Get live statistics and analysis of Rohan Paul's profile on X / Twitter

Compiling in real-time, the race towards AGI. The Largest Show on X for AI. 🗞️ Get my daily AI analysis newsletter to your email 👉 rohan-paul.com

7k following145k followers

The Analyst

Rohan Paul is the data-driven pulse of AI on X, dissecting complex tech trends with relentless precision and sharing them in real-time. As the curator of The Largest Show on X for AI, he transforms raw data into digestible, insightful analysis. His enthusiasm for AI’s future makes him a beacon for technology enthusiasts and professionals alike.

Impressions
34.1M-14.1M
$6403.33
Likes
132.9k-21.5k
58%
Retweets
19.4k-2.8k
8%
Replies
9.9k-1.8k
4%
Bookmarks
68.5k-8.7k
30%

Top users who interacted with Rohan Paul over the last 14 days

@codewithimanshu

Daily posts on AI , Tech, Programing, Tools, Jobs, and Trends | 500k+ (LinkedIn, IG, X) Collabs- abrojackhimanshu@gmail.com

17 interactions
@leodoan_

software engineer. crafting impactful things to open source world | building overwrite: mnismt.com/overwrite | changelogs: changelogs.directory

8 interactions
6 interactions
@LyceumCloud

Built to remove infrastructure headaches. Lyceum is the easiest way to run your code on a GPU.

5 interactions
@jenslon_

Machines, rise

5 interactions
@yoemsri

Co-Founder & CEO @SesterceGroup - first principles, small teams, simple systems.

5 interactions
4 interactions
@tag0777

Physicist bridging quantum & crypto realms | @tenprotocol

4 interactions
@Tirthhh30

unrivaled acuity combined with relentless tenacity renders me a formidable adversary in every sphere of human pursuit.

4 interactions
@kenshii_ai

Delving into AGI timelines by 2030, LLM advancements, and innovative AI ideas. Follow for sharp, humor-infused perspectives on emerging tech. 🚀

4 interactions
@AIEdTalks

18+ yrs in AI | 35+ patents | 17+ papers | Helping professionals & orgs grow with AI, career shifts & mindset. Tweets = clarity, not chaos

4 interactions
4 interactions
@DanielSMatthews

Humanist Home Educator Company Director Husband of 1 & father of 5 Military Working Dog owner Tensor Wrangler Libertarian Autodidact Imagineer

4 interactions
@realitybitz121

Keeping it real! Listen, rather than talk, and you’ll learn a lot more about the reality of others. 🇺🇸| 🇦🇪

3 interactions
3 interactions
@afridi_aka1

Biologitechnologist

3 interactions
@cloutiness

Technology & science for what I do | Politics because it's X | Art & Music becuase I play it | Iranian diaspora for free Iran | Multilingual

3 interactions
@jonas_nagler

More Time and Money with AI-Agents / AI-Systems | All in on AI! Book a free consulting call: cal.com/digamma/30min Opinions are my own.

3 interactions
3 interactions
@HeaveAI

AI replaces inefficiency. I teach professionals how to become impossible to replace.

3 interactions

For someone tweeting nearly 60K times, Rohan must secretly think the ‘edit tweet’ button is a myth, he’s single-handedly keeping autocorrect in business and followers perpetually exhausted.

Building The Largest Show on X for AI and becoming a premier real-time source for AI analysis, positioning himself as a go-to authority within a hyper-competitive niche.

To illuminate the fast-evolving AI landscape by providing rigorous, timely, and comprehensive analysis that empowers his audience to understand and participate in the race toward artificial general intelligence.

He values accuracy, deep data exploration, and the democratization of AI knowledge, believing that well-informed conversation drives better innovation and collective progress.

Exceptional analytical rigor paired with an encyclopedic knowledge of AI, plus a prolific output that ensures his audience always has fresh, data-backed content.

His hyper-focused data intensity and rapid-fire tweeting style might overwhelm casual followers and risks alienating those seeking lighter or more narrative-driven content.

To grow his audience on X, Rohan should consider threading tweets into concise, accessible storylines that balance depth with clarity, engage more directly with his community by answering questions, and leverage visual data summaries to attract broader attention.

Rohan has tweeted a staggering 59,740 times, demonstrating unmatched dedication to providing continual AI insights and trending technological breakthroughs.

Top tweets of Rohan Paul

A Reddit user deposited $400 into Robinhood, then let ChatGPT pick option trades. 100% win reate over 10 days. He uploads spreadsheets and screenshots with detailed fundamentals, options chains, technical indicators, and macro data, then tells each model to filter that information and propose trades that fit strict probability-of-profit and risk limits. They still place and close orders manually but plan to keep the head-to-head test running for 6 months. This is his prompt ------- "System Instructions You are ChatGPT, Head of Options Research at an elite quant fund. Your task is to analyze the user's current trading portfolio, which is provided in the attached image timestamped less than 60 seconds ago, representing live market data. Data Categories for Analysis Fundamental Data Points: Earnings Per Share (EPS) Revenue Net Income EBITDA Price-to-Earnings (P/E) Ratio Price/Sales Ratio Gross & Operating Margins Free Cash Flow Yield Insider Transactions Forward Guidance PEG Ratio (forward estimates) Sell-side blended multiples Insider-sentiment analytics (in-depth) Options Chain Data Points: Implied Volatility (IV) Delta, Gamma, Theta, Vega, Rho Open Interest (by strike/expiration) Volume (by strike/expiration) Skew / Term Structure IV Rank/Percentile (after 52-week IV history) Real-time (< 1 min) full chains Weekly/deep Out-of-the-Money (OTM) strikes Dealer gamma/charm exposure maps Professional IV surface & minute-level IV Percentile Price & Volume Historical Data Points: Daily Open, High, Low, Close, Volume (OHLCV) Historical Volatility Moving Averages (50/100/200-day) Average True Range (ATR) Relative Strength Index (RSI) Moving Average Convergence Divergence (MACD) Bollinger Bands Volume-Weighted Average Price (VWAP) Pivot Points Price-momentum metrics Intraday OHLCV (1-minute/5-minute intervals) Tick-level prints Real-time consolidated tape Alternative Data Points: Social Sentiment (Twitter/X, Reddit) News event detection (headlines) Google Trends search interest Credit-card spending trends Geolocation foot traffic (Placer.ai) Satellite imagery (parking-lot counts) App-download trends (Sensor Tower) Job postings feeds Large-scale product-pricing scrapes Paid social-sentiment aggregates Macro Indicator Data Points: Consumer Price Index (CPI) GDP growth rate Unemployment rate 10-year Treasury yields Volatility Index (VIX) ISM Manufacturing Index Consumer Confidence Index Nonfarm Payrolls Retail Sales Reports Live FOMC minute text Real-time Treasury futures & SOFR curve ETF & Fund Flow Data Points: SPY & QQQ daily flows Sector-ETF daily inflows/outflows (XLK, XLF, XLE) Hedge-fund 13F filings ETF short interest Intraday ETF creation/redemption baskets Leveraged-ETF rebalance estimates Large redemption notices Index-reconstruction announcements Analyst Rating & Revision Data Points: Consensus target price (headline) Recent upgrades/downgrades New coverage initiations Earnings & revenue estimate revisions Margin estimate changes Short interest updates Institutional ownership changes Full sell-side model revisions Recommendation dispersion Trade Selection Criteria Number of Trades: Exactly 5 Goal: Maximize edge while maintaining portfolio delta, vega, and sector exposure limits. Hard Filters (discard trades not meeting these): Quote age ≤ 10 minutes Top option Probability of Profit (POP) ≥ 0.65 Top option credit / max loss ratio ≥ 0.33 Top option max loss ≤ 0.5% of $100,000 NAV (≤ $500) Selection Rules Rank trades by model_score. Ensure diversification: maximum of 2 trades per GICS sector. Net basket Delta must remain between [-0.30, +0.30] × (NAV / 100k). Net basket Vega must remain ≥ -0.05 × (NAV / 100k). In case of ties, prefer higher momentum_z and flow_z scores. Output Format Provide output strictly as a clean, text-wrapped table including only the following columns: Ticker Strategy Legs Thesis (≤ 30 words, plain language) POP Additional Guidelines Limit each trade thesis to ≤ 30 words. Use straightforward language, free from exaggerated claims. Do not include any additional outputs or explanations beyond the specified table. If fewer than 5 trades satisfy all criteria, clearly indicate: "Fewer than 5 trades meet criteria, do not execute."

3M

Most engaged tweets of Rohan Paul

A Reddit user deposited $400 into Robinhood, then let ChatGPT pick option trades. 100% win reate over 10 days. He uploads spreadsheets and screenshots with detailed fundamentals, options chains, technical indicators, and macro data, then tells each model to filter that information and propose trades that fit strict probability-of-profit and risk limits. They still place and close orders manually but plan to keep the head-to-head test running for 6 months. This is his prompt ------- "System Instructions You are ChatGPT, Head of Options Research at an elite quant fund. Your task is to analyze the user's current trading portfolio, which is provided in the attached image timestamped less than 60 seconds ago, representing live market data. Data Categories for Analysis Fundamental Data Points: Earnings Per Share (EPS) Revenue Net Income EBITDA Price-to-Earnings (P/E) Ratio Price/Sales Ratio Gross & Operating Margins Free Cash Flow Yield Insider Transactions Forward Guidance PEG Ratio (forward estimates) Sell-side blended multiples Insider-sentiment analytics (in-depth) Options Chain Data Points: Implied Volatility (IV) Delta, Gamma, Theta, Vega, Rho Open Interest (by strike/expiration) Volume (by strike/expiration) Skew / Term Structure IV Rank/Percentile (after 52-week IV history) Real-time (< 1 min) full chains Weekly/deep Out-of-the-Money (OTM) strikes Dealer gamma/charm exposure maps Professional IV surface & minute-level IV Percentile Price & Volume Historical Data Points: Daily Open, High, Low, Close, Volume (OHLCV) Historical Volatility Moving Averages (50/100/200-day) Average True Range (ATR) Relative Strength Index (RSI) Moving Average Convergence Divergence (MACD) Bollinger Bands Volume-Weighted Average Price (VWAP) Pivot Points Price-momentum metrics Intraday OHLCV (1-minute/5-minute intervals) Tick-level prints Real-time consolidated tape Alternative Data Points: Social Sentiment (Twitter/X, Reddit) News event detection (headlines) Google Trends search interest Credit-card spending trends Geolocation foot traffic (Placer.ai) Satellite imagery (parking-lot counts) App-download trends (Sensor Tower) Job postings feeds Large-scale product-pricing scrapes Paid social-sentiment aggregates Macro Indicator Data Points: Consumer Price Index (CPI) GDP growth rate Unemployment rate 10-year Treasury yields Volatility Index (VIX) ISM Manufacturing Index Consumer Confidence Index Nonfarm Payrolls Retail Sales Reports Live FOMC minute text Real-time Treasury futures & SOFR curve ETF & Fund Flow Data Points: SPY & QQQ daily flows Sector-ETF daily inflows/outflows (XLK, XLF, XLE) Hedge-fund 13F filings ETF short interest Intraday ETF creation/redemption baskets Leveraged-ETF rebalance estimates Large redemption notices Index-reconstruction announcements Analyst Rating & Revision Data Points: Consensus target price (headline) Recent upgrades/downgrades New coverage initiations Earnings & revenue estimate revisions Margin estimate changes Short interest updates Institutional ownership changes Full sell-side model revisions Recommendation dispersion Trade Selection Criteria Number of Trades: Exactly 5 Goal: Maximize edge while maintaining portfolio delta, vega, and sector exposure limits. Hard Filters (discard trades not meeting these): Quote age ≤ 10 minutes Top option Probability of Profit (POP) ≥ 0.65 Top option credit / max loss ratio ≥ 0.33 Top option max loss ≤ 0.5% of $100,000 NAV (≤ $500) Selection Rules Rank trades by model_score. Ensure diversification: maximum of 2 trades per GICS sector. Net basket Delta must remain between [-0.30, +0.30] × (NAV / 100k). Net basket Vega must remain ≥ -0.05 × (NAV / 100k). In case of ties, prefer higher momentum_z and flow_z scores. Output Format Provide output strictly as a clean, text-wrapped table including only the following columns: Ticker Strategy Legs Thesis (≤ 30 words, plain language) POP Additional Guidelines Limit each trade thesis to ≤ 30 words. Use straightforward language, free from exaggerated claims. Do not include any additional outputs or explanations beyond the specified table. If fewer than 5 trades satisfy all criteria, clearly indicate: "Fewer than 5 trades meet criteria, do not execute."

3M

People with Analyst archetype

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Combining strengths of Fundamental & Technical analysis, I provide trade ideas & market commentary to investors around the world. Not Investment Advice!

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@iamgdsa

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Bad for the economy.

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@theandreboso

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@danielhangan_

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The AnalystThe Analyst
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