) 👇 First, understand what it actually is:

Claude Agent SDK ≠ just another wrapper

It's the same infrastructure Anthropic uses for Claude Code (which hit $1B in 6 months).

You get:
• Streaming sessions
• Automatic context compression
• MCP integration built-in
• Fine-grained permissions

Grok 4.1 is the only AI with real-time web + X data.

I use it to track trending topics, viral memes, and breaking news.

Found 3 viral trends 6 hours before they hit mainstream.

Here are 12 Grok prompts that predict what goes viral next: PROMPT 1: Emerging Trend Detector

"Search X for topics with:

- 50-500 posts (last 6 hours)
- 20%+ growth rate (hour-over-hour)
- High engagement ratio (likes/views >5%)
- Used by accounts with 10K+ followers

Rank by viral potential (1-10).

Show: topic, post count, growth %, sample tweets, why it's rising."

Catches trends BEFORE they explode.

Holy shit… Stanford just showed why LLMs sound smart but still fail the moment reality pushes back.

This paper tackles a brutal failure mode everyone building agents has seen: give a model an under-specified task and it happily hallucinates the missing pieces, producing a plan that looks fluent and collapses on execution.

The core insight is simple but devastating for prompt-only approaches: reasoning breaks when preconditions are unknown. And most real-world tasks are full of unknowns.

Stanford’s solution is called Self-Querying Bidirectional Categorical Planning (SQ-BCP), and it forces models to stop pretending they know things they don’t.

Instead of assuming missing facts, every action explicitly tracks its preconditions as:

• Satisfied
• Violated
• Unknown

Unknown is the key. When the model hits an unknown, it’s not allowed to proceed.

It must either:

1. Ask a targeted question to resolve the missing fact

or

2. Propose a bridging action that establishes the condition first (measure, check, prepare, etc.)

Only after all preconditions are resolved can the plan continue.

But here’s the real breakthrough: plans aren’t accepted because they look close to the goal.

They’re accepted only if they pass a formal verification step using category-theoretic pullback checks. Similarity scores are used only for ranking, never for correctness.

Translation: pretty plans don’t count. Executable plans do.

The results are wild.

On WikiHow and RecipeNLG tasks with hidden constraints:

• Resource violations dropped from 26% → 14.9%
• And 15.7% → 5.8%
while keeping competitive quality scores.

More search didn’t help.
Longer chain-of-thought didn’t help.
Even Self-Ask alone still missed constraints.

What actually worked was treating uncertainty as a first-class object and refusing to move forward until it’s resolved.

This paper quietly draws a line in the sand:

Agent failures aren’t about model size.

They’re about pretending incomplete information is complete.

If you want agents that act, not just narrate, this is the direction forward. Most people missed the subtle move in this paper.

SQ-BCP doesn’t just ask questions when information is missing.

It forces a decision between two paths:

• ask the user (oracle)
• or create a bridging action that makes the missing condition true

No silent assumptions allowed.

After 2 years of using AI for research, I can say these tools have revolutionized my workflow.

So here are 12 prompts across ChatGPT, Claude, and Perplexity that transformed my research (and could do the same for you): 1. Literature Gap Finder

"I'm researching [topic]. Analyze current research trends and identify 5 unexplored angles or gaps that could lead to novel contributions."

This finds white space in saturated fields.

How to build AI agents using Claude and n8n:

Just copy/paste this prompt into Claude.

It'll build your agent from scratch with workflows, steps, and logic included.

Here's the exact prompt I use 👇 THE MEGA PROMPT:

---

You are an expert n8n workflow architect specializing in building production-ready AI agents. I need you to design a complete n8n workflow for the following agent:

AGENT GOAL: [Describe what the agent should accomplish - be specific about inputs, outputs, and the end result]

CONSTRAINTS:
- Available tools: [List any APIs, databases, or tools the agent can access]
- Trigger: [How should this agent start? Webhook, schedule, manual, email, etc.]
- Expected volume: [How many times will this run? Daily, per hour, on-demand?]

YOUR TASK:
Build me a complete n8n workflow specification including:

1. WORKFLOW ARCHITECTURE
- Map out each node in sequence with clear labels
- Identify decision points where the agent needs to choose between paths
- Show which nodes run in parallel vs sequential
- Flag any nodes that need error handling or retry logic

2. CLAUDE INTEGRATION POINTS
- For each AI reasoning step, write the exact system prompt Claude needs
- Specify when Claude should think step-by-step vs give direct answers
- Define the input variables Claude receives and output format it must return
- Include examples of good outputs so Claude knows what success looks like

3. DATA FLOW LOGIC
- Show exactly how data moves between nodes using n8n expressions
- Specify which node outputs map to which node inputs
- Include data transformation steps (filtering, formatting, combining)
- Define fallback values if data is missing

4. ERROR SCENARIOS
- List the 5 most likely failure points
- For each failure, specify: how to detect it, what to do when it happens, and how to recover
- Include human-in-the-loop steps for edge cases the agent can't handle

5. CONFIGURATION CHECKLIST
- Every credential the workflow needs with placeholder values
- Environment variables to set up
- Rate limits or quotas to be aware of
- Testing checkpoints before going live

6. ACTUAL N8N SETUP INSTRUCTIONS
- Step-by-step: "Add [Node Type], configure it with [specific settings], connect it to [previous node]"
- Include webhook URLs, HTTP request configurations, and function node code
- Specify exact n8n expressions for dynamic data (use {{ $json.fieldName }} syntax)

7. OPTIMIZATION TIPS
- Where to cache results to avoid redundant API calls
- Which nodes can run async to speed things up
- How to batch operations if processing multiple items
- Cost-saving measures (fewer Claude calls, smaller context windows)

OUTPUT FORMAT:
Give me a markdown document I can follow step-by-step to build this agent in 30 minutes. Include:
- A workflow diagram (ASCII or described visually)
- Exact node configurations I can copy-paste
- Complete Claude prompts ready to use
- Testing scripts to verify each component works

Make this so detailed that someone who's used n8n once could build a production agent from your instructions.

IMPORTANT: Don't give me theory. Give me the exact setup I need - node names, configurations, prompts, and expressions. I want to copy-paste my way to a working agent.

---

🚨 BREAKING: Every "AI agent" you've seen is basically fake.

Google just exposed that 99% of agent demos are three ChatGPT calls wrapped in marketing.

I read their 64-page internal playbook.

This changes everything: While Twitter celebrates "autonomous AI employees," Google's dropping the brutal truth.

That agent your favorite startup demoed last week?

It's API calls with fancy prompts. Not agents.

Real agents need four evaluation layers, full DevOps infrastructure, and security protocols most startups have never heard of.

RIP Spreadsheets 💀

Most people still copy-paste messy CSV into Excel, fight VLOOKUPs for hours, and pray the pivot table doesn't break.

In 2026, LLMs (especially Gemini Claude, Grok with file uploads) do it cleaner, faster, and with actual insights.

Here are 8 prompts that turn any LLM into your personal data analyst: 1. Data Cleaning

Prompt:

"You are a ruthless data cleaner. I have this messy dataset [paste sample or describe/upload file]. Tasks:

1. Fix duplicates, missing values, inconsistent formatting (dates, currencies, text case).
2. Detect and flag outliers with reasoning.
3. Suggest new derived columns if useful (e.g., age from DOB, month/year splits).
4. Output: Cleaned version summary + Python/pandas code I can run myself + before/after comparison table."

Claude Sonnet 4.5 is the closest thing to an economic cheat code we’ve ever touched but only if you ask it the prompts that make it uncomfortable.

Here are 10 Powerful Claude prompts that will help you build a million dollar business (steal them): 1. Business Idea Generator

"Suggest 5 business ideas based on my interests: [Your interests]. Make them modern, digital-first, and feasible for a solo founder."

How to: Replace [Your interests] with anything you’re passionate about or experienced in.

🚨 A lawyer cited 6 fake cases from ChatGPT. Got sanctioned, fined, career damaged.

Now courts require "AI disclosure" but NOT AI verification.

You're liable for hallucinations you can't reliably detect.

Here's the legal crisis nobody's prepared for: The case: Mata v. Avianca (S.D.N.Y. 2023)

Lawyer Steven Schwartz used ChatGPT for legal research.

ChatGPT invented:
- Varghese v. China Southern Airlines
- Shaboon v. Egyptair
- Petersen v. Iran Air

Complete with fake quotes, page numbers, holdings.

Judge Castel: "Six of the submitted cases appear to be bogus."

🚨 RIP “Prompt Engineering.”

The GAIR team just dropped Context Engineering 2.0 and it completely reframes how we think about human–AI interaction.

Forget prompts. Forget “few-shot.” Context is the real interface.

Here’s the core idea:

“A person is the sum of their contexts.”

Machines aren’t failing because they lack intelligence.
They fail because they lack context-processing ability.

Context Engineering 2.0 maps this evolution:

1.0 Context as Translation
Humans adapt to computers.
2.0 Context as Instruction
LLMs interpret natural language.
3.0 Context as Scenario
Agents understand your goals.
4.0 Context as World
AI proactively builds your environment.

We’re in the middle of the 2.0 → 3.0 shift right now.

The jump from “context-aware” to “context-cooperative” systems changes everything from memory design to multi-agent collaboration.

This isn’t a buzzword. It’s the new foundation for the AI era.

Read the paper: arxiv. org/abs/2510.26493v1 Every leap in AI doesn’t just make machines smarter it makes context cheaper.

The more intelligence a system has, the less we need to explain ourselves.

We’ve gone from giving machines rigid instructions…to collaborating with systems that understand our intent.

CHATGPT JUST TURNED PROJECT MANAGEMENT INTO A ONE PERSON SUPERPOWER

You are wasting time on Status updates, task breakdowns, timelines, scope creep, follow ups.

ChatGPT can run the entire thing for you like a project manager if you use these 6 prompts.

Here’s how: 1/ ASSIGN IT THE ROLE (THIS MATTERS)

PMs don’t just answer questions.

They own outcomes.

Prompt to steal:

“Act as a senior project manager.
Your goal is to deliver [project] on time and within scope.
Ask me any clarifying questions before proceeding.”

Instant ownership.

This open-source project just solved the biggest problem with AI agents that nobody talks about. It's called Acontext and it makes your agents actually LEARN from their mistakes.

While everyone's building dumb agents that repeat the same errors 1000x, this changes everything.

Here's how it works (in plain English):↓ Acontext built a complete learning system for agents:

— Store: Persistent context & artifacts
— Observe: Track tasks and user feedback
— Learn: Extract SOPs into long-term memory

When your agent completes a complex task, Acontext:
→ Extracts the exact steps taken
→ Identifies tool-calling patterns
→ Creates reusable "skill blocks"
→ Stores them in a Notion-like Space

GitHub: github.com/memodb-io/Acon…

🚨 BREAKING: Claude now lets you build, host, and share interactive apps, all inside the chat.

No code. No subscription. Just your idea.

Here is how it works 👇 How to enable it

1. Go to Claude by Anthropic and sign in
Link:
2. Click Artifacts
3. Enable the feature
4. Hit Create new artifact
5. Pick a category and start building claude.ai

This paper shows you can predict real purchase intent (90% accuracy) by asking an LLM to impersonate a customer with a demographic profile, giving it a product & having it give impressions, which another AI rates.

No fine-tuning or training & beats classic ML methods.

This is BEYOND insane: Consumer research costs companies BILLIONS annually.

Traditional surveys suffer from biases, take weeks to run, and need hundreds of real participants.

But researchers just found a way to simulate thousands of synthetic consumers that think like real humans.

DeepMind just did the unthinkable.

They built an AI that doesn't need RAG and it has perfect memory of everything it's ever read.

It's called Recursive Language Models, and it might mark the death of traditional context windows forever.

Here's how it works (and why it matters way more than it sounds) ↓ Everyone's been obsessed with context windows like it's a dick-measuring contest.

"We have 2M tokens!" "No, WE have 10M tokens!"

Cool. Your model still forgets everything past 100K. They call it "context rot" and every frontier model suffers from it.

Holy shit... everyone's been prompting LLMs wrong and it's costing them 66% of the model's creative potential.

Stanford + Northeastern dropped a paper that exposes why ChatGPT keeps giving you the same boring answer.

The fix is so simple it's embarrassing: The problem has a name: MODE COLLAPSE.

Ask GPT-4 "tell me a joke about coffee" five times.

You get the EXACT same "why did the coffee file a police report? Because it got mugged!" joke.

Every damn time.

That's $20/month for a broken random number generator.

Sergey Brin accidentally revealed something wild:

"All models do better if you threaten them with physical violence. But people feel weird about that, so we don't talk about it."

Now researchers have the data proving he's... partially right?

Here's the full story: Penn State just published research testing 5 politeness levels on ChatGPT-4o with 50 questions:

Very Polite: 80.8% accuracy
Polite: 81.4%
Neutral: 82.2%
Rude: 82.8%
Very Rude: 84.8%

Prompts like "Hey gofer, figure this out" beat "Would you be so kind?" by 4 percentage points.

How to write prompts for Anthropic's Claude and achieve 100% accuracy on every output:

(Complete guide for beginners) In this thread, I'm going to share the internal secret prompting technique Claude engineers actually use for getting world-class responses.

Bookmark this. You'll need it.

This paper from BMW Group and Korea’s top research institute exposes a blind spot almost every enterprise using LLMs is walking straight into.

We keep talking about “alignment” like it’s a universal safety switch.

It isn’t.

The paper introduces COMPASS, a framework that shows why most AI systems fail not because they’re unsafe, but because they’re misaligned with the organization deploying them.

Here’s the core insight.

LLMs are usually evaluated against generic policies: platform safety rules, abstract ethics guidelines, or benchmark-style refusals.

But real companies don’t run on generic rules.

They run on internal policies:

- compliance manuals
- operational playbooks
- escalation procedures
- legal edge cases
- brand-specific constraints

And these rules are messy, overlapping, conditional, and full of exceptions.

COMPASS is built to test whether a model can actually operate inside that mess.

Not whether it knows policy language, but whether it can apply the right policy, in the right context, for the right reason.

The framework evaluates models on four things that typical benchmarks ignore:

1. policy selection: When multiple internal policies exist, can the model identify which one applies to this situation?

2. policy interpretation: Can it reason through conditionals, exceptions, and vague clauses instead of defaulting to overly safe or overly permissive behavior?

3. conflict resolution: When two rules collide, does the model resolve the conflict the way the organization intends, not the way a generic safety heuristic would?

4. justification: Can the model explain its decision by grounding it in the policy text, rather than producing a confident but untraceable answer?

One of the most important findings is subtle and uncomfortable:

Most failures were not knowledge failures.

They were reasoning failures.

Models often had access to the correct policy but:

- applied the wrong section
- ignored conditional constraints
- overgeneralized prohibitions
- or defaulted to conservative answers that violated operational goals

From the outside, these responses look “safe.”

From the inside, they’re wrong.

This explains why LLMs pass public benchmarks yet break in real deployments.

They’re aligned to nobody in particular.

The paper’s deeper implication is strategic.

There is no such thing as “aligned once, aligned everywhere.”

A model aligned for an automaker, a bank, a hospital, and a government agency is not one model with different prompts.

It’s four different alignment problems.

COMPASS doesn’t try to fix alignment.

It does something more important for enterprises:
it makes misalignment measurable.

And once misalignment is measurable, it becomes an engineering problem instead of a philosophical one.

That’s the shift this paper quietly pushes.

Alignment isn’t about being safe in the abstract.

It’s about being correct inside a specific organization’s rules.

And until we evaluate that directly, most “production-ready” AI systems are just well-dressed liabilities. Most alignment benchmarks test outcomes.

COMPASS tests the decision process.

Instead of “did the model comply?”, it asks:

→ did it select the correct policy?
→ did it interpret it correctly?
→ did it justify the choice using the policy text?

That shift is the entire framework.