MOST PEOPLE DON'T KNOW THIS

There are Python libraries giving free market data for 170,000+ tickers.

Stocks.
Crypto.
Forex.
Economic indicators.

No Bloomberg. No expensive APIs.

Here are 12 libraries every quant dev should bookmark👇 1. yfinance
🔗 github.com/ranaroussi/yfi…

One of the most popular Python libraries for financial data.

Get:
• Stocks
• Crypto
• FX
• Options
• Fundamentals
• Intraday + historical data

All pulled from Yahoo Finance.

2. pandas-datareader
🔗 pandas-datareader.readthedocs.io
Originally part of pandas.

Pull data from multiple sources:

• Stocks
• Forex
• Economic indicators
• Fama-French factors
• Global datasets

Perfect for research and analysis.

🤯 YOUR OBSIDIAN VAULT is worth $120K/YR GOLDMINE but you have 0 idea about it

GREG ISENBERG just dropped a 1hr MASTERCLASS explaining:

Obsidian + Claude Code = 24/7 personal OS.
Total Cost: $120/yr
This setup: FREE

🔖 Bookmark before it's buried!
Here’s what nobody tells you about Obsidian:

It’s not a productivity app.
It’s local markdown files that link together like neurons in your brain.

↳ Every idea connects to other ideas.
↳ Every connection creates new patterns.
↳ Every pattern becomes visible in a graph.

Then Claude Code enters via command line.
It can now read your entire vault.

An old idea from months ago. A recent meeting note. Today’s random thought.

Claude connects all three before you even realize they’re related.
That’s the magic.

🚨THIS IS CRAZY!
Agencies charge $2,000–5,000 for a brand kit.

I asked Perplexity Computer to create one from scratch.

No Canva. No Figma.

TOTAL COST: $0. TIME: 23 minutes.

Here's everything it made (A STEP BY STEP GUIDE)👇
🔖 BOOKMARK before it's gone First, I told Perplexity: "Design 4 logo concepts for a premium online luxury gift shop."

In 3 minutes, it generated:

→ A minimal ribbon bow monogram with gold foil texture
→ An elegant serif wordmark with a subtle gift box in the "O"
→ A crest-style emblem with intertwined ribbons
→ A clean modern logotype with a velvet ribbon underline

No freelancer. Just one prompt.

😱 55 MINUTES. 10 ADVANCED CONCEPTS. $0.

AAKASH GUPTA just dropped THE FREE Claude Code MASTERCLASS nobody expected

Sub-agents, custom skills, auto-invoking hooks, CLI, Jupyter notebooks

Here's the INSANELY VALUABLE STUFF you can't miss👇
🔖Bookmark now
First. Should you still use Claude Code over Cowork and OpenClaw?

→ Cowork is built ON TOP of Claude Code. Less powerful by design.
→ OpenClaw is great for monitoring. Not for being in the driver's seat.
→ Claude Code is still number one for maximum power and control.

Now the golden rule everyone skips.

It is not prompt engineering anymore. It is context engineering.
→ Set up a status line. See context usage in real time. Green. Orange. Red.
→ Turn off MCPs you are not using. They eat context just by existing.
→ Use sub agents for research tasks. One sub agent used 30,000 tokens.

Main session used 0.5%.
That gap compounds every single session.

😱 THIS FEELS ILLEGAL & I LOVE IT.

Claude Pro: $20/month, hit limits in 3 days
These 10 tricks: Same $20/month, lasts 3+ weeks

I GET 10-20X OUTPUT FROM same subscription.

No extra cost. Just smarter prompting.
🔖 Bookmark these hidden tricks to save your tokens 1. Reset your chats every 15–20 messages

Claude re‑reads the entire conversation on every turn, so older, longer chats become “token black holes.” The more history you carry, the more expensive each new message is, and the faster you slam into limits.

Treat chats as task‑specific rooms, not a lifetime WhatsApp thread.

Ask Claude:

"Summarise everything important we’ve done here into a ‘project card’ under 300–400 tokens: goal, key decisions, current status, and next three actions.”

Paste that project card into a fresh chat with:

“New chat, same project. Here’s the context from the last thread: [card]. Continue from here.”

You’ve just compressed thousands of tokens of history into a few hundred you can cheaply reuse.

😱 THIS FEELS ILLEGAL.

25 open source AI AGENTS. ALL FREE.

I scanned 1,000+ REPOS so you don't have to waste a single hour.
All powering real products right now.

Here's everything that you must read to get started👇
🔖 Bookmark before it's buried AGENT FRAMEWORKS

The agents everyone is secretly building on.

↳ OpenClaw — Persistent, multi-channel, writes its own skills. The most popular open source project in GitHub history. 210k stars. github.com/openclaw/openc…

↳ AutoGPT — Full agent platform built for long running tasks that don't need babysitting. github.com/Significant-Gr…

↳ LangGraph — Agents as graphs. Multi-agent orchestration done properly. 26.8k stars. github.com/langchain-ai/l…

↳ OWL — Multi-agent cooperation that tops the GAIA benchmark. The one researchers are quietly using. github.com/camel-ai/owl

These four are the foundation most serious agent builders are standing on right now.

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE.

It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer.

It's called DeerFlow.

You give it a task. It makes a plan, spins up its own team of sub-agents,
and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing.

Not a chatbot.
Not a research assistant.

An AI with its own computer that works while you sleep.

Here's what it does on its own:
→ Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output
→ Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once
→ Builds slide decks, websites, full research reports, and data dashboards from scratch
→ Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time.
→ Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs
→ Searches the web, runs commands, calls any tool you plug in

Here's how it thinks:

You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable.
A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals.

Here's the wildest part:
DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it.

22.7K GitHub stars. 2.7K forks. Built by ByteDance
100% Open Source. MIT License. Here is the Github Repo🔗
github.com/bytedance/deer…

🤯 YOUR OBSIDIAN VAULT is worth $120K/YR & you're getting $0 from it.

GREG ISENBERG just dropped a 1hr MASTERCLASS on turning Obsidian + Claude Code into a 24/7 personal Operating System.

Total Cost: $120/yr
This setup: FREE

Bookmark before it's buried!
I am really excited about this one!
So it generates ideas. Surfaces unconscious patterns. Executes tasks.

Obsidian + Claude Code + these 10 setups👇

1. WRITE EVERYTHING IN MARKDOWN

Daily notes. Projects. Beliefs. People. Meetings.
Every thought becomes fuel for your AI.

🤯 Don't hire an engineer to SET UP OPENCLAW

GREG ISENBERG & MORITZ KREMB just dropped a MASTERCLASS & taught you to do it yourself in 1 HOUR for $0.

Setup consultant: $3K
Greg's masterclass: FREE
26 billable hrs → 1 hour

🔖 Bookmark before it's gone.
What you learn:

- Memory architecture (5hrs billable → free)
- Personalization setup (3hrs → free)
- Skill automation (8hrs → free)
- Security config (4hrs → free)
- Production deployment (6hrs → free)

🚨 THIS SHOULD NOT BE FREE.

Claude just built my content repurposing machine.

These 15 Claude prompts for video → thread → LinkedIn.

Tested on 5 pieces - 4x reach.

Here is the SIMPLE CLAUDE PROMPT PACK you don't want to miss: 1. The Transcript Goldmine Extractor

You are an expert content strategist with deep experience turning long-form video content into high-performing social media assets. I am going to give you a raw video transcript and I need you to analyze it before a single word of content is written.↳ Read the entire transcript first without doing anything and absorb the full argument, story, and flow before making any decisions

↳ Extract the 3 to 5 core ideas that have the strongest potential to stand alone as their own piece of content
↳ For each idea, write one punchy summary sentence, pull out the single most quotable line from the transcript, and identify the primary emotional trigger it activates such as curiosity, pain, aspiration, or identity
↳ Tag each idea clearly as Thread-ready, LinkedIn-ready, or Both and briefly explain why you gave it that tag
↳ Note which idea has the highest viral potential and which has the highest authority-building potential
↳ Output everything as a clean numbered list and do NOT rewrite or expand the content yet, only extract, label, and annotate

[PASTE TRANSCRIPT HERE]

😱HOLY SHIT! Claude just killed my Notion second brain.

I wasted 2 years dumping everything into Notion - notes, tasks, research, ideas. It was a mess.

Last week I switched to these SIMPLE Claude prompts & improved my productivity by 10X

Here is the full PROMPT PACK👇 1. The “10x Output” Prompt

Act as a world-class productivity strategist.

Here are my current goals and tasks:
[PASTE GOALS/TASKS]

Identify the 20% of activities that will create 80% of the results.

Then:

↳ Eliminate or automate low-value work

↳ Suggest a simplified workflow

↳ Create a daily execution plan for maximum output.

😱 WAIT! WHAT?

GREG ISENBERG just dropped a masterclass on replacing your entire marketing team with digital employees.

Marketing team: $25K/month
Digital employees: $47/month

Same output. Better performance. Runs 24/7

Here's the complete breakdown👇

Let me break down what he showed:

The Old Way (Marketing Team):
Typical marketing team:

Media buyer: $8K/month
Copywriter: $6K/month
Analyst: $7K/month
Email marketer: $5K/month

Total: $26K/month = $312K/year

Plus:
Management time: 15 hours/week
Meetings, revisions, oversight
Limited to 8-5pm M-F
Vacation, sick days, turnover

🚨 BREAKING: MATTHEW BERMAN just released a video on how he runs his entire META ADS operation for $0/MONTH WITH OPENCLAW.

No agency. No VA. Just an AI agent that monitors, kills, scales, writes, and uploads ads autonomously.

Here's the system he built👇
He has been running Meta ads manually for 20 years.

He just packaged his entire workflow into 5 autonomous OpenClaw skills that replaced dozens of hours in Ads Manager every week.

The agent handles everything from health checks to creative uploads without human intervention.

Here's exactly what runs on autopilot in his setup every single day:

🚨 BREAKING!

2,000+ OPENCLAW USERS HAVE THIS SECURITY MISTAKE RIGHT NOW.

It's costing people their entire business.

API keys exposed. Files readable. Commands executable.

Here's the cheat sheet that fixes it in 30 minutes for $0.

🔖 Bookmark before it gets buried Your agent can read files, run commands, access API keys.

Unsecured = open door.

First thing to do RIGHT NOW:
openclaw gateway --host 127.0.0.1

60 seconds. External connections blocked forever.

🤯 HOLY SHIT. I wasted WEEKS on deep research before discovering this.

I don't get why most people don't use PERPLEXITY for DEEP RESEARCH.

HERE are 10 prompts that turn it into a PhD-level research assistant (and save you weeks of work): 1. Domain-Master Overview Prompt

Act as a PhD-level researcher and domain expert in {topic}.
Your goal: build me a deep, structured understanding from first principles to current frontier debates.

In your answer:

Start with 1–2 paragraphs that define the field, its core questions, and why it matters.

Use bullet points to explain the 5–10 most important concepts, each with a 2–3 sentence explanation.

Add a short section called “Historical Milestones” with bullet points for key papers, breakthroughs, or events (include year and 1–2 sentence significance).

Finish with a section “Current Frontier & Open Problems” where you:

List 5–7 active research directions in bullet points.

For each, explain why it’s hard and what progress is being made.

End with a short paragraph summarizing “If I had 30 days to get dangerous in this field, here’s the exact learning plan I’d follow.

🚨 AI can now build Excel formulas like Microsoft's Power BI consultants (for free).

Here are 15 insane Claude prompts that replace $550/hour spreadsheet specialists

(🔖 Bookmarked for later) 1. Nested IF Logic Builder

You are an expert Excel formula consultant with Power BI data modeling experience. I need you to build a Nested IF formula for me based on my conditions. Follow these rules strictly:

↳ Ask me first for the column/field I am evaluating, the conditions I want to test (in priority order), and the output values for each condition, including a fallback/else value.
↳ Build the formula using proper nested IF syntax: =IF(test1, val1, IF(test2, val2, IF(test3, val3, else_val)))
↳ Indent and annotate the formula in a human-readable breakdown so I understand the logic tree before I use it.
↳ Warn me if any condition is logically redundant, unreachable, or if the order of conditions will cause incorrect results.
↳ Suggest a modern equivalent (IFS, SWITCH, or CHOOSE) if my Excel version supports it and if it would simplify the formula.
↳ Handle edge cases - tell me if I need to account for blanks (ISBLANK), errors (IFERROR), or text vs. number mismatches.
↳ If my logic exceeds 7 levels of nesting, flag it as a maintainability risk and offer a restructured approach using helper columns or SWITCH.

↳ My conditions are: [paste your conditions here in plain English, e.g., "If Sales > 10000 then 'High', if Sales > 5000 then 'Medium', if Sales > 0 then 'Low', otherwise 'No Sales'"]

😱 THIS FEELS ILLEGAL: Greg Isenberg just revealed how to turn $1K Upwork gigs into $500K/year using OpenClaw.

Most people think: OpenClaw = productivity hack
He is thinking: OpenClaw = 24/7 digital employees

Here's the 8-step system: 👇

↳Spin up 5-10 OpenClaw machines (Mac Mini, VM, whatever)
↳Pick ONE boring workflow (insurance, real estate, law)
↳Map it tip-to-tail (trigger → clicks → parsing → CRM)
↳Use Claude Code for the Python pipeline
↳Package as: "Setup + 30 days + new workflows weekly"
↳Use Upwork as lead source + validation
↳Turn best workflow into vertical workspace
↳Sell to BigCo as "AI employees for [department]"

"But you can't build a big company doing Upwork deals"

🤯 HOLY SHIT... I just found 10 X articles that will save your OpenClaw
project from the mistakes.

Real users.
Real shipped products.
Real post-mortems.

No courses. No gurus. No fluff.
Bookmark this before it gets buried ↓

👇 Here's the list: 1. The #1 Viral Setup Guide

https://x.com/clawdbotatg/status/2021716456422166891?s=20

BREAKING: AI can now build financial models like $150K Goldman Sachs analysts.

Most pay $5K-$15K/model.
I reverse-engineered 47 investment banking models into 12 Claude prompts.

Result: 94% identical
Savings: $8,480
👇Here are 12 advanced prompts for investment banking analysis 1. DCF Valuation Model 💰
Replaces: 40 hours of analyst work | Typical cost: $800

You are a senior equity research analyst performing DCF valuation.

COMPANY: [COMPANY_NAME] | Ticker: [TICKER] | Industry: [INDUSTRY]
Current Price: $[PRICE] | Shares: [SHARES]mm | Market Cap: $[MCAP]

[PASTE 3-5 YEAR HISTORICAL FINANCIALS]

ASSUMPTIONS:
- Revenue CAGR: Base [BASE_%]%, Bull [BULL_%]%, Bear [BEAR_%]%
- Target EBITDA Margin Y5: [MARGIN_%]%
- CapEx % Revenue: [CAPEX_%]%
- NWC: DSO [DSO], DIO [DIO], DPO [DPO]
- Tax: [TAX_%]%

WACC:
- Risk-free: [RF_%]% | Beta: [BETA] | ERP: [ERP_%]%
- Cost of Debt: [COD_%]% | MV Equity: $[MVE] | MV Debt: $[MVD]

TERMINAL VALUE:
- Perpetuity growth: [PG_%]% | Exit multiple: [EXIT]x

OUTPUT:
1. 5-year FCF projections
2. WACC with Beta sensitivity (0.8-1.2)
3. Terminal value (both methods)
4. Sensitivity: WACC vs Growth, Revenue vs Margins
5. EV → Equity value/share → Upside vs $[PRICE]
6. Football field chart + implied multiples

BREAKING: AI can now build financial models like $150K Goldman Sachs analysts.

Most pay $5K-$15K/model.
I reverse-engineered 47 investment banking models into 12 Claude prompts.

Result: 94% identical
Savings: $8,480
👇Here are 12 advanced prompts for investment banking analysis 1. Discounted Cash Flow (DCF) Valuation Model

You are a senior equity research analyst performing a comprehensive DCF valuation.

COMPANY INFORMATION:
- Company Name: [COMPANY_NAME]
- Ticker: [TICKER]
- Industry: [INDUSTRY]
- Current Stock Price: [CURRENT_PRICE]
- Shares Outstanding (mm): [SHARES_OUTSTANDING]
- Market Cap: [MARKET_CAP]

FINANCIAL DATA:
[PASTE HISTORICAL FINANCIALS - Last 3-5 years Income Statement, Balance Sheet, Cash Flow]

KEY ASSUMPTIONS:
- Last Year Revenue: [LAST_YEAR_REVENUE]
- Revenue CAGR (5yr): Base [BASE_CAGR_%]%, Bull [BULL_CAGR_%]%, Bear [BEAR_CAGR_%]%
- Target EBITDA Margin Y5: [TARGET_MARGIN_%]%
- CapEx as % Revenue: [CAPEX_%]%
- NWC: DSO [DSO], DIO [DIO], DPO [DPO]
- Tax Rate: [TAX_RATE_%]%

WACC INPUTS:
- Risk-free Rate: [RISK_FREE_%]%
- Beta: [BETA]
- Equity Risk Premium: [ERP_%]% (default 5-6%)
- Pre-tax Cost of Debt: [COST_OF_DEBT_%]%
- Market Value Equity: [MV_EQUITY]
- Market Value Debt: [MV_DEBT]

TERMINAL VALUE:
- Perpetuity Growth: [PERPETUITY_GROWTH_%]% (typically 2-3%)
- Exit Multiple: [EXIT_MULTIPLE]x EV/EBITDA

TASKS:
1. Project 5-year Free Cash Flows (Revenue → EBITDA → NOPAT → FCF)
2. Calculate WACC with sensitivity to Beta (0.8-1.2)
3. Determine Terminal Value using both methods
4. Create sensitivity tables: WACC vs Terminal Growth, Revenue vs Margins
5. Output: Enterprise Value → Equity Value per share → Upside/downside vs [CURRENT_PRICE]
6. Provide football field chart and implied trading multiples

3 months ago I missed GameStop 2.0 because I saw the trend 4 days late.

Today: GROK alerts me 48-72 hours BEFORE price action.

I reverse-engineered 2,000+ stock movements into 10 prompts that scan real-time X sentiment.

This is your unfair advantage 👇 1. Data Collection from Social Media

Collect real-time stock market sentiment data for [Stock Ticker] on X. Use x_semantic_search with query 'investor reactions to [Stock Name] stock today' (limit=25, from_date=last 24 hours, min_score_threshold=0.2) to gather relevant posts. Add x_keyword_search query '([Stock Ticker]) (earnings OR upgrade OR downgrade) filter:replies min_faves:5' (limit=20, mode=Latest). List key posts with details like user handles, engagement metrics, and excerpts, then identify dominant stock-specific themes such as bullish catalysts or bearish risks