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Sep 18 8 tweets 4 min read Read on X
AI can now predict what you're thinking before you say it 🤯

New research from CMU introduces "Social World Models" - AI that doesn't just parse what people say, but predicts what they're thinking, what they'll do next, and how they'll react to your actions.

The breakthrough is S³AP (Social Simulation Analysis Protocol). Instead of feeding AI raw conversations, they structure social interactions like a simulation game - tracking who knows what, who believes what, and what everyone's mental state looks like at each moment.

The results are wild. On theory-of-mind tests, they jumped from 54% to 96% accuracy. But the real magic happens when these models start interacting.

The AI doesn't just respond anymore - it runs mental simulations first. "If I say this, how will they interpret it? What will they think I'm thinking? How does that change what I should actually say?"

This isn't just better chatbots. It's AI that can navigate office politics, understand when someone is lying, predict how a negotiation will unfold. AI that gets the subtext.

The researchers tested this on competitive vs cooperative scenarios. In competitive settings (like bargaining), the social world models helped even more - because modeling your opponent's mental state matters most when interests don't align.

Here's what's unsettling: the AI doesn't need to be the smartest model to build these social representations.

A smaller model can create the "mental maps" that help larger models reason better. Social intelligence might be more about representation than raw compute.

We're not just building AI that understands the world anymore. We're building AI that understands 'us'.Image
The key insight: humans navigate social situations by constantly running mental simulations. "If I say this, they'll think that, so I should actually say this other thing." AI has been missing this predictive layer entirely. Image
S³AP breaks down social interactions like a game engine.

Instead of messy dialogue, it tracks: who's in the room, what each person observed, what they're thinking internally, and what actions they take. Suddenly AI can follow the social physics. Image
The "Foresee and Act" algorithm is where it gets scary good. Before responding, the AI simulates how the other person will interpret its message, then optimizes for the actual goal.

It's not just reactive anymore - it's strategically predictive. Image
Tested on competitive negotiations vs cooperative tasks. The social world models helped more in competitive settings. Makes sense - when interests align, you can be direct.

When they don't, modeling the other person's mental state becomes critical. Image
What's wild: the AI that builds the best "mental maps" isn't necessarily the smartest overall model. Social intelligence might be more about representation than raw compute.

We're learning that understanding minds has different requirements than understanding physics.

Read the full paper: arxiv.org/abs/2509.00559Image
The AI prompt library your competitors don't want you to find

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Grab it before it's gone 👇
godofprompt.ai/pricing
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More from @godofprompt

Sep 17
Why the fuck no one is talking about this 🤯

Perplexity Comet AI browser is the most powerful agentic browser in the market.

I've been using it for email automation, writing documents, and doing all the lazy work I hate.

Here are 8 ways I use Perplexity's Comet browser: Image
1. Shopping
2. Writing & Content Creation
Read 12 tweets
Sep 16
OpenAI just published the first comprehensive study of how 700 million people actually use ChatGPT.

The results destroy every assumption about AI adoption.

Here's what they found: Image
MYTH BUSTED: "ChatGPT is mainly for work"

Reality check: Only 27% of ChatGPT usage is work-related. 73% is personal. And the gap is widening every month.

The productivity revolution narrative completely misses how people actually use AI. Image
THE REAL TOP 3 USE CASES

Forget coding and business automation. Here's what 700M people actually do:

1. Practical Guidance (29%) - Learning, how-to advice, tutoring
2. Seeking Information (24%) - Replacing Google searches
3. Writing (24%) - Editing emails, documents, content

These three account for 77% of ALL ChatGPT usage.Image
Read 13 tweets
Sep 15
holy sh*t

claude generated a 17-page AI startup opportunities report in the style of McKinsey.

all it took is one simple prompt.

take a look inside 👇 Image
1/ after 7 minutes of research, it gave me an entire presentation on opportunities for AI startups in the next 10 years.

scroll down to see the prompt I used.

executive summary:Image
Image
2/ i used this simple prompt:

"Adopt the role of an expert McKinsey consultant.

Perform deep research on [TOPIC] based on authoritative sources and create a PowerPoint Presentation listing [TOPIC] with SWOT Analysis, Opportunity Matrix, and Porter's 5 Forces."
Read 14 tweets
Sep 15
99.9% of prompt engineering guides are bullshit.

They overcomplicate everything.

If you have 3 minutes, I'm going to make you an expert prompt engineer.

Comment "Prompt" and I'll DM you my complete guide on prompting engineering.

(Open this thread)
Most people suck at prompting because they treat AI like Google.

They type random questions and expect magic. Wrong approach. AI is more like hiring a freelancer.

You need to be clear about what you want, give context, and set expectations. This mental shift changes everything.
WEEK 1-2: STOP BEING VAGUE

Bad prompt: "Help me with marketing"

Good prompt: "Write 5 email subject lines for a project management SaaS launching to small business owners. Make them curiosity-driven and under 50 characters."

See the difference? Specific request, clear audience, exact format. Practice this for 30 minutes daily.
Read 17 tweets
Sep 14
the default belief: chatgpt gets better → chatgpt gets more accurate.

but openai’s new paper reveals something different:

• hallucinations are mathematically inevitable
• “i don’t know” is punished more than lying
• fixing it would ruin chatgpt’s entire appeal

the truth will change how you see LLMs forever 🧵:Image
1/ Why do AIs like ChatGPT hallucinate?

It’s not bad data. It’s not poor training.

It’s how they’re built.

Language models predict one word at a time. That structure creates errors - even if the data is perfect. Image
2/ Just look at the numbers.

The paper shows this shocking insight:

A sentence’s error rate is at least 2x higher
than answering a yes/no question.

Why? Because errors snowball as more words are generated. Image
Read 17 tweets
Sep 12
I just asked Claude to build a full market analysis Excel model.. and it nailed it.

One prompt. Everything done. Pivot tables. Power Query. Monte Carlo simulations. Competitive landscapes.

Here's the prompt 👇: Image
1/ I gave Claude this single prompt:

Create a complete market analysis Excel model for the [INSERT INDUSTRY] industry. Conduct deep research, step by step, to find authoratitative data on the requested analyses below.

Build:
- TAM/SAM/SOM analysis 2024-2030
- Competitive landscape (top 20 players, market share evolution)
- Industry P&L benchmarks (margins by company size)
- Value chain analysis with cost breakdowns
- Technology adoption S-curves
- Regional market penetration models
- Unit economics by business model type
- Investment flow tracking (VC/PE/M&A deals)
- Regulatory impact scenarios
Include pivot tables, power query for updates, Monte Carlo simulation for forecasts.
Professional formatting, interactive dashboard. Output .xlsx.Image
2/ Most humans or tools would take days - or weeks - to deliver all this.

Claude delivered a fully functional model, professionally formatted, with interactive dashboards. Image
Read 10 tweets

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